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 the evaluation of the entrepreneurship framework conditions in Egypt, the country scored its highest on the physical infrastructure indicator, at ***. This was slightly higher than the Global Entrepreneurship Monitor (GEM) average. Moreover, the market dynamics upon which new businesses can enter the market obtained a rating of ***. Furthermore, the rest of the indicators received ratings lower than the GEM average, the lowest being the indicator on entrepreneurial education, at ***.

Batteries of questions for Entrepreneurship support by university and Entrepreneurial intention.

This dataset tracks annual reading and language arts proficiency from 2010 to 2012 for Business Entrepreneurship Science Technical Academy vs. Michigan and Business Entrepreneurship Science TECH. Academy School District

All the datasets uploaded contain all the variables required for the analysis carried out in the paper titled: “The impact of entrepreneurship training and credit on labour market outcomes of disadvantaged youth” psm_DP_Labd This dataset contains all the variables used to match the propensity scores. 1_Promise_single_974_DP_Labd This dataset has variables regarding ownership of businesses, savings and expenditure. 2_Promise_roster_974_DP_Labd Variables covering all the demographic characteristics are all gathered in this dataset. 3_Promise_q10_occup_974_DP_Labd Variables regarding employment are all in this dataset. 4_Promise_q12_loan_974_DP_Labd All the variables pertaining to loan are filed in this dataset.

This is a database constructed for conducting analysis of qualitative data based on IDIs for the purpose of preparing of PhD dissertation. The scope of analysis was development of social entrepreneurship in Poland - the role of the institutional environment. The database involves 20 respondents: company leaders of social enterprises and representatives of stakeholders from Poland.

The dataset used in the scientific research paper: Content Entrepreneurship on Social Media Platforms. It contains the 13 conducted interviews with content entrepreneurs and a detailed Codebook extracted from MAXQDA containing project information, the code system, code segments, and memos used during the analysis.

This dataset tracks annual graduation rate from 2011 to 2012 for Institute Of Business And Entrepreneurship High School vs. Maryland and Baltimore City Public Schools

This dataset tracks annual black student percentage from 1997 to 2013 for Milwaukee School Of Entrepreneurship vs. Wisconsin and Milwaukee School District

This dataset tracks annual total expenditure from 2019 to 2023 for School For Entrepreneurship And Technology School District

This study aims to propose and validate with experts a framework with elements for measuring social entrepreneurship for developing countries. The proposed model was designed based on a literature review of entrepreneurship models indexed in Web of Science and Scopus databases. The dimensions associated with social entrepreneurship and their potential analysis categories were identified, composing a preliminary framework of indicators validated by a panel of experts using the Delphi technique. The analysis tool was a four dimensions survey, and their sub-dimensions, resulting in 59 variables. It was available in Portuguese and English, which allowed international participation, and sent via e-mail to the respondents. A Likert-type scale was adopted, ranging from 1 to 7, where 1 is the least important and 7 the most important for a indicator. At the end of each group of questions, an open-ended question was included for suggestions and comments. The Delphi method was implemented in two rounds, and it was established as insertion criteria that at least 80% responses were equal or higher than 5. After the first data analysis round, the indicators were submitted to a second round. Initially, the indicators with consensus equal to or greater than 80% were evaluated, then those that did not reach consensus in the first round. In both cases, the specialist was asked to decide whether the indicator should be included or excluded. The analysis of the responses from the second round was carried out using the same level of consensus in first round (80%), for both inclusion and exclusion of the item in the model. After two rounds of Delphis questionnaires, it was possible to reach the most important indicators for the intended evaluation. Therefore, 46 out of 59 (77.97%) initially proposed indicators were taken into consideration to explain social entrepreneurship in developing countries. The model includes elements of entrepreneurship measurement related to the individual and organizational level, composing four dimensions, namely: Social Entrepreneurial Intention, Social Entrepreneurial Orientation, Processes, and Outcomes. It recognizes that social entrepreneurship in developing countries depends on the social context, which is reflected in the willingness to solve society's problems, generating not only economic value, but also social and environmental value as a result.

Time series data for the statistic Women, Business and the Law: Entrepreneurship Indicator Score (scale 1-100) and country Japan. Indicator Definition:The Entrepreneurship indicator measures constraints on women starting and running a business.. The Entrepreneurship indicator score is obtained by calculating the unweighted average of the following: 1. SG.CNT.SIGN.EQ; 2. SG.BUS.REGT.EQ; 3. SG.OPN.BANK.EQ; 4. SG.LAW.CRDD.GR (= 25 points each) and scaling the result to 100.

