Price endogeneity has been ignored in previous analyses of food demand in urban China. We exploit data provided by the China National Bureau of Statistics on agricultural commodity supply shifters and use reduced‐form price equations to account for price endogeneity. Applying our unique econometric approach to the analysis of provincial‐level food demand in China, we find strong statistical evidence of price endogeneity. Models that ignore price endogeneity result in substantially biased elasticities and misleading estimates of future food demand in China.

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With the increased volatility of feed prices, dairy farm managers are no longer concerned with managing only milk price volatility, but are considering the adoption of risk management programs that address income over feed cost (IOFC) margin risk. Successful margin risk management should be founded on an understanding of the behavior of IOFC margins. To that end, we have constructed forward IOFC margins using Class III milk, corn, and soybean meal futures prices. We focus on the characteristics of the term structure of forward IOFC margins, that is, the sequence of forward margins for consecutive calendar months, all observed on the same trading day. What is apparent from the shapes of these term structures is that both in times when margins were exceptionally high and in times when they were disastrously low, market participants expected that a reversal back to average margin levels would not come quickly, but rather would take up to 9 mo. Slopes of the forward margin term structure before and after most of the major swings in IOFC indicate these shocks were mostly unanticipated, whereas the time needed for recovery to normal margin levels was successfully predicted. This suggests that IOFC margins may exhibit slow mean-reverting, rather than predictable cyclical behavior, as is often suggested in the popular press. This finding can be exploited to design a successful catastrophic risk management program by initiating protection at 9 to 12 mo before futures contract maturity. As a case study, we analyzed risk management strategies for managing IOFC margins that used Livestock Gross Margin for Dairy Cattle insurance contracts and created 2 farm profiles. The first one represents dairy farms that grow most of their feed, whereas the second profile is designed to capture the risk exposure of dairy farms that purchase all their dairy herd, dry cow, and heifer feed. Our case study of this program encompasses the 2009 period, which was characterized by exceptionally poor IOFC margin conditions. We analyzed the dynamics of realized IOFC margins in 2009 under 4 different risk management strategies and found that optimal strategies that were founded on the principles delineated above succeeded in reducing the decline in IOFC margins in 2009 by 93% for the Home-Feed profile and by 47% for the Market-Feed profile, and they performed substantially better than alternative strategies suggested by earlier literature.

The Agricultural Act of 2014 replaced dairy product price supports and countercyclical income support payments with the Margin Protection Program for Dairy Producers. Using farm‐level data, producer decisions and aggregate policy costs under a variety of risk environments and policy design alternatives are simulated. Fixed premium rates may result in budget outlays that are substantially higher than for equivalent variable‐rate insurance subsidized at levels observed in revenue‐based crop insurance policies. Due to the absence of adjusted gross income or production eligibility constraints, a significant portion of benefits may accrue to a small share of large dairy farms.

Livestock Gross Margin Insurance for Dairy Cattle (LGM‐Dairy) is a risk management tool for protecting milk income over feed cost margins. In this article, we examine the assumptions underpinning the method used to determine LGM‐Dairy premiums. Analysis of the milk–feed dependence structure is conducted using copula methods, a rich set of tools that allow modelers to capture nonlinearities in dependence among variables of interest. We find a significant relationship between milk and feed prices that increases with time‐to‐maturity and severity of negative price shocks. Extremal, or tail, dependence is the propensity of dependence to concentrate in the tails of a distribution. A common theme in financial and actuarial applications and in agricultural crop revenue insurance is that tail dependence increases the risk to the underwriter and results in higher insurance premiums. We present, to our knowledge, the first case in which tail dependence may actually reduce actuarially fair premiums for an agricultural risk insurance product. We examine hedging effectiveness with LGM‐Dairy and show that, even in the absence of basis or production risk, hedging horizon plays an important role in the ability of this tool to smooth farm income over feed cost margins over time. Rating methodology that accounts for tail dependence between milk and feed prices extends the optimal hedging horizon and increases hedging effectiveness of the LGM‐Dairy program.