Women's Business Centers (WBCs) represent a national network of nearly 100 educational centers throughout the United States and its territories, which are designed to assist women in starting and growing small businesses. WBCs seek to "level the playing field" for women entrepreneurs, who still face unique obstacles in the business world. SBA’s Office of Women’s Business Ownership (OWBO) oversees the WBC network, which provides entrepreneurs (especially women who are economically or socially disadvantaged) comprehensive training and counseling on a variety of topics in several languages

Research data used in the paper entitled "Women founders of startups: an examination through the prism of the Queen Bee Phenomenon" published in Revista Brasileira de Gestão de Negócios (RBGN) V25, n3 (2023) Acess: https://rbgn.fecap.br/RBGN

Aha's Nenu Super Woman dataset

With 64 fields/features and 38 rows.

Dataset has below features/columns:

• Season Number - Season number
• Startup Name - Startup company name
• Season Start - Season first aired date
• Season End - Season last aired date
• Episode Number - Episode number within the season
• Anchor - Name of the anchor
• Pitch Number - Overall pitch number
• Industry - Industry name or type
• Business Description - Business Description
• Company Website - Company Website URL
• Entrepreneur Names - Name of the entrepreneurs
• Number of Presenters - Number of presenters
• Pitchers Average Age - All pitchers average age, <30 young, 30-50 middle, >50 old
• Started in - Year in which startup was started/incorporated
• Yearly Revenue - Yearly revenue, in lakhs INR, -1 means negative revenue, 0 means pre-revenue
• Monthly Sales - Total monthly sales, in lakhs
• Gross Margin - Gross margin/profit of company, in percentages
• Net Margin - Net margin/profit of company, in percentages
• SKUs - Stock Keeping Units, at the time of pitch
• Original Ask Amount - Original Ask Amount, in lakhs INR
• Original Offered Equity - Original Offered Equity, in percentages
• Valuation Requested - Valuation Requested, in lakhs INR
• Received Offer - Received offer or not, 1-received, 0-not received
• Accepted Offer - Accepted offer or not, 1-accepted, 0-rejected
• Total Deal Amount - Total Deal Amount, in lakhs INR
• Total Deal Equity - Total Deal Equity, in percentages
• Total Deal Debt - Total Deal Debt, in lakhs INR
• Debt Interest - Debt interest rate, in percentages
• Deal Valuation - Deal Valuation, in lakhs INR
• Number of Angels in deal - Number of sharks involved in deal
• Investment Amount Per Angel - Investment Amount Per Angel
• Equity Per Angel - Equity Per Angel
• Deal has conditions - Deal has conditions or not?
• Has Patents - Pitcher has Patents? yes/no
• Royalty deal - Deal has royalty or not?
• Super Woman Fund - Super Woman fund, in lakhs INR
• Deepa Investment Amount - Deepa Investment Amount, in lakhs INR
• Deepa Investment Equity - Deepa Investment Equity, in percentages
• Deepa Debt Amount - Deepa Debt Amount, in lakhs INR
• Renuka Investment Amount - Renuka Investment Amount, in lakhs INR
• Renuka Investment Equity - Renuka Investment Equity, in percentages
• Renuka Debt Amount - Renuka Debt Amount, in lakhs INR
• Sridhar Investment Amount - Sridhar Investment Amount, in lakhs INR
• Sridhar Investment Equity - Sridhar Investment Equity, in percentages
• Sridhar Debt Amount - Sridhar Debt Amount, in lakhs INR
• Rohit Investment Amount - Rohit Investment Amount, in lakhs INR
• Rohit Investment Equity - Rohit Investment Equity, in percentages
• Rohit Debt Amount - Rohit Debt Amount, in lakhs INR
• Sindhura Investment Amount - Sindhura Investment Amount, in lakhs INR
• Sindhura Investment Equity - Sindhura Investment Equity, in percentages
• Sindhura Debt Amount - Sindhura Debt Amount, in lakhs INR
• Sudhakar Investment Amount - Sudhakar Investment Amount, in lakhs INR
• Sudhakar Investment Equity - Sudhakar Investment Equity, in percentages
• Sudhakar Debt Amount - Sudhakar Debt Amount, in lakhs INR
• Karan Investment Amount - Karan Investment Amount, in lakhs INR
• Karan Investment Equity - Karan Investment Equity, in percentages
• Karan Debt Amount - Karan Debt Amount, in lakhs INR
• Deepa Present - Whether Deepa present in episode or not
• Renuka Present - Whether Renuka present in episode or not
• Sridhar Present - Whether Sridhar present in episode or not
• Rohit Present - Whether Rohit present in episode or not
• Sindhura Present - Whether Sindhura present in episode or not
• Sudhakar Present - Whether Sudhakar present in episode or not
• Karan Present - Whether Karan present in episode or not

