Periodic Reporting for period 1 - PenAgeing (Fair Pensions and Population Ageing)
Período documentado: 2021-01-01 hasta 2022-12-31
Resumen del contexto y de los objetivos generales del proyecto
Most of the traditional measures of ageing are based on the assumption that people are old at age 65 or even at age 60 irrespective of where they live and when they lived. However, people at age 65 were not the same everywhere, nor will they be the same in the future. For example, Japanese women at age 65 in 2020 are much healthier and have a much higher life expectancy than women of the same age living in 2020 in Chad or living in Japan 50 years ago. This means that at the same age, people have different characteristics such as health, life expectancy, cognitive abilities, and so on. We argued that those characteristics and not solely chronological age should be used to define who is old.
In the ERC ReAgeing project, we introduced several new measures of ageing based on characteristics such as physical and health conditions, cognitive abilities, life expectancy, and others. Among those new measures, we introduced an intergenerationally equitable pension age (IEPA), which is a fair pension age based on patterns of survival.
For the results of the ReAgeing grant to be relevant in the future, they must be extended to specific applications and be kept current. There was a gap between the new methodologies produced in the ReAgeing project and their use in policy analyses. The goal of the grant was to reduce this gap, to create a social benefit linking science and decision-making, and to enable better-informed policies concerning population ageing in general and pension ages in particular.
To reach this aim we developed a World Ageing Data Explorer (WADE) which easily allows policymakers, scientists, and even the general public to view our new measures of population ageing and compare them to conventional measures. WADE has very extensive functionality. It allows graphical comparison of different indicators, cross-country, and cross-regional comparisons, the ranking regions by different indicators, and the exporting of all presented data to output files. All results are available for the period 1950-2100. Intergenerationally equitable pension ages use 2000 as a reference year. This means that the pension age trajectories are presented in relation to the survivorship characteristics of the country under consideration in 2000 and we suggest that the age at retirement should be based on improvements in life expectancy assuming that in the year 2000, the age at retirement was 65.
WADE has a separate module that allows the calculation of ageing measures based on user-defined data. A special emphasis here was on the evaluation of Intergenerationally equitable pension ages. We allowed users to define their own reference year as well as pension ages in a reference year. It allows policymakers to evaluate intergenerationally equitable pension ages based on country-specific survivorship characteristics as well as on a particular pension age in a reference year.
Web Address: https://demog.iiasa.ac.at/apps/(se abrirá en una nueva ventana)
In our project, we had a special focus on the Austrian pension system. In the grant period, we developed an Austrian Ageing Data Explorer (AADE), which uses Eurostat data at the NUTS 2 level. The functionality of the AADE is similar to the functionality of the WADE. The AADE provides the ability to compare indicators at the regional level. A special emphasis was placed on the evaluation of IEPA. Here we used survivorship characteristics in Austria as a whole in 1996 as the reference.
The framework used to produce the IEPA is used to evaluate Beta Coefficients. We used those coefficients to understand how much retirement benefits would need to be adjusted to compensate for different patterns of regional survivorship. For example, data in 2019 suggests that, if the age at retirement is kept the same in all Austrian regions to compensate for inequality in survivorship, pension benefits would be higher by 4% in the Vienna region and lower by 6% in Vorarlberg (based on survivorship differences in 2019). On the other hand, if retirement benefits would be kept similar in Austrian regions, the IEPA would have to be different in different regions. For example, if Austrian survivorship in 1996 is used as a reference, the IEPA should be 67.0 and 67.2 in Vienna and Vorarlberg correspondingly. These numbers, albeit only tentative, point to how to implement an equitable differential pension policy in Austria.
The Austrian population is also used to evaluate Equal Survivorship Ages (ESAs). The survival rate from age 20 to the ESA is the same as the survival rate from age 20 to age 65 in Austria. This is a special type of calculation that we introduced for Austrian policymakers. It allows us to see the differences in survivorship to a pension age in different Austrian provinces. For example, in 2019 the probability for Austrian men to survive from age 20 to age 65 was 0.91. This means that out of 100 men at age 20 about 91 men would survive until age 65.
However, in Vienna, the same survival rate would occur at the earlier age of 63.8 years. On the other hand, in Vorarlberg, 91 men out of 100 would survive by age 66.5.
Provisional web address: https://demog.iiasa.ac.at/apps/austrianApp2022/(se abrirá en una nueva ventana)
Another important contribution of the project was preparing a chapter for the forthcoming Routledge Handbook of the Economics of Ageing edited by David E. Bloom et al. The chapter by W. Sanderson and S. Scherbov named “Ageing and Dependency” introduces the notion of Intergenerationally equitable pension age to a very large audience since the Handbook is expected to be the essential resource for advanced students, researchers, and policymakers looking at the economics of ageing across the disciplines of economics, demography, public policy, public health and beyond.
