Modeling new-firm growth and survival with panel data using event magnitude regression |
| |
| Institution: | 1. emlyon business school, 23, avenue Guy de Collongue, CS 40203 69134 Ecully Cedex, France;2. School of Economics and Management, Lund University, P.O. Box 7080, S-220 07 Lund, Sweden;1. University of Toronto, Canada;2. University of Pennsylvania, United States of America;1. Martin J. Whitman School of Business, Syracuse University, United States of America;2. Joseph M. Katz Graduate School of Business, University of Pittsburgh, United States of America;1. Rawls College of Business, Texas Tech University, USA;2. College of Business, Colorado State University, USA;3. Ivey Business School, Western University, Canada;4. Gustavson School of Business, University of Victoria, Canada;1. Department of Management & Entrepreneurship, Kelley School of Business, Indiana University, 1309 E 10th Street | Hodge Hall 3147, Bloomington, IN 47405, United States of America;2. École Polytechnique Fédérale de Lausanne, College of Management and Technology, Route Cantonale, 1015 St. Sulpice, Switzerland;3. Tom Love Division of Entrepreneurship & Economic Development, Price College of Business, University of Oklahoma, 1003 Asp Avenue – Suite 3005, Norman, OK 73019, United States of America;1. Lacy School of Business, Butler University, Indianapolis, IN, USA;2. School of Finance and Management, SOAS University of London, London, UK |
| |
| Abstract: | We introduce a new model to address three methodological biases in research on new venture growth and survival. The model offers entrepreneurship scholars numerous benefits. The biases are identified using a systematic review of 96 papers using longitudinal data published over a period of 20 years. They are: (1) distributional properties of new ventures; (2) selection bias; and (3) causal asymmetry. The biases make the popular use of normal distribution models problematic. As a potential solution, we introduce and test an event magnitude regression model approach (EMM). In this two-stage model, the first model explores the probability of four events: a firm staying the same size, expanding, contracting, or exiting. In the second stage, if the firm contracts or expands, we estimate the magnitude of the change. A suggested benefit is that researchers can better separate the likelihood of an event from its magnitude, thereby opening new avenues for research. We provide an overview of our model analyzing an example data set involving longitudinal venture level data. We provide a new package for the statistical software R. Our findings show that EMM outperforms the widely adopted normal distribution model. We discuss the benefits and consequences of our model, identify areas for future research, and offer recommendations for research practice. |
| |
| Keywords: | |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
