Using Factor Scores to Predict Metropolitan Growth: Regional Indicators

• Activity Number: 310
• Type: Contributed
• Date/Time: Tuesday, August 6, 2013 : 8:30 AM to 10:20 AM
• Sponsor: Business and Economic Statistics Section
• Author(s): Merissa C. Piazza*+ and Iryna V. Lendel
• Companies: Cleveland State University and Cleveland State University
• Keywords: Causal Inference ; Linear Regression ; Factor Analysis ; Data/Methods ; Urban Indicators ; Economic Development

Abstract

This paper examines the methodology behind the 2012 What Matters to Metros: Foundational Indicators for Economic Competitiveness ("Indicators"). The Indicators is a regional project that tracks over 100 middle-tier metropolitan statistical areas (within the population of 400,000 to 3.5 million) across 55 variables. Using factor analysis as a data reduction technique, these variables are clustered and expressed through factor scores to highlight overall themes in regional economies (i.e. entrepreneurship, innovation, and education, etc.). Using these factor scores as predictors, they are then regressed upon macroeconomic growth variables (employment, gross regional product, productivity, and per capita income). This paper will focus on important methodological implications and limitations to the factor and regression analyses, how statistical assumptions and data quality affect overall results, the generalizability of the study, and other relevant mythological modes of inquiry.