Wendy Barboza, Darcy Miller, Nathan Cruze United States Department of Agriculture National...
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“. . . providing timely, accurate, and useful statistics in service to U.S. agriculture.”
Wendy Barboza, Darcy Miller, Nathan CruzeUnited States Department of AgricultureNational Agricultural Statistical Service
Assessing the Impact of a New Imputation Methodology for the
Agricultural Resource Management Survey
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UNECE Work SessionApril 2014
The Agency and Mission
• The National Agricultural Statistics Service (NASS) is a statistical agency located under the U.S. Department of Agriculture (USDA).
• Mission: To provide timely, accurate, and useful statistics in service to U.S. agriculture.
• NASS conducts hundreds of surveys every year and publishes numerous reports covering virtually every aspect of U.S. agriculture.
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UNECE Work SessionApril 2014
Agricultural Resource Management Survey (ARMS)
• Provides an annual snapshot of the financial health of the farm sector and farm household finances.
• Only source of information available for objective evaluation of many critical policy issues related to agriculture and the rural economy.
• USDA and other federal administrative, congressional, and private-sector decision makers use this data when considering alternative policies/programs or business strategies for the farm sector or farm families.
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Adjusting for Item-level Nonresponse
• The ARMS survey questionnaire is long and complex: 51 pages and > 800 data items.
• The survey questions cover the characteristics, management, income, and expenses of both the farm operation and the farm household.
• Obtaining a response for every item is challenging; imputation is performed for missing items.
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Current Imputation Methodology
• Uses conditional mean imputation.• Groups formed by locality, farm type,
economic sales class (outliers excluded).• Specified collapsing order when not enough
records in a group.• Underestimates the variance and distorts
relationships between variables.
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New Imputation Methodology
• Uses multiple variables in imputation.• Data are transformed and a regression-based technique
is used.• Various criteria are used to select the covariates.• Parameter estimates for the sequence of linear models
and imputations are obtained using Markov chain Monte Carlo.
• Referred to as Iterative Sequential Regression (ISR).
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18 Key Variables- Agricultural Chemicals Expenditures- Farm Improvements and
Construction- Farm Services*- Farm Supplies and Repairs- Feed Expenditures- Fertilizer, Lime and Soil Conditioner
Expenditures- Fuels Expenditures- Interest- Labor Expenditures
- Livestock, Poultry, and Related Expenses
- Miscellaneous Capital Expenses- Other Farm Machinery
Expenditures- Rent- Seeds and Plants- Taxes*- Total Expenditures*- Tractor and Self-Propelled Farm
Machinery Expenditures- Trucks and Autos Expenditures
* Variable contains imputed values
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Analysis of 2011/2012 Data
• Examined the 18 key variables.• Percent change = 100*[(ISR-mean)/mean].• Positive (negative) value indicates that the
estimate using ISR is greater (less) than the estimate using conditional mean imputation.
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UNECE Work SessionApril 2014
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Conclusion
• There are major disadvantages with using the conditional mean imputation methodology.
• ISR will preserve important relationships, the distribution of the respondents’ data, and provide a better estimate of uncertainty.
• Preliminary analysis has shown that there is a significant difference between the two methodologies for some estimates.
• However, the results are promising and additional analysis is currently being conducted.
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Thank you!
Wendy [email protected]
United States Department of AgricultureNational Agricultural Statistics Service