Pay Referents and Satisfaction with Pay: Does Occupational Proximity Matter?

We explore whether employees compare their pay to the pay of others in a similarly prestigious occupation and, if so, whether this comparison has a negative impact on pay satisfaction. Using an experimental vignette methodology, Study 1 found that people are more inclined to compare with others from a similar or identical occupation and that comparison negatively impacts pay satisfaction. This comparison and its negative effect is particularly strong in high‐prestige occupations. Based on survey data, Study 2 also showed that the average pay of others in occupations of similar prestige is negatively correlated with employees’ pay satisfaction. This negative correlation was also stronger in higher‐prestige occupations. Our analysis highlights the importance of occupational prestige as a main factor influencing pay comparison.     

Status Traps

In this article, we explore nonlinearities in the intergenerational mobility process using threshold regression models. We uncover evidence of threshold effects in children's outcomes based on parental education and cognitive and noncognitive skills as well as their interaction with offspring characteristics. We interpret these thresholds as organizing dynastic earning processes into “status traps.” Status traps, unlike poverty traps, are not absorbing states. Rather, they reduce the impact of favorable shocks for disadvantaged children and so inhibit upward mobility in ways not captured by linear models. Our evidence of status traps is based on three complementary datasets; that is, the PSID, the NLSY, and US administrative data at the commuting zone level, which together suggest that the threshold-like mobility behavior we observe in the data is robust for a range of outcomes and contexts.     

Should Macroeconomic Forecasters Use Daily Financial Data and How?

We introduce easy-to-implement, regression-based methods for predicting quarterly real economic activity that use daily financial data and rely on forecast combinations of mixed data sampling (MIDAS) regressions. We also extract a novel small set of daily financial factors from a large panel of about 1000 daily financial assets. Our analysis is designed to elucidate the value of daily financial information and provide real-time forecast updates of the current (nowcasting) and future quarters of real GDP growth.