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We conducted spatial regression analysis to account for spatial clustering of sexually transmitted diseases (STDs) and to examine the state-level association between social capital (using Putnam’s public use data set) and rates of gonorrhea and syphilis. We conducted the analysis for the 48 contiguous states of the United States for 1990, 1995, and 2000 and controlled for the effects of regional variation in STD rates, and for state variation in poverty, income inequality, racial composition, and percentage aged 15–34 years. We compared the results of the spatial regression analysis with those of ordinary least squares (OLS) regression. Controlling for all population-level variables, the percentage of variation explained by the OLS regression and by the spatial regression were similar (mid-90s for gonorrhea and low-70s for syphilis), the standardized parameter estimates were similar, and the spatial lag parameter was not statistically significant. Social capital was not associated with STD rates when state variation in racial composition was included in the regression analysis. In this analysis, states with a higher proportion of residents who were African-American had higher STD rates. When we did not control for racial composition, regression analysis showed that states with higher social capital had lower STD rates. We conjecture that sexual networks and sexual mixing drive the association between social capital and STD rates and highlight important measurement and research questions that need elucidation to understand fully the relationship between social capital and STDs.