Presentation Title
Life Settlements in the U.S.: An Empirical Analysis of Regulatory Effects
Abstract
Ever since the AIDS crisis of the 1980s, the life settlement industry has experienced relatively rapid growth. With this growth, however, have come many regulations attempting to safeguard sellers in the market. This paper analyzes the effects of life settlement provider bond requirement and broker compensation disclosure regulations on the total life settlement transactions in each state. This is done through a time fixed effects multiple regression model, which is run on annual panel data covering three years. In addition, the structure and participants of the market are explored, and the theoretical economic significance of certain types of regulation is examined. Regression results show that estimators for the selected regulations are not statistically insignificant at any conventional level when controlling for relevant variables.
Primary Faculty Mentor Name
Dr. Jane Knodell
Secondary Mentor Name
Dr. Richard Sicotte
Status
Undergraduate
Student College
College of Arts and Sciences
Program/Major
Economics
Primary Research Category
Social Sciences
Life Settlements in the U.S.: An Empirical Analysis of Regulatory Effects
Ever since the AIDS crisis of the 1980s, the life settlement industry has experienced relatively rapid growth. With this growth, however, have come many regulations attempting to safeguard sellers in the market. This paper analyzes the effects of life settlement provider bond requirement and broker compensation disclosure regulations on the total life settlement transactions in each state. This is done through a time fixed effects multiple regression model, which is run on annual panel data covering three years. In addition, the structure and participants of the market are explored, and the theoretical economic significance of certain types of regulation is examined. Regression results show that estimators for the selected regulations are not statistically insignificant at any conventional level when controlling for relevant variables.