The 2002 Farm Security and Rural Investment Act contains two
features that added complexity to farmers' planting decisions, and
may have introduced new incentives that make cropping decisions based
partly on potential government payments, rather than expected market
returns. These are (a) the prospect of earning countercyclical payments
(CCPs) on the farmer's endowment of historically produced base crop
acreage when prices of these crops fall below pre-established levels;
and (b) the option for farmers to update the allocation of base
crops--from which direct payments (DPs) and CCPs are made--to reflect
recent (1998-2001) production history. (1)
These Farm Bill changes have positive and negative effects. For
farmers, the upside is reduced uncertainty and revenue risk.
Farmers' concerns over uncertain ad hoc supplemental payments
(given during 1998-2001 crop years to enhance payments in the 1996 Farm
Bill) are alleviated by "institutionalizing" a subsidy program
of CCPs through 2007 (Westcott, Young, and Price 2002). The base acre
updating option provided flexibility for farmers who wanted to change
the mix and amount of the different program crops eligible for subsidies
for 2002-2007. The disadvantage is that both CCP and updating can cause
a farmer to plant more base crop regardless of market conditions,
leading to an inefficient allocation of resources (see Orden 2002;
Miller, Barnett, and Coble 2003; Westcott 2005; Young et al. 2005).
Although CCPs would be made on the basis of historic, not current,
planting decisions, observers recognize that risk-averse producers may
face incentives to continue producing their base crops as a strategy to
minimize revenue risk and variability. In this event, producers may
align current plantings with their base acreage even when their price
expectations indicate that higher (current year) returns could be earned
by growing an alternative (nonbase) crop. (2) As for base acreage
updating, farmers' current production choices may be influenced by
their expectations of how each crop will be treated under future
legislation. If they expect an updating option, they may plant for base
to increase their expected future subsidy eligibility.
As a result, CCPs and the base acreage updating option under the
2002 Farm Act have potential supply response implications. The two open
questions we address in this paper are: (a) By increasing the lower
bound on income when the base crop is planted, do CCPs cause farmers to
shift investment toward the base crop and blunt price signals from
nonbase crops? (3) (b) Does the possibility of updating base acres cause
farmers to continue or enhance their plantings of program crops in an
attempt to secure future income from program payments? (4) We examine
these questions by designing a lab experiment on how producers with
heterogeneous risk preferences allocate resources under three cases: (a)
a baseline of price uncertainty without CCPs; (b) price uncertainty with
CCPs; and (c) price uncertainty with future policy uncertainty. We
assess how cases (b) and (c) affect income and markets relative to the
baseline.
Our results support some of the criticisms of CCPs and base acre
updating. We find that with CCPs, laboratory decision makers increased
their investment in the base crop relative to the baseline case. Adding
updating and policy uncertainty, they continued to rely relatively more
on the base crop than under a more policy-neutral environment. The
implications of increased base acre plantings are several: lower
potential income to producers who choose to reduce their revenue risk;
decreased efficiency of crop markets due to distorted allocation
decisions; depressed base crop prices, which further reduces income; and
an increased likelihood of subsidy payments.
Overview of 2002 Farm Act Commodity Provisions
The 2002 Farm Act employs three primary methods to provide income
support to field crop producers (principally wheat, feed grains, cotton,
rice, and oilseeds): direct payments (DPs), CCPs, and marketing loans.
Marketing loans and DPs were also available under the 1996 Farm Act,
while the target-price system of CCPs represents the reintroduction--in
modified form--of deficiency payments, which were eliminated by the 1996
Farm Act. Although this article focuses on the impact of CCPs and the
base updating option, we briefly summarize the main features of each
program to review the different sources of market income and program
payments available to eligible farmers. We also summarize the options to
establish and update base acres and yields, on which direct and CCPs are
made. (5)
Direct Payments
Under this program, eligible farmers entering into an agreement
with USDA receive annual fixed DPs during 2002-2007. Similar to the
annual production flexibility contract (PFC) payments made under the
1996 Farm Act, DPs are based on a producer's historical production
(base acres and yields) and are made (with some limitations) regardless
of his or her current planting decisions or current market prices. The
notable difference from the 1996 Act is that oilseed producers (e.g.,
soybeans, peanuts) became eligible. The payment equals a fixed payment
rate for each crop multiplied by the payment acres (85 % of base)
multiplied by the DP historical yield.
