What Drives Agricultural Profitability in the U.S.: Application of the DuPont Expansion Method

Transcription

1 What Drives Agricultural Profitability in the U.S.: Application of the DuPont Expansion Method Ashok K. Mishra 1, J. Michael Harris 2, Kenneth Erickson 2, and Charles Hallahan 3 1 Associate Professor, Louisiana State University Baton Rouge, Louisiana, Phone: , Fax: , amishra@lsu.edu 2 Economists, USDA-ERS-RRED Farm Business Economics Branch 1800 M Street, NW, Room N-4185 and Rooms N-4086 Washington, DC Phone: , Fax: , harris@ers.usda.gov Phone: , Fax: , erickson@ers.usda.gov 3 Mathematician, USDA-ERS-ISD Applications Development Branch Room S-2070 Washington, DC Phone: , hallahan@ers.usda.gov Selected Paper prepared for presentation at the American Agricultural Economics Association Annual Meeting, Orlando, FL, July 27-29, The authors gratefully acknowledge the very helpful contribution of Dr. Charles B. Moss, University of Florida, Food and Resource Economics Department, on previous versions of this paper, and of Dr. Hisham El-Osta, USDA-ERS, on this version. The views expressed here are those of the authors and do not necessarily represent the views or policies of ERS or the U.S. Department of Agriculture.

2 What Drives Agricultural Profitability in the U.S.: Application of the DuPont Expansion Method 1. Introduction Recent changes in the agricultural sector have focused increased attention on the role of agricultural specialization and integration in the sector. The received doctrine from Adam Smith is that specialization allows producers to gain production economies. Current trends in agriculture tend to support this revealed doctrine. The parochial picture of the family farm with livestock and a vast array of crops have been replaced with commercial enterprises focusing on the production of a limited combination of commodities. This specialization has been associated with increased size both in terms of land and equipment. However, this specialization comes at a cost, at least for crop farms. The combination of specialized equipment and single crops implies that equipment and labor are only used for limited time periods during the year. The classic case that an expensive combine sits in the barn for all but three weeks on a large corn farm implies that the effective cost of machinery is much higher under specialized crop farms than for farms with more diversified production. In addition to the notions of specialization, vertical integration in the farm sector has become an important theme over the past twenty years. The move toward vertical integration is probably more pronounced in the areas of livestock production, but vertical integration has also become important in some crops such as sugarcane. Economic theory suggests several causes for vertical integration. Joskow (2006) reviews alternative economic theories of vertical integration. Coase (1937) and Williamson (1979) suggest that vertical integration is a direct result of transaction costs. Grossman and Hart (1986) and Anderlini and Felli (2006) continue the theme, but refocus the discussion of

3 transaction cost of specific investments. This recasts the discussion in terms of the holdup problem and principal agent theory. In order to examine the significance of specialization and vertical integration on domestic agriculture, this study uses a financial approach based on the DuPont expansion (Reilly and Brown, 2000). The traditional DuPont Expansion decomposes the rate of return to equity into asset efficiency, gross margins, and solvency. In the current study, we hypothesize that agricultural specialization directly affects the asset efficiency and gross margin of the farm. Specifically, specialization would tend to decrease asset efficiency while increasing the gross margin. On the other hand, vertical integration may affect the gross margin and solvency directly. The effect on solvency would result from the integrator s use of credit as an incentive. However, the general type of agricultural enterprise integrated may also have implications for asset efficiency. Specifically, livestock operations may tend to have greater asset efficiency than crops. Further, we hypothesize that acres operated by the firm may lead to a scale effect (Kislev and Peterson, 1982; Cochrane, 1979). The objective of this paper is to examine the significance of specialization and vertical integration on the profitability (rate of return on equity, or ROE) in U.S. agriculture. We estimate a system of equations in log space using annual farm-level data from the U.S. Department of Agriculture s survey, We include all 10 ERS production regions and all farms. Alternatively, we estimate the model excluding farms with gross value of sales less than $100,000. We hypothesize that agricultural specialization directly affects the asset efficiency and gross margin of the farm. Specifically, specialization would tend to decrease asset efficiency while increasing the

