Life tables are the most precise instruments available to demographers for assessing overall mortality levels and trends in a population. Unfortunately, their construction is data-demanding: for both genders, and for each age x (from 0 to over 100), death risks (or rates) must be calculated. This requires a numerator (deaths) and a denominator (population), and results in more than 200 cells. If the observed population is small, random effects can easily predominate in one or more of these cells, producing unpredictable (and unreliable) results – even setting aside omissions, age misclassifications, and other forms of error. To be sure, a variety of methods exist to limit these distortions, but even they have their limits and can be stretched only so far. Is any analysis possible beyond that point?

Data, method and purpose

This question arose in our case. Exploiting an existing Istat database at municipal level (with a reconstruction of the series ensuring constant boundaries from 2002 to 2018 – a rare case indeed), we aimed to study small-scale mortality over the period across Italy’s almost 8,000 local administrative units (LAUs), some of them very small (down to 30 residents in the worst case). For that purpose, we needed a reliable and comparable measure of mortality, but also one that was not too demanding in terms of data input.

We therefore opted for the standardised mortality ratio (SMR), which is simply the ratio between observed and expected deaths. The former were available, thanks to the aforementioned Istat database, while the latter were calculated by applying a standard (national) mortality schedule to the local populations, broken down by gender and age.

Our SMR passed several preliminary tests, performing more than satisfactorily at the national level (Figure 1), as well as at the regional and provincial levels (NUTS2 and NUTS3, respectively – not shown here). Encouraged by this, we decided to apply it to Italy’s municipalities, the smallest possible territorial level (De Santis et al. 2025).

Our main curiosities (… oops, sorry. We meant: our main research questions) were:

1) How large were survival inequalities at the municipal level in Italy between 2002 and 2018?

2) How did these inequalities evolve over time?

3) Was any significant breakpoint detectable during this period?

4) Which contextual factors were most strongly associated with poor survival outcomes at the municipal level?

Main results

Figure 2 allows us to address our first three questions. Small-scale mortality variability was low in Italy during the period under examination (2002–2018): the coefficient of variation (i.e. the ratio between the standard deviation and the mean) ranged between 12% and 15% (panel a). On the negative side, it showed a slight upward trend, indicating somewhat greater relative heterogeneity (i.e. inequality) towards the end of the period.

No major breakpoints emerged over the 17 years we examined. Panel b of Figure 2 indicates that, of the total variance in LAU mortality, around 80% was attributable to within-region differences, and only about 20% to between-region differences. In other words, the frequently voiced concerns about wider inter-regional disparities following the regionalisation of the Italian health system – initiated in 1992 – appear to have been overstated, at least in terms of mortality. (It should be noted, however, that mortality is not the only metric by which health system performance should be assessed, but in our case, no other measures were available.)

Here too, however, a note of caution is warranted: although modest (around 20%, as noted), the between-region component of variability showed an upward trend. In other words, regions were more diverse at the end of the period than they had been at the beginning. Unfortunately, with our data, it is not possible to determine how much of this trend is attributable to policy choices (i.e. regionalisation of the health system) and how much to other factors.

Maps and correlations

Figure 3 visualises the distribution of small-scale mortality, averaged over the period 2002–2018. As is often the case in such analyses, the researchers’ hope of discerning a clear pattern is somewhat frustrated – particularly by the fact that male and female mortality do not appear to follow the same geographical distribution.

Lower mortality, especially among females, is typically observed in eastern Italy (Trentino–Alto Adige, the southern part of Veneto, Emilia-Romagna, Marche), in the centre (Umbria and Tuscany), and in southern Sardinia. Conversely, poorer survival conditions are found in south-western Italy (particularly the area around Naples and in Sicily), in the municipalities surrounding the capital city Rome (though not in Rome itself), and – somewhat unexpectedly – in numerous municipalities in the “affluent” North-West (Liguria, Valle d’Aosta, Piedmont, and parts of Lombardy), as well as in several municipalities along the river Po (running eastwards from Turin to just below Venice). Another area of concern can be identified in the north-east, covering parts of Friuli–Venezia Giulia and the northern municipalities of Veneto.

Regression results clearly indicate that mortality during the period was higher in municipalities characterised by greater “fragility” (an ad hoc index developed by Istat) and lower affluence (Table 1). They also show clear evidence of spillover effects between adjacent municipalities: mortality is geographically correlated (as captured by SAR models – neighbouring municipalities tend to exhibit similar levels), and so are the residuals (SEM models), suggesting that unobserved factors (e.g. air pollution, local policies, etc.) influence areas that extend beyond individual municipal boundaries.

Other potentially relevant variables – among the relatively few available in our database – did not yield consistent results. For instance, we expected mortality to be higher in municipalities with smaller and older populations, or those located in mountainous areas. This was not the case, or at least not consistently so: results varied by gender and proved difficult to interpret, at least for us.

Funding

We gratefully acknowledge financial support from:

– PRIN 2022 research project “The pre-Covid-19 stall in life expectancy in Italy: looking for explanations”, (n°2022CENE9F, PRIN 2022 call DD n.104 of 02/02/2022, funded by the European Union – NextGenerationEU)

– PRIN2022-PNRR research project “Foreign population and territory: integration processes, demographic imbalances, challenges and opportunities for the social and economic sustainability of the different local contexts (For.Pop.Ter)” [P2022 WNLM7], funded by European Union – Next Generation EU, component M4C2, Investment 1.1. 

* The views and opinions expressed are only those of the authors and may not reflect those of the financing institutions.

References

De Santis G., Benassi F., Carboni G., Maltagliati M. (2025). Local mortality patterns in Italy at the beginning of the 21st century. Genus, https://doi.org/10.1186/s41118-025-00262-3

The post Small-scale mortality in Italy: 2002–2018* appeared first on N-IUSSP.

Population change is one of the defining stories of our time. Longer lives and smaller families are reshaping our societies in ways that no previous generation has experienced. These trends are often portrayed as a crisis – a “demographic winter” or an impending burden on future generations. Yet there is another way to see them. Population ageing is also a testament to human progress: we live longer, healthier, and safer lives than ever before. Demographic change is not merely a challenge to be managed, but also a success to be valued and an opportunity to be shaped.

This e-book is grounded in a positive demographic perspective – a viewpoint centred on change and adaptation, echoing key recommendations from the World Health Organization, the United Nations, and the European Commission. As emphasised by the Italian Association for Population Studies (AISP), positive demography does not mean overlooking the real challenges posed by population ageing, low fertility, or social inequality. Rather, it invites us to shift our gaze from fear and alarm towards understanding and action. It encourages us to view demographic transformation as a new phase in the history of our societies: not better or worse than before, but different, and rich in possibilities for renewal.

Italy stands at the forefront of this transition. It is among the oldest countries in the world, with one in four citizens aged 65 or over and one of the highest life expectancies globally. Fertility remains very low, and regional differences continue to shape demographic realities across the country. Yet Italy also provides an exceptional setting in which to explore the future of ageing societies. Its achievements in longevity, vibrant local welfare systems, and long tradition of family solidarity make it a “natural laboratory” for studying how to age well in an ageing society. The Italian case therefore offers lessons not only for Europe, but also for many countries now undergoing similar demographic shifts.

