Bibliography

Adjemian, M.K. (2012). Quantifying the WASDE announcement effect. American Journal of Agricultural Economics, 94(2012), 238–256.

Administrative Data Liaison Service. (2015). Administrative Data Introduction. Available: http://www.adls.ac.uk/adls-resources/guidance/introduction.

Ahearn, M. (2013). Challenges in collecting data from complex farm operations: Review of perspectives from an international conference. In H.C.J. Vrolijk (Ed.), Complex Farms and Sustainability in Farm Level Data Collection, LEI Proceedings 13-054, The Hague.

Ahearn, M., Banker, D., Clay, D.M., and Milkove, D. (2011). Comparative survey imputation methods for farm household income. American Journal of Agricultural Economics, 93(2), 613–618.

Allan, D.W., and Lueck, D. (2002). The Nature of the Farm: Contracts, Risk, and Organization in Agriculture. Cambridge, MA: MIT Press.

Anderson, E. (2017). Administrative Data Used by NASS: A Discussion of Administrative Data Used by NASS Headquarters. (Staff report, National Agricultural Statistics Service). Washington, DC: U.S. Department of Agriculture.

Anderson, E., and Beckler, D. (2017). Administrative Data Used by NASS. Paper prepared for the Panel on Improving Data Collection and Reporting about Agriculture with Increasingly Complex Farm Structures, February 10, Washington, DC.

Bako, D. (2018). Guidelines on Improving and Using Administrative Data in Agricultural Statistics, Global Strategy to Improve Agriculture and Rural Statistics. Available: https://www.researchgate.net/publication/323280869_Guidelines_on_improving_and_using_administrative_data_in_agricultural_statistics.

Beckett, M.K., Elliott, M.N., Gaillot, S., Haas, A., et al. (2016). Establishing limits for supplemental items on a standardized national survey. Public Opinion Quarterly, 80(4), 964–976.

Beckler, D.G. (2013). Administrative data used by the National Agricultural Statistics Service. In Proceedings of the Sixth International Conference on Agricultural Statistics, Rio de Janeiro, Brazil, October 23–25, ISI Committee on Agricultural Statistics.

Berent, M.K., Krosnick, J.A., and Lupia, A. (2016). Measuring voter registration and turnout in surveys: Do official government records yield more accurate assessments? Public Opinion Quarterly, 80(3), 597–621.

Berg, E. (2015). Improving the Methodology for using Administrative Data More Broadly Across the Agricultural Statistics System: Administrative Data and the Statistical Pro-grammes of Developed Countries. Working Paper no. 3, Global Strategy to Improve Agricultural and Rural Statistics. Available: http://gsars.org/wp-content/uploads/2015/10/WP2-Improving-the-Methodology-for-Using-Administrative-Data-in-an-AgriculturalStatistics-System_14102015.pdf.

Bonnen, J.T. (1977). Assessment of the current agricultural database: An information system approach. In L.R. Martin and G.G. Judge (Eds.), A Survey of Agricultural Economics Literature. St Paul: University of Minnesota Press.

Bonnen, J.T. (1989). On the role of data and measurement in agricultural economics research. Journal of Agricultural Economics Research, 4.

Bonnen, J.T., Hildreth, R.J., Judge, G.G., Tolley, G., and Trelogan, H. (1972). Our obsolete data systems: New directions and opportunities.” American Journal of Agricultural Economics, 54(5), 867–875.

Bradburn, N.M. (1978). Respondent burden. In Proceedings of the Survey Research Methods Section of the American Statistical Association (pp. 35–40). Available: https://ww2.amstat.org/sections/srms/Proceedings/papers/1978_007.pdf.

Calus, M. (2009). Factors Explaining Farm Succession and Transfer in Flanders. PhD. thesis, Ghent University, Ghent, Belgium.

Commission on Evidence-Based Policymaking. (2017). The Promise of Evidence-Based Policymaking: Report of the Commission on Evidence-Based Policymaking. Washington, DC: Author. Available: https://www.cep.gov/content/dam/cep/report/cep-final-report.pdf.

Cruze, N.B. (2015). Integrating survey data with auxiliary sources of information to estimate crop yields. In Proceedings of the Survey Research Methods Section. Washington, DC: American Statistical Association.