4 gross margin. On the other hand, vertical integration may affect the gross margin and solvency directly. The effect on solvency would result from the integrator s use of credit as an incentive. Specifically, livestock operations may tend to have greater asset efficiency than crop farms. Our results are preliminary and, as expected, vary across farm production regions and across farm types (crop versus livestock). However we do find evidence that specialization and vertical integration are key factors driving farm profitability in the U.S. 2. The Empirical Model Following the DuPont formulation in Moss, Mishra, and Erickson, we begin with the expansion of the rate of return on equity: R S C S A = (1) E S A E where R is the rate of return on farm business equity, S is the level of agricultural sales, C is the cost of production, A is the level of agricultural assets, and E is the level of agricultural equity. The DuPont expansion in equation (1) shows how the rate of return to equity can be related to the gross margin on agricultural sales (S-C)/S, the asset efficiency measured by the asset turnover ratio (S/A) and the firm s solvency (A/E). Following Moss, Mishra and Erickson, we note that this traditional DuPont equation is linear in the logs: R S C S A ln = ln + ln + ln (2) E S A E Following this formulation, we posit a system of four equations in log space

5 R ln = α10 + α11j + α12a + α13x + ε1 E S C ln = α20 + α21j + α22a + α23i + α24 y + ε2 S (3) S ln = α30 + α31j + α32a + α32i + α34z + ε3 A A ln = α40 + α41j + α42a + α43i + α44z + ε4 E where J is the entropy measure of diversification to be discussed below, a is the acres operated by the firm used to capture scale effect, I is a measure of vertical integration, x, y, and z are vectors of variables used to remove other effects from the system and ε i are error terms. The first of the four equations in the log of the DuPont system can be dropped due to summing up conditions similar to a demand system. The entropy index is a measure of concentration proposed by Shannon and Weaver. We use an alternative measure of entropy, the Theil definition of entropy (TMI). If we define s i as the share of farm s revenues that are attributable to activity i, the entropy index can be defined as = i ( ) J s i ln s i (4) Note that s ln(s)=0 as s approaches 0. This measure of concentration reaches a minimum at 0 as the shares approach zero for all enterprises except one. This would represent a completely specialized firm producing a single output. At the other extreme, the entropy index reaches a maximum of ln(n) where N is the number of possible crops and livestock activities. 3. Data The rich and extensive data in the USDA- survey make our analysis possible. The survey is an annual survey covering farms in the 48 contiguous States. This

6 survey is designed to incorporate information from both a list of farmers producing selected commodities and a random sample of farmers based on area. Since stratified sampling is used, inferences regarding the means of variables for states and regions are conducted using weighted observations. We apply the USDA s in-house jackknifing procedure that it believes is most appropriate when analyzing data (Dubman, Kott; Cohen, and Jones). We link nine annual surveys to form a pooled time-series cross-section, assuming that the survey design for each year is comparable. Hence, we are able to use the annual survey data to examine structural changes over time. Incorporating the survey weights, and following the jackknifing procedure described in Kott, assures that regression results are suitable for inference to the population in each of the regions analyzed. 4. Estimation of the DuPont System The system of DuPont equations in Section 2 was estimated by OLS using repeated cross-sections regression (RCS) using annual farm-level data from the U.S. Department of Agriculture s Agricultural Resource Management Survey () data, , with (one-way) fixed effects: year. We estimate three of the four DuPont equations for the 10 USDA-ERS production regions for all farms (family and non-family farms). The first equation of the four, R E S C S A = (1) S A E can be dropped due to summing up conditions similar to a demand system.

7 Farm-level estimates of the R (the rate of return on farm business equity), S (the level of agricultural sales), C (the cost of production), A (the level of agricultural assets), E (the level of agricultural equity). The index of vertical integration (I) is estimated as the proportion of total value of production from contracts. Following the empirical model and DuPont system of equations in Section 2, we formulate four hypotheses about the factors driving the rate of return on equity: H1: Agricultural specialization (J)directly affects the asset efficiency (SA) and gross margin (S-C/S) of the farm. Specifically, specialization would tend to decrease asset efficiency while increasing the gross margin. H2: Vertical Integration, I, may affect the gross margin and solvency directly (A/E), and thus too the rate of return (R/E). H3: Acres operated, a, may lead to scale economies for larger farms. H4: Type farm (crop or livestock) may also affect rates of return. That is, the general type of agricultural enterprise integrated may also have implications for asset efficiency. Specifically, livestock operations may tend to have greater asset efficiency than crops. Our review of the literature on the firm, including recent transaction cost theory, reveals a set of complex links between agricultural specialization, vertical integration, scale, and farm type. To help us understand the key factors driving the profitability of the farm. We estimate the following DuPont model using farm-level RCS data, The econometric models presented here are:

