Between the Corporation and the Household: Commodity Prices, Risk- Management, and Agricultural Production in the United States.

Transcription

1 Between the Corporation and the Household: Commodity Prices, Risk- Management, and Agricultural Production in the United States November 9, 2008 Shawn Cole and Barrett Kirwan 1 Introduction How do individuals and enterprises manage risk? How should they? Financial theory has sharp predictions: non-diversified enterprises should eschew idiosyncratic risk, transferring it to diversified parties who are better equipped to bear these shocks. However, there is also evidence suggesting that individuals may not make optimal financial decisions, particularly when choices are complicated (Campbell 2006). We study these questions in the context of the U.S farming industryas firms, farms borrow, invest, and bear output and price risk. Yet, farms are often run by a single household, and may be unequipped to handle an increasingly complex menu of 1 Shawn Cole is assistant professor of Business Administration, Harvard Business School. Barrett Kirwan is assistant professor, College of Agriculture and Natural Resources, University of Maryland. This article was presented in an invited paper session at the ASSA annual meeting in San Francisco, CA, January The articles in these sessions are not subjected to the journal s standard refereeing process. 1

2 investment and risk-management choices. Farms thus represent a unique laboratory in which to explore risk-management. Agricultural commodity markets have recently experienced a torrent of new investment (New York Times, April 21, 2008). As much as $300 billion, much of it speculative, has entered the market at a time when volatility is at an historic high. The recent run-up and decline in commodity prices highlights the importance of effective agricultural riskmanagement. In this paper we employ a unique, nationally representative dataset to test predictions from financial theory about risk-management. To our knowledge, we are the first to employ multiple rounds of the Agricultural Resource Management Survey (ARMS) microfiles, combining data from , to study risk-management behavior in over 50,000 farm businesses. We find evidence that farms function in ways similar to firms and households. As the theory of the firm would predict, risk-management is increasing in leverage and decreasing in the entropy of production. 1 However, and in contrast to the theory of the firm, we find that risk-management is strongly increasing in education, relatively unrelated to experience, and decreasing in age. These results, consistent with household finance literature, suggest that cognitive costs of riskmanagement may affect farmers ability to manage risk. This paper proceeds as follows. We begin with a very brief review of the literature, describing antecedent work in agricultural economics as well as evidence of household financial behavior. We introduce the data, describing the creation of a pseudopanel. We then use the data to test theories of firm and household risk-management. Literature 2

3 Theoretical work has long recognized the value of futures prices and risk-management for agricultural production. Marcus and Modest (1984), for example, demonstrate that under fairly general assumptions, setting marginal cost equal to the futures price provides a good basis for making production decisions. Moschini and Henessy (2000) review the literature on agricultural risk management in the United States, noting that while theory suggests that hedging substantially increases farmer welfare, several important frictions such as basis risk and contracting costs may prevent farmers from participating in futures markets. Combined with production uncertainty, this suggests that farmers should not hedge their entire output. Indeed, evidence suggests that farmers participation in futures markets is quite rare. However, Moschini and Henessy conclude by noting that farmers may in fact benefit from these markets by locking in prices through forward contracts with local output aggregators. Several studies have examined farmers risk-management decisions in smallsample surveys. Asplund, Forster, and Stout (1989) studied 353 Ohio farms, finding a relationship between forward contracting and age, revenue, leverage, attendance at farm organization meetings and the use of consultants. Pennings and Leuthold (2000) find farmers attitudes towards the value of futures contracts are related to their use in the Dutch hog industry. In a paper closely related to this present study, Mishra and El-Osta (2002) examine one year of the ARMS data to measure correlates of hedging, finding that education, off-farm income, forward contracting, and computer use are all related to participation in futures markets. Relative to this literature, our paper makes several contributions. First, we analyze seven rounds of the dataset, which allows for greater flexibility in our specification and 3

