This statistic shows changes in the composition of new entrepreneurs in the United States as of April 2022, by age. In 2022, about * percent of entrepreneurs in the United States were aged between 20 and 30 years.

Key entrepreneur statistics for 2025, covering global numbers, demographics, education levels, new business applications, average age, and survival rates.

This statistic shows the rate of new entrepreneurs in the United States from 2000 to 2021, by age. In 2021, the rate of new entrepreneurs in the U.S. in the age group of 20 to 34 year olds was **** percent, or *** new entrepreneurs aged 20 to 34 years old for every 100,000 adults each month.

This statistic displays the percentage of the adult population involved in early-stage entrepreneurial activity in China as of June 2019, by age group. During the survey period, **** percent of respondents aged between 18 and 24 years in China reported to be in the process of starting their own business or have launched a business which is less than 42 months old.

Many observers, and many investors, believe that young people are especially likely to produce the most successful new firms. Integrating administrative data on firms, workers, and owners, we study start-ups systematically in the United States and find that successful entrepreneurs are middle-aged, not young. The mean age at founding for the 1-in-1,000 fastest growing new ventures is 45.0. The findings are similar when considering high-technology sectors, entrepreneurial hubs, and successful firm exits. Prior experience in the specific industry predicts much greater rates of entrepreneurial success. These findings strongly reject common hypotheses that emphasize youth as a key trait of successful entrepreneurs.

This statistic displays the percentage of the adult population involved in early-stage entrepreneurial activity in Malaysia as of *********, by age group. During the survey period, **** percent of respondents aged between 45 and 54 years in Malaysia reported to be in the process of starting their own business or have launched a business which is less than 42 months old.

This statistic shows the number of business establishments in the United States in March 2023, by age. In March 2023, there were **** million businesses that were less than * year old. At that time, there were ******* businesses that were 10 years old.

The Global Entrepreneurship Monitor [GEM] research program was developed to provide comparisons among countries related to participation of adults in the firm creation process. The initial data was assembled as a pretest of five countries in 1998 and by 2012 over 100 countries had been involved in the program. The initial design for the GEM initiative was based on the first US Panel Study of Entrepreneurial Dynamics, and by 2012 data from 1,827,513 individuals had been gathered in 563 national samples and 6 specialized regional samples. This dataset is a harmonized file capturing results from all of the surveys. The procedure has been to harmonize the basic items across all surveys in all years, followed by implementing a standardized transform to identify those active as nascent entrepreneurs in the start-up process, as owner-managers of new firms, or as owner-managers of established firms. Those identified as nascent entrepreneurs or new business owners are the basis for the Total Entrepreneurial Activity [TEA] or Total Early-Stage index. This harmonized, consolidated assessment not only facilitates comparisons across countries, but provides a basis for temporal comparisons for individual countries. Respondents were queried on the following main topics: general entrepreneurship, start-up activities, ownership and management of the firm, and business angels (angel investors). Respondents were initially screened by way of a series of general questions pertaining to starting a business, such as whether they were currently trying to start a new business, whether they knew anyone who had started a new business, whether they thought it was a good time to start a new business, as well as their perceptions of the income potential and the prestige associated with starting a new business. Demographic variables include respondent age, sex, and employment status.

While older entrepreneurs are more likely to be male, white, and have higher levels of human, social, and financial capital, we know less about interest in later-life entrepreneurship. This study estimates entrepreneurial interest in a nationally representative sample of Americans aged 50 to 70 using partial proportional odds modeling. We estimate that more than 31 million older Americans have some interest in entrepreneurship and reveal key predictors of this interest (e.g., younger age). Importantly, the findings indicate that a more diverse group of older adults are interested in entrepreneurship than have become entrepreneurs, suggesting the need for additional research on the potential disparities between entrepreneurial interest and action in later life.

In a survey conducted in 2022, about *** percent of respondents aged between 18 and 34 years in Japan reported being in the process of starting their own business or having launched a business that is less than 42 months old. The share was slightly higher among the group of respondents aged 35 to 64 years, with *** percent involved in early-stage entrepreneurial activity.

Means (and standard deviations) of the entrepreneurship tendency score, by age groups.

Annual counts of enterprises by age of owner (younger than 30 years, 30 to 39 years, 40 to 49 years, etc.) for Canada, provinces and the territories.

In 2024, approximately ** percent of the Brazilian population aged 18 to 34 was involved in early-stage entrepreneurship, i.e. managing businesses in their first *** years. This was the age range with the highest entrepreneurial engagement rate among the Brazilian adult population that year.

This dataset delves into the motivations and behaviours of elderly entrepreneurs in Cyprus - individuals who continue to engage in entrepreneurial activities past retirement age. Cypriot entrepreneurs often remain active post-retirement for varied reasons.

The dataset includes quantitative data from a survey of 122 elderly entrepreneurs collected in Cyprus (areas of Nicosia and Limassol), between the second half of 2018 and the first half of 2019. The dataset includes demographic characteristics and the measure of the following constructs: Self-efficacy, Passion Scale, Loneliness, Work-life Balance, Satisfaction with life, Personality, and Entrepreneurial intention.