Davis, J.H., and Goldberg, R.A. (1957). A concept of agribusiness. American Journal of Agricultural Economics, 39(4), 1042–1045.

Duncan, G. (2011). Commentary: Future U.S. national statistics use of administrative data. Journal of Privacy and Confidentiality, 3(2). https://doi.org/https://doi.org/10.29012/jpc.v3i2.603.

Dunn, R.A., and Hueth, B. (2017). Food and agricultural industries: Opportunities for improving measurement and reporting. American Journal of Agricultural Economics, 99(2), 510–523.

Earp, M.S., McCarthy, J.S., Schauer, N.D., and Kott, P.S. (2008a). Assessing the Effect of Calibration on Nonresponse Bias in the 2005 ARMS Phase III Sample Using 2002 Census of Agriculture Data. Washington, DC: U.S. Department of Agriculture, National Agricultural Statistics Service, Research and Development Division.

Earp, M.S., McCarthy, J.S., Schauer, N.D., and Kott, P.S. (2008b). Assessing the Effect of Calibration on Nonresponse Bias in the 2006 ARMS Phase III Sample Using Census 2002 Data. Washington, DC: U.S. Department of Agriculture, National Agricultural Statistics Service, Research and Development Division.

Endres, A.B., and Schlessinger, L. (2015). FDA revises definition of “farm.” Farmdoc daily (5), 185. (Department of Agricultural and Consumer Economics, University of Illinois at Urbana-Champaign). See http://farmdocdaily.illinois.edu/2015/10/fda-revises-definitionof-farm.html.

Eurostat. (2017). Agricultural holding. In Glossary. Available: http://ec.europa.eu/eurostat/statistics-explained/index.php/Glossary:Agricultural_holding.

Fay, R.E., and Herriot, R.A. (1979). Estimation of income from small places: An application of James-Stein procedures to Census data. Journal of the American Statistical Association, 74, 269–277.

Featherstone, A.M., Wood, M.A., Herbel, K.L., and Langemeier, M.R. (2012). Multiple entity farms: A growing and challenging phenomenon. Agricultural Finance Review, 72(2), 210–221. Available: https://doi.org/10.1108/00021461211250447.

Federal Statistical Office. (2008). NOGA 2008 General Classification of Economic Activities. Neuchâtel: Federal Statistical Office.

Food and Agriculture Organization of the United Nations. (2017). FAO World Program of the Census of Agriculture 2020, vol. 1. Rome, Italy.

Galesic, M., and Bosnjak, M. (2009). Effects of questionnaire length on participation and indicators of response quality in a web survey. Public Opinion Quarterly, 73(2), 349–360.

Galt, R.E. (2013). The moral economy is a double-edged sword: Explaining farmers’ earnings and self-exploitation in community-supported agriculture. Economic Geography, 89(4), 341–365.

Gardner, B.L. (1983). Fact and fiction in the public data budget crunch. American Journal of Agricultural Economics, 65(5), 882–888. Available: https://doi.org/10.2307/1240384.

Gardner, B.L. (2002). American Agriculture in the Twentieth Century: How It Flourished and What It Cost. Cambridge, MA: Harvard University Press.

Gasson, R., and Errington, A. (1993). The Farm Family Business. Wallingford, UK: CAB International.

Gerling, M., Tran, H., and Earp, M. (2008). Categorizing Nonresponse Occurring in the 2007 June Area Survey (California, Kansas, New York, Virginia and Washington). Research and Development Division Report RDD-08-09. Washington, DC: U.S. Department of Agriculture, National Agricultural Statistics Service.

Global Strategy to Improve Agricultural and Rural Statistics. (2017). Improving the Methodology for Using Administrative Data in an Agricultural Statistics System: Final Report. Technical Report No. 24. Rome: Global Strategy.

Goodhue, R.E., Heien, D.M., Lee, H., and Sumner, D.M. (2003). Contracts and quality in the California wine grape industry. Review of Industrial Organization, 23, 267–282.

Groves, R. (2006). Nonresponse rates and nonresponse bias in household surveys. Public Opinion Quarterly, 70(5), 646–675.

Hancock, D., and Ott, K. (2017). Total Respondent Burden for NASS for 2017. Presentation to the Panel on Improving Data Collection and Reporting about Agriculture with Increasingly Complex Farm Structures, February 10, Washington, DC.

