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Thursday, October 7
Thu, Oct 7, 1:15 PM - 2:30 PM
Celebrating Our Technical Expertise

Model-Based Estimates for Farm Labor Quantities (309892)

Nathan B. Cruze, NASS, USDA 
Linda J. Young, NASS, USDA 

Keywords: Hierarchical Bayes modeling, Small area estimation, Subarea models, Survey data

The United States Department of Agriculture’s (USDA’s) National Agricultural Statistics Service (NASS) conducts the Farm Labor Survey to produce the basis for employment and wage estimates for all workers directly hired by United States farms and ranches (excluding Alaska). The survey is conducted semi-annually, and the farm operators provide information on a reference week in the current quarter and a reference week in the preceding quarter; estimates are produced for each reference week. Historically, the survey estimates are reviewed by the Regional Field Offices (RFOs), headquarter (HQ) statisticians, and finally by the Agricultural Statistics Board (ASB). The ASB also considers the relationship between current year survey estimates and previous year official values when setting the final estimates. Small area models can be used to integrate survey estimates with auxiliary information and historical data. The resulting estimates are more reproducible and have valid measures of uncertainty. In this paper, several hierarchical Bayesian models are developed in support of different estimates of interest in the Farm Labor Survey. A 2019 case study illustrates that the direct survey estimates for areas with small sample sizes are improved by incorporating other sources of information and by borrowing information across areas and sub-areas. The resulting framework provides a complete set of coherent estimates for all required geographic levels; these methods were incorporated into the official Farm Labor publication for the first time in 2020.