Italy IT: New Business Density: New Registrations per 1000 People Aged 15 to 64 data was reported at 2.648 Number in 2016. This records an increase from the previous number of 2.577 Number for 2015. Italy IT: New Business Density: New Registrations per 1000 People Aged 15 to 64 data is updated yearly, averaging 2.221 Number from Dec 2006 (Median) to 2016, with 11 observations. The data reached an all-time high of 2.648 Number in 2016 and a record low of 1.906 Number in 2012. Italy IT: New Business Density: New Registrations per 1000 People Aged 15 to 64 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Italy – Table IT.World Bank.WDI: Businesses Registered Statistics. New businesses registered are the number of new limited liability corporations registered in the calendar year.; ; World Bank's Entrepreneurship Survey and database (http://www.doingbusiness.org/data/exploretopics/entrepreneurship).; Unweighted average; For cross-country comparability, only limited liability corporations that operate in the formal sector are included.

William Baumol proposes that there are two types of entrepreneurship: productive or unproductive. Productive entrepreneurship, characterized by innovation and efficient resource allocation, fosters economic growth and can act as a potent magnet for migration. Conversely, unproductive entrepreneurship, which often involves rent-seeking and regulatory circumvention, deters migration and potentially provokes out-migration. To test this link from the types of entrepreneurship and migration, we use a new index of entrepreneurship (productive and unproductive) in conjunction with a dataset covering migration to and from Metropolitan Statistical Areas (MSA) from 2005 to 2019. Our analysis reveals that regions high in productive entrepreneurship experience significant net in-migration, while those dominated by unproductive entrepreneurship see the opposite effect.

This dataset provides benchmarking of 220 undergraduate programs (BS BSc BA BBA). From this dataset, a paper has already been published:

Siddiqui, K., & Alaraifi, A. (2019). What they don't teach at entrepreneurship institutions? An assessment of 220 entrepreneurship undergraduate programs. Journal of Entrepreneurship Education, 22(6), 1-16.

Entrepreneurship is risky. We study the risk facing a well-documented and important class of entrepreneurs, those backed by venture capital. Using a dynamic program, we calculate the certainty-equivalent of the difference between the cash rewards that entrepreneurs actually received over the past 20 years and the cash that entrepreneurs would have received from a risk-free salaried job. The payoff to a venture-backed entrepreneur comprises a below-market salary and a share of the equity value of the company when it goes public or is acquired. We find that the typical venture-backed entrepreneur received an average of $5.8 million in exit cash. Almost three-quarters of entrepreneurs receive nothing at exit and a few receive over a billion dollars. Because of the extreme dispersion of payoffs, an entrepreneur with a coefficient of relative risk aversion of two places a certainty equivalent value only slightly greater than zero on the distribution of outcomes she faces at the time of her company's launch. (JEL G24, G32, L26, M13)

This study shows that young entrepreneurs see AI as a powerful tool for growing their businesses. They're using AI platforms like ChatGPT and analytics tools to learn about market trends, understand their customers better, and make smarter business decisions. These young business owners find AI helpful in many ways - it saves them time, helps them spot opportunities, and solves problems faster. However, they also face some challenges when using AI, like finding it complicated at first and worrying about whether they can trust the information it gives them. To deal with this, they often check AI's suggestions against their own experience and what their customers tell them. Despite these challenges, the entrepreneurs we talked to believe AI will become even more important for running a successful business in the future. They're excited about how AI could help them learn and adapt quickly in the fast-changing business world. This research highlights both the opportunities and the hurdles young entrepreneurs face when using AI, and suggests that with the right support and strategies, AI can be a game-changer for their businesses. The findings from this study can help other young entrepreneurs, business advisors, and policymakers understand how to better use and support AI in small businesses, potentially leading to more successful startups and stronger economic growth.

Explain the government's related achievements in promoting innovation and entrepreneurship policies.

Dataset of assessing how personality traits, extracurricular entrepreneurship education, and family influence affect the entrepreneurial intentions (EI) of Brazilian students in their final year at State Technical Schools (ETECs). ETECs are public educational institutions, in São Paulo State, that offer technical and secondary level courses. They are part of the Paula Souza State Center of Technological Education that is an autarchy linked to the State Secretariat of Science, Technology and Innovation.