Release Date: 2018-08-10.[NOTE: Includes firms with payroll at any time during 2016. Employment reflects the number of paid employees during the March 12 pay period. Data are based on Census administrative records, and the estimates of business ownership by gender, ethnicity, race, and veteran status are from the 2016 Annual Survey of Entrepreneurs. Detail may not add to total due to rounding or because a Hispanic firm may be of any race. Moreover, each owner had the option of selecting more than one race and therefore is included in each race selected. Respondent firms include all firms that responded to the characteristic(s) tabulated in this dataset and reported gender, ethnicity, race, or veteran status or that were publicly held or not classifiable by gender, ethnicity, race, or veteran status. Percentages are for respondent firms only and are not recalculated when the dataset is resorted. Percentages are always based on total reporting (defined above) within a gender, ethnicity, race, veteran status, and/or industry group for the characteristics tabulated in this dataset. Firms with more than one domestic establishment are counted in each geographic area and industry in which they operate, but only once in the U.S. and state totals for all sectors. For information on confidentiality protection, sampling error, nonsampling error, and definitions, see Survey Methodology.]..Table Name. . Statistics for U.S. Employer Firms That Had E-Commerce Sales by Sector, Gender, Ethnicity, Race, Veteran Status, and Years in Business for the U.S., States, and Top 50 MSAs: 2016. ..Release Schedule. . This file was released in August 2018.. ..Key Table Information. . These data are related to all other 2016 ASE files.. Refer to the Methodology section of the Annual Survey of Entrepreneurs website for additional information.. ..Universe. . The universe for the 2016 Annual Survey of Entrepreneurs (ASE) includes all U.S. firms with paid employees operating during 2016 with receipts of $1,000 or more which are classified in the North American Industry Classification System (NAICS) sectors 11 through 99, except for NAICS 111, 112, 482, 491, 521, 525, 813, 814, and 92 which are not covered. Firms with more than one domestic establishment are counted in each geographic area and industry in which they operate, but only once in the U.S. total.. In this file, "respondent firms" refers to all firms that reported gender, ethnicity, race, or veteran status for at least one owner or returned a survey form with at least one item completed and were publicly held or not classifiable by gender, ethnicity, race, and veteran status.. ..Geographic Coverage. . The data are shown for:. . United States. States and the District of Columbia. The fifty most populous metropolitan areas. . ..Industry Coverage. . The data are shown for the total of all sectors (00) and the 2-digit NAICS code level.. ..Data Items and Other Identifying Records. . Statistics for U.S. Employer Firms That Had E-Commerce Sales by Sector, Gender, Ethnicity, Race, Veteran Status, and Years in Business for the U.S., States, and Top 50 MSAs: 2016 contains data on:. . Number of firms with paid employees. Sales and receipts for firms with paid employees. Number of employees for firms with paid employees. Annual payroll for firms with paid employees. Percent of respondent firms with paid employees. Percent of sales and receipts of respondent firms with paid employees. Percent of number of employees of respondent firms with paid employees. Percent of annual payroll of respondent firms with paid employees. . The data are shown for:. . Gender, ethnicity, race and veteran status of respondent firms. . All firms. Female-owned. Male-owned. Equally male-/female-owned. Hispanic. Equally Hispanic/non-Hispanic. Non-Hispanic. White. Black or African American. American Indian and Alaska Native. Asian. Native Hawaiian and Other Pacific Islander. Some other race. Minority. Equally minority/nonminority. Nonminority. Veteran-owned. Equally veteran-/nonveteran-owned. Nonveteran-owned. All firms classifiable by gender, ethnicity, race, and veteran status. Publicly held and other firms not classifiable by gender, ethnicity, race, and veteran status. . . Years in business. . All firms. Firms less than 2 years in business. Firms with 2 to 3 years in business. Firms with 4 to 5 years in business. Firms with 6 to 10 years in business. Firms with 11 to 15 years in business. Firms with 16 or more ye...

