The growth of non-response rates for social science surveys has led to increased concern about the risk of non-response bias. Unfortunately, the non-response rate is a poor indicator of when non-response bias is likely to occur. We consider in this paper a set of alternative indicators. A large-scale simulation study is used to explore how each of these indicators performs in a variety of circumstances. Although, as expected, none of the indicators fully depict the impact of non-response in survey estimates, we discuss how they can be used when creating a plausible account of the risks for non-response bias for a survey. We also describe an interesting characteristic of the fraction of missing information that may be helpful in diagnosing not-missing-at-random mechanisms in certain situations.