4 permits us to include crop or region fixed effects and study non-parametric relationships. Second, and more importantly, in linking seven years of the ARMS database, we create a pseudo-panel. This allows us to explore variation in hedging behavior over time, both for the population as a whole and for important subsamples. Finally, we take seriously the possibility that farm businesses may not behave optimally. In particular we draw on recent evidence from the analysis of household finance that individuals do not behave in a manner consistent with the sharp predictions of financial theory. Calvet, Campbell, and Sodini (2007), for example, studying Swedish households investment in equity markets, document two significant inefficiencies. First, households do not diversify assets nearly as much as they should; second, financial market participation is lower than they would have expected. Description of Data In this paper we draw on information collected by ARMS every year from Each year, the U.S. Department of Agriculture (USDA) uses ARMS to collect detailed farm and household-level information from a randomly-selected, nationally representative sample of farms. These data include information on production and marketing practices, as well as farm and non-farm income and assets, along with demographic information about the farm operator. Forward (or marketing) contracts are one of the most important risk management tool used by farmers. Forward contracts allow a farmer and a buyer to lock in a crop s price long before harvest, typically at planting, thereby avoiding price risk. Importantly for this paper, ARMS collects detailed data about the marketing contracts used by farmers to lock in a price for their crop before harvest. In each year producers reported 4

5 the crop, quantity, and final price associated with each marketing contract. Using this information, we can observe a farmer s hedging behavior at the crop level. In the analysis below, we examine the extensive margin of farmers hedging behavior (i.e., the decision to hedge) with a binary variable indicating the use of a marketing contract for each crop. We examine the intensive margin by focusing on the proportion of the crop placed under a marketing contract. The ARMS data are not longitudinal; to utilize the most information available, we pool seven cross sections from the years 1999 to 2005, which results in information on 102,531 farms. When analyzing risk-management decisions, we restrict analysis to commercial farms whose annual revenue exceeds $100,000. The 2007 Family Farm Report (USDA, 2007) notes that of the nations 2.1 million farmers, 40 percent are hobby farms (i.e., the operator declares a non-farm primary occupation), 16 percent are retirement farms, and 10 percent are limited resource farms (i.e., low sales and low household income). By limiting attention to the remaining 34 percent, we study the behavior of professional agricultural workers. The final sample thus consists of 53,247 farms. Summary Statistics We begin with a discussion of summary statistics. Table 1 provides farm characteristics for the 53,247 farms that recorded sales greater than $100,000 in any of the years from 1999 to As the ARMS data are nationally representative, these represent over 600,000 farms. The first pair of columns gives the mean and standard deviation of the entire sample. The second pair gives these statistics for farms that manage price risk with futures, options, or marketing contracts; and the third pair of columns describes farms 5

6 that do not manage risk using these tools. The final two columns give the difference in means and the t-statistic from a test of equality of means. Amongst farms with over $100,000 in annual sales, the average farm is approximately 1,482 acres and records gross farm revenues of $465,205; farms that manage risk are not significantly larger than those that do not, but they earn substantially more revenue. Both gross revenues and net income for risk-managing farms are over 30 percent larger than for farms that do not manage risk. This is at least suggestive of a view that better farms manage risk, although of course the relationship could be driven by any number of other factors. 2 The summary statistics provide insight into the motivation for hedging. Consistent with theory, farms with marketing contracts are less diversified: They produce on average 0.13 fewer crops than farms without marketing contracts, and rely more on a single crop, garnering 66 percent of total revenue from a single crop, compared to 57 percent for farms without marketing contracts. Farms that hedge are also more leveraged, having a 15 percent higher debt-to-asset ratio. Table 2 provides descriptions of the characteristics of commercial farm operators. The average operator age in this group is 56. Education levels are modest: 13 percent of operators lack a high-school degree, 50 percent did not progress past a high-school degree and only 17 percent have finished college. There is no systematic difference in education levels between farms that manage risk and those that do not. Farms that manage risk receive a significantly lower share of income from off-farm activities (32 percent compared to 38 percent) and also hold significantly more assets on average ($260,397 compared to $190,939). 6

