This chapter examines how property regimes are likely to respond to the significant increase in average life expectancy predicted by “100-year life” theories. It takes a relatively pessimistic position, arguing that the optimal institutional response to demographic aging will be very difficult to produce: some countries, most notably the US, are likely to underrespond to the socioeconomic demands that demographic aging will probably impose on property law, whereas others, such as China and Japan, may well overrespond. This is because, within the realm of property rights and regulation, the economics and politics of demographic aging may well contradict each other: aging potentially reinforces political opposition to public governance even as it creates economic demand for it.

There are two barriers to realizing the promise of the 100-year life in the US. The first is that few get to live it: unlike peers in other high-income countries, the life expectancy of Americans is short. Paradoxically, however, boosting American longevity would aggravate a second problem: on important dimensions, Americans enjoy less independence in old age than their peers. These problems have something perhaps unexpected in common: a built environment that requires driving as the price of first-class citizenship. That bargain, a legacy of twentieth-century transportation and land use policy, first lops years off of life expectancy by claiming lives at disproportionately young ages and then saps independence and quality of life among the small share of Americans who are fortunate to reach very old age. This chapter proposes two solutions. First, it urges road safety interventions that maximize life expectancy and thus expand the promise of the 100-year life. Second, it develops a variant on the classic Tiebout model of residential sorting that applies the concept more narrowly to enable retirees to thrive (transportation-based “gray Tiebout sorting”). It details the instrumental promise of such a market and its potential for broad spillover benefits.

Lynda Gratton and Andrew Scott’s prediction that the median lifespan will exceed 100 years for children born today in high-income countries is “science fiction” within the dictionary definition: a “story featuring hypothetical scientific or technological advances.” This chapter argues that for a 100-year life to become the norm within the timeframe that Gratton and Scott envision, we will need to make extraordinary progress over the next several decades in reducing old-age mortality – advances that are qualitatively different from the disease-specific innovations that attract the vast majority of biomedical investment today. To achieve those advances will require not only scientific ingenuity but also legal and political innovation. Patent law – the most familiar tool in the innovation policy toolkit – is ill-fitted for the goal of attaining century-long lives. Instead of relying on private-sector sources, we will likely need governments to commit to moonshot investments in longevity akin to the Apollo project. Yet securing support for those investments will be challenging given the political economy of public funding for biomedical research. Thus the path to a 100-year life will likely require major breakthroughs not only in the laboratory but also in the legislature.

In the mid-twentieth century, income and wealth inequality was declining, leading to an era of comparative economic equality and a large middle class. But since the early 1980s, income and wealth inequality has been on the rise. The 100-year life describes how increasing life expectancy could transform the course of individual lives in detail – but how does this trend intersect with our age of inequality? And what does it mean for law and policymaking more generally? The dark side of rising life expectancy is that it is unequally shared. The first part of this chapter briefly discusses how age and inequality are connected – that is, that not everyone shares in rising life expectancy equally and that those who are living longer share many common attributes. The second part considers the possible impact of the skew in rising life expectancy on representative government. Inequality skews politics to favor the wealthy; inequality by age will likely do the same. The fundamental problem is that power is held unequally in most societies. The third part, on age and institutional design, outlines some ways in which political structures might be crafted to account for age-related differences.

A fundamental problem in descriptive epidemiology is how to make meaningful and robust comparisons between different populations, or within the same population over different periods. The problem has several dimensions. First, the data we have to work with (e.g. incident and prevalent cases, and deaths) is rarely usable in its raw form. We must therefore transform it in some way before undertaking the comparison itself. Second, our data usually tells us about fundamentally different attributes of the populations we are seeking to compare. If we are only ever interested in comparing any one of these attributes at a time (mortality, for example), then one of several simple and well-established transformations is all that is typically required. Increasingly, however, epidemiologists are being asked to bring these attributes together into more integrated and meaningful comparisons.

