Prediction of Future New Entrepreneurs Using Population Survey Data

Presentation Type

Poster

Presentation Type

Submission

Keywords

Entrepreneurship, Data Science, Population Survey, Machine Learning

Department

Nutritional Science

Major

Computer Science / Math

Abstract

Entrepreneurship is essential to the U.S. economy and understanding the characteristics of entrepreneurs can help organizations like the Small Business Administration (SBA) assist with growth of startup businesses. Understanding what makes an entrepreneur can be difficult and predicting future entrepreneurs even more so. Using data from the U.S. Census Bureau’s Current Population Survey (CPS), we are able to track annual survey respondents across a 16 month period from non-self-employment to self-employment. With our own technique, we successfully matched records from nearly 1.2 million respondents over a 10 year period from 2009 to 2019. Using machine learning techniques trained with an optimized set of features, we created one model to classify entrepreneurs and another to predict future entrepreneurs or the transition to entrepreneurship. The first model can be used to help understand defining factors of entrepreneurship and the second can be used to assist potential new or first time business owners. Other research conducted on the subject relies on niche datasets, which are expensive to collect and hard to come by; through machine learning and data mining approaches our technique shows the untapped potential of population data sets, such as the CPS, in business and economic research such as this.

Faculty Mentor

Alfonso Berumen

Funding Source or Research Program

Keck Scholars Program

Location

Waves Cafeteria

Start Date

22-3-2024 1:30 PM

End Date

22-3-2024 2:30 PM

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Mar 22nd, 1:30 PM Mar 22nd, 2:30 PM

Prediction of Future New Entrepreneurs Using Population Survey Data

Waves Cafeteria

Entrepreneurship is essential to the U.S. economy and understanding the characteristics of entrepreneurs can help organizations like the Small Business Administration (SBA) assist with growth of startup businesses. Understanding what makes an entrepreneur can be difficult and predicting future entrepreneurs even more so. Using data from the U.S. Census Bureau’s Current Population Survey (CPS), we are able to track annual survey respondents across a 16 month period from non-self-employment to self-employment. With our own technique, we successfully matched records from nearly 1.2 million respondents over a 10 year period from 2009 to 2019. Using machine learning techniques trained with an optimized set of features, we created one model to classify entrepreneurs and another to predict future entrepreneurs or the transition to entrepreneurship. The first model can be used to help understand defining factors of entrepreneurship and the second can be used to assist potential new or first time business owners. Other research conducted on the subject relies on niche datasets, which are expensive to collect and hard to come by; through machine learning and data mining approaches our technique shows the untapped potential of population data sets, such as the CPS, in business and economic research such as this.