Air Pollution Increases Influenza Hospitalizations

Gregor Singer; Joshua Graff Zivin; Matthew Neidell; Nicholas Sanders

We estimate the model with a pseudo-maximum likelihood estimator (68, 69) , which performs well with a large number of zeros and is consistent with over-or under-dispersion in the data (70) . We cluster standard errors at the county level to allow for arbitrary heteroskedasticity and serial correlation in the errors, and show robustness to two-way clustering at the added state-year level. Table S .3 provides falsification tests with outcomes unlikely to be correlated with air pollution. Column 1 repeats our baseline results for influenza patients. The next four columns use inpatient hospitalizations with a primary diagnosis of diabetes mellitus with complications, urinary tract infections, skull and face fractures, and osteoarthritis. Coefficients and standard errors indicate a precise zero effect for these outcomes. Table S .4 explores heterogeneous effects by age, gender and race. Estimates across different groups are statistically indistinguishable from one another, however, the point estimates for blacks and especially Hispanics are larger than for whites. Table S .5 explores robustness of our main results to different controls, fixed effects, and standard error calculations. Column (1) replicates the baseline results, and reports the estimates for our weather controls (reporting was suppressed in the manuscript for simplicity). Temperature and humidity controls are included as dummies for separate bins. While the coefficients on temperature are not statistically significant (county-year fixed effects absorb much of the large-scale variation), the sign is as expected.