8 A Model1:ln = α10 + α11j + α12a + α13x + ε1 E S C Model2:ln = α20 + α21j + α22a + α23i + α24 y + ε2 S S Model3:ln = α30 + α31j + α32a + α32i + α34z + ε3 A with a Year dummy for the one-way fixed effects model. Our specific parameterization of the DuPont equations is as follows: Model 1- (Solvency): Asset To Equity Ratio ln(a/e) = f(acres Operated, Theil Entropy, Index Of Vertical Integration, Farm Size, and Farm Size interacted with AcresOperated) Model 2- (Profitability): Net Profit Margin: ln(s-c/s) = f(acres Operated, Theil Entropy, Farm Type, Farm Size, and Farm Size interacted with AcresOperated) Model3 (Efficiency): Asset Turnover: ln(s/a) = f(acresoperated, TheilEntropy, FarmType, IndexOfVerticalIntegration, GetGovernmentPayments, GetOffFarmIncome, FarmSize, and FarmSize interacted with AcresOperated). 4. Results Before running the OLS regressions on the three DuPont equations, we split the sample into two groups: (1) All farms, Small, Medium, and Large Farms, and (2) Medium and Large Farms only (we excluded farms whose gross value of sales is less than $100,000). Statistical examination of the DuPont expansion indicates that the differences in the distribution for each ratio in a given time period are statistically significant. However, differences in the distribution of the rate of return on all factors across time are significant and primarily attributable to differences in the profit margin and solvency over time.

9 Specifically, statistical differences in the profit margins are observed in seven of the ten production regions. Similar differences are observed for the asset to equity ratio. The results of this study suggest that cross sectional variation in the rate of return to assets is primarily determined by the asset turnover ratio and the gross margin. We ran OLS regressions with one-way fixed effects (year), repeated cross sections, We parameterized the three regressions based on our literature review (e.g., Collins(1985), Coase (1937), Barnard and Boehlje(2004), and Anderline and Felli(2006). Our results must be viewed as preliminary for several reasons. First, we need to more thoroughly examine these initial results, checking for omitted variables, measurement error, etc. Second, we need to consider alternative parameterizations of the DuPont model. H1: Agricultural specialization (J) would tend to decrease asset efficiency while increasing the gross margin. To represent agricultural specialization, we used the Theil entropy measure (TMI). The TMI measures how concentrated total income from production is for the farm and the greater the diversity of crops and livestock produced, the greater the TMI. We included the TMI in all three equations and found evidence that agricultural specialization decreases asset efficiency (negative signs on the IndexVertIntegration coefficient and statistically significant) in the AssetTurnover equation for farms in the Northeast, Lake States, Corn Belt, Appalachia, Southeast and Delta. We found no evidence that agricultural specialization increases the gross margin since in the NetProfitMargin equation, the TMI was not significant in any region. H2: Vertical integration, I, may affect the gross margin and solvency directly, and thus too the rate of return. To represent vertical integration, we used the farm s share of value of production from contracting divided by the total value of production (Appendix Table A). We found that

10 vertical integration may increase solvency (raise the A/E ratio), but only in the Southeast. We did not test the effect of vertical integration on the gross margin, but will do so in future research. H3 Acres operated, a, may lead to scale economies for larger farms. Acres operated is reported in the USDA- survey. We included acres operated in each of the three equations. Acres operated was statistically significant in the AssetTurnover equation for the Corn Belt, Northern Plains, Appalachia, Southeast, Delta, Southern Plains, Mountain States, and Pacific. This suggests that acres operated may lead to scale economies through improving financial liquidity. Acres operated was statistically significant in the NetProfitMargin equation for the Northeast, Lake States, Corn Belt, Northern Plains, Delta, Southern Plains, and Mountain States. This also suggests that acres operated may lead to scale economies in these regions. H4: Type farm (crop or livestock) may also affect rates of return. Specifically, the general type of agricultural enterprise integrated may also have implications for asset efficiency. Specifically, livestock operations may tend to have greater asset efficiency than crops. We found that FarmType affected rates of return, both through the NetProfitMargin and through the AssetsToEquity ratio. FarmType was statistically significant in the Corn Belt, Northern Plains, Appalachia, Southeast, Delta, Southern Plains, and Mountain States. FarmType also affected profitability by affecting solvency (AssetsToEquity), but only in the Mountain States. 5. Conclusion In order to examine the significance of specialization and vertical integration on domestic agriculture, we used a financial approach based on the DuPont expansion. In the current study, we hypothesize that agricultural specialization directly affects the asset