It is within this context that the Age-It Research Programme – Ageing Well in an Ageing Society was established. Supported by the National Recovery and Resilience Plan and the Italian Ministry of University and Research, Age-It represents one of the largest and most ambitious research initiatives on ageing ever launched in Europe. With an investment exceeding €115 million and a broad interdisciplinary partnership, Age-It brings together experts in demography, medicine, biology, economics, psychology, engineering, and the social sciences. Its aim is not only to understand the causes and consequences of population ageing, but also to design practical, evidence-based solutions for individuals, families, and institutions.

Age-It is organised into ten thematic research centres, known as “Spokes”, each devoted to a key challenge of ageing: from the demographic foundations of longevity, fertility, and migration, to the biology of ageing, the complexity of multimorbidity, and the sustainability of care systems. Other Spokes address the silver economy and the role of older adults in the workforce, the cultural and political dimensions of ageing societies, and the development of innovative technologies for health and inclusion. Cross-cutting initiatives also focus on education and lifelong learning, data management, and the transfer of research findings into public policy.

The central message of Age-It is clear: ageing should not be seen solely as a cost, but as a shared opportunity to promote well-being, equity, and sustainability. By building bridges between scientific research and policymaking, the programme seeks to foster a holistic vision of ageing, one that combines social innovation with practical action. The challenge is not merely to adapt to demographic change, but to shape it, aligning demographic trends with the broader goals of sustainable development and intergenerational justice.

This e-book draws on Age-It’s research agenda and presents a selection of the most pressing population challenges facing Italy and other ageing societies today. It explores the demography of ageing, including fertility (Ch. 2 and 3), longevity and health (Ch. 4), and migration dynamics (Ch. 5 and 6); the growing focus on frailty and resilience in later life, examining how to assess and mitigate vulnerability (Ch. 7); the demography of care, analysing how families, institutions, and communities can respond to this growing issue (Ch. 8); the silver economy, where older adults play active roles as workers, consumers, and innovators (Ch. 9); and the question of intergenerational justice, concerning fairness between younger and older generations (Ch. 10).

Each of these topics is discussed with particular attention to their policy implications. The goal is to translate demographic knowledge into actionable insights for a sustainable ageing agenda. This involves rethinking traditional indicators – such as the old-age dependency ratio – which too often convey an overly pessimistic picture, and instead developing measures that better capture improvements in health, education, and life expectancy. It also entails identifying strategies that combine adaptation – preparing for the realities of an ageing population – with mitigation – addressing the underlying drivers of demographic imbalance, such as persistently low fertility or unequal access to care.

The positive demography perspective promoted by Age-It thus offers a new way forward. It recognises that demographic change is not destiny, but a domain for policy action. Just as societies have successfully adapted to industrialisation, urbanisation, and globalisation, they can also adapt to population ageing. The key lies in foresight, inclusiveness, and a steadfast commitment to fairness across generations.

Ultimately, the story of ageing is the story of humanity’s success – and its next great challenge. With the right policies and a shared vision, it can also become the story of our renewal. This e-book invites readers to engage with that challenge, exploring how a positive and sustainable approach to demographic change can help build societies that truly enable everyone to age well – today and tomorrow.

Acknowledgements

We acknowledge funding from the National Recovery and Resilience Plan, Next Generation EU, Project Age-It: “Ageing Well in an Ageing Society”. The views and opinions expressed are only those of the authors and do not necessarily reflect those of the European Union or the European Commission. Neither the European Union nor the European Commission can be held responsible for them.
Age-It: Ageing Well in an Ageing Society

References

Alderotti, G., Cozzani, M., Barbi, E., De Santis, G., Mezzanzanica, M., Miglio, R., Paterno, A., & Vignoli, D. (in press). Positive demography: Changing the perspective on population aging from the Age-It Research Program. The Journals of Gerontology: Series B, Psychological Sciences and Social Sciences.

Leeson, G. W. (2017). Realizing the potentials of ageing. Journal of Population Ageing, 10, 315–321.

Natale, F., Bernini, A., Seiger, F., & Kajander, N. (2024). Old age does not start at 65 (JRC137524). European Commission.

United Nations, Department of Economic and Social Affairs. (2023). World social report 2023: Leaving no one behind in an ageing world. United Nations.

Vignoli, D., & Paterno, A. (Eds.). (2025). Rapporto sulla popolazione – Verso una demografia positiva [Towards a positive demography]. Bologna: il Mulino.

Vignoli, D., Albertini, M., Chiatti, C., Aimaretti, G., Boccuzzo, G., Boffo, V., Brugiavini, A., Cavallo, F., Cenci, S., Cherubini, A., Cincotti, F., D’Adda di Fagagna, F., Ferrarese, C., Galasso, V., Galeotti, E., Graziani, A., Iaccarino, G., Lattanzio, F., Lucifora, C., & Petrucci, A. (in press). Aging well in an aging society: Italy at the forefront of global aging and the Age-It Research Program. The Journals of Gerontology: Series B, Psychological Sciences and Social Sciences.

Vignoli, D., Barbi, E., & Paterno, A. (2024). La demografia dell’invecchiamento: Una lettura positiva [The demography of ageing: A positive view]. Rivista il Mulino, 73(528), 12–30.

Vignoli, D., Paterno, A., & Stranges, M. (2025). Population report 2025: Towards a positive demography. Executive summary. Population Europe.

World Health Organization. (2015). World report on ageing and health. World Health Organization.

The post Beyond the “demographic winter” appeared first on N-IUSSP.

Before the invention of pension systems, at the end of the 19th century, the idea of “sponging off society”, and being morally entitled to do so, was simply non-existent. Nowadays, instead, in all developed nations and in a growing part of the developing world, this is almost taken for granted: “I did what I had to do when I was an adult. Now that I am too old to work, it is only fair that society should support me in return for my past contribution to its prosperity”. This mode of reasoning also highlights the shrinking role of the family in supporting its oldest members, a void progressively filled by the state.

The same line of reasoning could easily be applied to children: “I am too young to work now, but I need resources. I can borrow them from society, and repay my debt later, when I am old enough to start working for the market”. In this case, more so than for the others (older people), the family is still there, of course, but the state is already progressively (and ever more massively) stepping in. Indeed, child transfers already exist in virtually all societies, but they are not explicitly included in pension systems (which, if transformed in this way, should probably be relabelled “intergenerational transfer systems”, or something of the kind). Their purpose is two-fold: first, societies want to make sure that children are properly looked after (receive medical attention, go to school, etc.) and, second, societies wish to alleviate the cost of raising children, which is particularly high in a context where children cannot work before reaching adulthood, cannot be left alone, and must receive adequate education and medical care. Besides, when they are finally in a position to earn some money, children rarely use it to repay their parents but rather to build their own life. On top of that, their parents’ pension will normally be provided by the state so the children do not have to worry about their financial welfare.

Not surprisingly, fertility has drastically declined all over the world. Of course, the reasons are much more complex than I can possibly discuss here, but rising costs and declining economic benefits for parents are certainly among the causes. Public subsidies are one of the proposed solutions for addressing this problem. A moderate amount of child benefits within pension systems would simply work towards the same goal: that of sustaining fertility. Incidentally, even ideal pension systems, such as IPAYGO (De Santis 2024a,b), undergo considerable stress if births decline too much: preventing this outcome is in the best interest of pension systems themselves.