Release Date: 2017-07-13.[NOTE: Includes firms with payroll at any time during 2015. Employment reflects the number of paid employees during the March 12 pay period. Data are based on Census administrative records, and the estimates of business ownership by gender, ethnicity, race, and veteran status are from the 2015 Annual Survey of Entrepreneurs. Detail may not add to total due to rounding or because a Hispanic firm may be of any race. Moreover, each owner had the option of selecting more than one race and therefore is included in each race selected. Respondent firms include all firms that responded to the characteristic(s) tabulated in this dataset and reported gender, ethnicity, race, or veteran status or that were publicly held or not classifiable by gender, ethnicity, race, or veteran status. Percentages are for respondent firms only and are not recalculated when the dataset is resorted. Percentages are always based on total reporting (defined above) within a gender, ethnicity, race, veteran status, and/or industry group for the characteristics tabulated in this dataset. Firms with more than one domestic establishment are counted in each geographic area and industry in which they operate, but only once in the U.S. and state totals for all sectors. For information on confidentiality protection, sampling error, nonsampling error, and definitions, see Survey Methodology.]..Table Name. . Statistics for U.S. Employer Firms That Had E-Commerce Sales by Sector, Gender, Ethnicity, Race, Veteran Status, and Years in Business for the U.S., States, and Top 50 MSAs: 2015. ..Release Schedule. . This file was released in July 2017.. ..Key Table Information. . These data are related to all other 2015 ASE files.. Refer to the Methodology section of the Annual Survey of Entrepreneurs website for additional information.. ..Universe. . The universe for the 2015 Annual Survey of Entrepreneurs (ASE) includes all U.S. firms with paid employees operating during 2015 with receipts of $1,000 or more which are classified in the North American Industry Classification System (NAICS) sectors 11 through 99, except for NAICS 111, 112, 482, 491, 521, 525, 813, 814, and 92 which are not covered. Firms with more than one domestic establishment are counted in each geographic area and industry in which they operate, but only once in the U.S. total.. In this file, "respondent firms" refers to all firms that reported gender, ethnicity, race, or veteran status for at least one owner or returned a survey form with at least one item completed and were publicly held or not classifiable by gender, ethnicity, race, and veteran status.. ..Geographic Coverage. . The data are shown for:. . United States. States and the District of Columbia. The fifty most populous metropolitan areas. . ..Industry Coverage. . The data are shown for the total of all sectors (00) and the 2-digit NAICS code level.. ..Data Items and Other Identifying Records. . Statistics for U.S. Employer Firms That Had E-Commerce Sales by Sector, Gender, Ethnicity, Race, Veteran Status, and Years in Business for the U.S., States, and Top 50 MSAs: 2015 contains data on:. . Number of firms with paid employees. Sales and receipts for firms with paid employees. Number of employees for firms with paid employees. Annual payroll for firms with paid employees. Percent of respondent firms with paid employees. Percent of sales and receipts of respondent firms with paid employees. Percent of number of employees of respondent firms with paid employees. Percent of annual payroll of respondent firms with paid employees. . The data are shown for:. . Gender, ethnicity, race and veteran status of respondent firms. . All firms. Female-owned. Male-owned. Equally male-/female-owned. Hispanic. Equally Hispanic/non-Hispanic. Non-Hispanic. White. Black or African American. American Indian and Alaska Native. Asian. Native Hawaiian and Other Pacific Islander. Some other race. Minority. Equally minority/nonminority. Nonminority. Veteran-owned. Equally veteran-/nonveteran-owned. Nonveteran-owned. All firms classifiable by gender, ethnicity, race, and veteran status. Publicly held and other firms not classifiable by gender, ethnicity, race, and veteran status. . . Years in business. . All firms. Firms less than 2 years in business. Firms with 2 to 3 years in business. Firms with 4 to 5 years in business. Firms with 6 to 10 years in business. Firms with 11 to 15 years in business. Firms with 16 or more year...

