Responsive Design is a trend in recent survey literature concerned notably with managing data collection, through planning and appropriate intervention, so as to realize a well-balanced final set of respondents. In this effort, auxiliary variables, including paradata, are central. But regardless of what is done in the data collection, accurate estimation despite nonresponse is the ultimate goal. The auxiliary variables are important at the estimation stage as well, as when calibrated weights are used in the nonresponse adjustment. For accuracy, two factors intervene: (1) in the data collection, the level of imbalance achieved with the auxiliary information; and (2) in the estimation, the degree to which the auxiliaries explain the study variable. In practice, both objectives are less than completely satisfied. Reduced imbalance in data collection does not by itself guarantee low bias in the estimates. We ask: Is balancing worth a perhaps costly and demanding effort in data collection? Could one have done equally well by saving the use of the auxiliary information until the estimation stage? Complete bias elimination is not achieved at either stage. We outline a theory for a two-factor explanation of accuracy, and apply it to two important surveys at Statistics Sweden. The factors-thedegreeof imbalance and the degree of explanation-are systematically varied, and their joint effect on the accuracy of the estimates is evaluated empirically. The results show that reduced imbalance makes the adjustment of the simple estimate lose some of its importance. More importantly, the calibration-adjusted estimate realizes some accuracy improvement by having been preceded in data collection by a reduced imbalance. The explanation of why this happens is not simple, but a theoretical justification is outlined.