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Fixed effects estimation of a static model with robust or panel corrected standard errors is commonly used to model large N , large T panel data. However, this approach is biased and inconsistent in the presence of dynamic misspecification, slope heterogeneity, and cross-sectional dependence. Common correlated effects estimation of a dynamic model has been advanced to address these issues but is rarely used in sociology. Here, I provide an overview of the large N , large T panel data literature, and I conduct an array of Monte Carlo experiments to compare the fixed effects estimator to the common correlated effects estimator regarding the aforementioned issues. I show that fixed effects estimation with robust or panel corrected standard errors do not address these problems, which is most evident with high levels of slope heterogeneity and lag misspecification, and its performance worsens as the time dimension expands. In contrast, the common correlated effects estimator produces superior estimates as T increases and is robust to slope heterogeneity and cross-sectional dependence. Following the experiments, I present an example by examining the drivers of fossil fuel consumption at the U.S. state level from 1960 to 2018, and I conclude by presenting a decision-making framework for researchers to use to make informed decisions when modeling large N , large T panel data.

A central premise of development strategies is that nations use natural resources, such as fossil fuels, to raise population living standards and enhance well-being. However, research shows that the relationship among human well-being, resource use, and the associated emissions is complex and context specific. To better understand if natural resource use plays a historic role in generating human well-being in the United States, the authors conduct a time-series analysis of greenhouse gas emissions and average life expectancy from 1913 to 2017. The results show that increases in greenhouse gas emissions per capita have an instantaneous, negative effect on life expectancy. The authors also find evidence that income inequality has a long-run negative effect on life expectancy. Additional analyses provide mixed results regarding whether and how the effects of emissions on life expectancy are conditional on income inequality. These findings contradict the assumption that reductions in emissions necessitate trade-offs in human well-being in high-income contexts.

This cross-national study employs a time-series cross-sectional Prais-Winsten regression model with panel-corrected standard errors to examine the relationship between renewable energy consumption and economic growth, and its impact on total carbon dioxide emissions and carbon dioxide emissions per unit of GDP. Findings indicate that renewable energy consumption has its largest negative effect on total carbon emissions and carbon emissions per unit of GDP in low-income countries. Contrary to conventional wisdom, renewable energy has little influence on total carbon dioxide emissions or carbon dioxide emissions per unit of GDP at high levels of GDP per capita. The findings of this study indicate the presence of a “renewable energy paradox,” where economic growth becomes increasingly coupled with carbon emissions at high levels of renewable energy, and the negative effect of economic growth on carbon emissions per unit of GDP lessens as renewable energy increases. These findings suggest that public policy should be directed at deploying renewable energy in developing countries, while focusing on non-or-de-growth strategies accompanied with renewable energy in developed nations.

This study advances research at the intersection of environmental degradation, social stratification, and population health in the United States. Expanding the theoretical principles of power, proximity, and physiology, we hypothesize that the harmful effect of fine particulate matter on life expectancy is greater in states with higher levels of income inequality and larger black populations. To test our hypothesis, we use two-way fixed effects regression analysis to estimate the effect of a three-way interaction between fine particulate matter, income share of the top ten percent, and the percent of the population that is black on state-level average life expectancy for all US states and the District of Columbia (2000–2014). The findings support our hypothesis: the estimated effect of the three-way interaction on average life expectancy is negative and statistically significant, net of various socioeconomic and demographic controls. Using post-estimation techniques, we visually illustrate that the harmful effect of fine particulate matter on life expectancy is especially pronounced in states with both very high levels of income inequality and very large black populations. We conclude by summarizing the theoretical and substantive implications of our findings, the limitations of the study, and potential next steps in this evolving area of interdisciplinary research.