Developing Integer Calibration Weights for Census of Agriculture

When conducting a national survey or census, administrative data may be available that can provide reliable values for some of the variables. Survey and census estimates should be consistent with reliable administrative data. Calibration can be used to improve the estimates by further adjusting the...

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Bibliographic Details
Published in:Journal of agricultural, biological, and environmental statistics biological, and environmental statistics, 2019-03, Vol.24 (1), p.26-48
Main Authors: Sartore, Luca, Toppin, Kelly, Young, Linda, Spiegelman, Clifford
Format: Article
Language:English
Subjects:
Agricultural industry
Agricultural practices
Agriculture
Biostatistics
Calibration
Census
Censuses
Data processing
Estimates
Farms
Health Sciences
Integers
Mathematics and Statistics
Medicine
Monitoring/Environmental Analysis
Production methods
Rounding
Statistics
Statistics for Life Sciences
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Summary:When conducting a national survey or census, administrative data may be available that can provide reliable values for some of the variables. Survey and census estimates should be consistent with reliable administrative data. Calibration can be used to improve the estimates by further adjusting the survey weights so that estimates of targeted variables honor bounds obtained from administrative data. The commonly used methods of calibration produce non-integer weights. For the Census of Agriculture, estimates of farms are provided as integers so as to insure consistent estimates at all aggregation levels; thus, the calibrated weights are rounded to integers. The calibration and rounding procedure used for the 2012 Census of Agricultural produced final weights that were substantially different from the survey weights that had been adjusted for under-coverage, non-response, and misclassification. A new method that calibrates and rounds as a single process is provided. The new method produces integer, calibrated weights that tend to be consistent with more calibration targets and are more correlated with the modeled census weights. In addition, the new method is more computationally efficient.
ISSN:1085-7117
1537-2693
DOI:10.1007/s13253-018-00340-4