7 Table 3 describes in detail how farms manage risk for twelve common crops. Column (1) gives the number of farms in our sample which grow each of these crops, while column (2) gives the share of farms growing each particular crop that manages price risk. The use of marketing contracts varies greatly by crop: only 1 percent of households manage price risk for oats, while 87 percent of farms growing sugar beets manage risk. Much of this variation is likely attributable to the availability of liquid exchange-traded contracts, but other factors such as regional preferences and basis risk may play a role. Conditional on managing risk, the average share of production whose price is fixed in advance is relatively high. This number is given in column (3), and ranges from 40 percent for corn, to 97 percent for sugar beets. Hypotheses and Description of Results Financial theory makes several predictions about hedging behavior of enterprises, particularly those that are not held by diversified share-holders such as farms. First, as long as risk-management markets are efficient 3 and farmers are risk-averse, all farmers should hedge at least some product, as risk-averse farmers can hedge income volatility. Second, farms with a less diversified crop mix may benefit more from hedging, as their revenue stream is subject to greater idiosyncratic risk. Similarly, farms selling products whose price is more volatile will gain more from hedging. Finally, hedging serves as an important safeguard against the risk of financial distress. Bankruptcy can be costly. Theory therefore predicts that those households with higher amounts of leverage will hedge more. Several hypotheses have been advanced to explain low levels of participation in financial markets in a general setting; we look to these for guidance in our regressions. 7

8 First, a growing body of evidence suggests that financial literacy (Lusardi and Mitchell 2007) and education (Cole and Shastry 2008) are key determinants of financial market behavior. Unfortunately, the ARMS database does not provide any measure of financial literacy. We therefore focus on education as a predictor of hedging behavior. Table 4 presents our main results. In this table, we use a linear probability model to estimate which farm and farmer characteristics predict hedging behavior. Specifically, we regress a measure of whether a farmer hedges a particular crop c in year t, y ict on measures of household characteristics X it and farm characteristics W it : We cluster standard errors at the level of the farm. 4 Column (1) of table 4 presents results for a regression that pools all crops and all years. The first statistically significant finding is that older farmers are less likely to manage risk. While the coefficient on age 2 is positive, the magnitude is small enough that the quadratic term would not offset the linear term until well into a farmer s second century. There appears to be no relationship between experience and risk management, as both the linear and quadratic terms are small and statistically insignificant. In column (2), we add a fixed-effect for crop type. This is important because as demonstrated in table 3, there is significant variation in the average share of farms managing risk in each crop. If the characteristics of farmers growing different crops differ systematically (as they likely do), omitting a crop fixed-effect could confound differences in crop choice with differences in hedging behavior. The results in column (2) show that including crop fixed-effects generally does not alter the list of statistically significant variables, but does change the point estimates 8

9 of those variables. Controlling for crop fixed-effects reduces the magnitude of the revenue variable, although it remains statistically significant. This is consistent with crop-specific fixed costs to contracting. Including year effects in column (3) changes the point estimates for most variables slightly In column (4) we consider hedging more broadly to include the use of futures contracts and/or options. This analysis is limited to the period period, when ARMS collected futures and options use data. The results bear a similar pattern to the analysis of forward contracting alone, but with considerably larger point estimates. Many theories predict a cognitive cost of hedging. Indeed in contrast to the relationship shown in the summary statistics, after controlling for other variables, attaining a bachelor s degree or attending graduate school is associated with an increased likelihood of participating in futures markets. The effect of education is most pronounced in column (4); a high school diploma increases the likelihood of hedging by 7 percentage points, and an advanced degree increases the likelihood by 20 percentage points. Consistent with the summary statistics, we find that, after controlling for other factors, risk management is increasing in revenue. This is consistent with at least two hypotheses, which are difficult to distinguish from one another. There could be fixedcosts associated with hedging; alternatively, more able farmers may be able to generate higher revenue and also be more able to manage risk. Other results also reinforce the initial findings from the summary statistics. All regressions suggest that farms that produce more crops are less likely to manage risk, consistent with the hypothesis that crop diversification and hedging serve as substitutes. 9

