Comparative Profitability of Product Disclosure Statements

Burkovskaya, Anastasia; Li, Jian

doi:10.2139/ssrn.3532175

Cited by 1 publication

(5 citation statements)

References 17 publications

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“…Example The parameters of the numerical example are the same as in the experiment: probabilities of bad states

P (s_{1}) = P (s_{2}) = 0.05

, probability of no‐loss state

P (s_{3}) = 0.9

, income

I = 400

, losses

L_{1} = 400

and

L_{2} = 200

, and prices of $1 deductible reduction of each state

p_{1} = 0.4

and

p_{2} = 0.2

, implying the insurance premium would be

p = 200 - 0.4 d_{1} - 0.2 d_{2}

. Similar to Example 1 in Burkovskaya and Li ( 2021 ), we use the combination of CARA utility and CRRA curvature function: We compare the insurance choices for frames

π

and

τ

with

u (x) = \frac{1}{γ} (1 - e^{- γ x})

γ = 0.017

, and

ϕ (u) = \frac{u^{η}}{η}

with (1)

η = 1.2

(convex), (2)

η = 1

(linear), and (3)

η = 0.7

(concave). Table 13 shows insurance choices for each of these scenarios .…”

Section: Discussion Of the Resultsmentioning

confidence: 83%

“…The application of SASEU to insurance follows the framework set up in Burkovskaya and Li (2021). When facing the insurance problem we offered, the value functional of the SASEU consumer from an insurance plan

d = (p, d_{1}, d_{2})

and income

I

under the narrow frame

π = {s_{1}, s_{2}, s_{3}}

would be:

V_{π} (d) = P (s_{1}) ϕ (u (I - p - d_{1})) + P (s_{2}) ϕ (u (I - p - d_{2})) + P (s_{3}) ϕ (u (I - p));

whereas, under a broad frame

τ = {A, s_{3}}

with

A = {s_{1}, s_{2}}

, it is:

V_{τ} (d) = P (A) ϕ (P (s_{1} ∣ A) u (I - p - d_{1}) + P (s_{2} ∣ A) u (I - p - d_{2})) + P (s_{3}) ϕ (u (I - p)) .

…”

Section: Discussion Of the Resultsmentioning

confidence: 99%

Section: State Aggregation Subjective Expected Utilitymentioning

confidence: 99%

“…Our application of SASEU to insurance in this paper is a special case of a more general theoretical insurance framework developed in Burkovskaya and Li (2021).…”

Section: Set‐up Of Insurance Problemmentioning

confidence: 99%

“…Similar to Example 1 inBurkovskaya and Li (2021), we use the combination of CARA utility and CRRA curvature function: We compare the insurance choices for frames π and τ with u…”

mentioning

confidence: 99%

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Framing and insurance choices

Burkovskaya

Teperski

Atalay

2021

J of Risk & Insurance

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The presentation of objective information should not affect a rational decision-maker; however, in practice, it often does. This paper studies the effect of the framing of contingencies on insurance choices. In several experiments, we presented participants with differently framed insurance tasks, where they had to choose deductibles against two different types of losses.The treatment group received a broad description of the risky situation and was asked to decide on the deductibles jointly, whereas the control group received a narrow description of the same task and was asked to decide on the deductibles separately. We discovered that the distribution of the insurance premium paid was identical in all groups, but the actual choices of the insurance plans were significantly diverse: On average, the treatment group preferred more similar deductibles than did the control group. These results indicate the importance of framing in insurance decision-making.

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“…Example The parameters of the numerical example are the same as in the experiment: probabilities of bad states

P (s_{1}) = P (s_{2}) = 0.05

, probability of no‐loss state

P (s_{3}) = 0.9

, income

I = 400

, losses

L_{1} = 400

and

L_{2} = 200

, and prices of $1 deductible reduction of each state

p_{1} = 0.4

and

p_{2} = 0.2

, implying the insurance premium would be

p = 200 - 0.4 d_{1} - 0.2 d_{2}

. Similar to Example 1 in Burkovskaya and Li ( 2021 ), we use the combination of CARA utility and CRRA curvature function: We compare the insurance choices for frames

π

and

τ

with

u (x) = \frac{1}{γ} (1 - e^{- γ x})

γ = 0.017

, and

ϕ (u) = \frac{u^{η}}{η}

with (1)

η = 1.2

(convex), (2)

η = 1

(linear), and (3)

η = 0.7

(concave). Table 13 shows insurance choices for each of these scenarios .…”

Section: Discussion Of the Resultsmentioning

confidence: 83%

d = (p, d_{1}, d_{2})

and income

I

under the narrow frame

π = {s_{1}, s_{2}, s_{3}}

would be:

V_{π} (d) = P (s_{1}) ϕ (u (I - p - d_{1})) + P (s_{2}) ϕ (u (I - p - d_{2})) + P (s_{3}) ϕ (u (I - p));

whereas, under a broad frame

τ = {A, s_{3}}

with

A = {s_{1}, s_{2}}

, it is:

V_{τ} (d) = P (A) ϕ (P (s_{1} ∣ A) u (I - p - d_{1}) + P (s_{2} ∣ A) u (I - p - d_{2})) + P (s_{3}) ϕ (u (I - p)) .

…”

Section: Discussion Of the Resultsmentioning

confidence: 99%

Section: State Aggregation Subjective Expected Utilitymentioning

confidence: 99%

“…Our application of SASEU to insurance in this paper is a special case of a more general theoretical insurance framework developed in Burkovskaya and Li (2021).…”

Section: Set‐up Of Insurance Problemmentioning

confidence: 99%

“…Similar to Example 1 inBurkovskaya and Li (2021), we use the combination of CARA utility and CRRA curvature function: We compare the insurance choices for frames π and τ with u…”

mentioning

confidence: 99%

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