Worldwide, the male population is slightly higher than the female population, although this varies by country. As of 2024, Hong Kong has the highest share of women worldwide with almost ** percent. Moldova followed behind with around ** percent. Among the countries with the largest share of women in the total population, several were former Soviet states or were located in Eastern Europe. By contrast, Qatar, the United Arab Emirates, and Oman had some of the highest proportions of men in their populations.

Between August 2019 to July 2020, the site with the highest share of female users was etsy.com. Approximately ** percent of visitors to the e-commerce platform for selling handmade or vintage items were female. Additionally, Globo.com had the second-highest share of female users with **** percent.

The average for 2023 based on 12 countries was 50.47 percent. The highest value was in Uruguay: 51.51 percent and the lowest value was in Paraguay: 49.85 percent. The indicator is available from 1960 to 2023. Below is a chart for all countries where data are available.

About this dataset

Context

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

Key observations

Largest age group (population): Male # 30-34 years (11.65 million) | Female # 30-34 years (11.41 million). 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 United States population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the United States is shown in the following column.
• Population (Female): The female population in the United States 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 United States 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 United States Population by Gender. You can refer the same here

Population, female (% of total population) in World was reported at 49.71 % in 2023, according to the World Bank collection of development indicators, compiled from officially recognized sources. World - Population, female (% of total) - actual values, historical data, forecasts and projections were sourced from the World Bank on July of 2025.

The average for 2023 based on 53 countries was 50.08 percent. The highest value was in Zimbabwe: 52.38 percent and the lowest value was in the Seychelles: 44.82 percent. The indicator is available from 1960 to 2023. Below is a chart for all countries where data are available.

About this dataset

Context

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

Key observations

Largest age group (population): Male # 15-19 years (35) | Female # 45-49 years (41). 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 Sicily Island population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Sicily Island is shown in the following column.
• Population (Female): The female population in the Sicily Island 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 Sicily Island 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 Sicily Island Population by Gender. You can refer the same here

This map shows if an area had predominantly more males or females in the United States in 2010. If there are more females in the population, it is shaded pink. If there are more males, it is shaded blue. The map shows this pattern for states, counties, tracts, and block groups. There is increasing geographic detail as you zoom in, and only one geography is configured to show at any time. The data source is the US Census Bureau, and the vintage is 2010. The original service and data metadata can be found here.

Population, female (% of total population) in United States was reported at 49.76 % in 2024, according to the World Bank collection of development indicators, compiled from officially recognized sources. United States - Population, female (% of total) - actual values, historical data, forecasts and projections were sourced from the World Bank on July of 2025.

Population: Female: VR: Republic of Chuvashia data was reported at 628,058.000 Person in 2023. This records a decrease from the previous number of 630,553.000 Person for 2022. Population: Female: VR: Republic of Chuvashia data is updated yearly, averaging 679,174.000 Person from Dec 1989 (Median) to 2023, with 35 observations. The data reached an all-time high of 727,230.000 Person in 1992 and a record low of 628,058.000 Person in 2023. Population: Female: VR: Republic of Chuvashia data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Demographic and Labour Market – Table RU.GA010: Population: Female: by Region.

Social media was the most popular news platform amongst Americans as of February 2022 and was used most regularly by women, with 39 percent of female respondents to a survey saying that they used social networks for news on a daily basis. Meanwhile, twice the share of men than women reported reading newspapers each day.

About this dataset

Context

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

Key observations

Largest age group (population): Male # 30-34 years (7,947) | Female # 25-29 years (8,143). Source: U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.

Content

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

Age groups:

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

Scope of gender :

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

Variables / Data Columns

• Age Group: This column displays the age group for the Ontario population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Ontario is shown in the following column.
• Population (Female): The female population in the Ontario 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 Ontario 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 Ontario Population by Gender. You can refer the same here

About this dataset

Context

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

Key observations

Largest age group (population): Male # 25-29 years (68,680) | Female # 25-29 years (62,701). Source: U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.

Content

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

Age groups:

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

Scope of gender :

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

Variables / Data Columns

• Age Group: This column displays the age group for the San Diego population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the San Diego is shown in the following column.
• Population (Female): The female population in the San Diego 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 San Diego 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 San Diego Population by Gender. You can refer the same here

About this dataset

Context

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

Key observations

Largest age group (population): Male # 30-34 years (119,643) | Female # 30-34 years (123,624). Source: U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.

