1. Input-Output Analysis of Agriculture for Washington Counties Sean Ardussi, Phil Hurvitz Geography 440, Spring 2005 Prof. Bill Beyers

2. How would less agricultural dependence affect the economic base of certain counties?

3. Original Goals For highly agricultural dependent counties (King, Kitsap, etc.), develop an alternative set of economic assumptions based on more localized agriculture. Community based agriculture Less transportation expenditures Farmers Market Scenario

4. Overview Input-output analysis for each county in Washington State Focusing on agricultural industries Crops Livestock

5. Data Sources NAICS: employment, output, labor income BEA (does not match official WAIO model) USDA Census of Agriculture WA Employment Security Department

6. SIC → NAICS NAICS – North American Industry Classification System (1997 +) SIC – Standard Industrial Classification (1997 and prior)

7. NAICS Benefits Businesses that use similar production processes are grouped together Expanded sectors to reflect changes in economy Information sector Service sector NAFTA compatibility USA, Canada, Mexico

8. NAICS Drawbacks Less than 50% of SIC codes can be directly linked to a NAICS counterpart Conversion from SIC to NAICS is subject to error of judgment

9. Methods Washington State Input-Output Model (official WA Office of Financial Management model) Implemented within an R statistical/programming language environment

10. R Software for handling statistical operations Good for dealing with tabular data Handles generic and matrix math Reads & writes standard files Programming interface allows batch jobs

11. Example of R code # run the conflation and add to the employment matrix for (county in county.names) { # print (county) cty <- conflate.esd(county, 19) employment <- cbind(employment, cty) } colnames(employment) <- county.names # sum across rows to get WA totals of employment wa.employment <- rowSums(employment) wa.employment.sum <- sum(wa.employment) # make location quotients LQs <- NULL LQs.modified <- NULL for (i in 1:ncol(employment)) { lqs.county <- NULL lqs.county.modified <- NULL county.sum <- sum(employment[,i]) for (j in 1:nrow(employment)) { lq.local.component <- employment[j, i] / county.sum lq.state.component <- wa.employment[j] / wa.employment.sum lq <- lq.local.component / lq.state.component ifelse (lq < 1, lq.mod <- lq, lq.mod <- 1) lqs.county <- c(lqs.county, lq) lqs.county.modified <- c(lqs.county.modified, lq.mod) } LQs <- cbind(LQs, lqs.county) LQs.modified <- cbind(LQs.modified, lqs.county.modified) }

12. R output examples

13. Results Comparison of metrics across counties Location quotients Output Employment Labor income

14. Location Quotients: Livestock Livestock High Counties Adams – 11.82 Pacific – 8.62 Mason – 8.11 Yakima – 6.86

15. Location Quotients: Livestock Livestock Low Counties King – 0.13 Spokane – 0.16 Pierce – 0.62 Snohomish – 0.82

16. Location Quotients: Crops Livestock High Counties Okanagon – 17.11 Douglas – 15.8 Klickitat – 12.24 Grant – 10.82

17. Location Quotients: Crops Livestock Low Counties King –.03 Kitsap –.064 Spokane –.089 Snohomish –.114

18. Crop Output

19. Crop Employment

20. Crop Labor Income

21. Livestock Output

22. Livestock Employment

23. Livestock Labor Income

24. Limitations Needed to conflate data sets Needed to impute data Excel format not easy to translate to R

25. Benefits R code can be altered and simply run again to generate output statistics & figures Reduces user error when programmed correctly

26. Conclusions Different counties in the State vary widely with respect to agricultural economics Increased urbanization will have different effects on different locations in the State