Model-Based Estimates for Farm Labor Quantities

Lu Chen, Nathan B. Cruze and Linda J. Young
Additional contact information
Lu Chen: National Institute of Statistical Sciences, 1750 K Street NW Suite 1100, Washington, DC 20006, USA
Nathan B. Cruze: NASA Langley Research Center, Mail Stop 290, Hampton, VA 23681, USA
Linda J. Young: United States Department of Agriculture, National Agricultural Statistics Service, 1400 Independence Avenue SW, Washington, DC 20250, USA

Stats, 2022, vol. 5, issue 3, 1-17

Abstract: The United States Department of Agriculture’s (USDA’s) National Agricultural Statistics Service (NASS) conducts the Farm Labor Survey to produce estimates of the number of workers, duration of the workweek, and wage rates for all agricultural workers. Traditionally, expert opinion is used to integrate auxiliary information, such as the previous year’s estimates, with the survey’s direct estimates. Alternatively, implementing small area models for integrating survey estimates with additional sources of information provides more reliable official estimates and valid measures of uncertainty for each type of estimate. In this paper, several hierarchical Bayesian subarea-level models are developed in support of different estimates of interest in the Farm Labor Survey. A 2020 case study illustrates the improvement of the direct survey estimates for areas with small sample sizes by using auxiliary information and borrowing information across areas and subareas. 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.

Keywords: agricultural survey; auxiliary data; Bayesian diagnostic; official statistics; small area estimation; subarea models (search for similar items in EconPapers)
JEL-codes: C1 C10 C11 C14 C15 C16 (search for similar items in EconPapers)
Date: 2022
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