Hansen, K.M. (2007). The effects of incentives, interview length, and interviewer characteristics on response rates in a CATI-study. International Journal of Public Opinion Research, 19(1), 112.

Herzog, A.R., and Bachman, J.G. (1981). Effects of questionnaire length on response quality. Public Opinion Quarterly, 45(4), 549–559.

Hoppe, R.A. (2014). Structure and Finances of U.S. Farms: Family Farm Report. Washington, DC: U.S. Department of Agriculture, Economic Research Service.

Hoppe, R.A., and MacDonald, J.M. (2013). Updating the ERS Farm Typology. Washington, DC: U.S. Department of Agriculture, Economic Research Service.

Horowitz, K.J., and Planting, M.A. (2006). Concepts and Methods of the U.S. Input-Output Accounts. BEA Paper 0066. Washington, DC: Bureau of Economic Analysis.

Hotz, V., Joseph, B., Balzekas, J.D., Bradburn, N., et al. (1998). Administrative Data for Policy-Relevant Research: Assessment of Current Utility and Recommendations for Development. Report of the Advisory Panel on Research Uses of Administrative Data of the Northwestern University/University of Chicago Joint Center for Poverty Research. Available: http://public.econ.duke.edu/~vjh3/working_papers/adm_data.pdf.

International Financial Reporting Standards Foundation. (2017). Biological Assets Growing on Bearer Plants (IAS 41). Available: http://www.ifrs.org/projects/2017/ias-41-biological-assets-growing-on-bearer-plants.

Isengildina-Massa, O., Karali, B., and Cao, X. (2018). The market impact of USDA crop and livestock reports in the big data era. Unpublished manuscript. Submitted to the Journal of Agricultural Economics.

Judge, G.G., Day, R.H., Johnson, S.R., Rausser, G.C., and Martin, L.R. (1977). Survey of Agriculture Economics Literature. Vol. 2: Quantitative Methods in Agricultural Economics, 1940s to 1970s, pp. 386-407. Minneapolis: University of Minnesota Press.

Just, R.E. (1983). The impact of Less Data on the Agricultural Economy and Society. American Journal of Agricultural Economics, 65(5) (Proceedings Issue), 872–881.

Karali, B. (2012). Do USDA announcements affect co-movements across commodity futures returns? Journal of Agricultural and Resource Economics, 37(2012), 77–97.

Katchova, A., and Ahearn, M. (2015). Strategies to Link Farm Business and Household Data Over Time: Insights from Research on Farm Transitions. Presentation to the Linking Federal Data on Agriculture Workshop, Economic Research Service, U.S. Department of Agriculture, October 2, Washington, DC. Available: http://www.copafs.org/UserFiles/file/FarmTransitionsDataLinkagesKatchova2.pdf.

Kreiner, C., David, T., Lassen, D., and Leth-Petersen, S. (2013). Measuring the Accuracy of Survey Responses Using Administrative Register Data: Evidence from Denmark. Working Paper No. 19539. Cambridge, MA: National Bureau for Economic Research.

Lavallée, P. (2007). Indirect Sampling. New York. Springer.

Lindley, D.V., and Smith, A.F.M. (1972). Bayes estimates for the linear model. Journal of the Royal Statistical Society, 34, 1–41.

MacDonald, J.M., Hoppe, R.A., and Newton, D. (2018). Three Decades of Consolidation in US Agriculture. U.S. Department of Agriculture, Economic Research Service, Economic Information Bulletin, Number 189, March. Available: https://www.ers.usda.gov/webdocs/publications/88057/eib-189.pdf?v=0.

MacDonald, J.M., Korb, P., and Hoppe, R.A. (2013). Farm Size and the Organization of U.S. Crop Farming. ERR-152. Washington, DC: U.S. Department of Agriculture, Economic Research Service.

Mattos, F.L., and Silviera, R.L.F. (2016). Futures price response to crop reports in grain markets. Journal of Futures Markets, 36(10), 923–942.

McCarthy, J.S., Beckler, D.G., and Qualey, S.M. (2006). An analysis of the relationship between survey burden and nonresponse: If we bother them more, are they less cooperative? Journal of Official Statistics, 22(1), 97–112.

McCarthy, J., Wagner, J., and Sanders, H.L. (2017). The impact of targeted data collection on nonresponse bias in an establishment survey: A simulation study of adaptive survey design. Journal of Official Statistics, 33(3), 857–871.

