Research Papers

We evaluated inequality of opportunity across 31 EU countries by applying Bayesian model averaging and tree-based machine learning, focusing on how uncertainty in the choice of circumstance variables and functional forms affects estimates.

Standard “kitchen sink” models often overstate the role of circumstances in shaping income outcomes. By systematically addressing model uncertainty, we sought to produce more accurate and robust measures that reflect how social class, parental education, and household conditions shape opportunity.

Our findings show that ignoring model uncertainty can mislead policy by exaggerating the extent of inequality of opportunity. Recognizing the roles of class, education, and migrant background informs better strategies for reducing unjust disparities across societies.

We reexamined the relationship between religion and economic growth, considering the impact of specific religious beliefs and practices on growth rates.

We aimed to provide a nuanced analysis of the role of religion in economic growth by investigating the detailed aspects of religious beliefs and practices, which previous research may have overlooked.

We contribute to a deeper understanding of the complex relationship between religion and economic growth, emphasizing the importance of considering specific aspects of religious influence for more accurate policy implications.

We investigated the empirical evidence for various growth theories under conditions of model uncertainty. Using model averaging methods, we assessed the roles of fundamental growth theories, macroeconomic policy effects, regional heterogeneity, and parameter heterogeneity in explaining aggregate growth.

We aimed to address the uncertainty regarding the correct growth model and to evaluate the strength of empirical evidence for different growth theories. By doing so, we sought to understand the limitations of cross-country growth regressions in adjudicating these theories.

We highlight that cross-country growth regressions have limited capacity to determine the relative importance of alternative growth theories. Our findings underscore the significance of macroeconomic policy effects and regional heterogeneity, suggesting a need for more nuanced models and approaches in growth economics research.

We surveyed the recent literature on growth empirics, comparing modern empirical findings and methodologies to the traditional growth accounting analyses from the 1960s and 1970s. We evaluated new methodologies, findings, and the critical role of Total Factor Productivity (TFP), statistical convergence, and the search for robust growth determinants.

We aimed to assess the progress and limitations of modern growth empirics compared to traditional growth accounting. By doing so, we sought to identify the areas where modern methodologies have advanced our understanding and where substantive conclusions remain elusive.

Bridging the gap between modern empirical claims and growth theories is crucial. Unifying statistical findings into a coherent growth model and connecting them to economically meaningful notions of convergence can enhance our understanding of growth dynamics and inform effective policy-making.

We generalized the empirical analysis of the Solow growth model to account for country-specific heterogeneity, relaxing the common assumption that all countries have identical aggregate production functions. Our analysis revealed significant country-specific variations in Solow parameters linked to differences in initial income levels.

We aimed to enhance the explanatory power of the Solow growth model by incorporating country-specific factors. This approach allows for a more accurate representation of the diverse economic environments across different countries.

Recognizing and incorporating country-specific heterogeneity in growth models improves their explanatory value and accuracy. This enhanced model provides better insights into the economic growth processes of individual countries, leading to more tailored and effective economic policies.