Content

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

Age groups:

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

Scope of gender :

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

Variables / Data Columns

• Age Group: This column displays the age group for the Brooklyn borough population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Brooklyn borough is shown in the following column.
• Population (Female): The female population in the Brooklyn borough 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 Brooklyn borough 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 Brooklyn borough Population by Gender. You can refer the same here

About this dataset

Context

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

Key observations

Largest age group (population): Male # 25-29 years (10,111) | Female # 20-24 years (9,216). 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 San Bernardino population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the San Bernardino is shown in the following column.
• Population (Female): The female population in the San Bernardino 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 San Bernardino 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 San Bernardino Population by Gender. You can refer the same here

About this dataset

Context

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

Key observations

Largest age group (population): Male # 20-24 years (1,250) | Female # 20-24 years (1,302). Source: U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.

Content

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

Age groups:

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

Scope of gender :

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

Variables / Data Columns

• Age Group: This column displays the age group for the Sweden town population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Sweden town is shown in the following column.
• Population (Female): The female population in the Sweden 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 Sweden 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 Sweden town Population by Gender. You can refer the same here

About this dataset

Context

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

Key observations

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

Content

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

Age groups:

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

Scope of gender :

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

Variables / Data Columns

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

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for White Earth Population by Gender. You can refer the same here

