The small sample size of cities used in the analysis (at most 75 cities per year) limits the generalizability of the findings and increases the risk of spurious correlations.
Data Granularity Mismatch
The data on influenza deaths and other variables are not always available at the same level of granularity. For example, influenza mortality is recorded at the regional level, while other data might be at the city level. This requires the author to make assumptions about the homogeneity of influenza mortality within regions, which might not always hold true.
Confounding Effects of World War I
The study period covers the years following World War I, a period of significant social and economic upheaval in Germany. Disentangling the effects of the influenza pandemic from the impacts of the war, such as population changes and economic disruption, is challenging.
The lack of data on city spending during the war and the hyperinflation period before 1925 makes it difficult to control for pre-existing differences between cities, which could influence both spending patterns and voting behavior.
The paper acknowledges the possibility of omitted variable bias, where unobserved factors unrelated to the influenza pandemic might be driving the observed correlations. For instance, regional differences in cultural values or political preferences could influence both mortality rates and voting patterns.
Instrumental Variable Limitations
While the instrumental variable approach using railway density addresses some endogeneity concerns, the instrument might not be perfectly exogenous, and the small sample size can lead to instability in the estimates.