Agricultural Investment and Internal Cash Flow Variables

Jensen, Farrell E.; Lawson, John; Langemeier, Larry N.

doi:10.2307/1349449

Cited by 16 publications

(5 citation statements)

References 14 publications

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“…In this study, a lagged dependent variable is used under a system GMM estimation approach to capture investment dynamics. A similar approach of utilizing lagged variables and/or the system GMM estimator with a lagged dependent variable has been used by other studies, such as Ariyaratne and Featherstone (2009), Bokusheva et al (2007), Hadrich et al (2013), Hart and Lence (2004), Jensen et al (1993), Micheels et al (2004), and Weersink and Tauer (1989).…”

Section: Dynamic Modelmentioning

confidence: 99%

“…Additional benefits of using the reduced form approach include: not having to specify a specific functional form for profits or costs, being able to incorporate factors affecting investment beyond the prices and quantities of outputs and inputs, and the ability to represent adjustment costs by including lagged independent variables. See Jensen et al (1993) and Weersink and Tauer (1989) for additional justification.…”

Section: Notesmentioning

confidence: 99%

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Differences across farm typologies in capital investment during 1996-2013

Stutzman

2017

AFR

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Purpose The purpose of this paper is to examine the impact of changes in farm economic conditions and macroeconomic trends on US farm capital expenditures between 1996 and 2013. Design/methodology/approach A synthetic panel is constructed from Agricultural Resource Management Survey (ARMS) data. A dynamic system GMM regression model is estimated for farms as a whole and separately within farm typology categories. The use of farm typologies allows for comparison of the relative magnitudes of these estimates across farms by farm sales level and the operator’s primary occupation. Findings Changes in gross farm income levels, tax depreciation rates, and interest rates have a significant impact on crop farm investment, while changes in output prices, net cash farm income levels, tax depreciation rates, and farm specialization levels have significant impacts on livestock farm capital investment. The relative significance and magnitudes of these impacts differ within farm typologies. Significant differences include a greater responsiveness to change in tax policy variables for residential crop farms, greater responsiveness to changes in output prices and debt to asset ratios for intermediate livestock farms, and larger changes in commercial crop and livestock farm investment given equivalent changes in farm sales or the returns to investment. Research limitations/implications These findings are of interest to agricultural economists when constructing farm investment models and employing pseudo panel methods, to those in the agricultural equipment and manufacturing sector when constructing models to manage inventories and plan for production needs across regions and over time, to those involved in drafting tax policy and evaluating the potential impacts of tax changes on agricultural investment, and for those in the agricultural lending sector when designing and executing agricultural capital lending programs. Originality/value This study uniquely identifies differences in the level of investment and the magnitude of investment responsiveness to changes in farm economic conditions and macroeconomic trends given differences in income levels and primary operator occupation. In addition, this study is one of the few which utilizes ARMS data to study farm capital investment. Utilizing ARMS data provides a rich panel data set, covering producers across many different crop production types and regions. Finally, employing pseudo panel construction methods contributes to efforts to effectively employ cross-sectional data and dynamic models to study farm behavior across time.

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Section: Dynamic Modelmentioning

confidence: 99%

Section: Notesmentioning

confidence: 99%

Differences across farm typologies in capital investment during 1996-2013

Stutzman

2017

AFR

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“…They found that farm machinery and equipment decreased by $589 million in 1987 and $417 million in 1988. Jensen et al (1993) also examined the effect of changes made by TRA86. Utilizing farm-level observations from the Kansas Farm Management dataset over the period 1973-1988 and including cash flow measurements in the model, they found that changes in TRA86 decreased the average farm machinery and equipment investment by between $6,081 and $9,679.…”

Section: Background and Literaturementioning

confidence: 99%

“…Farm production-specific differences, such as crop type, farm size, and degree of specialization will impact the demand function. Cash flow and farm operator characteristics are important in explaining investment behavior ( Jensen et al, 1993;Bierlen and Featherstone, 1998). Interest rates and interest expenses may also impact investment choice.…”

