- How large errors am I willing to tolerate?

- How pervasive is missing data in my indicators of interest?

- Is the probability of a country not having data likely correlated with the indicator of interest?

- [If producing time series] How much do the global statistics change from year to year and do the same countries consistently have missing values?

- [If missing data is imputed] How confident am I in the precision of the imputations?

- [If producing sub-global statistics] How large are the groups and how much of the variation happens between subgroups rather than within subgroups?

Jointly answering these questions should afford an approximate slope of the error as a function of the population coverage, as well as a ceiling on how large an error one is willing to tolerate. Judging from the table comparing standard deviations with original units, our (admittedly, subjective) take is that errors should never on expectation exceed 0.25 standard deviation. Even in the less optimistic cases we presented, this roughly corresponds to not publishing statistics when data for less than half of the relevant population is available. For certain purposes, such as comparing statistics over time, it is likely that much lower errors are needed. A corollary of these recommendations is that it is always optimal to use the share of the global population covered rather than the share of countries covered as the coverage threshold. A corollary of this is that rather than conditioning on data being present for populous countries, it would be better to increase the coverage threshold.

# References

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