Moving beyond the Mean: A Quantile Analytic Approach to Cross-National Comparative Research in Development Sociology

• In Event: 3247 - Open Topic on Sociology of Development

Abstract

Quantitative studies in the field of development sociology employ a wide range of modeling techniques depending on the characteristics of the data. However, our systematic review of the most highly cited studies in the field reveals a common focus on the conditional mean. That is, they estimate the relationship between predictors and outcomes, for any given sample, on average. In this study, we demonstrate and problematize this trend. We then present an alternative: quantile regression analysis. In taking a quantile analytic approach, we remove the assumption that the drivers of cross-national inequality are uniform in magnitude and direction across the distribution of development outcomes. We use quantile regression analysis adapted for panel data to analyze societal impacts on the natural environment measured as carbon dioxide emissions per unit of production and per capita. We test three predictors highlighted in previous work: economic development, integration into the world economy, and ties to world society. We find evidence that the effects of each predictor depend on the size of states’ environmental impact. These results suggest that, in addition to traditional distinctions between political economic positions and point in time, sources of cross-national stratification differ across the distribution of development outcomes.

Authors

• Kristen Shorette, Stony Brook University

• Jamie Marie Sommer, Stony Brook University