10 Finally, all regressions are consistent with financial distress hypotheses: farms that have higher levels of leverage are more likely to manage risk, although the size of this effect is modest: moving from the lowest to highest quartile of leverage increases the probability of managing risk by approximately 2 percentage points. In columns (5) and (6) we examine the intensive margin as well, regressing the share of output hedged with forward contracts for each crop on the same set of farm and household characteristics. For farmers who do not manage risk, we set the share hedged to zero. We again use Ordinary Least Squares estimation, clustering standard errors at the farm level. We focus our discussion on column (6), which includes crop and year fixed effects. We again find no systematic relationship between age and risk-management. Education is again statistically significant but of limited practical importance: collegeeducated households hedge just 2.9 percentage points more of their output than those with the lowest level of education. The effect of gross revenue is positive. At the mean a 1 percent increase in gross revenue is associated with a 0.9 percentage point increase in the proportion of the crop hedged. This result might indicate the effect of bargaining power whereby larger farms command a higher marketing contract price, consequently contracting more of their output. Of course this specification cannot rule out the change that unobservable farmer characteristics cause the correlation (e.g., successful farmers who have high revenue also manage risk effectively). 10

11 We again find that firms at the highest levels of leverage hedge the largest quantity of output, but the effect is very small: one percentage point of the value of output. Notably, off-farm income is unrelated to the hedging decision. It is generally believed that working off-farm is a common risk-mitigation tool (Mishra and Goodwin, 1997) that crowds out other risk-mitigation strategies (Blank, 2005). These beliefs are not supported by the data; the coefficient on the proportion of total income from off-farm sources is both statistically and economically insignificant, being very close to zero. The summary statistics demonstrated that farmer participation in risk management is low. This is true even for crops for which liquid futures and option markets exist. Evidence from ARMS suggests that farm and individual characteristics can explain only a small amount of this variation: the incremental improvement in R 2 from adding these characteristics is small. In addition to cross-section variation, time-series variation in hedging behavior may be informative. In figure 1 we plot the share of farmers who manage risk through forward contracts for ten crops. The top graph gives the relationship for corn, barley, soybeans, sorghum, and wheat, where risk-management is somewhat rare. The bottom graph gives the relationship for sugarbeets, peanuts, cotton, rice, and potatoes, where hedging is more common. While we are not observing a panel of households, the sample size of our data set is large enough that estimates are representative of the population. All ten crops exhibit significant time-series variation. The fluctuation in percent terms ranges from a decrease of 53 percent, to an increase of 112 percent from year-toyear. Indeed, the temporal variation is much more significant than the amount of 11

12 variation predicted by the regression models in response to changes in age, experience, or farm size. Why might farmers decisions to hedge change so drastically from year to year? It is difficult to explain these fluctuations in a neo-classical model. If the only friction were financial distress, and the probability of financial distress depended significantly on the level of the commodity price, one might imagine hedging requirements would fluctuate from year to year. However, given the scope for significant swings in commodity prices, it seems unlikely that the optimal amount of hedging varies so quickly. An alternative explanation may be that farmers are attempting to time the market, selling output forward when they feel the price will fall but declining to hedge when they feel the price may rise. This type of behavior could generate the significant fluctuations that we observe. While we do not present evidence in favor of this hypothesis, we note that it is consistent with evidence from behavioral finance literature that suggests that individual investors attempts to profit by taking active positions in financial markets are hazardous to the investors wealth. (Barber and Odeen 2002) Conclusion and Directions for Future Research This paper represents a first attempt at exploring the individual, temporal, and regional determinants of participation in agricultural risk management. We find that in general relatively little agricultural risk is hedged. Few farmers sell product forward or trade in futures or options markets; moreover, even when farms do participate in these markets, they hedge only a small fraction of their output. Hedging behavior varies in systematic ways that correspond to standard models of behavior. Education matters, as does farm size and the diversification of the farm. 12