The disadvantages of including child benefits in pension systems

The disadvantages of a reform of this type (introducing child benefits in pension systems, with resources taken from payroll contributions) are obvious: as the number of beneficiaries increases (older persons and children), either payroll contributions must rise, or pensions must decrease, because a balanced budget is, or at least should be, one of the guiding principles of all pension systems. In the former case working adults have to bear the additional cost; in the latter, pensioners do. And if a compromise is struck between these two extremes (somewhat higher contributions and slightly lower pensions), the entire electorate will be worse off in the short run, an outcome which considerably reduces the likelihood of a reform of this type ever being adopted.

The advantages of including child benefits in pension systems

However, there are also a number of advantages. There is, for instance, the pro-fertility argument discussed earlier, which would bring with it some simplification, because other forms of delayed parental support usually embedded in pension systems could then be abolished (e.g. earlier retirement or extra pension benefits). There would also be an implicit redistributive component, from the rich to the poor, if child benefits are flat (same for every child) but contributions proportional to labour income.

Beyond that, two more advantages are particularly worth mentioning. The first is that this arrangement would help to stabilize the required (or equilibrium) payroll contribution rate. When societies grow older, there are more seniors around, but fewer children, and although the positive and negative components of the transfer system would not precisely match each other, at least they would move in such a way as to attenuate oscillations, making the system easier to manage.

The second advantage is that in PAYGO pension systems, a mysterious mechanism is at work, whereby population increases lead to (“quasi capital”) gains while population declines create (“quasi capital”) losses. As developed countries are facing population declines, or are about to face them, losses are currently much more worrying than gains. One of the causes of these imbalances is the difference between the average ages at receiving and paying transfers. Without child benefit, this difference is in the order of 30 years (if, for instance, contributions are paid at age 40 on average and pensions received at age 70 on average). The formula is not given here (you can find it in De Santis 2024a), but suffice it to say that this age difference enters multiplicatively into the calculation, and therefore has a very strong impact.

Introducing non-trivial child benefits would drastically reduce the average age at receiving transfers and would thereby also reduce this age difference. If this age difference declined to zero, no quasi-capital gains and losses would ever affect the functioning of pension systems, and the “demographic time bomb” would be totally defused. Nobody thinks that we should go that far, but there are strong grounds for arguing that a few steps in that direction could (and perhaps should) be taken.

In my view, these two benefits are strong enough to make it worthwhile to at least start considering the possibility of introducing child benefits in modern PAYGO pension systems. although the majority of readers may not agree.

Funding

I acknowledge funding from Next Generation EU, in the context of the National Recovery and Resilience Plan, Investment PE8—Project Age-It: “Aging Well in an Aging Society’. This resource is co-financed by the Next-Generation EU (DM 1557 11.10.2022). The views and opinions expressed are my own and do not necessarily reflect those of the funding agencies.

References

De Santis G. (2024a) Demography, Economy and Policy Choices: The Three Corners of the Pension Conundrum. Statistics, Politics, and Policy, online first. https://doi.org/10.1515/spp-2023-0013 

De Santis G. (2024b) Threshold ages and AAM in PAYGO pension systems, Neodemos.

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Pension systems serve several purposes, the most important of which is probably to protect citizens from the risk of surviving to old age with consumption needs but no, or insufficient, income. However, there are also other objectives (e.g. “rewarding” past commendable behaviours, such as paying contributions, serving the country, or performing arduous or dangerous jobs) and various constraints, especially a balanced budget, which is particularly difficult to guarantee in a world where commitments are long term, but the demo-economic scenario keeps changing unpredictably.

This contradiction can be resolved in one of two ways:
1) Either some kind of pension arrangement is set up, which seems to work decently for the time being. However, despite inevitable bombastic assertions as to its “historical” character, everybody knows that things will soon get out of balance, and a change of rules will prove necessary, at the very least to address population ageing, probably the single most important driver of disequilibrium in the long run; 

2) Or a self-adjusting pension system is set up, one that can always work, come what may, and guarantee certain predefined (and important) results.

Solution #1 is, by far, the one that societies seem to prefer in practice. Indeed, it is the only one that has ever been adopted, to the best of my knowledge, and it is also the reason why changes in pension legislation are so frequent (see e.g. OECD 2023, pp. 63 onwards). We are so used to this type of setup that most observers think that no other way is possible, in part because no viable alternative is in sight and in part because they deem that things may, in fact, be better like this. After all, all over the world, it is normal for rulers (parliaments, governments…) to issue all sorts of new laws, reflecting in part current circumstances and in part the prevailing “spirit of the age”. Why should pension laws be any different, and based on rules written in stone?

The specificity of pensions systems

Well, there are indeed a couple of reasons why pension laws should be revised rarely, if ever, and both relate to the fact that the Lexis diagram is constantly at work in this field (Figure 1). In pay-as-you-go (PAYGO) pension systems, the only type discussed here, money flows cross-sectionally: each year contributions are paid (by working adults) and converted into pension benefits, received by retirees (red arrow). However, and here comes the black arrow, subjective expectations, individual balance sheets of contributions and benefits, and the very spirit of pension laws act longitudinally: “Pay this now, you will receive that in the future”. Rule changes renege on past promises, and, while necessary to keep the system on track (with an approximate balance between income and expenditure), they undermine people’s trust in the institution and, ultimately, in democracy. This will result in protests; hopefully, they may be peaceful ones, but voters will remember this “betrayal” at the next elections.

Besides, it’s not just about breaking promises: there is also a serious issue of (inter)generational equity and equality, and this is the second reason why pensions rules should not be changed without very good reason.

Ignoring, for the sake of simplicity, the problem of the changing value of money (e.g. due to inflation), or, if you prefer, reasoning in “real terms”, let us imagine that the members of a given birth cohort paid 100 in contributions during their active lives. Upon retirement, they should receive a pension of 100, and get back what they gave. However, if pension rules need to be revised (and benefits cut), one or more birth cohorts will receive less than their due and thus be treated unfairly, both within the cohort itself and in comparison with others. For each fiscal year (red arrow) everything seems to be well-balanced, but longitudinally (black arrow) this may result in unwarranted gains for some birth cohorts and losses for others.

What’s the alternative? AAMs and PAYGO

The alternative to rule-changing is to set a self-adjusting system, with constant rules that determine, in a known way, the necessary changes in the key variables of the system. Note that this idea does not exclude the possibility of ad-hoc adjustments: it merely makes them more rarely necessary (if at all).

And this notion does not come “out of the blue”: it expands on the recently developed idea of introducing automatic adjustment mechanisms (AAMs) in pension systems, aiming to resolve specific problems (e.g., what to do with retirement age when survival improves). The difference is that, while each AAM can address only one specific issue, a series of them could address many more – ideally all of them.

Of course, AAMs have their downsides. The first is that control seems to be lost over the dependent variable, the one that is automatically adjusted when the corresponding independent variable changes. In the previous example, retirement age will respond automatically to changes in life expectancy, and the result may be socially undesirable, e.g. increases may be too fast or, over time, excessive. In fact, the objection is much less compelling than it seems, because policy makers can always change the law if such tendencies emerge, which is no worse than having to operate in the absence of AAMs.

In all cases, most observers doubt that everything can be automatically adjusted and kept under control, given the large number of variables at play in the pension field: unemployment, labour productivity, inflation, population ageing, migration, etc. 