Section: Capital Investment Demandmentioning

confidence: 99%

Tax policy and farm capital investment: Section 179 expensing and bonus depreciation

Williamson

Stutzman

2016

Agricultural Finance Review

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Purpose – The purpose of this paper is to estimate the impact of Internal Revenue Code cost recovery provisions – Section 179 and “bonus depreciation” – on farm capital investment. Design/methodology/approach – The authors construct a synthetic panel of data consisting of cohorts of similar farms based on state and production specialization using the USDA’s Agricultural Resource Management Survey for years 1996-2012. Employing panel data methods, the authors are able to control for time-invariant fixed effects, as well as the effects of past investment on current investment. Findings – The authors estimate statistically significant investment demand elasticities with respect to the Section 179 expensing deduction of between 0.28 and 0.50. A change in bonus depreciation, on average, had little impact on capital investment. Practical implications – The estimates suggest there is a modest effect of the cost recovery provisions on investment overall, but a stronger effect on farms that have more than $10,000 in gross cash farm income. There are other implications for the agricultural sector: the provisions may encourage technology adoption with its associated benefits, such as reduced cost of production and improved conservation practices. On the other hand, the policy could contribute to the growing concentration in production as large commercial farms expand their operated acreage to take advantage of increasingly efficient physical capital. Originality/value – To the authors’ knowledge, this is the first research to use a nationally representative dataset to estimate to impact of Section 179 and “bonus depreciation” on farm investment. The findings provide evidence of the provisions’ impact on farm capital purchases.

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“…Only two of the variables were consistently significant in the fermer's investment decision, operator ^e and gross income. They found that large &rms and young &rmers were the most likely to expand, while older fermers and small ferms were likely to make no investment Jensen, Lawson, and Langemeier (1993) built a conq)osite model based upon accelerator and neoclassical investment models and added internal cash flow variables. The cash flow variables were justified by pointing to the studies of agricultural lenders' methods for evaluating a form's credit rating.…”

Section: Agricultural Investment Studiesmentioning

confidence: 99%

Examining agricultural investment

Hart¹

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The conclusion can also be readied under the less restrictive assumption d>at finns and investors have the same investment opportunities (Stiglitz, 1969). ' Most of this section is based upon an excellent review of &e investment modeling literature provided by Chirinko(1993). 7 A^ere Q'^t represents average Q and p't is the relative price of investment (relative to output). Marginal Q is equal to average Q only under certain conditions (Hayashi, 1982, 1985). These are: 1) input and output markets are conipetitive, 2) the production and adjustment cost fimctions are linearly homogeneous, 3) coital is homogeneous, and 4) investment decisions are separate from other financial decisions. Thus, to apply this model to investment, one again assumes that funds are readily available and that their source is immaterial to the investment decision. Another problem in appfyii^ this model to agriculture is that most &rms do not sell stocks in financial markets, making Q hard to determine. In each of these models, internal and external finance are treated as perfect substitutes. The firm is unconcerned or unaffected by the choice of internal or external flmds. This would be true if financial coital markets were perfect. There would be no transaction costs or asymmetric information problems between lenders and borrowers. Although this type of assun:q)tion may be adequate in some studies, it is hard to justify for agricultural investment at the &rm level Many studies have investigated agricultural lenders' credit rating procedures, essentially examining the asymmetric information problem. These studies suggest that asymmetric iiiformation problems are significant in agricuhursil lending. this study, we seek to find the connection between the asymmetric information literature and the investmCTt Ihoature including financial variables into investment UKidels. In the Bayesian con^site regression approach, elements from the ' Often refored to in tiie literature as Tobin's Q. * This section is derived from Chapter 11 in Bayesian DataAnafysis Gelman et al. (1995) and Brooks (1998).

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Agricultural Investment and Internal Cash Flow Variables

Cited by 16 publications

References 14 publications

Differences across farm typologies in capital investment during 1996-2013

Differences across farm typologies in capital investment during 1996-2013

Tax policy and farm capital investment: Section 179 expensing and bonus depreciation

Examining agricultural investment

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