Search results

This study examines the impact of college education on incorporated and unincorporated self-employments. It specifically compares the effects on African Americans and Hispanics with the effects on Whites.

The study sample was drawn from the US Current Population Survey between 1989 and 2018. Based on a sample size of 1,657,043 individuals, this study employed logit regression models to test the hypotheses. Racial variations were examined using African Americans and Hispanics as moderators.

The results suggest that college education increases incorporated self-employment and reduces unincorporated self-employment. The impact of college education on incorporated self-employment is stronger for African Americans and Hispanics than for Whites. In contrast, its effect on unincorporated self-employment is stronger for Whites than for African Americans and Hispanics.

The findings provide empirical evidence of how college experience changes the motivation of starting an incorporated or unincorporated business. The results suggest that college education impacts African Americans and Hispanics differently than Whites in pursuing their career path of entrepreneurship.

It is the first study that examines the relationship between college education and incorporated/unincorporated self-employment. It also sheds light on radical variations.

From the standpoint of a policy maker who has access to a number of expert forecasts, the uncertainty of a combined or ensemble forecast should be interpreted as that of a typical forecaster randomly drawn from the pool. This uncertainty formula should incorporate forecaster discord, as justified by (i) disagreement as a component of combined forecast uncertainty, (ii) the model averaging literature, and (iii) central banks’ communication of uncertainty via fan charts. Using new statistics to test for the homogeneity of idiosyncratic errors under the joint limits with both T and n approaching infinity simultaneously, the authors find that some previously used measures can significantly underestimate the conceptually correct benchmark forecast uncertainty.