Countercyclical Payments
CCPs are available to producers for covered commodities with base
acres whenever the effective price for that commodity is below a
predetermined target price. The per-unit payment rate for CCPs equals
the amount by which the target price exceeds the effective price. The
effective price equals the direct payment rate plus the higher value of
(a) the market price or (b) the commodity marketing loan rate. Similar
to DPs, CCPs are made regardless of what crop the producer currently
grows (with some limits). The amount of the CCP is equal to the payment
rate for each crop multiplied by the payment acres (85 % of base)
multiplied by the CCP payment yield. (6) Similar to DPs, producers do
not have to grow the base crop to receive CCPs, but unlike DPs, CCPs
depend on current market prices for the base crop. If the effective
price is above the target price, no CCP is received on the base crop.
Marketing Assistance Loans and Loan Deficiency Payments
Nonrecourse loans with marketing loan provisions operate as they
did under the 1996 Farm Act, with some revisions to loan rates, and with
eligibility extended to additional commodities (peanuts, wool, mohair,
honey, pulses). Farmers must produce the covered program crop to be
eligible for marketing loans. When market prices are below the loan
rate, producers benefit from the program in two ways. First, farmers can
repay the commodity loan at a lower "loan repayment rate" that
reflects market prices. The difference between the initial loan and the
amount repaid is the marketing loan gain. Second, a producer can opt not
to take the loan and instead receive the marketing loan benefit directly
by taking the difference between the loan rate and (if lower) the loan
repayment rate as a loan deficiency payment (LDP).
Base Acreage and Base Update Option
The 2002 farm legislation allows farmers who received direct (PFC)
payments during 19962002 to choose between keeping their old base
acreage or updating base acres to reflect average planted acres for
eligible commodities during the 1998-2001 crop years. Producers select
one of the two options for all covered commodities. Although base yields
for DPs still reflect yields during 1981-1985, producers who update
their base acres to reflect 1998-2001 plantings have the option of
updating yields on which CCPs are made. CCP yields are set at the higher
of (a) 93.5% of average yields on planted acres during 1998-2001, and
(b) average 1998-2001 yields plus 70% of the difference between program
yields for PFC payments and average 1998-2001 yields.
Experimental Design: General Structure and Specific Elements
We designed the experiment to reflect the underlying incentives of
the 2002 Farm Act. In the experiment, a participant allocated his or her
acres into either a base crop or a nonbase crop, or both. We mimicked
current planting choices by asking subjects to allocate a fixed number
of tokens (i.e., acres) to a Blue option (base crop) and a Red option
(nonbase crop). (7) For example, if a subject had 100 tokens, he might
allocate 40 to Blue and 60 to Red, or in some other combination totaling
100.
Each subject's task was to allocate tokens under different
experimental environments defined by economic and policy circumstances.
Three cases were defined: (a) the Baseline case: price uncertainty with
DPs only; (b) the CCP case: the baseline plus the potential of CCPs; and
(c) Policy risk case: the CCP case plus policy uncertainty. Ten rounds
were used for each case, giving a total of thirty rounds. This design
allowed us to compare behavior in the CCP case and the Policy risk case
against the Baseline case to better understand how CCPs and policy risk
(including the possibility of mandatory base updating) affect behavior.
(8) Consider each case in detail.
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.
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).
Third, the possible market effects have ramifications for
government spending and trade. If producers switch from nonprogram to
program crops so the supply of each program crop increases, program crop
prices will fall (holding demand steady). As each program crop price
decreases, the chance increases each individual program crop will
qualify for CCPs, which increases government expenditures. Another issue
is whether CCPs would be classified as "blue box" or
production and trade distorting "amber box" domestic subsidies
under World Trade Organization rules. Both spending categories are
subject to limits under recent (October 2005) U.S. negotiating proposals
and including CCPs in either category increases the possibility of
exceeding WTO spending limits. (28)
Westcott (2005) points out that production distortion from CCPs may
be "limited" in naturally occurring markets due to several
factors. For example, farmers have other risk management tools at their
disposal; large and less risk-averse farms tend to dominate production
of program crops; and other programs such as marketing loan provisions
already offer price protection. These factors underscore the difficulty
of separating the effects of CCPs from other influences in observed
annual production data, a difficulty reduced when using experimental
methods.
As noted by Roth (1995), experimental methods can provide rapid
feedback to policy makers about issues that are not easily teased out
with observed data. Roe and Randall (2002) further suggest that
"the field of agricultural risk analysis could benefit ... from
continued research using experimental methods" in policy analysis.
Our design isolated the CCP incentives under risk. Result 1 supports the
idea that CCPs can be production distorting, as participants altered
production choice toward planting more in the base crop. This result is
supported by Anton and Le Mouel (2004) who find the risk reducing
incentives of CCPs are significant as revealed by the estimated risk
premia.