This chapter proposes a framework for estimating the investment in human capital from health improvement or activities that improve life expectancy and reduce morbidity rates. The measurement framework builds on and extends the Jorgenson-Fraumeni income-based approach for estimating human capital to account for the effect of health on human capital. This economic approach to measuring health human capital differs from the welfare-based approach that estimates the economic effect of health improvements on the quality of life and well-being of individuals. The framework is then implemented for Canada, and the investment in health human capital for the period from 1970 to 2020 is estimated. The estimated investment in health human capital based on the income approach was found to be lower than health expenditures in Canada. This suggests that much of the health expenditures should be classified as consumption rather than as an investment that increases earnings.

For decades, researchers have tried to identify ecological and biological correlates of longevity, often using life expectancy and maximum lifespan as the gold standards. The recent increase in demographic data collected in non-model species has also led researchers to develop alternative metrics of longevity, especially in comparative analyses (e.g. 90% longevity). As a result, studies focused on longevity rely on heterogeneous statistical methodologies and use a variety of longevity metrics that are not always clearly defined. This lack of clarity has led to confusion in the interpretation of results and makes it difficult to compare results across studies. This chapter discusses the statistical interpretation of each metric and highlights potential biases associated with the missus of longevity metrics; conducts a systematic review of the various longevity metrics used across the scientific literature and analyses the content of scientific articles on longevity using topic modelling methodology; and illustrates, using two examples, the importance of selecting the appropriate metric based on the research question. Based on these insights, it provides a list of recommendations aimed at helping researchers to think carefully about the choice of metrics when studying longevity.

The Global South, that groups together low- or middle- income countries mainly located in Africa, Asia and Latin America, concentrates most of the world population. Population ageing, caused by the demographic transition and a large decrease in fertility and mortality rates, make these countries face numerous challenges. Among regions in the Global South, the differences in life expectancy at birth were still large in 2022: almost 74 years in Latin America and the Caribbean but only 60 years in sub-Saharan Africa, with some countries barely exceeding 50. Due to many factors that play on health transition, high mortality countries suffer a cumulative burden from both infectious and non-communicable diseases (NCDs). In addition, the lack of old-age mortality data is a dramatic issue when studying longevity in these countries.

During the nineteenth century, Iberia entered the path towards modern economic growth. Although industrialization occurred later than in other Western European countries, economic progress ultimately led to an unprecedented improvement in the standards of living. This chapter aims to analyse the evolution of such advances and, when possible, compare Iberia with its Western European counterparts. In so doing, it presents several indicators capturing different dimensions of well-being, average income, consumption patterns, height, life expectancy, and a synthetic measure, the Human Development Index (HDI). Income distribution is examined by looking at alternative inequality indicators: Gini coefficient, the extraction ratio and top income shares. Based on this information the long-run evolution of economic inequality is assessed. All in all, the evidence presented shows that economic progress and well-being significantly improved in Iberia since mid-nineteenth century, although this happened at a slower pace than in Western Europe.

That differences in health outcomes exist between groups is unsurprising and, in some cases, seems subject to ‘natural law’. Such ‘common sense’, arguably unavoidable differences are termed ‘health disparities’ – a term usually understood to be value-neutral. By contrast, more complex differences in health outcomes which seem to derive from differences in opportunities or systemic bias are deemed ‘unfair’ and are referred to as ‘health inequalities’ or ‘health inequities’.

This chapter delves further into how we describe health inequalities and different measures and data that illustrate these differences. Causes and mechanisms of inequality are explored, followed by examples of inequality across groups with certain population characteristics, including ethnicity; gender, sexual orientation and gender identity; disability; and socially excluded groups. Finally, approaches and strategies for reducing health inequalities are presented, with potential actions described at the micro-, meso- and macro-levels.

Best places in the world to grow old based on income, employment, health, education, and environment. In Sweden, health has improved in the older population over the last decades, so Sweden’s health care needs have decreased overall. Sweden has the largest health care workforce in the world serving citizens over 65. 94 % of people over 65 live at home! Elders receive in-home assistance when needed. Only 4% of all care—health care or home care-is paid for by patients themselves. Municipal fixers—people who can come and do chores to help reduce falls, such as change a lightbulb. They come to your home. Totally free. If your needs are high enough, someone can come in every two hours around the clock to help care for you—totally without cost to you. High satisfaction. No stigma around dementia. Swedish government develops list of drugs that older people should not be prescribed. Sweden has implemented community-based care and practical approaches to older adult safety.