Model-Based Estimates for Farm Labor Quantities
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National Institute of Statistical Sciences, 1750 K Street NW Suite 1100, Washington, DC 20006, USA
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United States Department of Agriculture, National Agricultural Statistics Service, 1400 Independence Avenue SW, Washington, DC 20250, USA
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NASA Langley Research Center, Mail Stop 290, Hampton, VA 23681, USA
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Author to whom correspondence should be addressed.
Academic Editor: Wei Zhu
Stats 2022, 5(3), 738-754; https://doi.org/10.3390/stats5030043
Received: 1 July 2022
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Revised: 29 July 2022
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Accepted: 30 July 2022
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Published: 3 August 2022
(This article belongs to the Special Issue Small Area Estimation: Theories, Methods and Applications)
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.
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Keywords:
agricultural survey; auxiliary data; Bayesian diagnostic; official statistics; small area estimation; subarea models
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MDPI and ACS Style
Chen, L.; Cruze, N.B.; Young, L.J. Model-Based Estimates for Farm Labor Quantities. Stats 2022, 5, 738-754. https://doi.org/10.3390/stats5030043
AMA Style
Chen L, Cruze NB, Young LJ. Model-Based Estimates for Farm Labor Quantities. Stats. 2022; 5(3):738-754. https://doi.org/10.3390/stats5030043
Chicago/Turabian StyleChen, Lu, Nathan B. Cruze, and Linda J. Young. 2022. "Model-Based Estimates for Farm Labor Quantities" Stats 5, no. 3: 738-754. https://doi.org/10.3390/stats5030043
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