Another important achievement of the project is a request from the UN Population Division for the algorithms and codes to evaluate our new measures of ageing. In December 2022, the UN Population Division in New York organized a hybrid seminar where we presented our new measures and discussed ways to incorporate them into the next revision of the World Population Prospects. As a result, the UN Population Division is planning to incorporate our measures beginning with the 2024 Revision World Population Prospects. This will ensure the widest possible dissemination of our new measures of ageing since the UN’s World Population Prospects are the main source of population data around the world.
In the ERC ReAgeing project, we introduced several new measures of ageing based on characteristics such as physical and health conditions, cognitive abilities, life expectancy, and others. Among those new measures, we introduced an intergenerationally equitable pension age (IEPA), which is a fair pension age based on patterns of survival.
For the results of the ReAgeing grant to be relevant in the future, they must be extended to specific applications and be kept current. There was a gap between the new methodologies produced in the ReAgeing project and their use in policy analyses. The goal of the grant was to reduce this gap, to create a social benefit linking science and decision-making, and to enable better-informed policies concerning population ageing in general and pension ages in particular.
To reach this aim we developed a World Ageing Data Explorer (WADE) which easily allows policymakers, scientists, and even the general public to view our new measures of population ageing and compare them to conventional measures. WADE has very extensive functionality. It allows graphical comparison of different indicators, cross-country, and cross-regional comparisons, the ranking regions by different indicators, and the exporting of all presented data to output files. All results are available for the period 1950-2100. Intergenerationally equitable pension ages use 2000 as a reference year. This means that the pension age trajectories are presented in relation to the survivorship characteristics of the country under consideration in 2000 and we suggest that the age at retirement should be based on improvements in life expectancy assuming that in the year 2000, the age at retirement was 65.
WADE has a separate module that allows the calculation of ageing measures based on user-defined data. A special emphasis here was on the evaluation of Intergenerationally equitable pension ages. We allowed users to define their own reference year as well as pension ages in a reference year. It allows policymakers to evaluate intergenerationally equitable pension ages based on country-specific survivorship characteristics as well as on a particular pension age in a reference year.
Web Address: https://demog.iiasa.ac.at/apps/(se abrirá en una nueva ventana)
In our project, we had a special focus on the Austrian pension system. In the grant period, we developed an Austrian Ageing Data Explorer (AADE), which uses Eurostat data at the NUTS 2 level. The functionality of the AADE is similar to the functionality of the WADE. The AADE provides the ability to compare indicators at the regional level. A special emphasis was placed on the evaluation of IEPA. Here we used survivorship characteristics in Austria as a whole in 1996 as the reference.
The framework used to produce the IEPA is used to evaluate Beta Coefficients. We used those coefficients to understand how much retirement benefits would need to be adjusted to compensate for different patterns of regional survivorship. For example, data in 2019 suggests that, if the age at retirement is kept the same in all Austrian regions to compensate for inequality in survivorship, pension benefits would be higher by 4% in the Vienna region and lower by 6% in Vorarlberg (based on survivorship differences in 2019). On the other hand, if retirement benefits would be kept similar in Austrian regions, the IEPA would have to be different in different regions. For example, if Austrian survivorship in 1996 is used as a reference, the IEPA should be 67.0 and 67.2 in Vienna and Vorarlberg correspondingly. These numbers, albeit only tentative, point to how to implement an equitable differential pension policy in Austria.
The Austrian population is also used to evaluate Equal Survivorship Ages (ESAs). The survival rate from age 20 to the ESA is the same as the survival rate from age 20 to age 65 in Austria. This is a special type of calculation that we introduced for Austrian policymakers. It allows us to see the differences in survivorship to a pension age in different Austrian provinces. For example, in 2019 the probability for Austrian men to survive from age 20 to age 65 was 0.91. This means that out of 100 men at age 20 about 91 men would survive until age 65.
However, in Vienna, the same survival rate would occur at the earlier age of 63.8 years. On the other hand, in Vorarlberg, 91 men out of 100 would survive by age 66.5.
Provisional web address: https://demog.iiasa.ac.at/apps/austrianApp2022/(se abrirá en una nueva ventana)
Another important contribution of the project was preparing a chapter for the forthcoming Routledge Handbook of the Economics of Ageing edited by David E. Bloom et al. The chapter by W. Sanderson and S. Scherbov named “Ageing and Dependency” introduces the notion of Intergenerationally equitable pension age to a very large audience since the Handbook is expected to be the essential resource for advanced students, researchers, and policymakers looking at the economics of ageing across the disciplines of economics, demography, public policy, public health and beyond.
Another important achievement of the project is a request from the UN Population Division for the algorithms and codes to evaluate our new measures of ageing. In December 2022, the UN Population Division in New York organized a hybrid seminar where we presented our new measures and discussed ways to incorporate them into the next revision of the World Population Prospects. As a result, the UN Population Division is planning to incorporate our measures beginning with the 2024 Revision World Population Prospects. This will ensure the widest possible dissemination of our new measures of ageing since the UN’s World Population Prospects are the main source of population data around the world.