Miller, D., and O’Connor, T. (2012). Item Response Rates for the Agricultural Resource Management Survey III in 2006 and 2007. NASS Research Report No. RDD-11-07. Washington, DC: U.S. Department of Agriculture, National Agricultural Statistics Service.

Moss, C.B., Featherstone, A.M., and Wilson, C.A. (2012). Review of the financial data provided by the agricultural resource management survey. Agricultural Finance Review, 72(2). Available: https://doi.org/10.1108/afr.2012.42172baa.001.

Myers, R.J., Sexton, R.J., and Tomek, W.G. (2010). A century of research on agricultural markets. American Journal of Agricultural Economics, 92, 376–402.

National Academies of Sciences, Engineering, and Medicine. (2017a). Federal Statistics, Multiple Data Sources, and Privacy Protection: Next Steps. Washington, DC: The National Academies Press. doi: https://doi.org/10.17226/24893.

National Academies of Sciences, Engineering, and Medicine. (2017b). Innovations in Federal Statistics: Combining Data Sources While Protecting Privacy. Washington, DC: The National Academies Press. doi: https://doi.org/10.17226/24652.

National Academies of Sciences, Engineering, and Medicine. (2017c). Improving Crop Estimates by Integrating Multiple Data Sources. Washington, DC: The National Academies Press. doi: https://doi.org/10.17226/24892.

National Academies of Sciences, Engineering, and Medicine. (2018). Reengineering the Census Bureau’s Annual Economic Surveys. Washington, DC: The National Academies Press. doi: https://doi.org/10.17226/25098.

National Agricultural Statistics Service. (2014). 2012 Census of Agriculture, Appendix B: General Explanation and Census of Agriculture Report Form. Washington, DC.

National Agricultural Statistical Services/National Institute of Statistical Sciences Technical Expert Panel. (2017). Publication of Agriculture Census Data on Farm Operator Demographics. Washington, DC.

National Research Council. (2007). Understanding Business Dynamics: An Integrated Data System for America’s Future. J. Haltiwanger, L.M. Lynch, and C. Mackie (Eds.). Panel on Measuring Business Formation, Dynamics, and Performance. Committee on National Statistics, Division of Behavioral and Social Sciences and Education. Washington, DC: The National Academies Press.

National Research Council. (2008). Understanding American Agriculture: Challenges for the Agricultural Resource Management Survey. Panel to Review USDA’s Agricultural Resource Management Survey. Committee on National Statistics, Division of Behavioral and Social Sciences and Education. Washington, DC: The National Academies Press.

National Research Council. (2013). Nonresponse in Social Science Surveys: A Research Agenda. R. Tourangeau and T.J. Plewes (Eds.). Panel on a Research Agenda for the Future of Social Science Data Collection. Committee on National Statistics, Division of Behavioral and Social Sciences and Education. Washington, DC: The National Academies Press.

Nickerson, C., and Borchers. A. (2012). How is land in the United States used? A focus on agricultural land. Available: https://www.ers.usda.gov/amber-waves/2012/march/data-feature-how-is-land-used.

North American Industrial Classification System. (2017). See: https://www.census.gov/eos/www/naics/2017NAICS/2017_NAICS_Manual.pdf.

O’Connor, T. (1992). Identifying and Classifying Reasons for Nonresponse on the 1991 Farm Costs and Returns Survey. SRB Research Report SRB-92-10. Washington, DC: U.S. Department of Agriculture, National Agricultural Statistics Service.

O’Donoghue, E.J., Hoppe, R.A., Banker, D.E., and Korb, P. (2009). Exploring Alternative Farm Definitions: Implications for Agricultural Statistics and Program Eligibility. Economic Information Bulletin No. 49. Washington, DC: U.S. Department of Agriculture.

O’Donoghue, E.J., Hoppe, R.A., Banker, D.E., Ebel, R., Fuglie, K., Korb, P., Livingston, P., Nickerson, C., and Sandretto, C. (2011). The Changing Organization of U.S. Farming, EIB-88. Washington, DC: U.S. Department of Agriculture.

O’Hara, A. (2016). The Census Bureau Linkage Infrastructure. Presentation at the Joint Statistical Meetings on August 1, Chicago, IL. Available: http://ww2.amstat.org/misc/OHaraJSM2016.pdf.