Our design did not address two features of the 2002 Act, which
could affect the interpretation of our results. First, there are no
adjustments made in our bonuses for the fact that direct and CCPs are
made only on a percentage (85%) of base acres. If these adjustments were
incorporated, the lump-sum bonuses would have been lower, implying our
results could overstate the effects of CCPs. Second, we excluded the
marketing loan program to focus on the basic CCP structure--target
price, market price, and direct rate. Adding the marketing loan program
into our design would temper the basic effects of CCPs by providing an
additional price support mechanism.
Discussion of H2 Results
The impact of the base acre updating clause depends on expected
benefits from future programs, which in turn depend on the continuation
of such programs (Westcott, Young, and Price 2002). By introducing
policy uncertainty (with the possibility of mandatory updating) along
with CCPs, we examine how compounding these two risks affect production
choices compared to our baseline, which we summarize as Result 2.
RESULT 2. We find evidence in favor of hypothesis H2: A CCP
style-subsidy program and policy uncertainty (with the possibility of
mandatory base acreage updating) induced subjects to allocate more to
the base option.
Support. We reject at the 5% significance level the null to
hypothesis H2 that people are equally responsive to price signals
between crop (token) allocation choices in the Policy risk case and the
Baseline case. The coefficient suggests that there is an average
increase of 7.92% toward investment in the base crop option in the
Policy risk case compared to the Baseline case, holding all else equal.
Introducing a CCP along with policy uncertainty, including mandatory
updating, shifted crop allocation toward the base crop. The implications
for the agricultural economy are similar to those discussed for Result
1.
The Policy risk case included both price and policy risk in a
simplified setting, which created one key caveat. In our design, if the
update option was realized, our players had to update (no choice to opt
out). In reality, producers had the option not to update base acres in
the 2002 Farm Act, and it is possible that the same could occur under
future legislation. Most producers would likely use this "opt
out" feature if it were added to our design, which implies less
incentive to plant the base crop since the current design starts each
participant with all base acres. Unless the player allocated all tokens
to Blue (base), updating base would reduce per round earnings. If
players never updated, the results should be indistinguishable from the
CCP case.
Discussion of H3 Results
Another policy question is whether producers changed cropping
strategies between the CCP and Policy risk case. Did mandatory updating
cause them to ignore market signals and "plant to maintain
base" in an attempt to maximize available subsidies? Result 3
suggests producers had a limited reaction to this policy risk.
RESULT 3. We find insufficient evidence to support hypothesis H3:
The coefficients of the CCP and Policy Risk case variables are not
statistically different from each other.
Support. We cannot reject at the 5% significance level the null
hypothesis that the effects of the CCP case and Policy risk case are the
same. The Wald test indicates that there is no statistical difference
between the CCP case and Policy risk case coefficients (p-value 0.20).
The lack of statistical evidence makes it challenging to disentangle the
effects of policy uncertainty. Did participants not consider the change
between the CCP case and the Policy risk case, or did the opposing
effects of our policy uncertainty cancel out? Since the coefficients
indicate the total effect with CCPs and policy uncertainty is probably
at least as large as with just the CCP, it is possible the incentive to
plant the base (Blue) crop is stronger with mandatory updating despite
the countervailing incentive created by the chance of policy
elimination. This result suggests that participants were "planting
to maintain base" (to secure future program payments). These
participants disregarded current market signals to maintain or enhance
program payments.
These results are similar to findings in Lusk and Coble (2003).
Their experimental study had players make decisions over choices similar
to our risk preference X-test, in which some of them faced an additional
background mean-zero lottery. They tested for levels of risk aversion
and found players who faced background risk were slightly more risk
averse. Our results are adding policy risk on top of price risk induced
incrementally more allocations to the base crop. Again this is a risk
minimizing choice.
Conclusion
This study examined the production effects of CCPs and base acre
updating under price and policy uncertainty in an experimental market.
The experimental design allowed us to isolate how CCPs affect the mix of
base and nonbase crops. The evidence suggests CCPs influence crop
allocation decisions in the lab--the average player allocated more acres
toward the base crop option relative to the absence of CCPs. The results
were similar after adding policy uncertainty with a possibility of
mandatory base updating. Our findings do not rule out nontrivial impacts
to producers' planting choices, income, crop markets, and
allocative efficiency.
Several extensions to our design could be considered.
