U.S. farm capital investment 1996–2013: Differences by farm size and operator primary occupation
This study analyzes U.S. farm level investment in machinery, equipment and structures between 1996-2013. A synthetic panel is constructed using annual cross-sectional farm level observations from the Agricultural Resource Management Survey (ARMS). Cohorts are formed by grouping farms into similar categories based upon farm production type, region and farm typology. This methodology allows the use of fixed effects to control for cohort specific and time-invariant similarities in investment levels, addresses non-investment in a single period by using cohort average investment rates, and allows links between investment levels and other key determinants across cohorts over time. Within farm typologies, farms are classified based on levels of gross cash farm income (GCFI) and operator primary occupation. Commercial farms have GCFI greater or equal to $350,000. Resident farms have GCFI less than $350,000 and a primary operator occupation other than farming. Intermediate farms also have GCFI less than $350,000 but identify their primary occupation as farming. Making these distinctions is important if investment behavior is related both to GCFI levels and primary occupation. Previous studies find differences between in farm capital investment rates and changes in sales or income measurements, tax policy variables, and cash flow measurements based on farm size and levels of off-farm income. To test if these same relationships hold when using the ARMS data and farm typology categories, I develop three hypotheses based upon these three commonly found and/or asserted relationships. The three hypothesis developed are that compared to the other farm typologies there is a greater increase in investment rates given: 1) an increase in output prices and returns on investment for commercial farms, 2) changes in tax policy variables for resident farms, and 3) changes in measures of credit constraints for intermediate farms. I test these hypotheses by allowing these key coefficients to vary across farm typologies. Given the results of these tests, I find evidence to support the first two hypotheses, though this varies by commodity type, but little evidence to support the third. Using the estimated model, changes in specific model coefficients are used to explain differences in investment levels in 2013 vs. 1996 and to estimate average farm investment levels in 2024. Changes in farm capital investment in 1996 vs. 2013 can be attributed to changes in output prices, interest rates and year specific impacts. Decreases in net farm incomes on commercial grain and livestock farms, declining output prices for intermediate livestock farms, lower bonus tax depreciation expense limits on resident livestock farms, and rising interest rates for grain farms across typologies lead to large declines in average farm investment in 2024 compared to 2013.
Baker, Purdue University.
Off-Campus Purdue Users:
To access this dissertation, please log in to our