The goal of this project is to analyze the effect of mandated health benefits on labor market outcomes
Topic is influenced by Simon (2001) and Gruber (1994)
Simon (2001) looks at the effects of Small Group Health Insurance on coverage rates
In the early 1990s, millions of Americans were uninsured. Workers in small firms (<25 workers) were disproportionally less likely to have health insurance through their employer
Simon believes this is due to industry redlining - where insurance providers deny access to coverage to certain industries that they deem "high risk"
Statewide insurance reform dropped several barriers to coverage
Despite increased access, Simon found that coverage rates actually decreased
She theorizes this is due to adverse selection - due to asymmetric information, high risk individuals drive out the low risk individuals
Gruber (1994) explores the effect of mandatory benefits on wages
Theory predicts that offering benefits, such as health insurance, is associated with a decrease in wage so long as the workers value that benefit more than its cost to the firm
Gruber took an easily identifiable group, women of childbearing age, and analyzed the effects of mandatory maternity benefits on their wages
Using a DDD model, he identifies women ages 20 to 40 as the treatment group. He then compares the mean wages between the control group (men, women with ages > 40 ) before and after the reform
Gruber found that mandating maternity benefits was associated with a decrease in wage for the control group
The goal of my model is to:
1) Run a regression to estimate the effect of mandated benefits on coverage rates for workers in risky occupations*
Simon's paper found that broad coverage rates decreased
In line with Gruber's paper, I'm expecting coverage rates to increase for the subgroup of risky workers
2) If coverage rates among the subgroup increases, analyze the extent to which those workers value the benefits by estimating the effect on wages
Based on Gruber's findings, I expect wages to decrease
*risky occupations is defined in the model as working a job whose fatality rate is above the median.
Baseline Model
The goal of my model is to:
1) Run a regression to estimate the effect of mandated benefits on coverage rates for workers in risky occupations*
2) If coverage rates among the subgroup increases, analyze the extent to which those workers value the benefits by estimating the effect on wages
*risky occupations is defined in the model as working a job whose fatality rate is above the median.