Social Science

Gender differences and new venture performance


  Peer Reviewed

Abstract

Purpose This study compares the performance of female majority-owned new ventures (FNV) vs. male majority-owned new ventures (MNV). It analyzes the differences in levels of variables such as education, the same industry work experience of owners, and other venture level attributes between FNVs and MNVs. More importantly, this study employs decomposition techniques to determine the individual contribution from the intergender difference of each attribute on the performance of the new venture. For example, the study finds that, on average, the owners of an MNV possessed 3.4 years more of the same industry work experience than their FNV counterparts. This difference in work experience accounted for 47% of the “explained” gap [1] in Net Profits between the FNVs and MNVs. Design/methodology/approach This paper utilizes the Kauffman Firm Survey, a longitudinal dataset of 4,928 new ventures started in the USA in 2004. It employs Blinder-Oaxaca and Fairlie decomposition techniques in conjunction with OLS and Logit regressions. Both methods provide point estimates of contributions to the performance gap due to the heterogeneity in each attribute across the groups (FNV and MNV). This approach has a significant advantage over OLS or mediation analysis, which can only provide a directional analysis of the contributions of differences in attributes to performance. Findings The paper finds no performance gap between MNVs and FNVs. It further investigates whether the heterogeneous characteristics of MNVs vs FNVs are related to different effects on survival and performance. It finds that characteristics such as owners’ work experience in the same industry, average hours worked by owners in the new venture, the technology level of the venture, and its incorporation status are related with a differential impact on new venture survival and performance. Research limitations/implications All firms in the dataset belonged to a single cohort (2004) of new ventures started in the US. Future studies are encouraged to develop a dataset from multiple geographies and founding over several years so that the results may be more generalizable. Practical implications The paper provides crucial practical guidance to policymakers, investors, and entrepreneurs. In general, policies that enhance the work experience of women entrepreneurs and provide access to infrastructure such as daycares, which may allow them to work more hours, would probably improve the performance of FNVs. Originality/value The paper furthers the literature on women entrepreneurship by analyzing point estimates of differential contribution of disparate variables to performance. From a methodological perspective, the study reconciles the results between regression and decomposition analyses.

Key Questions and Answers

1. What is the purpose of the study on gender differences in venture performance?

The study compares the performance of female majority-owned (FNVs) and male majority-owned (MNVs) ventures, analyzing factors like work experience, education, and other attributes to determine their impact on performance.

2. What methodology was used in the research?

The study used the Kauffman Firm Survey dataset with 4,928 new ventures and applied Blinder-Oaxaca and Fairlie decomposition techniques combined with OLS and Logit regressions to measure performance differences.

3. What were the key findings?

The study found no performance gap between FNVs and MNVs but identified that factors like industry experience, hours worked, and technological level influenced performance and survival differently between the groups.

4. What are the practical implications?

The study suggests that supporting women entrepreneurs with better work experience and infrastructure (e.g., childcare) could improve the performance of FNVs.

5. What is the study's originality?

This paper uniquely analyzes the differential contribution of various attributes to the performance gap between FNVs and MNVs and reconciles results from regression and decomposition analyses.