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

The Baseline case. We introduced the idea of DPs and price uncertainty. Direct payment rates are fixed for each base crop and are independent of current production and prices, i.e., decoupled (Westcott, Young, and Price 2002). (9) In the Baseline case (Case (a), each player faced the crop allocation choice and received an additional exogenous direct payment (BONUS1). Each crop came with inherent price risk. Before a subject made an allocation decision, he or she knew that three price outcomes were possible (Zero, Low, and High Price) with given values and probabilities for each crop. After a participant allocated his tokens, two independent random draws determined the realized Blue and Red prices. (10) Over the ten rounds, we used ten lotteries that cover a range of expected values and variances for the Blue and Red crops. Tables 1 and 2 show the pattern. (11) We added this feature for two reasons: to cover a variety of base and nonbase lottery decisions, and to give players distinct lotteries to keep them focused on earning more money, i.e., experimental dominance. (12)

The CCP case. We added the possibility of CCP payments to the Baseline case. In the 2002 Act, CCPs are determined by comparing a target price to the base crop price, and are based on the formula:

CCP payment rate

= Target price - [Maximum {commodity price, loan rate} + Direct payment rate]. (13)

We fixed the target price minus the DP rate to be above the given Low Price and below the given High Price. In addition to the direct payment (BONUS1), this created two potential CCP subsidies. We presented these subsidies to the subjects as lump-sum bonuses: a bonus if Zero is the realized base crop price (BONUS2), and a smaller bonus if the Low Price is the realized base crop price (BONUS3). We incorporated the decoupled (from production) nature of CCPs as follows: if the realized base crop price is Zero or Low, then participants received the corresponding base crop bonus regardless of their current allocation of Red and Blue tokens. For example, if the Low price is realized, players receive BONUS1 and BONUS3 irrespective of how they allocated their tokens.

The policy risk case. We introduced three potential policy outcomes to be realized after allocation decisions are made: (a) repeal of the CCPs (which recreates the Baseline case); (b) DPs and CCPs (which recreates the CCP case); (c) or people must update their base acres (for that round only) given their allocation in that round (this represents a mandatory base updating). If the mandatory updating policy is realized, the participant earned Realized BONUSES based only on the percentage of base crop planted. For example, if the subject chose fifty Red and fifty Blue with a realized Blue price of Zero, he or she earned total Realized BONUSES = [(50 Blue tokens)/100 total tokens] * (BONUS1 + BONUS2); i.e., 50% of BONUS1 plus BONUS2. (14) A participant that updated was only affected in that current round; he or she started the next round with 100 (blue) base acres (each round therefore mimics the beginning of a new Farm Act).

Experimental Design: Specifics

We conducted the experiment in a computer lab at the University of Wyoming with approximately twenty-five terminals. Each session had a varying number of subjects. Students entered the room, and sat down at a private computer terminal. The computer program employed has four useful features. First, the moderator can select the maximum round time, prices, probabilities, bonuses, round order, case order, and between fixed outcomes or random draws based on specified probabilities. All the results are based on random draws. Second, round earnings were privately displayed to the subject after a decision, and random draws determine the realized prices in each round. Third, subjects could view their own allocations, earnings, and cumulative earnings in previous rounds for a given case, including when making their current allocation decisions. Fourth, subjects could use the Decision Tool. The Decision Tool is a calculator, given the subject's preliminary allocation of Blue and Red, that could be used to determine the joint probabilities and earnings for each of the nine price combination possibilities (e.g. Blue Low Price/Red High Price, etc.). The Decision Tool also showed the total expected value and variance of that choice. (15)

Our specific design followed two stages--measuring risk preferences (X-test) and individual decision making under risk. (16) This two-stage experiment served to examine how people with heterogeneous risk preferences make their production decisions facing price uncertainty with and without CCPs, and with and without policy uncertainty on base acre updating. Following standard experimental procedures, the first stage measured each participant's risk preferences by asking them to make choices over nine monetary lotteries. (17) This permitted us to classify subjects as risk averse, risk neutral, or risk loving and to explore whether risk preferences were associated with different behavior under each case.

After completing the risk preference questions, the subjects moved on to the main experiment with the Baseline, CCP, and Policy risk cases. The computer program provided a quick overview of the experimental instructions, informing subjects that earnings would be given in lab dollars, with a 2000 lab dollars to $1 dollar conversion rate. The Baseline case instructions were independently read followed by an ending quiz to test/help subjects better understand the instructions. (18) Each subject allocated their 100 tokens in each of ten rounds. Subjects had four minutes per round to make their allocation decision; if the limit was exceeded, he or she would receive zero earnings for that round (no subject exceeded the time limit in any round). Each subject faced the ten lotteries (see table 1) presented in random order.

After the Baseline case, about half the subjects participated in the CCP case for ten rounds and then the Policy risk case for another ten rounds; the other participants did so in reverse order. Again the instructions for each case were independently read, and quizzes administered. (19) After all thirty rounds were completed, the program displayed the results of the risk preference test (either the $2.50 sure bet or the realized lottery results), and then each case-specific earnings, and finally, total earnings. The students were paid in cash and left the room. Total laboratory time varied between forty-five and ninety minutes; total earnings were between $18.85 and $36.06, with an average of $28.10.

Variable parameters. We had three key parameters that varied in value--price uncertainty, bonuses, and policy risk. For price uncertainty, each lottery was resolved as either Zero, Low Price, or High Price. (20) We used zero to emphasize the effects a CCP with a very low price on the base crop, and used a relatively low probability of realization (a constant 10%). The Low Price varied from 8 to 18 lab dollars and the High Price varied from 14 to 32 lab dollars, with probabilities for each ranging from 30% to 60% (see table 1).

The potential bonuses were presented as lump-sum payments. (21) We did this since base acreage did not change--subjects had 100 base acres in each round. The direct payment (BONUS1) was fixed at 1.5 lab dollars/Blue base acre (150 lab dollars for all 100 Blue base acres). (22) In the CCP and Policy risk cases, a fixed target price of 15 lab dollars/base acre was set for the base crop. Once the target price and DPs were fixed, the possible CCP payments were determined by:

(1) BONUS2 or BONUS3 = (Blue target price - Blue realized price) * 100 - BONUS1.

If the realized base price was Zero, BONUS2 was set at 1,350 lab dollars. The Low Price CCP (BONUS3) adjusts so equation (1) is satisfied and varies between 50 and 550 lab dollars depending on the lottery (table 2).

In the Policy risk case, we assigned fixed probabilities to the likelihood of different policy outcomes to reflect policy uncertainty. We used a 60% chance the policy would be the same as the CCP case (both DPs and CCPs available) and a 20% chance for each of (a) the mandatory updating outcome (in which both bonuses varied with the percent of base crop planted) and (b) the Baseline case outcome (maintaining DPs but dropping the CCP subsidies).

Data and Hypotheses

The data were generated by running the experiment on students recruited in Economics and Finance undergraduate classes. Eighty-eight students participated, which created a balanced panel data set with thirty rounds of play. Table 3 provides descriptive statistics of the dependent variable, which is the proportion of tokens allocated to the Blue (base) option over ten rounds in each case. Relative to the Baseline case, our (alternative) hypotheses are:

* Hypothesis 1 (H1)--subjects will be less responsive to price signals with the introduction of a CCP system;

* Hypothesis 2 (H2)--subjects will be less responsive to price signals (relative to baseline) under a system with CCPs and policy uncertainty (including potential mandatory base updating); and

* Hypothesis 3 (H3)--the effects of the CCP and policy uncertainty will be greater than without potential mandatory updating/policy uncertainty.