11 efficiency and gross margin of the farm. Specifically, specialization would tend to decrease asset efficiency while increasing the gross margin. On the other hand, vertical integration may affect the gross margin and solvency directly. The effect on solvency would result from the integrator s use of credit as an incentive. However, the general type of agricultural enterprise integrated may also affect asset efficiency. Specifically, livestock operations may tend to have greater asset efficiency than crops. We estimated a system of equations in log space using annual farm-level data from the USDA s data, We included all production regions in the contiguous 48 states, and all farms. We found evidence that specialization and vertical integration are among the key factors driving farm profitability in the U.S.

12 References Anderlini, Luca, and Leonardo Felli. Transaction Costs and the Robustness of the Coase Theorem. The Economic Journal. Volume 116, Issue 508, January Barnard, Freddie L., and Michael Boehlje. Using Farm Financial Standards Council Recommendations in the Profitability Linkage Model: The ROA Dilemna Journal of the American Society of Farm Managers and Rural Appraisers, pp Coase, Ronald The Nature of the Firm. Economica 4: Cochrane, Willard W. The Development of American Agriculture. Minneapolis, MN: University of Minnesota Press, Cohen, S., J. Xanthopoulos, and G. Jones, An Evaluation of Available Statistical Software Procedures Appropriate for the Regression Analysis of Complex Survey Data, Journal of Official Statistics 4 (1988): Collins, R. A. Expected Utility, Debt-Equity Structure, and Risk Balancing. American Journal of Agricultural Economics 67(1985): Dubman, R.W., Variance Estimation with USDA s Farm Costs and Returns Surveys and Agricultural Resource Management Study Surveys, Washington DC: U.S. Department of Agriculture, Economic Research Service Staff Paper AGES 00-01, Featherstone, A. M., C. B. Moss, T. G. Baker, and P. V. Preckel. The Theoretical Effects of Farm Policies on Optimal Leverage and the Probability of Equity Loss. American Journal of Agricultural Economics 60(1988): Grossman, Sanford J. and Oliver D. Hart. The Costs and Benefits of Ownership: A Theory of Vertical and Lateral Integration. The Journal of Political Economy, Vol. 94, No. 4 (August, 1986), pp Hirschberg, Joseph G. and Daniel J. Slottje. The Reparameterization of Linear Models Subject to Exact Linear Restrictions. University of Melbourne, Department of Economics, Working Paper Series No. 702, July Hopkins, Jeffrey and Mitchell Morehart. An Empirical Analysis of the Farm Problem: Comparability in Rates of Return. Select Paper for Challenging the Agricultural Economics Paradigm A symposium honoring the career of Luther G. Tweeten, September 10-11, 2000, Columbus, Ohio. Joskow, Paul L. Vertical Integration. Chapter XX. Prepared for the American Bar Association Antitrust Section s Issues in Competition Law and Policy project. (revised December 5, 2005). Kislev, Yoav, and Willis Peterson, Prices, Technology, and Farm Size, Journal of Political Economy 90 (1982),

13 Kott, P.S., Using the Delete-A-Group Jackknife Variance Estimator in NASS Surveys, U.S. Department of Agriculture, National Agricultural Statistics Service, (USDA/NASS), NASS Research Report, RD-98-01, Washington, DC, Melvin, Jon, Michael Boehlje, Craig Dobbins, and Allan Gray. The DuPont Profitability Analysis Model: An Application and Evaluation of an E-Learning Tool. Agricultural Finance Review, Spring 2004, pp Mood, A. A., F. A. Graybill, and D. C. Boes. Introduction to the Theory of Statistics. New York: McGraw-Hill, Moss, Charles B., Ashok K. Mishra and Kenneth W. Erickson, Decomposition of Differences in Agricultural Profitability: A DuPont Expansion. Unpublished manuscript, Moss, C. B., S. A. Ford, and W. G. Boggess. Capital Gains, Optimal Leverage, and the Probability of Equity Loss: A Theoretical Model. Agricultural Finance Review 49(1989): Reilly, Frank K. and Keith C. Brown. Investment Analysis and Portfolio Management. The Dryden Press, 6 th. edition. Fort Worth: 2000, pp Schmitz, Andrew. Boom/Bust Cycles and Ricardian Rent. American Journal of Agricultural Economics 77(1995): Williamson, Oliver. The Vertical Integration of Production: Market Failure Considerations. The American Economic Review, Vol. 61, No. 2, Papers and Proceedings of the Eighty-Third Annual Meeting of the American Economic Association. (May, 1971), pp Williamson, Oliver. Transaction Cost Economics: the Governance of Contractual Relationships. Journal of Law and Economics October 1979, 22:

14 Table 1. Regression results for investment share equations by region (one-way fixed effects): Small, Medium, and Large Farms DuPont equation 1: lnassetturnoverratio = f(acres, TheilEntropy, Farm type, FarmSize, FE: year) Northeast Lake States Corn Belt Northern Plains Appalachia Variable Estimate t-value Estimate t-value Estimate t-value Estimate t-value Estimate t-value LnAssetTurnoverRatio Acres *** *** *** ** 2.25 TheilEntropy *** *** *** *** Farm type *** *** *** *** *** 6.84 Index VertIntegration *** *** GetGovtPayments *** *** *** *** *** GetOffFarmIncome *** *** *** *** *** SmallFarm *** *** *** *** *** MediumFarm *** *** *** 3.34 Acres*SmallFarm *** *** *** *** Acres*MediumFarm *** *** *** *** *** Year *** *** *** *** *** DuPont equation 2: lnnetprofitmargin = f( Acres, TheilEntropy, Farm type, FarmSize, FE: year) lnnetprofitmargin Acres *** *** *** *** *** TheilEntropy ** *** ** Farm type *** *** *** *** SmallFarm *** *** *** MediumFarm *** *** *** *** *** 3.53 Acres*SmallFarm *** *** *** *** *** Acres*MediumFarm *** *** *** *** *** Year *** *** *** *** *** DuPont equation 3: lnassetstoequity = f(acres, TheilEntropy, Farm type, FarmSize, Acres*FarmSize, FE: year) lnassetstoequity Acres *** *** TheilEntropy ** *** *** *** Farm type ** 1.85 IndexVertIntegration ** * SmallFarm ** MediumFarm ** ** Acres*SmallFarm ** Acres*MediumFarm ** ** *** Year * NS ** NS NS *** = statistical significance at the 0.01 confidence level; ** =.05 confidence level; * =.10 confidence level; NS = not statistically significant. Source: USDA- survey.

15 Table 2. Regression results for investment share equations by region (one-way fixed effects):medium and Large farms DuPont equation 1: lnassetturnoverratio = f(acres, TheilEntropy, Farm type, FarmSize, FE: year) Southeast Delta Southern Plains Mountain States Pacific Variable Estimate t- Estimate t-value Estimate t-value Estimate t-value Estimate t-value value lnassetturnoverratio Acres *** *** *** *** *** TheilEntropy *** *** *** *** *** Farm type *** *** *** *** *** IndexVertIntegration *** *** 3.99 GetGovtPayments *** *** *** *** *** GetOffFarmIncome *** *** *** *** *** SmallFarm *** *** *** *** *** MediumFarm * *** *** *** Acres*SmallFarm *** *** Acres*MediumFarm *** *** *** *** *** Year *** *** *** *** *** DuPont equation 2: lnnetprofitmargin = f( Acres, TheilEntropy, Farm type, FarmSize, FE: year) lnnetprofitmargin Acres *** *** *** TheilEntropy *** ** * *** Farm type *** *** *** SmallFarm *** *** *** *** MediumFarm *** * ** ** * Acres*SmallFarm *** *** * ** *** Acres*MediumFarm * *** *** ** ** Year *** *** *** *** *** DuPont equation 3: lnassetstoequity = f(acres, TheilEntropy, Farm type, FarmSize, Acres*FarmSize, FE: year) lnassetstoequity Acres *** *** TheilEntropy ** *** *** ** 1.71 Farm type *** ** * IndexVertIntegration * 1.29 SmallFarm * * * ** 2.02 MediumFarm Acres*SmallFarm Acres*MediumFarm Year NS NS NS NS NS *** = statistical significance at the 0.01 confidence level; ** =.05 confidence level; * =.10 confidence level; NS = not statistically significant. Source: USDA- survey.