IPAYGO

In fact, sometimes dreams (or nightmares?) come true. I recently proposed an improved version of a PAYGO pension system, called IPAYGO, which does precisely this: it predefines what society deems important (balanced budget, a certain trade-off between actuarial equity and redistribution towards the less well-off, etc.) and then adjusts automatically to obtain precisely the desired results, in all possible demo-economic scenarios (De Santis, 2024). 

I have run out of space, and I cannot even start to describe my proposal, here. Let me just underline this: each year (cross sectionally, red arrow in Figure 1) the working population produces something, which can be thought of as a pie. Then, separately, there are claims on this pie, some of them deriving from “previous merits” (i.e. pensions: black arrow in Figure 1). A pension system is merely a complex transfer game, where nothing is produced, claims cannot exceed production in any given year, pensions take away resources from those who produced them, and the size of the pie is not known in advance.

The best that can be done in this context, in my opinion, is to predefine a set of rules on the socially acceptable relative share of this pie that can be allocated to all claimants, in such a way that claims sum up to 100%. No disequilibrium is ever possible (claims sum to 100 and therefore coincide with production), and the final result (who gets what) is the one that society has previously agreed upon.

Unfortunately, this is not how any actual pension systems function, and this is why imbalances emerge, deficits accumulate, and (painful) adjustments are frequently needed.

Funding

I acknowledge funding from Next Generation EU, in the context of the National Recovery and Resilience Plan, Investment PE8—Project Age-It: “Aging Well in an Aging Society”. This resource is co-financed by the Next-Generation EU (DM 1557 11.10.2022). The views and opinions expressed are my own and do not necessarily reflect those of the funding agencies.

References

De Santis G. (2024) Demography, Economy and Policy Choices: The Three Corners of the Pension Conundrum. Statistics, Politics, and Policy, online first. https://doi.org/10.1515/spp-2023-0013OECD (2023)  Pensions at a glance 2023, Paris.

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Demographers have long debated about the main drivers of population ageing. The contest is really only between fertility and mortality, as migration is known to play but a minor role, with the exception of small and peculiar realities (e.g. the United Arab Emirates in 2022 – Figure 1), and even then, usually just for limited periods of time.

The two opposing points of view are well summarized in these words:

Why are populations of rich countries so much older today than they were a century or two ago? … Common sense suggests that longer life is responsible for population ageing, but decades ago the work of demographers such as Coale (1956, 1957) and Keyfitz (1975) persuaded us that in fact fertility decline was more important. More recently, a new wave of demographic analysis suggests that mortality decline is the main demographic source of continuing population ageing.  Here we … consider this possibility, but reject it.
(Lee and Zhou, 2017, pp. 285-287).

What do you think about it? And why should non-demographers care?

Levels and variations of old age measures

We recently addressed this issue in two distinct publications (De Santis and Salinari 2023, 2024). Our main conclusion is much closer to the “common sense” evoked in the above quote: the culprit of population ageing is longer average life spans. This is not particularly surprising, all in all, because the reason we can observe individuals living up to 40 years, for instance (which is not the case for mosquitoes), but not to the age of 1,000 (as it happens with sequoias), is that human mortality has certain characteristics, which translate into and shape population age structures (De Santis and Salinari, 2024).

Note, however, that there is a small ambiguity in this debate. Imagine you are interested in measuring the depth of the water of a large lake, where waves are frequent and high. Your indicator, an anchored cork in this example, will go up and down “erratically”, and predictions on direction and amount of change between two short-spaced instants depend on whether the cork is currently on the peak or a trough of a wave, how big the wave is, and when the next waves are due. On average, however, the cork will indicate the “normal” water level in that lake, which depends on the underlying structural and climatic conditions (e.g. average rainfall and its tendency to increase or decrease over time).

If we transpose this metaphor to the field of demography we can think of waves as “short-term disturbances” to the lake surface (=population age structure) produced by changes in number of births and, to a lesser degree, migrants. However, the underlying tendency towards higher or lower water level is due to more structural conditions (average rainfall).

Let us consider the average age as an indicator of population ageing. Alternatives are possible, of course, such as the old age dependency index, OADI, but nothing changes in practice (not shown here). We can calculate two versions of the average age. The “standard” one, A_t, is based on the observed population of age x at time t, P_x,t. The “reference” one, A*_t, is calculated instead on the stationary population at time t, L_x. As the stationary population derives from a (period) life table, which depends only on survival conditions (not fertility or migration), we can use it as a measure of what would happen to ageing (as measured by the average age A_t) if only current mortality mattered. All the rest (previous mortality, fertility and migration) goes into the difference between the expected (by us) value A*_t and the observed value At. 

Figure 2 shows what can be observed in practice in a few selected developed countries, over a long time span. If the “waves” (fertility, migration and past mortality) had no effect on the observed age structure, all the points would lie on the bisector, where A_t=A*_t, and where, in other words, 100% of ageing depends on survival. Moving rightwards (from low to high survival, so from low to high levels of ageing in the corresponding stationary populations), we observe instead that points move “in waves”. There is a rather large trough when A* is about 36 years (corresponding to an average length of life e₀ of some 55/60 years), which historically occurred in the middle of the demographic transition – a very impressive wave indeed. For higher values of A*, however, after the end of the demographic transition, and up to today, things tend to return to “normal”. Our prediction is that new waves will hit the lake (starting from the ongoing second demographic transition), with new peaks and troughs, that will propagate and intersect with occasional new disturbances of all kinds. But A*_t will always be a fairly good guide to the expected value of At.

Why should non-demographers care about all this?

If the age structure has effects on the socio-economic life of a community, understanding what drives its levels and change, both in the short and in the long run, matters for everybody, not just for population specialists and their academic discussions. The demographic bonus and pension systems, for example, are two areas where these ideas could potentially be applied.

The demographic bonus (or dividend, or window of opportunity) has traditionally been defined as a phase of “structural improvement” over a recent past. For instance, Lee and Mason (2010, p. S159) call it a “phase of … three to five decades … [when] support ratios rise well above their pre-transition levels”. We suggest that the comparison be made also with the stationary age structure of that time. This means that a demographic dividend can be considered to exist as long as the observed age structure is “better” than that of the corresponding stationary population. 

Let us refer to Figure 3, for instance. Due to its impressive fertility decline, China’s old-age dependency index (OADI) started to worsen (i.e. to increase) in the 1980s, and is now deteriorating rapidly. However, until about 2036, the observed OADI in China will be lower than its standard equivalent OADI*. In our interpretation, this means that, until then, China will still benefit from a demographic bonus, which means that it will be younger than what would be expected, based on survival level, although this advantage will shrink progressively as time passes. 

Most pension systems have been reformed (retrenchments) in recent years due to population ageing. In several cases, these adjustments have been  pegged to recent life tables and current survival conditions, and translate into higher retirement ages, or lower pension benefits (because retirees are expected to draw resources from the system for a longer period of time). Our findings suggest that this is the right approach, that it should become more widespread and systematic than is currently the case, and that it should be explicitly presented to the general public as a (comparatively small) price to pay for the great success of ever longer survival that benefits everyone. 