13 However, there is significant time-series variation that cannot be explained by these relatively fixed factors. Moreover, this time-series variation is substantially greater than the cross-sectional variation. This suggests several directions for future research. First, it would be useful to apply the lens of behavioral economics to farm financial decision-making. An important feature which we feel has not received as much attention in the agricultural economics literature is the fact that most households are farms, headed by individuals with often low levels of education and likely low levels of financial literacy. A large and growing body of evidence suggests that individuals with low levels of education make mistakes in financial markets, and in particular are less likely to participate. (Campbell 2006; Schwartz 2007). Second, it is important to understand the dynamics of household behavior. Are farmers in fact attempting to time the market in their hedging decisions? This is important because it is unlikely that farmers have superior information that would allow them to earn excess returns in the market. More likely is the possibility that they are overconfident and make mistakes. Measuring the welfare costs of investment mistakes is often difficult because one rarely observes the entire portfolio of an investor. (An important exception is Calvet, Campbell and Sodini 2007). The ARMS data are perhaps one of the very few comprehensive, representative data sets that include information about investment, production, and risk management decisions and therefore represent a unique and compelling opportunity to understand how individuals and small enterprises manage risk. 13

14 References Asplund, N.M., D.L. Forster, and T.T. Stout Farmers Use of Forward Contracting and Hedging. Review of Futures Markets 8(1): Barber, B., and T. Odeen Trading Is Hazardous to Your Wealth: The Common Stock Investment Performance of Individual Investors. Journal of Finance 55(2): Blank, S.C The Relationship Between Price Risk Management Tools and Offfarm Income. Giannini Foundation of Agricultural Economics. ARER Update 8(6): Calvet, L. E., J.Y. Campbell, and P. Sodini Down Or Out: Assessing the Welfare Costs of Household Investment Mistakes. Journal of Political Economy 115: Campbell, J. Y Household Finance. Journal of Finance 61: Cole, S. and K. Shastry If You Are So Smart, Why Aren t You Rich? The Effects of Education, Financial Literacy and Cognitive Ability on Financial Market Participation. Working Paper, Harvard Business School and University of Virginia. Henriques, D. New Threat to Farmers: The Market Hedge. The New York Times, April 21, 2008, se, accessed December

15 Lusardi, A., and O.S. Mitchell Baby Boomer Retirement Security: The Roles of Planning, Financial Literacy, and Housing Wealth. Journal of Monetary Economics 54: Marcus, A.J. and D.M. Modest Futures Markets and Production Decisions. Journal of Political Economy 92(3): Mishra, A.K. and B.K. Goodwin Farm Income Variability and the Supply of Offfarm Labor. American Journal of Agricultural Economics 79(3): Mishra, A.K. and H.S. El Osta Managing Risk in Agriculture through Hedging and Crop Insurance: What Does a National Survey Reveal. Agricultural Finance Review 62(2): Moschini, G. and D.A. Hennessy Handbook of Agricultural Economics. Bruce Gardner and Gordon Rausser, eds. Amsterdam: Elsevier Science Publishers. Pennings, J.M.E. and R.M. Leuthold The Role of Farmers Behavioral Attitudes and Heterogeneity in Futures Contracts Usage. American Journal of Agricultural Economics 82(4): Schwartz, A Household Financing Behavior in Fixed Rates Mortgages. Ph.D Disseration, Harvard University. U.S. Department of Agriculture Agricultural Resource Management Survey. 1 Entropy is a measure of the diversity of crops grown on a farm. It is calculated by the USDA, based on responses from each farm. 2 For example risk management may be more common near urban areas where input prices are lower and farms are smaller. 15

16 3 The assumption of no significant fixed costs to hedge is also required. 4 We choose a linear probability model because it is simplest to interpret, particularly with fixed-effects. 16

17 Proportio on of Farms corn, grain barley soybean wheat sorghum, grain Figure 1: Crop level Marketing Contract Prevalence

18 Prop portion of Farms sugarbeets peanuts cotton rice potatoes Figure 1, cont.: Crop level Marketing Contract Prevalence

19 Table 1. Business Characteristics of U.S. Farms with Sales Greater than $100,000, All Farms Farms with Marketing Contracts Farms with No Marketing Contracts Difference T-Stat Mean Std Dev Mean Std Dev Mean Std Dev Acres Operated Gross Farm Income 465,205 1,454, ,793 1,172, ,064 1,535, , Livestock Income 146, ,305 51, , ,383 1,020, , Total Farm Expenses 346,231 1,135, , , ,736 1,198, , Net Farm Income 118, , , , , ,541 34, Number of commodities produced Primary Crop Revenue Share Value of Crop Inventory 80,947 1,182, ,141 2,244,933 68, ,799 49, Debt-Asset Ratio Observations 53,247 15,189 38,058 Weighted Obeservations 2,231, ,219 1,674,187 Notes: Data from pooling the Agricultural Resource Management Surveys. Sample includes all farms with cropland or self-classified as crop farms. All dollar amounts in 2005 dollars.

