  Common practice to address nonresponse in probability surveys in National
Statistical Offices is to follow up every nonrespondent with a view to lifting
response rates. As response rate is an insufficient indicator of data quality,
it is argued that one should follow up nonrespondents with a view to reducing
the mean squared error (MSE) of the estimator of the variable of interest. In
this paper, we propose a method to allocate the nonresponse follow-up resources
in such a way as to minimise the MSE under a quasi-randomisation framework. An
example to illustrate the method using the 2018/19 Rural Environment and
Agricultural Commodities Survey from the Australian Bureau of Statistics is
provided.