Mean-preserving spreads of prices and probabilities would test whether
CCPs would have more impact on planting decisions. Understanding the
extent of this impact would further clarify the key incentives affecting
cropping decisions with available program payments. Second, one could
examine how subjects react to more downside "hits" and how
long it takes to recover from these shocks. Changing the Policy risk
case probabilities could provide more insight into whether
"planting to maintain base" occurs in the lab. A third
extension is to allow for variable base acres and optional updating.
Fourth, bankruptcy could be added, which would increase incentives to
use a maximin strategy. Finally, marketing loans and other
risk-reduction options can be added to test the robustness of our
results to a broader range of outside options.
[Received November 2005; accepted November 2006.]
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"Economic Analysis of Base Acre and Payment Yield Designations
under the 2002 U.S. Farm Act." ERS Economic Research Report Number
12.
(1) "Base acreage" refers to a farm's crop-specific
acreage of wheat, feed grains, cotton, rice, oilseeds, or peanuts
eligible to participate in commodity programs under the 2002 Farm Act.
Base acres and yields determine the level of government (direct and
counter-cyclical) payments and reflect a farm's historical level of
acres and yields. Under the 1996 Farm Act, production flexibility
contract (PFC) acreage and payment yields for most producers were
generally based on--as in prior legislation--the crop mix and prevailing
yields during the 1981-1985 period. The 2002 Act allows farmers to
update this mix by (a) adding newly eligible crops (i.e., oilseeds) to
their current mix, or (b) revising base acreage to reflect plantings
during 1998-2001. We refer to "farmers" or
"producers" assuming they own the base acreage.
(2) By "nonbase" crop, we take the farmer's
perspective: crops for which the producer does not have a production
history or an established base, or a crop ineligible for program
payments.
(3) Planting a base crop ensures a higher minimum income received
due to CCP subsidies and therefore can create an incentive for farmers
to allocate crops such that they maximize their minimum possible
revenue, usually called a maximin solution in decision theory.
(4) The more risk averse the person, the more likely they would
engage in a maximin strategy or one that allows for a higher maximin
earnings in the future (planting for or maintaining base).
(5) For a more detailed presentation of the main commodity policy
provisions of the 2002 Farm Act, and a comparison with provisions
available under the 1996 Farm Bill, see the ERS, USDA side-by-side
analysis available on the ERS website at:
http://www.ers.usda.gov/Features/FarmBill/Titles/TitleICommodities.htm.
(6) For each crop, the CCP payment rate = (Target price )--(Direct
Payment rate)--{Maximum [commodity price, loan rate]}.
(7) Following standard experimental procedures, we used
context-neutral terms. Although farmers typically posses base acreage
for several crops, we endowed the subjects with base acreage of one crop
("blue"). One can consider the other crop ("red")
either a crop not eligible for government payments (a nonprogram crop)
or a program crop the farmer had not previously planted. Because we
exclude the marketing loan program, the "red" crop can be
thought of as any nonbase crop.
(8) We control for order of play of the cases by using two
sequences (called treatments): (a) Baseline, CCP, then Policy risk case;
and (b) Baseline, Policy risk, then CCP.
(9) The 2002 Act provides DPs similar to the former production
flexibility contract (PFC) payments. Direct payments are tied to base
acreage, but are completely decoupled from a farmer's current
planting choices and current market prices. We include DPs in the
baseline case because, although not affecting current production
decisions, they constitute one part of government payments that may be
at risk if mandatory base updating is instituted (a possibility
introduced in the Policy risk case). Recall that CCPs are also decoupled
from production decisions, but are linked to current market prices of
the farmer's base crops.
(10) These random draws made the prices of our crop options
independent. With correlated prices, CCPs still provide maximin
incentives for planting base crops.
(11) The inequalities (presented in Table 2) either within or
across the lotteries are not necessarily the same numerically or by
percentage; it is a general pattern.
(12) Dominance means here that the monetary rewards dominate the
subjective costs of making choices in the experiment, or any other
motivation.
(13) We isolated the impact of CCPs by assuming no marketing loan
program.
(14) Note a caveat about our experimental design. We recognize our
design has people making one-time decisions over many rounds. Our
representation of a base acre updating policy does not reflect current
legislation. The 2002 Act gave farmers the one-time option of updating
base acres to reflect recent planting history and this base acreage is
in effect for the remainder of the Farm Act. In the Policy risk case, if
the updating policy was randomly chosen the participants faced a
mandatory updating of their base acres based on the token allocation
decisions made earlier that round. Current program benefits (bonuses)
were potentially reduced for that round while base acres in subsequent
rounds were unaffected (start with 100 base acres in each round). This
is a simplification of the current Farm Act's updating procedure,
in which base acres could have been changed based on average plantings
of program crops during 1998-2001 (Westcott, Young, and Price 2002).
