Objectives: What is the main driver of life expectancy across societies and over time? This study aims to document a systematic and quantitatively sizeable relationship between income levels and life expectancy. Method: A panel data set of 197 countries over 213 years is analyzed with different regression methods. Robustness tests are provided. Results: By itself, GDP per capita explains more than 64 percent of the variation in life expectancy. The Preston curve prevails even when accounting for country- and time-fixed effects, country-specific time trends, and alternative explanatory variables such as health-care expenditure, malaria prevalence, or political institutions. If anything, this link has become stronger over recent decades when data quality has improved. Results from instrumental variable estimations suggest this finding to be largely unaffected by reverse causality. Quantile regression results suggest the relationship between income and life expectancy to be persistent across different levels of life expectancy. Conclusion: Income matters for life expectancy. If policymakers want to prolong people's lives, economic growth appears to be the predominant medicine.