20 Table 2. Household Characteristics of U.S. Farms with Sales Greater than $100,000, All Farms Farms with Marketing Contracts Farms with No Marketing Contracts Mean Std Dev Mean Std Dev Mean Std Dev Age Education Less than High School High School Some College College Graduate Graduate School Experience Off-farm Household Income Share Household Assets ( ) Total 208, , , , , ,789 Financial Assets (Non-retirement) 61, ,888 61, ,925 61, ,972 Retirement Assets 37, ,177 48, ,074 33,652 93,278 Non-farm Real Estate 102, , , ,215 88, ,938 Other Assets 38, ,263 51, ,148 33, ,331 Notes: Data from pooling the Agricultural Resource Management Surveys. Sample includes all farms with cropland or self- classified as crop farms. All dollar amounts in 2005 dollars.

21 Table 3. Crop-Level Crop Marketing Contract Use Farms with Revenue > $100,000 Production N Farms with Marketing Contracts Under a Marketing Contract Barley 87, Canola 19, Corn 1,084, Cotton 120, Oats 128, Peanuts 35, Potatoes 16, Rice 45, Sorghum 120, Soybean 1,034, Sugarbeets 25, Wheat 609, Notes: Data are pooled ARMS data from Columns 1 give the number of farms growing each crop. Columns 2 reports the proportion of farms with a marketing contract for each crop. Columns 3 reports the proportion of crop output placed under contract for farms with marketing contracts.

22 Table 4. Determinants of Hedging-Regression Results Dependent Variable: Any Forward Contracting Forward or Futures Contracting Fraction of Output Contracted Age * ( ) ( ) ( ) ( ) ( ) (0.0009) Age ( ) ( ) ( ) ( ) ( ) ( ) Experience ** *** ( ) ( ) ( ) (0.0018) ( ) ( ) Experience * ( ) ( ) ( ) ( ) ( ) ( ) High School *** *** *** *** (0.0058) (0.0053) (0.0053) ( ) ( ) ( ) Some College *** *** *** *** ( ) ( ) ( ) ( ) ( ) ( ) Bachelor's *** *** *** *** *** *** ( ) ( ) ( ) ( ) ( ) ( ) Grad School *** *** *** *** *** *** ( ) (0.01) ( ) ( ) ( ) ( ) Revenue *** *** *** *** *** *** (0.036) ( ) (0.0312) ( ) ( ) ( ) 2 Revenue *** *** *** *** *** *** ( ) ( ) ( ) ( ) ( ) ( ) Entropy *** *** *** *** *** *** ( ) ( ) ( ) (0.0521) ( ) ( ) Number of Commodities *** *** *** *** *** *** ( ) ( ) (0.001) ( ) ( ) ( ) Leverage_ ** *** ( ) ( ) ( ) ( ) ( ) (0.0043) Leverage_ *** *** *** *** ( ) ( ) ( ) ( ) ( ) ( ) Leverage_ *** *** *** *** ** ( ) ( ) ( ) (0.0183) ( ) ( ) Off-farm income (0.0008) ( ) ( ) ( ) (0.0006) ( ) Observations 85,931 85,931 85,931 38,055 85,931 85,931 R-squared Year FE No No Yes Yes No Yes Crop FE No Yes Yes Yes No Yes Note: Independent variables are defined as follows: age in years, age squared, experience in years, experience squared, a dummy for completing high school, a dummy for ar attending some college, a dummy for completing college, a dummy for completing graduate school, the log of farm revenue, log farm revenue squared, a dummy for growing only one crop, a measure of crop diversification known as entropy, dummies for being in the second lowest, second highest, and highest quartiles of leverage, and the ratio of off-farm income to net farm income. Forward and futures contracting regression uses ARMS data.