Oremus, W. (2017). How “big data” went bust: And what comes next. Slate (online), October 16. http://www.slate.com/articles/technology/technology/2017/10/what_happened_to_big_data.html.

Parsons, J.L. (2011). USDA/NASS Current and Imminent Data Collection Challenges Faced by Statistical Agencies. Paper presented at the Enhancing Data Collection for Complex Agricultural Establishments Workshop, Niagara on the Lake, Ontario, Canada, June 26–28.

Peytchev, A., and Peytcheva, E. (2017). Reduction of Measurement Error Due to Survey Length: Evaluation of the Split Questionnaire Design Approach.” Survey Research Methods 11(4), 361–68.

Porter, S.R., and Whitcomb, M.E. (2005). Non-response in Student Surveys: The Role of Demographics, Engagement and Personality. Research in Higher Education, 46(2), 127–152.

Prell, M., Bradsher-Fredrick, H., Comisarow, C., Cornman, S., et al. (2009). Profiles in Success of Statistical Uses of Administrative Data. Available: http://www.bls.gov/osmr/fcsm.pdf.

Prewitt, K. (2010). Science starts not after measurement, but with measurement. ANNALS of the American Academy of Political and Social Science, 631(1), 7–16.

Pruitt, J.R., Tonsor, G.T., Brooks, K.R., and Johnson, R.J. (2014). End user preferences for USDA market information. Food Policy, 47, 24–33.

Puts, M.J.H., Tennekes, M., Daas, P.J.H., and Blois, C.D. (2016). Using huge amounts of road sensor data for official statistics. In Proceedings of the European Conference on Quality in Official Statistics (Q2016). Madrid. Available: http://www.pietdaas.nl/beta/pubs/pubs/q2016Final00177.pdf.

Reinhardt, N., and Bartlett, P. (1989). The persistence of family farms in United States agriculture. Sociologia Ruralis, 29, 203–225.

Ridolfo, H., Harris, V., McCarthy, J., Miller, D., Sedransk, N., and Young, L. (2016). Developing and testing new survey questions: The Example of new questions on the role of women and new/beginning farm operators. In Proceedings of the Fifth International Conference of Establishment Surveys, June 20–23. Geneva, Switzerland: American Statistical Association.

Robbins, M.W., and White, T.K. (2011). Farm commodity payments and imputation in the Agricultural Resource Management Survey. American Journal of Agricultural Economics, 93(2), 606–612.

Sanginabadi, B. (2018). USDA Forecasts: A Meta-analysis Study. Available: https://arxiv.org/abs/1801.06575.

Smith, J., Beaulieu, M., Kumar, E., and O’Neill, L. (2013). Recent developments in the production of agriculture statistics. In Proceedings of the 6th International Conference on Agricultural Statistics, Rio de Janeiro, Brazil.

Sumner, D.A. (1982). The off-farm labor supply of farmers. American Journal of Agricultural Economics, 64(3), 499–509.

Sumner, D.A. (1985). Farm programs and structural issues. In B. Gardner (Ed.), U.S. Agricultural Policy: The 1985 Farm Legislation. Washington, DC: American Enterprise Institute.

Sumner, D.A. (1990). Targeting and the distribution of program benefits. In K. Allen (Ed.), Agricultural Policies in a New Decade. Washington DC: Resources for the Future.

Sumner, D.A. (1991). Targeting farm programs. Contemporary Policy Issues, IX, 93–106.

Sumner, D.A. (2007). Farm Subsidy Tradition and Modern Agricultural Realities. Paper prepared for the American Enterprise Institute project on Agricultural Policy for the 2007 Farm Bill and Beyond. Available: http://aic.ucdavis.edu/research/farmbill07/aeibriefs/20070515_sumnerRationalesfinal.pdf.

Sumner, D.A. (2008). Agricultural subsidy programs. In The Concise Encyclopedia of Economics, 4 Library of Economics and Liberty. Available: http://www.econlib.org/library/Enc/AgriculturalSubsidyPrograms.html.

Sumner, D.A. (2014). American farms keep growing: Size, productivity, and policy. The Journal of Economic Perspectives, 28(1), 147–166.