16 Table 3. Regression results for investment share equations by region (one-way fixed effects): Medium and Large Farms DuPont equation 1: lnassetturnoverratio = f(acres, TheilEntropy, Farm type, FarmSize, FE: year) Northeast Lake States Corn Belt Northern Plains Appalachia Variable Estimate t-value Estimate t- Estimate t- Estimate t-value Estimate t-value value value lnassetturnoverratio Acres *** *** *** *** 4.36 TheilEntropy *** *** *** * *** 5.36 Farm type *** *** *** *** *** 6.64 IndexVertIntegration *** *** *** *** GetGovtPayments *** * *** *** 4.86 GetOffFarmIncome *** *** *** ** *** MediumFarm *** *** *** *** *** 5.08 Acres*MediumFarm *** *** *** *** *** Year *** *** *** *** *** DuPont equation 2: lnnetprofitmargin = f( Acres, TheilEntropy, Farm type, FarmSize, FE: year) lnnetprofitmargin Acres *** *** *** *** *** TheilEntropy * ** Farm type.2527* *** *** *** 2.94 MediumFarm.8985*** *** *** *** *** 4.73 Acres*MediumFarm *** *** *** *** *** Year *** *** *** *** *** DuPont equation 3: lnassetstoequity = f(acres, TheilEntropy, Farm type, FarmSize, Acres*FarmSize, FE: year) lnassetstoequity Acres *** *** TheilEntropy * *** *** *** Farm type * IndexVertIntegration * ** MediumFarm ** ** Acres*MediumFarm * ** ** Year NS NS * * NS *** = statistical significance at the 0.01 confidence level; ** =.05 confidence level; * =.10 confidence level; NS = not statistically significant. Source: USDA- survey.

17 Table 4. Regression results for investment share equations by region (one-way fixed effects): Medium and Large Farms DuPont equation 1: lnassetturnoverratio = f(acres, TheilEntropy, Farm type, FarmSize, FE: year) Southeast Delta Southern Plains Mountain States Pacific Variable Estimate t-value Estimate t-value Estimate t-value Estimate t-value Estimate t-value lnassetturnoverratio Acres *** *** *** TheilEntropy *** *** *** *** ** Farm type *** *** *** *** *** IndexVertIntegration *** *** * GetGovtPayments *** *** *** *** 3.84 GetOffFarmIncome *** *** *** *** MediumFarm *** *** *** ** 2.30 Acres*MediumFarm *** *** *** *** *** Year *** *** *** *** *** DuPont equation 2: lnnetprofitmargin = f( Acres, TheilEntropy, Farm type, FarmSize, FE: year) lnnetprofitmargin Acres * *** *** *** * TheilEntropy *** ** *** Farm type.4621*** *** *** *** MediumFarm *** *** * Acres*MediumFarm * *** *** ** *** Year *** *** *** *** *** DuPont equation 3: lnassetstoequity = f(acres, TheilEntropy, Farm type, FarmSize, Acres*FarmSize, FE: year) lnassetstoequity Acres *** TheilEntropy * * ** ** Farm type *** ** IndexVertIntegration *** MediumFarm * *** Acres*MediumFarm Year NS NS NS NS NS *** = statistical significance at the 0.01 confidence level; ** =.05 confidence level; * =.10 confidence level; NS = not statistically significant. Source: USDA- survey.

18 Appendix Table A. Variable description and source Variable Description Calculated using variables as: NetProfitMargin Net profit margin ((INFI-V22A- V22E+EFINT)/IGFI*100) AssetTurnover Asset turnover (IGFI)/ATOT ratio AssetsToEquity Asset to equity ATOT/NETW ratio Source AcresOperated Acres operated Not calculated. TheilEntropy Theil measure of Described in text. entropy FarmSize (small, medium, large) Acres_SmallFarm Acres_MediumFarm IndexVerticalIntegration GetGovtPayments GetOffFarmIncome Year Interaction variable Interaction variable Index of farm vertical integration Dummy variable 1=yes 0 = no Dummy variable 1=yes 0 = no Year of survey Small: gross value of sales < $100,000; Medium: $100,000 <= gross value of sales < $250,000; Large: $250,000 <= gross value of sales < $500,000. AcresOperated * SmallFarm AcresOperated*MediumFarm Value of production under contract/total value of production IGOVT TOTOFI Year Calculated using data.

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