Intergenerational Socioeconomic Mobility and Cognitive Impairment Among Chinese Older Adults: Gender Differences

Abstract

This study examined the impact of intergenerational socioeconomic mobility on the risk of cognitive impairment in a cohort of Chinese older adults aged 60 years and older. Data were derived from the 2014 wave of the Chinese Longitudinal Healthy Longevity Survey. Logistic regression models were performed to assess the impact of three dimensions of socioeconomic mobility (occupational mobility, educational mobility, and residential mobility) on the risk of cognitive impairment. We found that men who were stable with non-professional jobs across generations had a higher risk of cognitive impairment than their counterparts who experienced upward occupational mobility compared to their father. This pattern was not observed in women. There was little evidence that educational mobility or residential mobility affected cognitive impairment in later life. The findings have implications for advancing supportive policies and practices related to maximizing the benefits of education and career advancements for cognition in later life.

Keywords

cognitive function social mobility socioeconomic status gender differences

Introduction

Consistent evidence has shown age-related decline in cognition, but there are wide individual variations in the timing and extent of cognitive decline (Hughes et al., 2018). Previous research has linked low educational attainment and low occupational status to poorer cognitive function and a greater risk of dementia and Alzheimer’s disease in later life (Liu & Lachman, 2020; Zeki Al Hazzouri et al., 2011). Indeed, socioeconomic factors play an important role in the cognitive function of older adults. There is emerging evidence that differences in cognitive aging can be traced to early-life conditions such as childhood socioeconomic conditions (Liu & Lachman, 2020; Zeki Al Hazzouri et al., 2011). Socioeconomic status (SES) is both dynamic and generational, with the capacity to change throughout life and across generations (Stern, 2012). It is documented that objective and subjective improvement in SES might benefit health and well-being in later life (Johnson-Lawrence et al., 2013; Wilkinson et al., 2012). On the other hand, degradation in SES may lead to health problems over time (Fu et al., 2019; Guimarães et al., 2018).

Researchers have studied resilience to understand which individual characteristics or institutional context may buffer the detrimental effect of early-life adversity on health in later life (Greenfield & Moorman, 2019). A related issue is whether socioeconomic mobility (i.e., change in socioeconomic conditions over the life course) can compensate for the detrimental effect of low childhood SES on health in later life. Empirical studies in Western countries have documented the impact of socioeconomic mobility on subjective and objective health outcomes in younger and older adulthood (Guimarães et al., 2018; Wilkinson et al., 2012). Systematic reviews of literature have linked socioeconomic mobility to health behaviors and chronic illness over the life course (Elhakeem et al., 2017; Fu et al., 2019). Overall, though, little research has examined the impact of socioeconomic mobility on cognitive health in later life, especially in developing countries such as China.

The life-course perspective provides a dynamic framework for the study of socioeconomic transitions and cognitive health. The cumulative risk theory suggests that the detrimental effect of lower socioeconomic conditions accrue throughout the life course (Cohen et al., 2010). Therefore, the risk for developing poor adult health increases as the duration of exposure to such disadvantage increases. In our study context, this theory implies that consistently lower SES would be associated with the highest risk of cognitive impairment. On the other hand, the social mobility theory suggests that early-life conditions can be either enhanced or diminished by later effects (Mishra et al., 2009). In terms of the current study, this theory implies that downward socioeconomic mobility would be associated with a higher risk of cognitive impairment, especially as compared to consistently high SES or upward mobility.

The life-course perspective also emphasizes the interdependent relationships among family members (Hagestad, 2003). The parent-child relationship, as one of the most important family relationships, has been indicated to affect the health and well-being of the offspring over the life span (Gimenez et al., 2013; Li et al., 2019). Within a family, the SES of adult children could be highly dependent upon the educational and occupational achievement of their parents (Huang et al., 2021). The change of SES across generations can be termed as intergenerational socioeconomic mobility. In line with Birnie et al. (2011), we define intergenerational socioeconomic mobility as changes in the level of SES from parents to offspring, or from early childhood to older adulthood.

In social science research, education and occupation have been commonly used to measure SES (Guimarães et al., 2018). In traditional Chinese society, educational and employment opportunities are highly gendered. There is evidence that women in traditional Chinese society had fewer opportunities for formal education and high-skilled occupations than men (Cong, 2008). Even today, gender inequality has been pervasive in China, especially in relatively poor areas of the country. The devaluation of Chinese women can be traced to long-standing cultural values derived from the patriarchal family structure that institutionalizes power and gender relations (Gui, 2017). Gender inequality in China is observable in many aspects, including socioeconomic characteristics and health conditions. We therefore aim to investigate potential gender differences in the association between socioeconomic mobility and cognition.

