Abstract
This thesis explores the balance between equity and efficiency in government granting, particularly in regards to government grants to remediate environmental pollution. Through statistical analysis, I analyze California cities to see whether there is a link between pollution and staff capacity, and whether that link impacts a city’s likelihood to apply for and receive state money to reduce pollution in the community. My research employs ordinary least squares (OLS) regression and logistic regression and utilizes city data obtained from the California Environmental Protection Agency, Strategic Growth Council, State Controller’s Office, and the US Census. I find that cities with more pollution also have statistically fewer staff per capita. Further, I find that cities with higher administrative staff capacity are more likely to apply for funding from the state’s Affordable Housing and Sustainable Communities program, and applicants with higher administrative staff capacity are more likely to be awarded funding from the state. Furthermore, administrative staff capacity is the only variable that has a real impact on the outcome for a city or an applicant, with only a very weak effect of poverty on a city’s likelihood of applying for funds and no statistically significant effect from any other variable in the models. Based on this research, I recommend that the state begin tracking staff capacity for all cities in California to identify the cities that will be less likely to have capacity to apply for and win government grants. The state should then use these data to conduct proactive outreach to cities that are unlikely to apply for funds and that may statistically be in the most need of funds. I also suggest that the state set aside special funds for cities with low staff capacity, and that policy makers consider other policy mechanisms besides competitive grants, like providing direct services to cities with the highest need.