Supply response to countercyclical payments and base acre updating under uncertainty: an experimental study.

Hypothesis H1 follows because choosing to allocate resources to the program crop reduces downside risk. In the CCP case, a player allocating to the base crop (Blue) is guaranteed to receive at least the target price per token (15 lab dollars per token). In contrast, if he allocates to the nonbase (Red) crop and the realized Red price is Zero with a realized High Blueprice (implying no CCP given), he receives only the direct rate per token (1.5 lab dollars per token). Allocating tokens in the Blue option maximizes the minimum possible earnings per token, i.e., the maximin solution. (23)

Why might adding the option of revenue risk reduction influence production decisions? In the Baseline case, players were given lotteries and asked to choose to maximize expected net returns. In contrast, the CCP provides another option to be considered--revenue risk reduction. The producer's problem is transformed. A producer faces the joint optimization problem of balancing (a) maximum expected net return and (b) minimum revenue risk (see Westcott, Young, and Price 2002). At the individual level, a player placing more weight on revenue risk reduction allocates additional tokens in the Blue option. Westcott, Young, and Price (2002) note this individual expected return/revenue risk trade-off implies that aggregate equilibrium production levels would reflect both profit maximization and revenue stabilization.

Hypothesis H2 builds on the above discussion and includes policy uncertainty on CCP availability and possible mandatory base updating. In the Policy risk case, we have a 20% chance of DPs only, a 60% chance of DPs with CCPs, and a 20% chance of mandatory updating. Since the 20% DP-only is identical to the Baseline case, there should be no additional effect. The 60% DP-CCP is the same as the CCP case; so we expect more base acreage allocation. The 20% mandatory updating should also induce a subject to allocate more to the Blue option since potential subsidies are directly tied to the production decision. Since the combined probability of CCP and mandatory updating exceeds the DP-only case (80% versus 20%), we expect more tokens to be allocated to the base crop compared to the baseline.

For Hypothesis H3, the intuition on whether the effects of the CCP with updating and policy uncertainty will be greater than the CCP case alone is more intricate than the other hypotheses. The 20% DP-only bonus outcome should lead to less reliance on the base crop than the CCP case. The 60% chance of a DP and CCP bonus is a neutral outcome, with no expected difference compared to Hypothesis 2. The 20% chance of a mandatory updating outcome may induce more investment in the Blue option than the CCP case since any level of Red reduces potential subsidies. There are predicted positive and negative effects compared to the CCP case.

Our prediction is again based on the maximin solution. Policy uncertainty means a subject has a chance to earn nothing if he allocates all resources to the nonbase crop. This occurs if the realized policy was mandatory updating and the nonbase realized price was Zero. Since all plantings are in the nonbase, no subsidies are available. While this outcome has a low probability (2%), the possibility of receiving zero lab dollars in a single round may create movement toward the base crop. The only way to avoid this risk is to continue to invest in the base crop, which is a maximin strategy. We know in the Policy risk case the only way to guarantee a minimum income of at least the DPs (the minimum per round earnings in the CCP case) is to invest all tokens in the base option. (24)

Model

We explore the panel data allocation choices using a random-effects model: (25)

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]

where PBlue is the proportion of tokens each player allocated to the Blue option (base crop) each round and is presented in decimal form. Now consider our covariates. The constant term reflects the Baseline case, lottery one, and risk neutral players. Because lottery one should induce a higher proportion of blue investment (same mean with lower variance), we expect the constant to be positive. T2 is the treatment (sequence) variable when players first faced the CCP case and then the Policy risk case. We do not anticipate a treatment effect. CCP case and Policy risk case are dummy variables for the rounds in which each player faces the CCP and Policy risk cases. Hypotheses H1 and H2 predict the [[beta].sub.2] and [[beta].sub.3] coefficients to be positive and significant. Hypothesis H3 predicts Policyriskcase to be larger in magnitude than CCPcase, [[beta].sub.2] < [[beta].sub.3]. The Lottery variables are binary variables to capture the different lotteries (2 through 10 in table 1). We expected players to choose the lottery with the larger expected value, however, lotteries 3 and 7 are difficult to predict since the expected value is larger but the variance is much larger for the same option (higher risk).

