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- All Subjects: Economics
- Creators: School of Mathematical and Statistical Sciences
- Member of: Barrett, The Honors College Thesis/Creative Project Collection
- Resource Type: Text
implement large fiscal adjustments in response to the financial crisis, various other economic consequences
were felt, such as inflation, public debt growth, and a decrease in purchasing power. A result from these
consequences that typically occur every recession are demand shocks within the employment sector. As firms
are put into tight financial positions, employers are forced to make employment decisions to cut costs for
long-term sustainability, such as laying off workers, or reducing their working hours.
This paper aims to investigate how weekly working hours are impacted by shocks to the economy across European countries. Using the 2008 recession as the basis, an empirical analysis was conducted with panel data for 32 countries over 33 years, with average weekly working hours across four occupational groups as the variable of interest, and various economic indicators such as GDP growth as independent variables. Additionally, countries were split up and grouped based on geographical location to examine potential country and region-specific trends.
Over time, there is a decreasing trend in weekly working hours across all observed occupations and countries. This decreasing trend continues during the 2008 recession, but the slope of decrease is not significant relative to the entire time period. However, when dis-aggregated into occupational groups with a distinction between full-time and part-time workers, the trends in working hours are a much more noticeable, both during the recession and over the entire time frame of observation.
Using a dataset of ASU students from the 2016-2017 cohort, we interact gender and parent education level to observe gaps in academic achievement. We see a statistically insignificant achievement gap for males across parent education level, but a statistically significant achievement gap for females across parent education level. We also observe dropout gaps among these interaction groups. We see the widest dropout gap being between males across parent education level, with the smallest dropout gap being between females across parent education level. So with males we see an insignificant achievement gap but the widest dropout gap across parent education level, and with females we see a significant achievement gap but the smallest dropout gap across parent education level. What is driving these gaps and causing more similarly performing students to drop out at wider rates? At the aggregate level, we see larger gaps in grade- associated dropout probability across parent education level for males which may be able to explain the larger difference in overall proportions of dropouts between males. However, when predicting dropout probability of the semester with the most first generation and non-first generation dropouts, we see that females have the largest differences across parent education level in grade-associated dropout probability. This suggests that our model may be best suited in using college achievement data to predict overall dropout probabilities, not next-semester dropout probabilities using current semester data. Our findings also suggest that first generation students’ dropout probability is more sensitive to the grades they receive than non-first generation students.