Women Entrepreneurs Boston (WE BOS) provides the skill-building opportunities, technical help, and networks to help women entrepreneurs launch and grow their businesses. WE BOS hosts an annual series of free events called WE BOS Week. As part of that week, WE BOS created this dataset, which showcases self-identified Women-Owned businesses in the City of Boston.

You can learn more about WE BOS and WE BOS Week here: https://www.boston.gov/economic-development/women-entrepreneurs-boston

This study aims to examine the correlation between various types of entrepreneurial motivations and the corporate performance of self-employed micro-businesses operated by women in China. Through the application of Structural Equation Modeling (SEM) estimation on a sample of 160 female entrepreneurs, our findings reveal that female entrepreneurs driven by pull motivation prioritize non-financial performance as their primary goal. Conversely, those driven by push motivation exhibit a greater emphasis on financial performance. Furthermore, the cross-group analysis indicates that a high level of motivation among necessity-driven female microbusiness entrepreneurs contributes to achieving a high level of financial performance, whereas a high level of motivation among opportunity-based female microbusiness entrepreneurs does not significantly influence non-financial performance. The implications of these findings for research and policy development pertaining to Chinese female online microbusinesses are also discussed.

This table presents data on the distribution of business sizes (micro, small, and medium) across two genders of entrepreneurs (man and woman), using counts and percentages to illustrate the breakdown.Key Insights:Microbusinesses dominate the dataset, accounting for the vast majority of businesses. Women represent 69.2% of this category, while men make up 30.8%.Small businesses are more evenly split, with 54.4% women and 45.6% men.Medium Businesses are male-dominated, with men accounting for 61.5% while women represent 38.5%.The gender distribution across all business sizes shows that women are the majority (67.7%), while men make up 32.3%.

This file contains data at the firm level for the 2011 Finance and Gender Issues Survey that is a follow-up of Enterprise survey 2010 implemented in Trinidad and Tobago financed by IDB. This data specially concentrates on gender ownership of the firm. It is possible to study firm´s performance and finance, in others. It is also attached technical information regarding data and questionnaires.

The current body of research on the gender-energy nexus has largely concentrated on the effects of energy poverty within households, highlighting the impact on women in domestic settings. Nonetheless, women entrepreneurs involved in various productive activities are also crucial in adopting new energy technologies. This dataset presents raw and processed data obtained from 27 face-to-face interviews conducted across multiple African countries, focusing on micro and small-sized enterprises with at least one female owner. The data can be used to assess energy access among women entrepreneurs in Africa, focusing on the potential for renewable energy adoption.

The data collection through semi-structured, face-to-face interviews occurred between February and September 2023. The semi-structured interviews were guided by a predetermined questionnaire featuring predominantly open-ended questions designed to collect quantitative and qualitative data. The main areas of data collection presented in this dataset span socio-economic factors related to the enterprise and entrepreneur, energy access characteristics including appliances, processes, and energy supply, and the potential for adopting renewable energy technologies, highlighting current barriers to and drivers for future energy access implementation.

Key components of the dataset include the following:

Socio-economic factors: Enterprise location, ISIC division and industry sector classification, main production goods, gender-based ownership structures, enterprise formality (based on registration), year of establishment or business start, enterprise size (number of employees), profit margins, and business challenges related to the owner's gender.

Energy access characteristics: Type of energy carriers used, subapplication, grid blackout or fuel shortage, energy consumption levels, type, number and power rating of appliances used, temperature requirements, and energy expenditure.

Potential for renewable energy adoption: Type and amount of process waste, perceived barriers and drivers for renewable energy adoption, willingness to invest in new technologies, and preferred financing methods for these new technologies.

