Willingness to insure people infected with COVID-19: Surveys with Insurance Professionals and Lay People

Last registered on January 03, 2023

View Trial History

Pre-Trial

Trial Information

General Information

Title

Willingness to insure people infected with COVID-19: Surveys with Insurance Professionals and Lay People

RCT ID

AEARCTR-0010613

Initial registration date

December 12, 2022

Initial registration date is when the trial was registered.

It corresponds to when the registration was submitted to the Registry to be reviewed for publication.

First published

January 03, 2023, 3:38 PM EST

First published corresponds to when the trial was first made public on the Registry after being reviewed.

Locations

Country

China

Region

Beijing

Primary Investigator

Name

Wanxin DONG

Affiliation

Renmin University of China

Contact Primary Investigator

Other Primary Investigator(s)

PI Name

Jiakun Zheng

PI Affiliation

Renmin University of China

Contact Investigator

Additional Trial Information

Status

Completed

Start date

2022-12-05

End date

2022-12-31

Keywords

Behavior, Finance & Microfinance, Health

Additional Keywords

Insurance, Ambiguity, Prosociality, COVID-19

JEL code(s)

D80, D91

Secondary IDs

Prior work

This trial does not extend or rely on any prior RCTs.

Abstract

Covid-19 is a worldwide pandemic. Some medical studies suggest that contracting Covid-19 increases the risk of developing other health problems. However, there is significant ambiguity about the severity of sequelae. In the Chinese insurance industry, we observe that some insurance companies charge higher premiums or even refuse to provide coverage to people infected with Covid-19. We conjecture that insurers' willingness to cover is influenced by their ambiguity attitudes and perceived ambiguity about the severity of sequelae. In this study, we use questionnaires to investigate individuals' risk perceptions on Covid-19 and attitudes toward insurers' underwriting decisions. We recruit two types of respondents including insurance professionals and lay people. Their levels of knowledge of risk differ considerably, which may lead to different opinions. In the questionnaires, we use some self-reported attitudinal scales to measure individuals' willingness for coverage, ambiguity and pro-sociality preferences. We conjecture that the more averse insurance professionals are to ambiguity, the less willing they are to cover people infected with Covid-19. Conversely, the more averse lay people are to ambiguity, the more they expect insurers to cover Covid-19. Moreover, respondents with a higher level of pro-sociality are more willing to cover people infected with Covid-19.

External Link(s)

Registration Citation

Citation

DONG, Wanxin and Jiakun Zheng. 2023. "Willingness to insure people infected with COVID-19: Surveys with Insurance Professionals and Lay People." AEA RCT Registry. January 03. https://doi.org/10.1257/rct.10613-1.0

Sponsors & Partners

Experimental Details

Interventions

Intervention(s)

We prepare two different questionnaires for insurance professionals and lay people respectively. Each questionnaire consists of three parts. The first part involves some socio-demographic questions, including gender, year of birth, education, type of work, location, income, marital status, etc. The second part involves questions on subjects' risk perceptions related to Covid-19 and attitudes toward insurers' underwriting decisions. The third part includes questions to measure subjects' risk attitudes, ambiguity, and pro-sociality preferences. In the second and third parts, we use self-reported attitudinal scales. Subjects are asked to indicate the extent to which they agree or disagree with a list of statements on a scale from 0 to 10. The experiment is expected to be completed within 5-10 minutes.
We recruit insurance professionals through Wechat groups and other means. We recruit lay people through a professional questionnaire company. Both groups of people face similar questions. However, for questions about underwriting decisions, we provide insurance professionals with the following phrase"As an insurance professional".

Intervention Start Date

2022-12-17

Intervention End Date

2022-12-25

Primary Outcomes

Primary Outcomes (end points)

- To what extent do you agree or disagree with insurance companies refusing to cover people infected with COVID-19?
- Perceived ambiguity about the sequelae of COVID-19 infection.
- Subjects' ambiguity preferences.
- Subjects' level of pro-sociality.
- Beliefs about contracting COVID-19 within a year

Primary Outcomes (explanation)

Assumptions for insurance professionals:
- the more averse insurance professionals are to ambiguity, the less willing they are to cover people infected with Covid-19.
- respondents with a higher level of pro-sociality are more willing to cover people infected with Covid-19.
- respondents with a higher beliefs about contracting COVID-19 are more willing to cover people infected with Covid-19.
Assumptions for lay people:
- the more averse lay people are to ambiguity, the more they expect insurers to cover Covid-19.
- respondents with a higher level of pro-sociality are more in favor of insuring people infected with Covid-19.
- respondents with a higher beliefs about contracting COVID-19 are more in favor of insuring people infected with Covid-19.

Secondary Outcomes

Secondary Outcomes (end points)

Secondary Outcomes (explanation)

Experimental Design

We prepare two different questionnaires for insurance professionals and lay people respectively. Each questionnaire consists of three parts. The first part involves some socio-demographic questions, including gender, year of birth, education, type of work, location, income, marital status, etc. The second part involves questions on subjects' risk perceptions related to Covid-19 and attitudes toward insurers' underwriting decisions. The third part includes questions measuring subjects' risk attitudes, ambiguity, and pro-sociality preferences. In the second and third parts, we use self-reported attitudinal scales. Subjects are asked to indicate the extent to which they agree or disagree with a list of statements on a scale from 0 to 10. The experiment is expected to be completed within 5-10 minutes.

Experimental Design Details

For the second part, the questions include:
- To what degree do you know how infection with Covid-19 affects the likelihood of developing other illnesses?
- (As an insurance professional,) do you think it is reasonable for an insurer to obtain information related to insureds' record of infection with Covid-19?
- (As an insurance professional,) do you think it is reasonable for an insurer to refuse coverage to the insureds infected with Covid-19?
- (As an insurance professional,) do you think it is reasonable for an insurer to increase the overall premium for the insureds infected with Covid-19?
- In your opinion, what percentage of the Chinese population will be infected with Covid-19 in the coming year?
- etc.
For the third part, the questions include:
- One risk attitude question by Dohmen et al. (2011):
The extent to which you consider yourself a risk taker.
- Four ambiguity preference questions: Intolerance of Ambiguity Scale by Kirton (1981).
There is a right way and a wrong way to do almost everything.
Practically every problem has a solution.
I feel relieved when an ambiguous situation suddenly becomes clear.
I find it hard to make a choice when the outcome is uncertain.
- Three prosociality questions: Prosociality Scale (Luengo Kanacri et al., 2021)
I try to help others.
I try to be close to and take care of those who are in need.
I easily share with friends any good opportunity that comes to me.

Randomization Method

Random sampling.

Randomization Unit

Individual.

Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters

Two types of people including insurance professionals and lay people.

Sample size: planned number of observations

300 insurance professionals and 400 lay people. For 400 lay people, half male and half female; 100 people in each of the four age groups (20-30, 30-40, 40-50 and 50-60).

Sample size (or number of clusters) by treatment arms

300 insurance professionals and 400 lay people.
For 400 lay people, half male and half female; 100 people in each of the four age groups (i.e., 20-30, 30-40, 40-50 and 50-60).

Minimum detectable effect size for main outcomes (accounting for sample design and clustering)

Supporting Documents and Materials

IRB