Crop Insurance Today February 2018 - 23

As discussed in detail in the article, budget
effect may stem from tight profit margins and
mental accounting. Facing tight operating profit
margins over the last 15 years, farmers appear to
spend about at most five to ten percent of their
crop budget on crop insurance premiums. On the
other hand, a budget heuristic can be implicit or
explicit and can apply to high income individuals
as part of mental accounting. In light of research
from behavioral economics and marketing science, farmers may be assigning low budgets for
goods such as crop insurance that may be perceived to be vague in benefits and yet certain in
costs at the moment of decision (that is, salient
in costs), despite being desirable in the long run
(Thaler, 2008; Du, Feng, and Hennessy, 2017).
Farmers may also be comparing crop insurance
premium to a benchmark expenditure in their
production budget. The upshot is that, under
such a heuristic, farmers may begin with a set
amount of money and ask their agents to find
them the best value within that constraint.
The article points out that the budget constraint hypothesis is consistent with a suite of
empirical observations. The bulk of corn, soybean, wheat, and upland cotton acres are within
five percent of the expected crop value, yet some
acres (especially for wheat and cotton) are within five to ten percent of the expected crop value. Limited acres with expenditures beyond ten
percent have been observed. Coverage demand is
lower in such regions where the insurance product is more expensive - despite the price being
commensurate with the risk. Beyond coverage
choices, farmers have markedly shifted their
preferences from basic and optional units to enterprise units since a provision of the 2008 Farm
Bill increased subsidy rates for enterprise units.
Meanwhile, the 2014 Farm Bill introduced supplemental area based products (the Stacked Income Protection (STAX) program specifically for
upland cotton and the Supplemental Coverage
Option, SCO, for other crops). Apparently, the
legislative intent was to ameliorate the aforementioned differences in insurance coverage uptake
across regions. However, SCO and STAX products saw limited uptake. Finally, a recent empirical finding in the literature (Du, Feng and Hennessy, 2017) indicates that farmers reveal some
aversion to incurring out of pocket premium in
their crop insurance coverage choices.
In light of these observations, the article extends Dr. Bulut's earlier work (Bulut and Collins,
2014) by adding a budget constraint effect to the
simulation analysis. The budget effect is modeled

as it has been revealed through the data on actual choices. Covering fairly representative farm
situations in 2015, the article demonstrates that
farmers' choices are generally consistent with the
expected utility maximization behavior so long
as a budget constraint in effect. The overall conclusion is that when it comes to farmers' choices
regarding crop insurance products and coverage
levels, budget constraints matter. Moreover, the
simulation analysis confirms the intuition that,
whenever farmers operate under a budget constraint, SCO and STAX do not appear to be the
solution to increase coverage levels in counties
that had predominantly low coverage levels (below 70 percent) such as those in the Great Plains
and the South. The article ends with a number of
suggestions for future research avenues.
Both articles make contributions toward a
better understanding of crop insurance demand.
The JARE article emphasizes a "reference price"
effect, while the AFR article emphasizes "budget heuristic" effect - as a modification of the
standard expected utility theory framework. To
reiterate, both effects can be found in Thaler
(2008) as part of mental accounting2 and can be
consistent with the modeling of farmers' aversion
for out of pocket payments in Du, Feng and Hennessy (2017). In the absence of such effects, the
expected utility model predicts that a risk-averse,
rational farmer facing actuarially fair premium
rates (without even requiring any premium support) should buy insurance at the highest possible level, which would be 85 percent with individual crop insurance plans. That in turn poses
a question as to the gap between this theoretical
prediction and farmers' actual choices as discussed above. To explain this gap, one could turn
to the alternative decision-making framework
cumulative prospect theory (Babcock, 2015).
The cumulative prospect theory was originally
developed in Tversky and Kahneman (1992) and
may be employed to explain some of the anomalies that are not compatible with expected utility
theory. 3 The AFR article carries out an extensive
simulation analysis with respect to the cumulative prospect theory framework and evaluates the
findings in Babcock (2015) as well.
In closing, both research endeavors undertaken by Dr. Bulut were recognized as contributions
to the agricultural economics literature. These

articles have been published in academic journals and featured here in Crop Insurance TODAY®
magazine (see the references for a select list) have
been already cited by outside researchers and are
expected to stimulate future research. Such an
impact adds to the credibility of the association's
responses regarding program and policy changes. Just as it is vitally important to incorporate the
latest agronomic research into the crop insurance
industry's loss adjustment procedures, it is equally important that the industry maintain a viable
analytical research program, most particularly in
the field of agricultural economics. NCIS needs
to be part of the academic conversation as crop
insurance has become a mainstay of the profession's focus for at least the past decade.

References:

Babcock, B. A. (2015), "Using Cumulative Prospect Theory
to Explain Anomalous Crop Insurance Coverage Choice,"
American Journal of Agricultural Economics, Vol. 97 No.
5, pp. 1371-1384.
Bulut, H., Collins, K.J., and Zacharias, T.P. (2012), "Optimal
Coverage Demand with Individual and Area Plans of Insurance," American Journal of Agricultural Economics,
Vol. 94 No. 4, pp. 1013-1023.
Bulut, H. and Collins, K.J. (2014), "Designing Farm Supplemental Revenue Coverage Options on Top of Crop Insurance Coverage," Agricultural Finance Review, Vol. 74 No.
3, pp. 397-426.
Bulut, H. and K.J. Collins. (2011), "Systemic Risk and Crop
Insurance in Retrospect and Prospect," Crop Insurance
TODAY, NCIS, November.
Bulut, H., F. Schnapp and K.J. Collins. (2011), "Volatility
Factor in Concept and Practice," Crop Insurance TODAY,
NCIS, May.
Collins, K.J. and H. Bulut (2011), "Crop Insurance and the
Future Farm Safety Net," Choices. 26(4).
Congressional Budget Office. (2017), "Options to Reduce the
Budgetary Costs of the Federal Crop Insurance Program,"
December.
Du, X. H. Feng and D. A. Hennessy. (2017), "Rationality of
Choices in Subsidized Crop Insurance Markets," American Journal of Agricultural Economics, Vol. 99 No. 3, pp.
732-756.
Government Accountability Office. (2014), "Crop Insurance:
Considerations in Reducing Federal Premium Subsidies
GAO-14-700," Washington, DC, August.
Innes, R. (2003), "Crop Insurance in a Political Economy: An
Alternative Perspective on Agricultural Policy," American
Journal of Agricultural Economics, Vol. 85 No. 2, pp.
318-335.
Thaler, R.H. (2008), "Mental Accounting and Consumer
Choice," Marketing Science, Vol. 27 No. 1, pp. 15-25.
Tversky, A. and Kahneman, D. (1992), "Advances in Prospect Theory: Cumulative Representation of Uncertainty,"
Journal of Risk and Uncertainty, Vol. 5, pp. 297-323.

2 Richard Thaler was the winner of 2017 Nobel prize in economics.
3 In modeling how individuals make decisions under uncertainty, the cumulative prospect theory blends economics
and psychology and states that the individual would compare each possible outcome to a reference point first and
code those outcomes as gains or losses. The individual would then weigh the values he or she is deriving from gains
or losses with the subjective probabilities.
CROPINSURANCE TODAY®

Table of Contents for the Digital Edition of Crop Insurance Today February 2018