Funding

We acknowledge co-funding from two sources: 1) Next Generation EU, in the context of the National Recovery and Resilience Plan, Investment PE8—Project Age-It: “Aging Well in an Aging Society’. This resource is co-financed by the Next-Generation EU (DM 1557 11.10.2022). 2) the Italian MUR (PRIN 2022–No. 2022CENE9F, “The pre-Covid-19 stall in life expectancy in Italy: looking for explanations”). The views and opinions expressed are only those of the authors and do not necessarily reflect those of the funding agencies.

References

Coale A.J. 1956. The effects of changes in mortality and fertility on age composition, Milbank Memorial Fund Quarterly, 34:79–114.

Coale A.J. 1957. How the age distribution of a human population is determined, Cold Spring Harbor Symposia on Quantitative Biology, 22.

De Santis G., Salinari G. 2023. “What drives population ageing? A cointegration analysis”, Statistical Methods and Applications, 32:1723–1741. https://doi.org/10.1007/s10260-023-00713-1

De Santis G., Salinari G. 2024. Long-term impact of mortality on population age structures. International Journal of Population Studies, https://doi.org/10.36922/ijps.377 

Keyftz N. 1975. How do we know the facts of demography?, Population and Development Review, 1 (2): 267–288. 

Lee R., Zhou Y. 2017. Does fertility or mortality drive contemporary population aging? The revisionist view revisited. Population and Development Review, 43:285–301. https://doi.org/10.1111/padr.12062. 

United Nations. 2022. World Population Prospects: The 2022 Revision. New York, United Nations Publications.

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The age-old dispute about whether funding is better or worse than pay-as-you-go (PAYG) as a public pension arrangement has subsided in recent years. With funding, birth cohorts save in their adult years, forming a capital (or reserve fund) that they consume little by little in old age. With PAYG, the currently employed pay the pension benefits of previous birth cohorts, and hope that future generations will return the favour.

Once PAYG is in place, however, reverting to funding has proved practically impossible, because the adult population would be required to pay twice, both for those who are currently old (under PAYG) and for themselves (under funding). This probably explains why the current debate has instead focused on the best possible PAYG arrangement, in most cases suggesting small (“parametric”) improvements to existing systems (OECD 2019, 2020; Chamie 2021). Some authors, however, believe the NDC (or notional-defined contribution) system to be markedly better than other PAYG schemes, and hope that this arrangement will soon be adopted more widely, beyond the handful of countries – Sweden, Italy, Latvia, Norway, and Poland – that currently use it (Holzmann 2006, 2017).

The rationale of NDC

Very schematically, NDC works as follows. Each contribution paid into the pension system, while not saved (that would be funding), is earmarked. These series of contributions, duly revalued (old payments cannot be directly compared to recent ones), form the “virtual capital” K_s (s for senior) of pensioners at the time of their retirement. As retirement takes place at a known age β (say, 65 years) and life expectancy at age β, or e_β, is also known, the initial annual pension of each senior s can be calculated as

1) P_s=K_s/e_β.

and then revalued over time for inflation. For instance, those who retire with a virtual capital of €200,000 and have a residual average length of life (as pensioners) of 20 years, will receive an initial annuity of €10,000. 

To be sure, the actual formula is more complicated than eq. (1), for various reasons: anticipated revaluation of the virtual capital, survivor provisions, floors and ceilings, possibility of early retirement for several categories, etc. But let’s not lose the main thread, here.

NDC: pros and cons

NDC pension systems provide a solution to the problem of how to calculate individual pension benefits in a world with no “real” private savings: they treat past contributions as money saved in an investment fund, with a predetermined rate of return, usually linked to the evolution of the economy, such as per-capita income. The transparency and actuarial equity that characterize the system typically represent substantial progress over previous or alternative solutions. Even NDC systems, however, have their shortcomings. The most important are:

1) Eq. 1 gives the initial amount of the pension benefit, but no “natural” solution exists as to how this should evolve over time. Besides, if survival conditions change (usually: improve), pensioners who retire today will spend more than the expected e_β years in retirement, but this is nowhere taken into account.

2) Well-functioning pension systems tend to depress fertility, because they deprive children of part of their utility as an economic support to parents in old age. However, no counterbalancing mechanism is foreseen, although, again, in practice, ad hoc arrangements may be, and in fact are, introduced to “compensate” parents (mothers, mostly) for the time they invest in rearing children.

3) The “natural” consequence of longer life spans is lower pensions (see eq. 1). Retirement age can be (and in fact is) raised, but with ad hoc solutions.

4) Redistribution towards the poor gets lost (although in practice it is often reintroduced via ad hoc arrangements). Yet, protecting the old from poverty is one of the very reasons why public pension systems exist. This shortcoming is all the more disturbing because the poor die earlier, and this differential mortality has anti-redistributive effects: if uncorrected, the system takes from the poor and gives to the rich. 

5) Expenditures and revenues of the pension system do not coincide, either in each single year or over the long run. Ad hoc adjustments are needed to ensure budget balance.

Is there anything else? IPAYG, maybe…

The IPAYG, or improved pay-as-you-go pension system, is a possible alternative (De Santis 2021). Its details are too long and complex for this article, but its general philosophy is simple: “everything is relative”, and therefore nothing can be predetermined in absolute terms. Real-life pension systems adopt a different philosophy: they fix rules by law, but then change the law when the system proves unviable. IPAYG, instead, never needs to change the law (although this can be done), precisely because all of its “laws” are relative.

In terms of money, for instance, all values are pegged to a numeraire (the average net labour earnings of the adult population of that year) that reflects the current economic situation of society. In prosperous years, this numeraire will increase, inflating pensions (and possibly also child benefits, which can be introduced in the system); in years of scarcity, it will decrease. Besides, a specific parameter explicitly regulates the relative degree of actuarial equity and redistribution that societies prefer, and clarifies that the two are alternatives: the greater the former, the lesser the latter.

Demography matters (also in IPAYG)

In demographic terms, IPAYG offers the possibility of introducing (moderate) child benefits to sustain fertility and reduce the anti-natalist effect of pension systems.

As for survival, two threshold ages are relevant: α (separating youth from adulthood) and β (retirement age). In both cases, IPAYG assumes that it makes more sense to predetermine the shares of life that an average individual should spend in childhood, adulthood and old age: therefore, both α_t and β_t become variables that depend on current survival conditions, and that move in such a way that these shares remain constant.

Let us take the Italian case as an example. Four life tables (age profile of the corresponding stationary populations) are drawn in the left panel of Figure 1 (the right panel displays the corresponding real populations), between 1900 and 2019. 

Assuming that the preferred shares are (and remain) Y*=20% (average share of life spent as a youth), S*=20% (average share of life spent as a senior), and therefore A*=60% (average share of life spent in adulthood), in 1900, the correct threshold ages can be calculated as α₁₉₀₀=11.5 years and β₁₉₀₀=54 years (Figure 2).

When applied to the actual population, these threshold ages α_t and β_t produce the actual shares of life as a youth Y_t, adult A_t and senior S_t displayed in Figure 3 – not coinciding with the target values Y*, A* and S*. This, however, is just natural: Y_t, A_t and S_t vary over time, with a long-term average that is (very close to) Y*, A* and S*, respectively, but they do not necessarily coincide with their “reference” values in a given year. 

Table 1 gives an idea of how these threshold ages must evolve over time (in this case, between 1900 and 2020, in Italy), if preferences about Y*, A* and S* do not change, but survival does, with e₀ (average length of life) rising from 41.9 years to 83.2 years (both sexes). The lower bound of adulthood (α) passes from 11.5 years to 16.7 years, while its upper bound (β, or retirement age) increases from 54 years to 68 years.