Sumner, D.A. (2017). Economics of U.S. state and local regulation of farm practices, with emphasis on restrictions of interstate trade. Annual Review of Resource Economics 9.

Sumner, D.A., and Leiby, J.D. (1987). An econometric analysis of the effects of human capital on size and growth among dairy farms. American Journal of Agricultural Economics, 69(2), 465–470.

Sumner, D.A., and Wolf, C.A. (2002). Diversification, vertical integration and the regional pattern of dairy farm size. Review of Agricultural Economics, 24(2), 442–457.

Thompson, K., and Kaputa, S. (2017). Investigating adaptive nonresponse follow-up strategies for small businesses through embedded experiments. Journal of Official Statistics, 33(3), 835–856. doi:10.1515/jos-2017-0038.

United Nations Economic and Social Council. (2016). Report of the Global Working Group on Big Data for Official Statistics. Available: http://unstats.un.org/unsd/statcom/47thsession/documents/2016-6-Big-data-for-official-statistics-E.pdf.

United Nations Economic Commission for Europe. (2011). Using Administrative and Secondary Sources for Official Statistics: A Handbook of Principles and Practices. Available: http://www1.unece.org/stat/platform/display/adso/Using+Administrative+and+Secondary+Sources+for+Official+Statistics.

United Nations Economic Commission for Europe. (2015). Guidelines on Statistical Business Registers. Available: http://www.unece.org/index.php?id=40574.

U.S. Bureau of the Census. (1996). 1992 Census of Agriculture: Volume 2 Subject series. Part 4 History. Washington, DC. Available: http://usda.mannlib.cornell.edu/usda/AgCensusImages/1992/02/History.pdf.

U.S. Department of Agriculture. (2014). 2012 Census Highlights: Farm Economics. ACH12-2, May. Washington, DC: Author.

U.S. Department of Agriculture and Economic Research Service. (2017). America’s Diverse Family Farms: 2017 Edition. Washington, DC: Author.

U.S. Department of Agriculture and Economic Research Service. (2018). Agricultural Productivity in the U.S. Washington, DC: Author. Available: https://www.ers.usda.gov/dataproducts/agricultural-productivity-in-the-us.aspx.

U.S. Department of Agriculture, National Agricultural Statistical Service Advisory Committee on Agriculture Statistics. (2015). ARMS Progress Report: USDA’s National Agricultural Statistics Service and Economic Research Service Respond to Recommendations by the Agricultural Resource Management Survey Review Panel. Washington, DC: Author. Available: https://www.nass.usda.gov/Surveys/ARMS_Progress_Report.pdf.

U.S. Department of Agriculture, National Agricultural Statistical Service Advisory Committee on Agriculture Statistics. (2016). Summary and Recommendations. Annual Meeting, November 8–9. Available: https://www.nass.usda.gov/About_NASS/Advisory_Committee_on_Agriculture_Statistics/2017/ACAS%20November%202016%20Meeting%20Executive%20Summary.pdf.

U.S. Office of Management and Budget. (2006). Standards and Guidelines for Statistical Surveys. Washington, DC. Available: http://www.whitehouse.gov/omb/inforeg/statpolicy/standards.pdf.

Vrolijk, H.C.J., and Poppe, K.J. (2016). Structural change in Dutch agriculture; Impact on farm level statistics. In 7th International Conference on Agricultural Statistics, Rome, October 10–28. Available: https://www.researchgate.net/publication/309616377_Structural_change_in_Dutch_agriculture_impact_on_farm_level_statistics.

Weber, J.G., and Clay, D.M. (2013). Who does not respond to the Agricultural Resource Management Survey and does it matter? American Journal of Agricultural Economics, 95(3), 755–771.

Weber, J.G., Key, N., and O’Donoghue, E. (2016). Does federal crop insurance make environmental externalities from agriculture worse? Journal of the Association of Environmental and Resource Economists, 3(3), 707–742.

Wolf, C., and Sumner, D.A. (2001). Are farm size distributions bimodal? Evidence from kernel density estimates of dairy farm size distributions. American Journal of Agricultural Economics, 83(1), 77–88.

Young, L.J., Lamas, A.C., and Abreu, D.A. (2017). The 2012 Census of Agriculture: A capture–recapture analysis. Journal of Agricultural, Biological and Environmental Statistics, 22(4), 523–539.