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Volatility of Farming and Operator’s Family Income of Canadian Farmers

Volatility of Farming and Operator’s Family Income of Canadian Farmers. Kenneth Poon University of Guelph AGRI Research Group, Statistics Canada. Farm Income Characteristics . Farming income highly variable compared to other sectors Variability in production and price

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Volatility of Farming and Operator’s Family Income of Canadian Farmers

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  1. Volatility of Farming and Operator’s Family Income of Canadian Farmers Kenneth Poon University of Guelph AGRI Research Group, Statistics Canada

  2. Farm Income Characteristics • Farming income highly variable compared to other sectors • Variability in production and price • Farm families are more financially vulnerable • Volatility as a measure of financial well-being • Volatility = variability over time

  3. Support Program • Growing Forward • Objectives: Foster competitive and innovative sector • improve welfare of farm operators and families • Business Risk Management (BRM) Suite • Programs designed to reduce income / margin volatility • Catch-all program: no specific commodity/group targeted

  4. Why Volatility? • Currently, no clear picture of income volatility for Canadian agricultural sector • Few datasets are formatted to examine volatility • Studying volatility can identify… • Any sectors are relatively more vulnerable • Trends in volatility • Factors related to volatility

  5. Data Source • The Farm Micro-Longitudinal Dataset • Operator-level Panel data, 2001 - 2006 • Source incorporated & unincorporated income tax • Data cleaned • NO T3 records (i.e. community farms) • NO duplicates: 1 operator per farm per family • NO entries with 0 revenue or expenses for all 6 years • NO entries labeled as ‘non-farm’ for 3+ consecutive years • Final Sample Size: 32692 operators • 5355 operators dropped

  6. Measuring Volatility • Volatility = variation over time • Measurement: Coefficient of Variation (CV) • Standard deviation over time / mean over same period • % variation from mean • CV=0: no volatility, CV=1: SD=mean, CV undefined: mean=0 • Interested Variables • Net Operating Revenue (NOR) • Operator’s Family Income (OFI), unincorporated only

  7. Relating Volatility • Relating volatility: Spearman Rank Correlation • Similarity in ranking of CV between NOR, OFI • Unweighted comparison between 2 variables • If Spearman’s rho = • 1: ranking matches perfectly • 0: non of the rankings match • -1: ranking exactly opposite

  8. Typology • Typology based on AAFC definition • Determined by typology @ start of sample (2001) *Low income cutoff in 2001, for family with 2 parents, 2 children under 18 SOURCE: Farm Micro-Longitudinal Dataset, 2001-2006

  9. Commodity Groups • Commodity Groups determined by NAISC • Commodity consist of >50% sales • Determined @ start of sample (2001) SOURCE: Farm Micro-Longitudinal Dataset, 2001-2006

  10. Typology vs. Commodity Groups SOURCE: Farm Micro-Longitudinal Dataset, 2001-2006

  11. Volatility of Typology • CV ranked within typology, into quartiles • Max CV of 25th, 50th, 75th percentile reported Volatility of NOR, 2001-2006 CV of NOR at selected percentiles, 2001-2006 SOURCE: Farm Micro-Longitudinal Dataset, 2001-2006

  12. Weighted Annual Mean of NOR, 2001-2006 SOURCE: Farm Micro-Longitudinal Dataset, 2001-2006

  13. Median 3-year CVs by Typology, 2001-2006 SOURCE: Farm Micro-Longitudinal Dataset, 2001-2006

  14. Volatility of NOR by Commodity Groups CV of NOR at selected percentiles, 2001-2006 SOURCE: Farm Micro-Longitudinal Dataset, 2001-2006

  15. Weighted Annual Mean of NOR, 2001-2006 SOURCE: Farm Micro-Longitudinal Dataset, 2001-2006

  16. Median 3-Year CVs by Commodity, 2001-2006 SOURCE: Farm Micro-Longitudinal Dataset, 2001-2006

  17. Volatility by Typology SOURCE: Farm Micro-Longitudinal Dataset, 2001-2006 Volatility of OFI, 2001-2006

  18. Weighted Annual Mean of OFI, 2001-2006 SOURCE: Farm Micro-Longitudinal Dataset, 2001-2006

  19. CV of Total Household Income SOURCE: Farm Micro-Longitudinal Dataset, 2001-2006

  20. Volatility of Commodity Groups Volatility of OFI, 2001-2006 SOURCE: Farm Micro-Longitudinal Dataset, 2001-2006

  21. Weighted Annual Mean of OFI by Commodity Group, 2001-2006 SOURCE: Farm Micro-Longitudinal Dataset, 2001-2006

  22. 3-year CVs of OFI by Commodity Group, 2001-2006 SOURCE: Farm Micro-Longitudinal Dataset, 2001-2006

  23. Correlation between CV of NOR & THI • All positive, significant at 5% Spearman’s rho between NOR & OFI by commodity groups, 2001-2006 SOURCE: Farm Micro-Longitudinal Dataset, 2001-2006 Spearman’s rho between NOR & OFI by typology, 2001-2006

  24. Volatility & Correlation: NOR vs OFI SOURCE: Farm Micro-Longitudinal Dataset, 2001-2006

  25. Major Trends in Volatility • Volatility of NOR, OFI increasing over time • OFI volatility increases with farm size • NOR volatility lowest for medium farms • High volatility not always linked with low NOR or OFI • Operators of very large farms have high volatility and NOR • Correlation Between NOR and OFI volatility increases with farm size

  26. Possible Explanation for Trends • Farm size vs OFI Volatility • Operators of smaller farms more likely to adopt off-farm work, stabilize off-farm income • Large-farm operators likely take on higher risk, specialize, reliant on farming as main source of income • Low-income farm operators may not have time/resource for effective risk management, relies on farming as main source of income

  27. Possible Explanation for Trends • Correlation Between NOR, OFI volatility increases with farm size • Large-farm operators likely more reliant on farming as main source of income • Operators of smaller farms likely have off-farm work • Medium farms have lowest volatility in NOR • Dairy operators make up majority of medium farm operators • Efficiency of scale? Best combination of diversification & risk management?

  28. Further Research • Relationship between size, volatility, and correlation • Explain by off-farm work opportunities? • Regression between CV & size, farm type, typology, off-farm labour market characteristics • Low-income farm operators financially vulnerable • How does current support programs affect household income for these individuals?

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