The Panel Study of Entrepreneurial Dynamics (PSED) research program was designed to longitudinally examine the startup process with multi-year cohort tracking, so as to enhance the scientific understanding of how individuals start businesses. The project provided data on the process of business formation based on nationally-representative samples of nascent entrepreneurs, those active in business creation. PSED I (available from ICPSR as study 37203) began with screening in 1998-2000 to select a cohort of 830 with three follow-up interviews. The panel participants were identified prior to launch of their firms and were tracked through gestation, launch and eventual growth or death of the firm. A control group of those not involved in firm creation were available for comparisons. PSED II began with screening in 2005-2006, followed by six yearly interviews. The information obtained as part of the PSED research program included data on the nature of those active as nascent entrepreneurs, the activities undertaken during the start-up process, and the characteristics of start-up efforts that become new firms. The PSED II data included as part of this collection includes: Dataset 1 and 2: Screener Data (58 variables, 31,845 cases) Dataset 3 and 4: Waves A-F plus Screener Data (7,821 variables, 1,214 cases) Demographic variables included as part of this collection comprises age, race, ethnicity, gender, household income, educational attainment, employment status, marital status, citizenship, household characteristics, and business characteristics.

Enterprises managed by the founder, by entrepreneur age (2005)

With the collision between the green and low-carbon economy and the accelerating digital economy, how to realize the effect of "1+1>2" has gradually become an important topic for contributing to the high-quality development of regions and enterprises. Entrepreneurship in the digital age continues to exhibit new characteristics, and its impact on green development is also more closely related. This article focuses on the context of the low-carbon strategy, incorporating the digitalization level, entrepreneurship, and green development into the same framework. It then takes 2011–2021 Chinese provincial panel data and enterprise panel data as samples to conduct research. The results indicate the following: (1) The digitalization level has a significant positive promoting effect on the green development of regions and enterprises, and blockchain technology has the strongest promoting effect on the green development of enterprises. (2) The digitalization level drives the green development of regions and enterprises through three channels: entrepreneurs’ innovative spirit, entrepreneurs’ entrepreneurial spirit and entrepreneurs’ contract spirit. Entrepreneurship is the intermediary bridge for the digitalization level to promote green development. (3) Environmental regulations partially serve as "accelerators" of the impact of green development. The findings of this article will provide empirical support for evaluating the impact of digitalization on green development and offer useful insights for better stimulating and cultivating entrepreneurship in the new era to empower comprehensive green development.

This statistic displays the percentage of the adult population involved in early-stage entrepreneurial activity in India as of June 2017, by age group. During the survey period, *** percent of respondents aged between 45 and 54 years in India reported to be in the process of starting their own business or have launched a business which is less than 42 months old.

Social entrepreneurship has been advocated by both government and non-government groups in Bangladesh as a strategy to boost the country's economy (Adnan, Yusoff & Ghazali, 2018, p. 1). Social entrepreneurship in a country provides sustainability in socio-economic development. Social entrepreneurs create a good value in our society and also they increase the private value.

Due to the nature of the research, we were required to administer the survey to participants online; we did so by creating a Google form and posing a series of questions and/or statements with multiple-choice answers to the participants. Respondents were given a 5-point scale on which to indicate how much they agreed or disagreed with the statement. It was suggested that we use Smart PLS software to run the samples, compute the findings, and establish the dependability of each variable. From a total of 225 samples, 95% or more of the responses were considered satisfactory for 205 of them. These samples were analyzed using statistical approaches including descriptive statistics, intercorrelation analysis, regression analysis, and similar techniques.

The questionnaire was divided into three sections where first section is the introduction, brief description about the purpose of research. From Table-1 (Demographic Variable) it can be seen that most of the respondents are between 20 to 29 years old in terms of age and it is (88.71%) comparing to the total number of people. Graduate between thirty to thirty nine age are (10.25%) and graduates between forty to forty nine age are only (1.02%). In terms of gender male have more respond to social entrepreneurship and the percentage is (66.15%) where female is lower (33.15%). In term of education level or qualification bachelor’s frequency is 149 (76.41%) out of total 195. Respondents from master’s level have 46 (23.58%) out of 195. The respondents from public university are more (63.58%) and from private (57.25%). Model construction and test using PLS-SEM To draw the model in Smart PLS-3 factors were uploaded initially. Every factor is called latent variable in this software.

Entrepreneurial Readiness (Tabular)

Target: entrepreneurial_readiness (numeric 0–10) Features: savings_amount, monthly_income, monthly_expenses, monthly_entertainment, sales_skills, risk_level, age, dependents, assets, confidence, difficulty_of_business_idea. Suggested splits: train / test (80/20). Intended use: Tabular regression baselines (LinearRegression, RandomForestRegressor, XGBoost), EDA, scaling/outlier handling demos. Notes:

Consider StandardScaler or RobustScaler and… See the full description on the dataset page: https://huggingface.co/datasets/King-8/entrepreneurial-readiness-data-v2.