LDCE is lagged dollar cumulative earnings. (26) Assuming players exhibit decreasing absolute risk aversion (DARA) preferences, we expect as players accumulate larger incomes they are more likely to move toward the nonbase (riskier) option (see, for example, Chavas and Holt (1990), who report results supporting DARA preferences for corn and soybean plantings). LHITIND is lagged hit for an individual, in which we define a hit as when a player received a blue High Price and a red Zero Price in the same round, which eliminated the potential of either CCP subsidy (BONUS2 or BONUS3). This coefficient is predicted to be positive. Receiving a "hit" should push a player toward the maximin strategy in subsequent rounds in the CCP and Policy risk case by reminding participants of the risk reducing effects of planting the base crop. RAVER reflects those participants identified as risk averse; we expect risk aversion to induce greater allocations to Blue. RLOV indicates a risk-loving person, which should have a negative coefficient. (27) Table 4 displays the predicted signs for the coefficients.

Results and Discussion

Table 4 also shows the results of the random-effects model. The regression model is significant with an R-squared of 0.255. We see the constant term is positive and significant as predicted. We find no apparent treatment effect, T2. The lottery coefficients followed the pattern of subjects planting more acres in whichever investment option had the highest expected value. The lagged cumulative earnings variable LDCE indicates that as earnings increased, players chose a higher percentage of the nonbase option. This does not contradict the notion that players had DARA preferences, although the coefficient is significant only at the 10% level. LHITIND is insignificant, perhaps due to the paucity of "hits" in the data set.

The two risk-preference coefficients, RAVER and RLOV, are the opposite of the predicted signs, although both coefficients are relatively small and not significant at the 5 % significance level. One explanation for the signs is that players may have not treated the two stages as independent; i.e., they tried to balance their portfolios of risk across the risk preference lotteries (i.e., the X-test) and allocation decision experiments. The CCP case and Policy risk case coefficients were the predicted sign and significant at the 1% and 5% levels. We now explore what the results suggest for our three hypotheses.

RESULT 1. We cannot reject (alternative) hypothesis H1: Adding a CCP subsidy induced subjects to allocate more to the base option (Blue option).

Support. We reject at the 1% significance level the null to hypothesis H1, which says that people are equally responsive to price signals for nonbase crops in the presence of a CCP-style subsidy compared with the Baseline case. The results suggest on average there was a 5.43% shift toward the base crop given CCP subsidies relative to the Baseline case, holding the other effects constant. The result is the CCP system dampened the responsiveness to market signals.

Result 1 has three economic implications. First, CCPs offer producers a way to reduce their revenue risk by following a maximin strategy. This can be perceived as a benefit to risk-averse producers. But there is also a potential cost. By planting more acres of base crops and giving less consideration to market conditions for nonbase crops, producers might pass up opportunities to increase their revenue. CCP style subsidies may lower the incomes, over the longer term, of participating producers by creating risk-reducing strategies only for selected (base) crops.

Second, shifting production from base crops to nonbase crops affects markets for both crop types. Simplifying to a two-crop world, greater base crop production increases the supply and, holding demand constant, lowers the equilibrium price. If the new effective price exceeds the target price, no additional subsidies are provided and producers' per acre revenues from base crop production fall. But if the new effective price falls below the target price, the CCP partially compensates for the reduced market revenue (see the 2002 Farm Act). Also, assuming greater base crop allocation reduces nonbase crop production, the price for the nonbase crop increases. If the nonbase crop price exceeds the base crop price, a producer would have earned more revenue by planting the nonbase crop (assuming he or she is a price taker).