These dynamic adjustment of the threshold ages keeps the system on track. The basic message is that had IPAYG been applied in Italy in 1900, it could still be in force 120 year later, with unchanged rules, despite the huge economic and demographic variations of the period.

Not too bad, for a pension system.

Funding

This work was supported by the Italian MIUR through the JPI MYBL / CREW Project (Joint Programme Initiative: More Years Better Life, 2016 Call. CREW: Care, retirement and wellbeing of older people across different welfare regimes». MIUR Decree: n. 3266/2018; Official Bulletin no. 32, 7 Feb 2019).

References 

Chamie J. (2021) America’s retirement riddle, N-IUSSP, 17 May,

De Santis G. (2021) An improved pay-as-you-go pension system, in comparison with a notional defined contribution system: demographic, economic and policy characteristics. DiSIA Working Paper 2021/08, . 

Holzmann R. (2006). Toward a Coordinated Pension System in Europe: Rationale and Potential Structure. In R. Holzmann and E. Palmer (Eds.), Pension reform: issues and prospect for nonfinancial defined contribution (NDC) schemes. Washington, DC: The World Bank, 225–265

Holzmann, R. (2017). The ABCs of Nonfinancial Defined Contribution (NDC) Schemes. IZA Policy Paper No. 130.

OECD (2019) Pensions at a Glance 2019

OECD (2020) Pension Outlook 2020

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Health declines in old age, but this process is accelerated if adulthood is affected by one or more adverse events, in terms of unemployment, financial hardship, stress, or bad health.How relevant are these episodes and what is the role played by persistence and coexistence of disadvantages? The former refers to the duration of periods when individuals are “under pressure”, not necessarily because of the same types of problems. The latter focuses on periods of “multiple disadvantages” (simultaneous occurrence of adverse events) and, again, on their duration.

Data, variables, and method

In a recent paper, we measured these effects on data taken from SHARE, the Survey of Health, Ageing, and Retirement in Europe (2004‒2017) (Zanasi, De Santis and Pirani, 2021). Our dependent variable is frailty in late adulthood, at ages 60‒64, 65‒69, 70‒74, and 75‒79. Frailty is measured with a 40-item index validated on the SHARE dataset measuring the number of health, or health-related, deficits reported by respondents. The index is normalized, so as to range between 0 (perfect conditions) and 1 (presence of all the 40 possible deficits). In practice, however, the observed maximum of the index is much lower (less than 0.5), and its average is relatively low, between 0.11 and 0.16 (Table 1).

Our main independent variables refer to the disadvantages suffered in working-age adulthood (ages 25 to 59 years) in four domains (unemployment, financial hardship, severe stress, and bad health). For each disadvantage, we computed a variable describing its persistence in working-age adulthood, i.e. the share of life (between ages 25 and 59 years) spent with a certain disadvantage: never, occasionally, or frequently, where occasionally means between 1% and 24%, and frequently mean 25% or more. Beyond that, we also measured the frequency of coexisting disadvantages, that is, the share of working-age adult life that an individual spent with two or more disadvantages simultaneously. We classified these occurrences as before: never, occasionally, or frequently.

We used a set of linear regressions, which we ran both with and without covariates, to better grasp the role played by the persistence and coexistence of disadvantages. Here, however, we will refer exclusively to the simple case, without covariates. 

The legacy of the past 

Figure 1 shows the predicted evolution of frailty by age under the assumption that no other disadvantage was experienced in adulthood (linear regression, no covariates). Frailty increases with age: this holds also for individuals who never experienced any disadvantage in adulthood, but levels and slope reflect past adverse events. The worst case emerges in relation to past health conditions (illness). For instance, those whose health was frequently poor at working ages (more than 25% of the time) have a frailty index (0.17) at ages 60‒64 years, which is higher than that of consistently healthy people at much older ages, between 75 and 79 years (0.15).

Let us look at things from a slightly different perspective. Instead of measuring frailty in “percentage points”, let us transform it into “years of life lost”. On average, frailty increases by 5 points over 15 years, from ages 60-64 to ages 75-79 (first line of Figure 1), i.e. by approximately 1/3 of a point every year. Therefore, a frailty difference of merely one percentage point corresponds to some three years of life; five percentage points correspond to 15 years of life, etc. People who had difficult experiences in working-age adulthood may well have the same chronological age as the reference group (no disadvantage, ever) but, physiologically, they turn out to be 3, …, 15, … ny ears older. 

More disadvantages, worse outcomes

Turning to coexisting disadvantages, the upper panels of Figure 2 show the most common disadvantage profiles for individuals who experienced simultaneous disadvantages (financial hardship and stress) only occasionally (1–25% of their working-age adult lives). The bottom panels, instead, show the most common profiles for those who experienced coexisting disadvantages frequently, that is more than 25% of their working-age adult life: stress and financial hardship (bottom left), illness and stress (bottom right). The latter situation appears to be particularly harmful. For example, while the “independent” experience of frequent stress is associated to a frailty score of roughly 0.16 (Figure 1) by the age of 75-79 years, the combination with financial hardship raises it to 0.20 (bottom left panel of Figure 2). The worst case concerns individuals who were both sick and under severe stress at the same time (frequently, during their working-age adult years): their frailty index starts very high (0.20 at age 60-64) and reaches 0.25 by ages 75-79.

Conclusions

Frailty inequalities may be profound and, although this is frequently overlooked, they depend on the individual life course, which means that poor conditions tend to persist up to late in life. All the disadvantages suffered in the past, in all life domains (at least those that we could consider here) matter, but it is their cumulative effects that turns out to be particularly harmful. This calls for early action against disadvantages, if the goal of “active ageing” is to be pursued. 

Funding

This work was supported by the Italian MIUR through the JPI MYBL / CREW Project (Joint Programme Initiative: More Years Better Life, 2016 Call. CREW: Care, retirement and wellbeing of older people across different welfare regimes. MIUR Decree: n. 3266/2018; Official Bulletin no. 32, 7 Feb 2019).

References 

Ben-Shlomo, Y., & Kuh, D. (2002). A life course approach to chronic disease epidemiology: Conceptual models, empirical challenges and interdisciplinary perspectives. International Journal of Epidemiology, 31(2), 285–293.

Corna, L. M. (2013). A life course perspective on socioeconomic inequalities in health: A critical review of conceptual frameworks. Advances in Life Course Research, 18(2), 150–159.

Heap, J., Lennartsson, C., & Thorslund, M. (2013). Coexisting disadvantages across the adult age span: A comparison of older and younger age groups in the Swedish welfare state. International Journal of Social Welfare,22(2), 130–140.

Kuh, D., Ben-Shlomo, Y., Lynch, J., Hallqvist, J., & Power, C. (2003). Life Course Epidemiology. Journal of Epidemiology and Community Health, 57(10), 778–783.

Lu, W., Benson, R., Glaser, K., Platts, L. G., Corna, L. M., Worts, D., … Sacker, A. (2017). Relationship between employment histories and frailty trajectories in later life: Evidence from the English Longitudinal study of ageing. Journal of Epidemiology and Community Health, 71(5), 439–445.

