Estimating Trends in the Total Fertility Rate with Uncertainty Using Imperfect Data

We develop a method to estimate and assess uncertainty in the total fertility rate over time. Our estimates are based on multiple imperfect observations from different data sources including surveys and censuses. We take account of measurement error by decomposing it into bias and variance and we estimate both by linear regression on a variety of data quality covariates. We estimate the total fertility rate for seven countries in West Africa using a local smoother, and we assess uncertainty using the weighted likelihood bootstrap. We find that accounting for differences in data quality between observations gives better calibrated confidence intervals and reduces bias.