The dataset is valuable for researchers, policymakers, and practitioners aiming to understand the energy access landscape for women entrepreneurs in Africa. It provides a foundation for developing targeted interventions that promote gender equity in energy access and foster the adoption of renewable energy technologies. Researchers can use the data to perform analyses of the socio-economic and technical factors influencing energy use in micro- and small-sized enterprises. Policymakers can leverage the insights to design gender-sensitive energy policies and support mechanisms that address the specific needs of women entrepreneurs. Practitioners can develop innovative business models and financing solutions tailored to the unique challenges and opportunities identified in the dataset.

This Dataset contains year and state-wise data on loans extended to women Entrepreneurs under Pradhan Mantri Mudra Yojana (PMMY)

The Mekong Vitality Expanded (MVE) is transforming women from micro-enterprise operators to entrepreneurs who are business leaders, fully engaged in their communities. The project will enable women by equipping them with an understanding of sound business practices, market forces, and trade related opportunities. Facilitated by mobile technology, the program increases women’s access to key sources of market information and guides them to use that information to identify and seize more business opportunities. Pact is augmenting the business development training for an estimated 300 of the 6,000 participants in its WORTH program. At the start of the project, Pact conducted a rapid assessment of barriers to women’s full engagement in business, adapted from the USAID Diagnostic of Women Entrepreneurs. The purpose of the assessment was to garner an in-depth understanding of the business environment as well as the capacity and needs of Vietnamese participants. Pact then developed an augmented business training curriculum based on the results of the startup assessment. In order to introduce mobile technology for an e-learning application/platform of the business materials, the assessment integrated information about participants’ current level of capacity, the type of technology and devices currently most used by the women and their frequency of use. Members of existing WORTH groups will be given the opportunity to self-select into the MVE activities.

Women entrepreneurs in Nigeria face higher barriers than men do to access finance, especially in providing traditional forms of collateral for loans, since most assets that lenders accept are typically registered to men. The Nigeria We-Fi project aims to address key constraints faced by women in the access to credit by facilitating the development and delivery of innovative credit products and supporting capacity building for women-led SMEs.

This impact evaluation will measure the impact of an innovative credit product designed to surmount longstanding collateral constraints faced by women entrepreneurs by using cash flow to determine credit worthiness. It will also measure the impact of tailored outreach campaigns by banks to reach women entrepreneurs. Since evidence of the impact of large, flexible-collateral loans for women entrepreneurs is limited globally, this innovative pilot also has the potential to inform broader We-Fi programming and credit interventions for women entrepreneurs.

As part of a proof-of-concept pilot study, this baseline survey was conducted from August – December 2021 on a sample of 214 business owners across four states in Nigeria: Abuja, Lagos, Oyo, and Port Harcourt.

The instrument was designed to collect enterprise and household level data from the sampled male and female enterprise owners that have been approved for a cashflow based loan at their place of business. Survey topics include (i) household demographics, (ii) business activities and ownership, (iii) access to finance (iv) entrepreneurs’ characteristics, and (v) intra-household dynamics.

Cultivating business growth intentions in rural, regional, and remote women entrepreneurs is crucial, considering the unique challenges they face in rural areas. The growth intentions of rural, regional, and remote women entrepreneurs remain understudied. This study pioneers research on the interplay between entrepreneurial leadership competency, identity, and growth intentions of rural, regional, and remote Australian women. We surveyed rural, regional, and remote women entrepreneurs in Queensland, Australia, using structural equation modeling for analysis. Results revealed a positive relationship between entrepreneurial leader identity, business growth intentions, and entrepreneurial leadership competency. Moreover, entrepreneurial leadership competency positively correlated with growth intentions. The study indicated that entrepreneurial leadership competency partially mediates the link between identity and growth intentions. This research addresses a theoretical gap by introducing a new model showcasing the relationships between entrepreneurial leadership identity, entrepreneurial leadership competency, and venture growth intentions. From a practical standpoint, our findings strengthen the business case for improving tailor-made rural, regional, and remote entrepreneurial development programs.

Dataset collected from South African female entrepreneurs.Questionnaire available from natanyam@uj.ac.za

Loading, reliability, and validity statistics of higher-order components of EL competency.