Romero-Ortuno, R., & Kenny, R. A. (2012). The frailty index in Europeans: association with age and mortality. Age and Ageing, 41, 684–689.

Stolz, E., Mayerl, H., Waxenegger, A., Rásky, E., & Freidl, W. (2016). Impact of socioeconomic position on frailty trajectories in 10 European countries: Evidence from the Survey of Health, Ageing and Retirement in Europe (2004-2013). Journal of Epidemiology and Community Health, 71(1), 73–80.

SHARE – Survey of Health, Ageing and Retirement in Europe

Zanasi, F., De Santis, G., Pirani E. (2021) Frailty in late years: the legacy of coexistence and persistence of disadvantages in working-age adulthood, DiSIA Working Paper 2021/09, https://local.disia.unifi.it/wp_disia/2021/wp_disia_2021_09.pdf.

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Despite a large body of literature, the relationship between retirement and health is far from clear. The loss of a sense of belonging and usefulness, or the reduction of intellectually engaging activities may make retirement harmful for health (Behncke 2012, Bonsang et al. 2012; Mazzonna and Peracchi 2012), whereas relief from work-related stress may instead trigger a health-enhancing mechanism (Coe and Zamarro 2011; Eibich 2015; Kesavayuth 2014, Kolodziej and García-Gómez 2019). The relationship may also depend on other variables, such as family circumstances, job-related characteristics, or institutional settings, which in turn may affect the transition to retirement. Needless to say, this complicates considerably the identification of causal links (Whitley and Popham 2017).

Definitions matter. Strictly speaking, retirement is a formal transition from employment to the status of retiree, with the receipt of a pension benefit after reaching a predetermined threshold age. However, other reasons may push people out of the labour market. These include job loss or the need (or desire) to do something else, such as taking care of sick relatives, although separating the various cases may be problematic.

Endogeneity is another big issue, because retirement is not independent of previous health conditions and “reverse causality” (health affecting retirement, rather than vice versa), if not properly controlled for, may invalidate all conclusions (Behncke 2012; Bonsang et al. 2012; Eibich 2015; Mazzonna and Peracchi 2012).

SHARE and PSM

Using the longitudinal Survey of Health, Ageing and Retirement in Europe (SHARE, waves 1, 2, 4, 5 and6, 2004-05 to 2014-15), in a recent paper we focused on 15 countries that participated in at least two consecutive waves (Austria, Belgium, Czech Republic, Denmark, Estonia, France, Germany, Greece, Italy, the Netherlands, Poland, Slovenia, Spain, Sweden, and Switzerland) (Pirani, De Santis and Zanasi 2021). We retained only respondents aged between 50 and 70 years who were interviewed twice, and who were employed at wave w,and no longer employed (cases) or still in employment (controls) at wave w+1.

We applied a propensity score matching approach (PSM), to identify the causal effect of leaving the labour market on various health measures, using a large set of individual information. In short, we estimated the health effect of discontinuing one’s work (“treatment”) by comparing two groups of individuals who were as similar as possible in all observed respects (includinghealth) at wave w, before retirement, but who followed two distinct paths later on, and were found in different states at wave w+1: some exited the labour market (treated individuals, or cases) and others did not (matched individuals, or controls).

We evaluated the health differences between those who did and did not leave the labour market using three health outcomes measured at wave w+1:

• the frailty index(a composite measure including objective and subjective aspects, Romero-Ortuno and Kenny 2012);
• the Euro-D scale(Prince et al. 1999), a commonly used and reliable measure of depression, and
• self-perceived health.

Initially we included all leavers, i.e. all those who had left the labour market between two consecutive waves for anyreason. Then, we focused on two separate sub-groups:

(i)Retirees: those who formally retired between two consecutive waves;

(ii)Drop-outs: those who stopped working between two consecutive waves, while not formally retiring.

(A third, small and highly selected category – the Sick– we left aside.)

Results

Table 1 reports the estimates of the ATT (Average Treatment on the Treated) effects on the three health outcomes, according to the different definitions of retirement considered. A positive sign means that treated individuals exhibit worse health conditions than controls, thus revealing a damaging health effect of exiting the labour market.

With regard to the comprehensive group of leavers, exiting the labour market between 50 and 70 years of age seemsto cause a general deterioration of health, especially in terms of frailty, although the magnitude of the effects is small. Closer scrutiny, however, reveals that this negative effect depends on the route out of the labour market. Formal retirement (group i) does not appear to affect health in our sample, in either way (a slightly worsening effect, significant at 10% level, can be detected only for the frailty index of men). Conversely, those who leave their job not because of formal retirement but for other reasons, i.e. the drop-outs(group ii), later have worse health outcomes, regardless of the health indicator considered. Withdrawing from the labour market because of unemployment or for family reasons generally leads to higher frailty levels, a higher number of depressive symptoms, and poorer self-reported health.

Conclusions

Trying to overcome the typical intrinsic limitations of observational studies in this field, which we did as best we could with a PSM approach, we show that leaving the labour market in late midlife (between 50 and 70 years of age) is a more complex transition than it may seem. Loose definitions of retirement, such as “all those who, past a certain age (e.g., 50 years), leave their occupation for whatever reason” may lead to wrong conclusions. In fact, formal retirement itself has virtually no effect on health, either good or bad. This is good news, in our opinion: health concerns cannot be used as an argument against postponing the retirement age in case of need, for instance to safeguard pension systems.

Rather, increased efforts should be devoted to keeping older people at work, even when difficulties arise, either in the labour market (unemployment) or in the family sphere. Those who quit their job for these reasons end up being worse off – at least, shortly after retirement, the only effect that we could measure here. Our findings are largely independent of the health indicator considered: all of them signal a worsening of health following withdrawal from the labour market for reasons other than formal retirement.

Funding

This work was supported by the Italian MIUR through the JPI MYBL / CREW Project (Joint Programme Initiative: More Years Better Life, 2016 Call. CREW: Care, retirement and wellbeing of older people across different welfare regimes. MIUR Decree: n. 3266/2018; Official Bulletin no. 32, 7 Feb 2019).

Main references

Behncke, S. (2012). Does retirement trigger ill health? Health Economics, 21, 282–300.

Bonsang, E., Adam, S., & Perelman, S. (2012). Does retirement affect cognitive functioning? Journal of Health Economics, 31(3), 490–501.

Coe, N. B., & Zamarro, G. (2011). Retirement effects on health in Europe. Journal of Health Economics, 30(1), 77–86.

Eibich, P. (2015). Understanding the effect of retirement on health: Mechanisms and heterogeneity. Journal of Health Economics, 43, 1–12.

Kesavayuth, D., Rosenman, R. E., & Zikos, V. (2018). Retirement and health behaviour. Applied Economics, 50(54), 5859–5876.

Kolodziej, I. W. K., & García-Gómez, P. (2019). Saved by retirement: Beyond the mean effect on mental health. Social Science & Medicine, 225(August 2018), 85–97.

Mazzonna, F., & Peracchi, F. (2012). Ageing, cognitive abilities and retirement. European Economic Review, 56(4), 691–710.

Pirani, E. De Santis, G. & Zanasi, F. (2021) Retirement? Other ways out of the labour market are far more worrying for health. Results from a matching approach study, DiSIA Working Paper 2021/10, https://local.disia.unifi.it/wp_disia/2021/wp_disia_2021_10.pdf.

