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5 Table 14 Weighted means and (standard errors) for selected variables, 2007 Internet use All family farms Variable Name No Internet Dial-up Broadband 1997 Continuous variables: Operator age OP_AGE (0.45) (0.34) (0.38) (0.20) No. broadband providers NOPROVIDERS (0.09) (0.09) (0.09) (0.05) Household income TOTHHI 61,614 77, ,141 87,523 (3,912.25) (2,396.71) (5,286.60) (2,548.60) No. farmworkers NOWORKERS (0.04) (0.03) (0.03) (0.01) Dummy variables: Population change MIGCLS (0.01) (0.01) (0.01) (0.01) Children CHILD (0.01) (0.02) (0.02) (0.01) Spouse working off-farm SPOFF (0.01) (0.02) (0.02) (0.01) College education COLLEGE (0.01) (0.01) (0.02) (0.01) Urban RURAL (0.01) (0.01) (0.02) (0.01) Not urban or rural (0.01) (0.01) (0.02) (0.01) Rural (0.01) (0.01) (0.01) (0.01) Source: ERS using FCC and 2007 ARMS (USDA, NASS and ERS). are not the odds of using dial-up or broadband, only the odds relative to not being connected to the Internet at all. The sign of the coefficient gives the direction of the relationship: increase or decrease in probability due to the predictor. For example, as the age of the farm operator increases, the probability of having dial-up or broadband Internet access relative to no Internet access declines, as indicated by the negative and significant coefficients. (More discussion on the underlying methodology of this analysis is presented in Appendix D.) Other significant model results include: Larger farm businesses, as indicated by more hired workers, have a higher probability of broadband Internet access. Farm households with income above \$50,000 have a higher probability of broadband Internet access. The relative probability of broadband Internet use does not increase as the number of providers in an area increases. 35

7 Conceptually, farmers may choose to (1) purchase inputs in the nearest local town, (2) bypass the nearest local town but purchase inputs within the market reach of the nearest farm service center, or (3) bypass the farm service center altogether. Purchasing patterns are examined for three broad categories of resource inputs: farm inputs (feed, seed, and fertilizer), farm machinery and equipment, and farm credit. Comparing input purchases in each of these three mutually exclusive categories allows us to observe the changing nature of farm/local area interrelationships and the use of the Internet. We present results using logistic regression from one of these models in table 16. Our results suggest that the market reach of the nearest town may no longer define what the farm operator perceives as local. Making farm purchases over the Internet is the strongest factor increasing the likelihood of the operator bypassing the nearest town and even the more distant farm service center (table 16). As farm operators increase their participation in e-commerce, their relationships with local suppliers are likely to weaken. Farm operators may increasingly opt for distant suppliers to secure lower prices or better access to niche inputs. Suppliers with an established Internet presence, including local ones, would appear better positioned to retain customers within the local economy. Table 16 Logistic regression results 1 Choice variable: Bypass the nearest town? Type of purchase Farm inputs Farm equipment Farm credit Odds ratios Farm level variables: Log (gross farm sales) *** *** *** Operator s years experience ***.9964 Years of education ** * Internet farm purchase *** *** * County-level variables: No. of farm inputs merchants.9360*** No. of farm equipment dealers.9303** Remote county.7456*.7417* Log (highway miles) *** ** Log (population density) ** * Log (per capita income) Model statistics No. of observations 2,793 2,793 2,793 Log pseudolikelihood -1, , , Wald Prob > Pseudo R ^ ^ Odds ratio i = exp ( β i ). Signifi cance level of the coeffi cient estimates ( β i ):* p<.1; ** p<.05; *** p<.01. Source: ERS analysis of 2004 ARMS data (USDA s NASS and ERS) and other data. 37

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