Expert Forecasts of Longevity Beliefs and Pension Knowledge

We conducted a survey on key knowledge areas relevant to individuals over the economic life-cycle, namely financial literacy, pension-specific knowledge, and life expectancy. The survey focused on how individuals integrate these different areas of knowledge, the relationship between individuals’ beliefs about their own life expectancy and that of their peers– defined as individuals of the same gender, age, and place of residence–, and included an information treatment providing some participants with actuarial estimates of their expected lifespan. This treatment was intended to study how such information influences the formation of subjective longevity expectations. The survey experiment was pre-registered AEARCTR-0012807 ("Cultural Aspects of Retirement Saving Decisions").

To interview individuals, we opted for a survey conducted by a commercial survey company using Computer-Assisted Telephone Interviewing. The study was done in Trentino-South Tyrol, a region in northeastern Italy. The commercial survey company SWG randomly selected 1,000 individuals from a pool of eligible respondents living in the region.

TESTING KNOWLEDGE. We examined respondents’ factual knowledge of the three main factors influencing life-cycle behavior: financial literacy, pension knowledge, and longevity beliefs. Financial literacy is evaluated through three questions on the fundamental topics: interest compounding, inflation, and risk diversification. Pension specific knowledge is assessed by testing individuals’ understanding of how their state pension is calculated and financed. Lastly, longevity accuracy is derived from the ability to accurately predict the life expectancy of similar individuals.

In particular, to test financial literacy we asked:

i. Suppose you had $100 in a savings account and the interest rate was 2% per year. After 5 years, how much do you think you would have in the account if you left the money to grow?
(a) More than $110
(b) Exactly $110
(c) Less than $110
(d) Don’t know

ii. Imagine that the interest rate on your savings account was 1% per year and inflation was 2% per year. After 1 year, how much would you be able to buy with the money in this account?
(a) More than today
(b) Exactly the same
(c) Less than today
(d) Don’t know

iii. Please tell me whether this statement is true or false. “In general, investing $1,000 in shares of a single company is a less risky investment than investing $1,000 in shares of 10 different companies.”
(a) True
(b) False
(c) Don’t know

Next, to test pension-specific knowledge we asked:

i. Based on current legislation, do you know how the state pension of a young person newly hired by a company will be calculated?
(a) Yes, with a contribution-based system, i.e., based on all contributions paid during the
working period
(b) Yes, with a earnings-based system, i.e., based only on salaries and contributions from the
final years of work
(c) Yes, with a mixed system, i.e., partly contribution-based and partly earnings-based
(d) Don’t know

ii. Based on current legislation, in your opinion, the contributions that workers pay to the National Institute for Social Security today:
(a) Are used to pay pensions to those who are already retired
(b) Are set aside in workers’ pension accounts and invested in the financial markets
(c) Are partly used to pay pensions to those who are already retired and partly set aside in
workers’ pension accounts and invested in the financial markets
(d) Don’t know

Finally, to test the factual knowledge about life expectancy we asked:
What is, in your opinion, the average life expectancy (i.e., age at death) of a [respondent’s age] years-old [respondent’s gender] living in [respondent’s county]?

INFORMATION TREATMENT. The survey also included an experiment in which a randomly selected sub-sample of respondents was provided with information about their actuarial life expectancy based on the respondent specific gender, age, and county of residence. This experiment allows us to assess whether individuals incorporate this information when forming their subjective longevity expectations and, if so, whether such updates may influence other long-term investment decisions and overall satisfaction in their financial planning.

The control group question was:
Based on the latest available data, the National Statistical Office provides estimates of average life expectancy (i.e., age at death) by sex, age, and county of residence. Do you think this information is relevant to you?

The treatment group question was:
Based on the latest available data, the National Statistical Office estimates that the average life expectancy (i.e., age at death) for a [respondent’s gender] aged [respondent’s age] and living in [respondent’s county of residence] is [actuarial life expectancy based on respondent’s gender, age an county of residence]. Do you think this information is relevant to you?

2025-11-17

2026-02-28

The expert forecasts about:
- the life expectancy individuals estimate for their peers;
- the life expectancy individuals estimate for their own life;
- the knowledge individuals have about financial concepts;
- the knowledge individuals have about the state pension;
- the life expectancy individuals estimate for their own life, given the life expectancy they estimated for their peers;
- the life expectancy individuals estimate for their own life, given both the life expectancy they estimated for their peers and the official actuarial life expectancy (specific to their age, gender, and county of residence) provided to them.

We will recruit approximately 200+ experts both through the new letters of academic associations in the fields of behavioral economics, public economics, labor economics, and finance, and through an ad hoc list of authors who have recently published on related topics.

The invitation will include a link to a Qualtrics survey containing a brief explanation of the study’s purpose and structure. We collect information on each expert to capture their academic rank and areas of expertise, which may allow us to assess how forecast accuracy varies with expertise. Experts will then be asked to provide their forecasts for the assigned cases (constructed from the survey data) through a three-block survey, which takes approximately five minutes to complete. The forecasts concern individuals’ beliefs about longevity as well as their knowledge of basic financial concepts and the pension system.

Experts will not receive any direct compensation for their participation. However, as a token of appreciation, we will donate €1 to Save the Children for each completed questionnaire, up to a maximum total donation of €200.

The survey will consist of three blocks of questions.

In the first two blocks, experts will be asked to predict how individuals estimate the life expectancy of their peers and of themselves, as well as how they perform on questions related to basic financial concepts and the state pension system.
To this end, we created 15 cases per gender, representing equally sized age groups (ranging from 25 to 60 years old), for a total of 30 cases.

In the first block, we present the randomly selected case A (defined by age, gender, and official actuarial life expectancy) to the expert and ask them to predict the answers that A provided regarding:
- the life expectancy of A's peers (same age, gender, and county of residence);
- A’s own subjective life expectancy.

In the second block, we present again A’s information and ask the expert to predict how many questions A answered correctly regarding:
- 3 questions on Financial literacy (i.e., interest compounding, inflation, and risk diversification);
- 2 questions on Pension knowledge (i.e., how the state pension benefits are calculated, and how the state pension system is financed).

In the third block, we ask experts to predict the effect of providing individuals with information about the actuarial life expectancy of their reference group (defined by age, gender, and county of residence) on the formation of their subjective longevity beliefs.
We constructed 24 representative cases capturing the beliefs that individuals held about the life expectancy of their peers, disaggregated by gender and county of residence (the analyzed region comprises two counties). Specifically, we proceeded as follows: first, we divided the range of peer life expectancy estimates — from the lowest (65 years) to the highest (100 years) — into 5-year intervals. We then excluded the extreme values (those below 67.5 and above 97.5 years), which represent less than 1 percent of the sample,1 and finally computed the average actuarial life expectancy shown to treated individuals within each range-by-gender-by-county cell.

We present case B (defined by the individual’s estimate of their peers’ life expectancy) to the expert and ask them to predict:
- B’s own subjective life expectancy when they have NOT RECEIVED any information about the official life expectancy estimates;
- B’s own subjective life expectancy when they have RECEIVED information that the official life expectancy for an individual of the same age, gender, and county of residence is [B's official life expectancy] years.

Each expert will be randomly assigned by the Qualtrics platform to one of the 30 cases presented in the first and second blocks, and to one of the 24 cases presented in the third block.

Individual subject

No

Please see below. The number of clusters is the same as the number of observations.

About 200 experts.

N/A; there is a single treatment arm, with the only source of variation originating from the representative cases presented.