Segmenting the Construction Industry: A Quantitative Study of Business Interest Groups in a Low Salience Policy Setting

Abstract

The intent of this research is to detect if business interest group involvement in urban sustainability policymaking increases or decreases the likelihood of policy adoption. Extant research reports both positive and negative effects with varying magnitude. This study segments the construction industry into distinctive categories to explain conditions under which types of business interest groups support or oppose building regulations drawing from competing theoretical angles—private and public interest group theory. It analyzes the effects of two groups—traditional construction and green building association members—on the adoption of building energy codes, a low salience policy issue that attracts technical experts more so than citizen groups. After applying web scraping algorithms, logistic regression explains the probability of code stringency given differences in the presence of trade association members in cities while controlling for demographic, social, and political factors. Findings suggest that this approach to operationalizing interest groups has merit. Despite being from the same industrial category, the segmented business interest groups have divergent effects on the local building policies with traditional construction interest groups having a greater negative effect on the odds of a city’s energy code adoption compared to the green builder interest group.

Keywords:

• Building codes
• business influence
• microeconomics
• sustainability
• urban policy

Disclosure Statement

There are no conflicts of interest associated with this research.

Notes

1 The member proportion variable is generated from two other independent variables. Testing for multicollinearity, the Variance Inflation Factor on the member proportion variable is 1.76, indicating a very moderate level of multicollinearity. Withholding the member proportion variable from the model does not substantially alter the coefficients or significance on any other variables.