. 2020 Oct 15;18(2):239–255. doi: 10.1007/s10433-020-00582-7

Table 2.

Synthesising intersectionality and life course analyses to understand unequal ageing

Areas of synergy	Description	Examples and potential approaches
People change intersectional subgroups over the life cycle and could therefore be said to follow an ‘intersectional trajectory’	People occupy a series of structural positions/social identities over the life cycle Social axes of inequality are both causes and consequences of social stratification Gender and ethnicity are relatively stable characteristics, while SEP changes over the life cycle Intersectional trajectories might be consistent with age as leveller, persistent inequality, or cumulative disadvantage/advantage patterns of unequal ageing Social roles in relation to reproduction and production, as well as personal relationships, are intertwined with intersectional trajectory Social roles and relationships channel individual actions and decisions The norms and meanings regarding key roles and transitions may be intersectionally patterned	Examine how people navigate role transitions and intersectional patterning in this Analyse ethnicity by gender outcomes depending on time spent in certain SEPs (duration) at what age (timing/critical periods), how the order of SEP statuses might influence health (sequential effects), or how certain SEP transitions might constitute a big life change (turning points) Aisenbrey and Fasang (2018) find that high prestige careers are mainly only accessible for Black men if they are in stable relationships with a maximum of one child Dressel et al. (1997) highlight how retirement might have little meaning among low income African Americans Richardson and Brown (2016) find a multiplicative effect of ethnicity and gender on hypertension risk trajectories
People employ agency to resist discrimination and shape their own identities across the life cycle, within given constraints	Resource constraints and institutional structures limit the possibilities for agency Risks in relation to knowledge and information, e.g. in relation to the pension system may be intersectionally patterned People actively shape their positions/identities over time based on their age, gender, ethnicity, class, and other characteristics	Holman et al. (2018) suggest women disadvantaged according to SEP and ethnicity were less able to mitigate the damaging effects of increases to the women’s state pension age because they were less likely to be aware of the change and less likely to have the financial knowledge to make complex pensions decisions Walker and Naegele (1999) showed the variations in political participation between different groups of older people, based on SEP, in different EU countries
Intersectional patterning and its significance for unequal ageing varies by historical time and spatial context	Different schools, neighbourhoods, regions, or countries have different intersectional diversity, which changes over time (e.g. changing numbers of professional ethnic minority women) Intersectional subgroups take on different meanings in different contexts. It means something different to be a working-class 55-year-old Black woman in 1920 versus 2020, and in the UK versus the USA Intersectional patterning may have more explanatory power with respect to unequal ageing in certain historical times and spatial contexts than in others From an intersectionality perspective, discriminatory norms, policies, and institutions are key explanations for why intersectional outcomes vary by context (see below)	Examine time/place differences in intersectional diversity Conduct MAIHDA analysis to examine within and between intersectional variation in different historical times and spatial locations to understand explanatory power of intersectional patterning Examine the ageing of different cohorts of disabled people and people with intellectual disabilities Compare relevance of area deprivation versus individual SEP in explaining health inequalities, and how this varies by age, gender, and other axes of inequality Examine the relevance of age in deprived neighbourhoods or deprivation in neighbourhoods with a high average age Conduct intersectional analyses on a local or regional scale to generate place-specific evidence Informal care role of older adults may change in neighbourhoods with high unemployment (Dressel et al. 1997)
People are affected by multiple forms of discrimination over the life cycle and according to historical time and spatial context	The impact of discrimination on unequal ageing depends on life course dynamics, e.g. duration, timing/critical periods, sequential effects Sustained discrimination over the life cycle on the basis of characteristics other than age influences how later life age discrimination is experienced Meso-level discriminatory mechanisms, e.g. labelling of individual potential, and the social-psychological dynamics of internalised incompetence reproduce inequalities over the life cycle Individuals experience differential ‘institutional imbrication’, i.e. exposure to multiple policies/institutions/stereotypes not only based on their intersectional position/identity, but on their country, age, and cohort Experiences of discrimination varies by time and place depending on the prevalent ‘matrix of domination’ (see below)	Analyse inequalities in life expectancy and healthy life expectancy according to age, cohort, gender, race, and ethnicity Examine differences in reported interpersonal discrimination across time and place Examine how individuals experience multiple forms of discrimination across different contexts, e.g. ageism in one policy and sexism in another, and multiplicative forms of discrimination, e.g. some policies are both sexist and ageist Policy contexts can be used to interpret individual outcomes, can be directly linked to individual data, or cross-national panel data can be used for a comparative perspective of changing policy contexts over time Bécares and Zhang (2018) found that accumulated interpersonal discrimination has a negative impact on the mental health of older ethnic minority women Dressel and Barnhill (1994) found that for African American grandmothers age was not seen as a prominent social identity in the face of lifelong experiences of poverty, racism, and sexism
Ageism, sexism, racism, and other forms of discrimination and their interconnections (the ‘matrix of domination’) vary by historical time and spatial context	Social policies and institutional practices, such as the welfare state, education, immigration, social care, and retirement are more or less discriminatory depending on historical time and spatial context Such policies and practices can discriminate on the basis of single or multiple social characteristics The wider socio-political context, e.g. austerity and neoliberalism shapes discrimination and oppression The nature and prevalence of stereotypes changes over time	Policy analysis of how policies discriminate based on both single and multiple axes of inequality at a time, e.g. ageism and stereotypes of older men versus women The transformation from ‘worn out’ older workers of the early twentieth century to the ‘productive ageing’ of the early twenty-first century (Macnicol 2006) Shifts in the visual stereotypes of older women over time (Warren 2018) The cultural turn in ageist stereotypes from physical limitations to cosmetic appearance, with a particularly severe impact on older women (Twigg 2013)