How Does Gender Stereotype Affect Women’s Career Progression Toward Top-Leadership Positions in the Corporate Industries of Bangladesh?AbstractNotwithstanding the sharp rise in women’s engagement in the workforce, leadership positions in Bangladesh remain predominantly occupied by men, perpetuated by deep-rooted gender stereotypes and the belief that women lack managerial capabilities. Before commenting on women's leadership ability in Bangladesh, how often they receive the chance to lead compared to males, which consequences they encounter, and why they can't continue are matters of question. To answer this, we have leveraged the Leader-Member Exchange Theory and Career Curve to systematically explore overlooked facets and scrutinize the impact of gender preconceptions on women's leadership advancement. Our findings unveil that women play the out-group role due to the persistent adherence to the "think manager think male" paradigm, the male ego and fear of accepting women's leadership, critiques and prejudice, and double standards within the organization. Hence, women have longer mid-career crises than men and are marginalized in succession planning, which leads women to have self-doubt, moral dilemmas, and the propensity to step down from the lead role and to switch organizations even at inferior positions. The authors urge organizations to foster diversity, challenge biases, ensure equitable professional progression, and adopt think manager think contingent over the think manager think male paradigm.Data Collection and Analysis We have employed a qualitative research methodology, specifically utilizing in-depth interviews with 16 participants to conduct our investigation. The primary target population of this study was female employees who were employed in the mid-level to top-level. The reason behind targeting these populations is primarily to identify the barriers they are facing due to gender in their mid-career that is troubling them to secure the top position and to know the experience faced by the top-level female employees when they lead in the male-dominated corporate sector. Then this study secondarily targeted female employees who are working at the entry level to know their leadership aspirations in the top position. Furthermore, to validate the findings of the study, the authors found the importance of cross-verification. Hence, this study also targeted male employees from the mid-level to top-level to know their perception regarding the gender stereotype and female leadership in top management. The data analysis process was carried out using Nvivo 14 software for the in-depth interview transcripts. Upon importing the data into Nvivo, a systematic coding strategy was employed to identify recurring themes and patterns within the dataset. The process of thematic analysis involved the development of codes that aligned with the core concepts discovered in the interviews. Project and concept maps were generated using Nvivo to visually illustrate the connections, providing a comprehensive view of the findings.Discussion on findingsGender stereotype indeed affects women in Bangladesh not only to secure top leadership position as well as to sustain the leadership position. As per participant reports, networking emerges as a pivotal factor in shaping employees' professional development within the workplace. Regrettably, female professionals continue encountering challenges in establishing and benefiting from such essential networks. Most female employees face tight control from their boss as they play the “out-group”, whereas male employees are higher in the in-group. The interview findings reveal a discernible gender gap in learning opportunities, especially in predominantly male-dominated organizations. Male employees benefit from more inclusive and unrestricted communication with leaders, allowing them to have more opportunities to learn and improve their skills. In contrast, female employees face obstacles that prevent them from participating in leadership discussions. The limited access is ascribed to factors such as derogatory remarks, cases of sexual harassment, and omnipresent preconceptions that cast doubt on women's aptitude for strategic thinking. This study identifies a prevalent dichotomy in leadership dynamics, designating female employees to the "outgroup" and male counterparts to the "in-group." This outgroup positioning subjects female employees to heightened formal scrutiny and stringent control mechanisms from their leaders. The prevalence of the male ego of working under women and fear of losing control also pushes male-dominated organizations in Bangladesh to limit the learning opportunities for females. The awareness of male employees' performance, stemming from established in-group relationships, translates into a discernable bias favoring males in promotion considerations. The perceptible influence of in-group and outgroup dynamics significantly governs leaders' cognitive processes when designing succession planning strategies.Consistent with the observations made by Amakye et al. (2021), our study corroborates a discernible inclination among subordinates to display less compliance toward female leaders compared to their male counterparts, which raises concerns for organizational management, potentially influencing their reluctance to appoint females in managerial positions.In mid-career progression, female employees experience prolonged periods of crisis compared to their male counterparts, largely attributable to the dynamics of in-group/out-group politics. Despite having female representation in management, female participants observed biased evaluations, attributing it to stereotypical perceptions prevalent among male-dominated department heads. For women in Bangladesh, attaining and maintaining leadership positions poses considerable challenges. The continuous need to substantiate their leadership capabilities places them under perpetual scrutiny, where any mistake is met with immediate negative feedback, raising questions about their competence vis-à-vis their male counterparts. Biased perception and disparate treatment towards female leaders instigate self-doubt moral dilemmas and often compel them to transition to female-oriented organizations, albeit in lower positions. However, this challenge has enabled women to augment their skills, positioning them to showcase their value consistently. In Bangladesh, current findings highlight women's growing confidence, visionary mindset, adept navigating challenges, fostering strength, and acquiring diverse, adaptive leadership patterns. The persistent adherence to the "think manager, think male" mindset may inadvertently create a glass ceiling for male advancement in future leadership scenarios. It is imperative for our business culture to acknowledge and embrace the virtue of empathy exhibited by women, incorporating it as a fundamental value in leadership.ConclusionsIn conclusion, our study contributes to the existing literature on gender stereotypes and female leadership by delving into the association between stereotypes and women's leadership in Bangladesh's corporate sector. Unlike previous studies that predominantly focused on social role congruity theory and factors affecting women's leadership, our research incorporates leader-member exchange theory, contingent leadership theory and career trajectories to understand the effect of gender stereotype on women’s leadership sustainability. Despite performance and qualifications, we find that these stereotypes act as barriers, hindering women from attaining and exercising top leadership positions. Nevertheless, our study unveils resilience among effective female leaders who strategically navigate these barriers, viewing setbacks as opportunities for self-improvement. Female leaders, faced with gender stereotypes, adopt distinctive leadership behaviors, prompting a reevaluation of leadership paradigms traditionally associated with males. This challenges organizations to foster inclusive environments, promoting diversity and performance regardless of gender, and shifting from the entrenched "think manager, think male" paradigm to embracing a "think manager, think contingent and leader-made" approach. This research brings forth significant theoretical implications, urging a reevaluation of leadership paradigms. It highlights the importance of in-group and out-group dynamics, prompting a deeper exploration of their influence on learning opportunities, promotions, and leadership perceptions. Leveraging the Leader-Member Exchange (LMX) theory and mid-career crisis as theoretical lenses, the study elucidates the limited representation of women in top leadership positions in Bangladesh.Additionally, the study underscores the significance of emotional intelligence and empathy, challenging the conventional "think manager, think male" mindset.The research findings have important practical implications for organizations developing inclusive and fair leadership environments. This investigation illuminates the lack of attention given to the underutilized capabilities of women in leadership positions at various organizational levels. The study questions traditional measures of commitment, highlighting that assessing dedication only based on continuous accessibility and extended working hours is erroneous. Promoting diversity and inclusion while empowering female employees through skill enhancement is pivotal for dismantling current challenges and fostering gender equity

About this dataset

Context

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

Key observations

Largest age group (population): Male # 25-29 years (557) | Female # 55-59 years (326). Source: U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.

Content

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

Age groups:

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

Scope of gender :

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

Variables / Data Columns

• Age Group: This column displays the age group for the Berlin population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Berlin is shown in the following column.
• Population (Female): The female population in the Berlin 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 Berlin 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 Berlin Population by Gender. You can refer the same here

About this dataset

Context

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

Key observations

Largest age group (population): Male # 25-29 years (411,152) | Female # 25-29 years (402,863). Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Content

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

Age groups:

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

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 Los Angeles County population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Los Angeles County is shown in the following column.
• Population (Female): The female population in the Los Angeles County 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 Los Angeles County 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 Los Angeles County Population by Gender. You can refer the same here