While our design represents a potential policy for updating, it is a
simplification of the potential range of future updating options, should
they occur at all. Attempting to include updating based on current
policy would have greatly complicated the current design without
necessarily providing much additional insight.
(15) Using the Decision Tool was optional. As in the wilds,
subjects who understand expected value and variance may use such tools
to help their decisions, while others need not.
(16) Experimental instructions can be found on-line at the AgEcon
Search website, http://agecon.lib.umn.edu/.
(17) This test is designed to elicit the subjects' risk
preference by asking them to make nine choices. Each choice is called a
"game" and involves selecting either the sure bet of $2.50 or
playing a tottery with a chance of winning $0 or $5. In each game the
probability of the $5 payoff changes ranging in 10% increments from 10%
to 90% (presented as numbers from 1 to 10; e.g., $0 if a 2, 3, 4, 5, 6,
7, 8, 9, or 10 is drawn, $5 if a 1 is drawn). A random draw determines
which "game" is played. If the subject chooses the lottery for
the randomly drawn game a randomly drawn whole number between one and
ten determines which payoff they receive ($0 or $5). The draws were not
done until subjects were finished with the main experiment. Similar
designs have been used by Holt and Laury (2003) and Lusk and Coble
(2003).
(18) Subjects had to answer all true/false case quiz questions
correctly before proceeding. If after several attempts on their own the
student was unable to answer them all correctly, the moderator would
have them fill out the answers they believed to be correct and discuss
any wrong answers until they were understood and the answers corrected.
(19) The same ten lotteries were used in all three cases. Within
each case, however, the lotteries were randomized for each player to
avoid influences from ordering.
(20) See Table 1 for the prices, probabilities, expected value per
token, and variance per token of the ten lotteries for both the base and
nonbase crop.
(21) These lump-sum payments are calculated with all acres eligible
for DPs and CCPs (they do not include adjustments for payment acres or
payment yields). In practice, CCPs cover only a portion of the shortfall
between the target price and the effective price (market price plus
direct payment) since payments are made on 85% of base acres times a
historical yield. DPs are also subject to payment acre and yield
adjustments. Recall we assume no marketing loan program.
(22) Setting BONUS1 at 1.50 lab dollars/acre made DPs 10% of the
income receive from the target price. Direct payments rates in the 2002
Farm Act range from 1.71% (Oats) to 22.4% (Rice) of the target price,
with a mean of 10.9% (Westcott, Young, and Price 2002).
(23) Consider the possibilities of the realized Blue prices: (a) If
the price is the High Price, the participant receives the market price
(at least 17 per token, see Table la) plus the direct rate (1.5 per
token) which, in sum, is greater than the target price per token. (b) If
the price is the Low Price. the participant receives the market price
plus the direct rate plus BONUS 3 which, in sum, is the target price per
token (1,500 total lab dollars divided by 100 tokens equals the target
price of 15 per token). (c) If the price is the Zero Price, the
participant receives a market price of zero plus the direct rate plus
BONUS 2 which, in sum, is again the target price per token (1,500 total
lab dollars divided by 100 tokens equals the target price of 15 per
token). Thus the minimum received from a token allocated to the Blue
option is the target price. The minimum possible earnings for investment
in Red occurs when the realized Zero Red price and a realized High Blue
price occur: (a) If the price is the Zero Price and the Blue price is
High, the participant receives a zero market price plus the direct rate
(1.5 per token) only (no bonuses when blue price > target price)
which, in sum, is equal to 1.5 per token. Thus the minimum received from
a token allocated to the Red option is the direct rate.
(24) Recall under the mandatory base update outcome, blue remains a
program or base crop, and red remains ineligible for bonus payments.
(25) The Lagrange Multiplier test (p-value less than 0.005)
indicated that random-effects model is preferred to the classic
regression model (Greene 2000). The fixed-effects model cannot include
the treatment or the risk preference variables due to perfect
colinearity with the individual intercepts. The Hausman test was
conducted on the above model excluding T2, RAVER, and RLOV. The Hausman
(1978) test (p-value of 1.00) indicated no significant difference
between the fixed and random-effects models, which suggests that the
random-effects model may be preferred since it is efficient (Greene
2000). The fixed-effects model yielded similar results with slightly
lower case coefficients (0.0472 for CCP case and 0.0647 for Policy risk
case) and lower significance levels (p-values 0.0