Prince, M., Reischies, F., Beekman, A. T. F., Fuhrer, R., Jonker, C., Kivela, S.-L., et al. (1999). Development of the EURO–D scale – a European Union initiative to compare symptoms of depression in 14 European centres. British Journal of Psychiatry, 174(4), 330–338.

Romero-Ortuno, R., & Kenny, R. A. (2012). The frailty index in Europeans: association with age and mortality. Age and Ageing, 41, 684–689.

Whitley, E., & Popham, F. (2017). Leaving the labour market later in life: How does it impact on mechanisms for health? Occupational and Environmental Medicine, 74(12), 877–886.

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Cultural distances are frequently evoked, especially by researchers who need to justify what their models cannot explain, but they are very hard to define and measure. When it comes to groups (in our case national groups: Italians and foreigners of various nationalities living in Italy) the very notion of cultural distance may be based on the false assumption of internal homogeneity.

Not very clear, is it? OK, let us try with an example. Consider Figure 1 and assume that the five cultural clusters ‘make sense’, i.e. that they correctly identify five different cultural typologies. Note that we ignore the cultural characteristics of each of these typologies, and we do not need to know them, at least initially. On the y axis we read proportions: among Italians (from the South and from the two main Islands) about 30% of respondents are of type A, ~25% are of type B, … and ~17% are of type E. As the proportions are approximately the same in the two cases, we conclude that the two groups of Italians are very similar, although not internally homogeneous.

Conversely, the Chinese living in Italy have a markedly distinct distribution of individuals across typologies: only about 4% are of type A, ~28% of type B, … and as many as ~45% of type E. Muslims (a short term for immigrants ‒ of unknown religion ‒ from predominantly Muslim countries) appear to be somewhere in-between, although in their case the share of typologies B (~30%) and C (~31%) is larger than in other instances.

Two things emerge from this analysis.

1. ‘National culture’ is not a specific typology. Each national group has (at least) five such typologies (and probably more, but in our case there are statistical indications that stopping at five categories is enough). What changes is the proportion of the members of that group (nation or area of origin) who can be classified in each of the five typologies.
2. Based on these proportions (better: on their differences), an estimate of the relative distance between (national) groups may be attempted.

An application to Italy (2011-2013)

This is the approach that we followed in a recent paper, with data taken from two similar ISTAT surveys conducted in Italy between 2011 and 2013, the latter covering the general population, the former focused on foreigners (De Santis, Maltagliati and Petrucci, 2021).

The main results of our analysis are displayed in Figure 2, which uses proportions (those of Figure 1, but for all groups) to calculate (Euclidean) distances, and then (with little loss of information, i.e. little distortion) forces these distances to appear on a Cartesian plane.

Italians form a homogeneous group, relatively speaking. However, within Italy, a clear north-to-south gradient appears. Northern Italians are culturally closer to immigrants from neighbouring European countries (France, Germany, and North-Central Europe). Southern Italians, instead, appear closer to other nationalities, starting with those around the Mediterranean Basin.

Some countries from homogeneous regions are highlighted, such as those from Latin America, or from the Indian peninsula. The fact that they are close, in Figure 2, means that their members have a comparable distribution among the five cultural typologies mentioned at the beginning.

The Chinese living in Italy are the culturally farthest immigrant group, according to our estimates, an outcome that conforms to expectations and to other sources (e.g. ISTAT 2020).

Finally, people of foreign origin, but who later acquired the Italian nationality (labelled ‘IT2’), lie somewhere in-between: they are somewhat detached from Italians with Italian origin, but they are closer to them than any other foreign group living in Italy.

What lies behind (or better, inside) our clusters

The construction of our five clusters is based on the 11 empirical indicators listed in Figure 3: how often certain ‘objects’ are used (e.g. personal computers), or certain activities are carried out (reading books, attending concerts, etc.) or politics is discussed. Admittedly, it is not culture proper that is being evaluated here: rather, it is the use of (free) time, which is influenced by culture but also by several other factors that we cannot control for (e.g. personal resources). Our indicators are less than ideal, we admit, but this is as far as we could go if we wanted to merge the two ISTAT surveys that were needed to compare foreigners (ISTAT 2016a) with Italians (ISTAT 2016b).

Figure 3 gives an idea of the main characteristics of the members within each cluster. For example, the members of cluster C (Secluded from the social world, if we want to label them) do few or none of the listed activities, similar in this respect to the members of cluster B (Semi-secluded), who are just slightly more active when it comes to dancing, or reading newspapers and magazines. (This was the case of immigrants from predominantly Islamic countries, remember?)

In cluster E (Surfers) we find individuals who frequently use PCs and surf the internet, go to the cinema, or to concerts (all types of live music) and go dancing, but who never talk about politics (as is the case for the Chinese, in Figure 1). The members of cluster A (Active) are quite or very active in all the activities indicated, and they also talk a lot about politics: many Italians are like that (see again Figure 1). Finally, there are the members of group D, the Committed: they talk a lot about politics, keep themselves decently informed, and disdain certain facets of modernity, such as PC, internet and discos.

However, one point should be emphasized: in any national group, there are individuals of all clusters (that is, of all typologies, which seems inconsistent with such notions as ‘national homogeneity’, ‘typical traits’, and so on: what changes is (merely?) the relative frequency of the various typologies.

What do we learn from all this?

First, we must caution readers against hasty interpretation. We refer them to the original article for the limitations of our findings, in terms, for instance, of available empirical indicators to assess the ‘cultural orientation’ of our respondents, clustering criteria, and impossibility of controlling for certain covariates (age, sex, education, income, etc.; De Santis, Maltagliati and Petrucci, 2021).

Also, we do not have any empirical indication of how time and selection influence our results. For instance, let us consider people of foreign origin who are now Italians. Acquiring the Italian nationality is a long and painful process. Therefore the ‘IT2’ group has, on average, a longer period of permanent stay in the country than any other group. The fact that they are culturally closer to Italians than all others may be interpreted in two main ways:

• they were selected from the start, and it is precisely this affinity that made them stay in the country and decide eventually to become Italians,
• they gradually got used to the ‘Italian way of thinking’, and their proximity to Italians at the end of the process indicates that people can and do change: cultural convergence is possible and is taking place.

Either way, cultural distances do not seem to be insurmountable obstacles when it comes to integrating foreigners in a host country. And, in all cases, those who evoke them can now start to measure them (if empirical data permit, of course).

Acknowledgements

Financial support is gratefully acknowledged from two sources:

1) JPI MYBL / CREW Project (Joint Programme Initiative: More Years Better Life, 2016 Call. CREW: Care, retirement and wellbeing of older people across different welfare regimes. MIUR Decree: n. 3266/2018; Official Bulletin no. 32 7. Feb 2019), and

2) MIUR-PRIN 2017 Grant (Italian Ministry of University and Research, Prot. N. 2017W5B55Y).

References

De Santis G., Maltagliati M., Petrucci A. (2021) So Close, So Far. The Cultural Distance of Foreigners in Italy. Social indicators Research, online first

ISTAT (2016a) Condizione e integrazione sociale dei cittadini stranieri: file per la ricerca,

ISTAT (2016b) Aspetti della vita quotidiana: file per la ricerca,

ISTAT (2020) Identità e percorsi di integrazione delle seconde generazioni in Italia Cultural identity and integration of second generations in Italy, Rome.

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