Areas of synergy

Description

Examples and potential approaches

People change intersectional subgroups over the life cycle and could therefore be said to follow an ‘intersectional trajectory’

People occupy a series of structural positions/social identities over the life cycle

Social axes of inequality are both causes and consequences of social stratification

Gender and ethnicity are relatively stable characteristics, while SEP changes over the life cycle

Intersectional trajectories might be consistent with age as leveller, persistent inequality, or cumulative disadvantage/advantage patterns of unequal ageing

Social roles in relation to reproduction and production, as well as personal relationships, are intertwined with intersectional trajectory

Social roles and relationships channel individual actions and decisions

The norms and meanings regarding key roles and transitions may be intersectionally patterned

Examine how people navigate role transitions and intersectional patterning in this

Analyse ethnicity by gender outcomes depending on time spent in certain SEPs (duration) at what age (timing/critical periods), how the order of SEP statuses might influence health (sequential effects), or how certain SEP transitions might constitute a big life change (turning points)

Aisenbrey and Fasang (2018) find that high prestige careers are mainly only accessible for Black men if they are in stable relationships with a maximum of one child

Dressel et al. (1997) highlight how retirement might have little meaning among low income African Americans

Richardson and Brown (2016) find a multiplicative effect of ethnicity and gender on hypertension risk trajectories

People employ agency to resist discrimination and shape their own identities across the life cycle, within given constraints

Resource constraints and institutional structures limit the possibilities for agency

Risks in relation to knowledge and information, e.g. in relation to the pension system may be intersectionally patterned

People actively shape their positions/identities over time based on their age, gender, ethnicity, class, and other characteristics

Holman et al. (2018) suggest women disadvantaged according to SEP and ethnicity were less able to mitigate the damaging effects of increases to the women’s state pension age because they were less likely to be aware of the change and less likely to have the financial knowledge to make complex pensions decisions

Walker and Naegele (1999) showed the variations in political participation between different groups of older people, based on SEP, in different EU countries

Intersectional patterning and its significance for unequal ageing varies by historical time and spatial context

Different schools, neighbourhoods, regions, or countries have different intersectional diversity, which changes over time (e.g. changing numbers of professional ethnic minority women)

Intersectional subgroups take on different meanings in different contexts. It means something different to be a working-class 55-year-old Black woman in 1920 versus 2020, and in the UK versus the USA

Intersectional patterning may have more explanatory power with respect to unequal ageing in certain historical times and spatial contexts than in others

From an intersectionality perspective, discriminatory norms, policies, and institutions are key explanations for why intersectional outcomes vary by context (see below)

Examine time/place differences in intersectional diversity

Conduct MAIHDA analysis to examine within and between intersectional variation in different historical times and spatial locations to understand explanatory power of intersectional patterning

Examine the ageing of different cohorts of disabled people and people with intellectual disabilities

Compare relevance of area deprivation versus individual SEP in explaining health inequalities, and how this varies by age, gender, and other axes of inequality

Examine the relevance of age in deprived neighbourhoods or deprivation in neighbourhoods with a high average age

Conduct intersectional analyses on a local or regional scale to generate place-specific evidence

Informal care role of older adults may change in neighbourhoods with high unemployment (Dressel et al. 1997)

People are affected by multiple forms of discrimination over the life cycle and according to historical time and spatial context

The impact of discrimination on unequal ageing depends on life course dynamics, e.g. duration, timing/critical periods, sequential effects

Sustained discrimination over the life cycle on the basis of characteristics other than age influences how later life age discrimination is experienced

Meso-level discriminatory mechanisms, e.g. labelling of individual potential, and the social-psychological dynamics of internalised incompetence reproduce inequalities over the life cycle

Individuals experience differential ‘institutional imbrication’, i.e. exposure to multiple policies/institutions/stereotypes not only based on their intersectional position/identity, but on their country, age, and cohort

Experiences of discrimination varies by time and place depending on the prevalent ‘matrix of domination’ (see below)

Analyse inequalities in life expectancy and healthy life expectancy according to age, cohort, gender, race, and ethnicity

Examine differences in reported interpersonal discrimination across time and place

Examine how individuals experience multiple forms of discrimination across different contexts, e.g. ageism in one policy and sexism in another, and multiplicative forms of discrimination, e.g. some policies are both sexist and ageist

Policy contexts can be used to interpret individual outcomes, can be directly linked to individual data, or cross-national panel data can be used for a comparative perspective of changing policy contexts over time

Bécares and Zhang (2018) found that accumulated interpersonal discrimination has a negative impact on the mental health of older ethnic minority women

Dressel and Barnhill (1994) found that for African American grandmothers age was not seen as a prominent social identity in the face of lifelong experiences of poverty, racism, and sexism

Ageism, sexism, racism, and other forms of discrimination and their interconnections (the ‘matrix of domination’) vary by historical time and spatial context

Social policies and institutional practices, such as the welfare state, education, immigration, social care, and retirement are more or less discriminatory depending on historical time and spatial context

Such policies and practices can discriminate on the basis of single or multiple social characteristics

The wider socio-political context, e.g. austerity and neoliberalism shapes discrimination and oppression

The nature and prevalence of stereotypes changes over time

Policy analysis of how policies discriminate based on both single and multiple axes of inequality at a time, e.g. ageism and stereotypes of older men versus women

The transformation from ‘worn out’ older workers of the early twentieth century to the ‘productive ageing’ of the early twenty-first century (Macnicol 2006)

Shifts in the visual stereotypes of older women over time (Warren 2018)

The cultural turn in ageist stereotypes from physical limitations to cosmetic appearance, with a particularly severe impact on older women (Twigg 2013)