In the context of China, another important indicator of SES is the category of residence (e.g., urban and rural). There is an institutionalized rural-urban division in China due to the establishment of a household registration system (hukou) in 1955. The rural-urban residency in China has substantially influenced the availability and accessibility to social welfare and medical resources. A systematic review of 48 studies on the prevalence of mild cognitive impairment in China reported a higher prevalence of cognitive impairment among older adults living in rural areas (Xue et al., 2018). A recent study in China reported that the urban community setting had a protective effect on cognitive impairment in later life, though a faster cognitive decline was also observed in urban-dwelling older adults (Xiang et al., 2018). It is therefore plausible to believe that residential mobility (e.g., from rural to urban areas) might also have an impact on cognitive health.

Recent gerontological research in the Chinese population has emphasized the importance of investigating life-course risk factors of dementia and other cognitive problems (Chan et al., 2013; Fu, 2019). Cognitive function of Chinese older adults has been linked to life-course factors such as early-life conditions, social support, social engagement, and health-related behaviors (Fu, 2015, 2016; Wei et al., 2014; Zhang et al., 2008). It is still understudied, however, whether intergenerational socioeconomic mobility would affect the cognitive function of Chinese older adults in their later decades of life.

The present study aims to (1) investigate the impact of intergenerational socioeconomic mobility on the risk of cognitive impairment in Chinese older adults; and (2) test whether gender influence the relationship between socioeconomic mobility and cognitive impairment. We give attention to three dimensions of socioeconomic mobility: educational mobility, occupational mobility, and residential mobility. Based on the aforementioned theoretical and empirical evidence, we propose the following hypotheses:

H1: Those with consistently low SES across generations will have a higher risk of cognitive impairment than their counterparts in any other mobility groups.

H2: Those with downward socioeconomic mobility will have a higher risk of cognitive impairment compared to their upwardly mobile counterparts.

H3: The impact of socioeconomic mobility on cognitive impairment will be more robust in men than in women.

Methods

Data and Sample

This study used the 2014 wave of the Chinese Longitudinal Healthy Longevity Survey (CLHLS). The CLHLS is a nationally representative large-scale survey, covering 23 provinces and cities in China. The CLHLS attempted to interview all centenarians who voluntarily agreed to participate in the study in the selected provinces and cities. For each centenarian, one near-by (e.g., in the same village) octogenarian (aged 80–89) and one near-by nonagenarian (aged 90–99) of pre-designated age and sex was interviewed. The goal was to have comparable numbers of male and female octogenarians and nonagenarians at each age from 80 to 99. The CLHLS provides high-quality information concerning demographic characteristics, socioeconomic conditions, health-related behaviors, psychological health, and physical health of older adults. All information was obtained through face-to-face questionnaire interviews in the homes of participants. Systematic assessments of the CLHLS regarding reliability and validity indicate that the data quality is high (Gu, 2008). The analytical sample consists of 5087 Chinese adults aged 60 years and older, including 2367 men and 2720 women.

Outcome Variable

Cognitive function was measured by the Chinese version of the Mini-Mental State Examination (MMSE), which evaluated the cognitive functions of memory, attention, language, calculation and orientation (Zhang, 2006). The MMSE index used in this study includes 24 items (see Table A1). Apart from Item #6 (naming different kinds of food within 1 minute), all the other items were dichotomized and coded as one if respondents were able to give a correct answer (otherwise, 0). For Item #6, one point was given for each food named by respondents and up to seven points could be earned. Following Zhang (2006), proxy responses or “unable to answer” responses for the MMSE index were counted as incorrect answers (coded as 0). The range of the MMSE score was from 0 to 30 (Cronbach’s α = .92 in the study sample), with higher scores indicating better cognitive function. Respondents were defined as having cognitive impairment if their MMSE score was less than 25 (Riedel et al., 2008; Schaller et al., 2012). Specifically, cognitive impairment is a binary variable, where the value one indicates “being cognitively impaired” (MMSE < 25 points) and the value 0 indicates “otherwise” (MMSE ≥ 25 points).

Explanatory Variables

We used three explanatory variables to measure intergenerational socioeconomic mobility: educational mobility, residential mobility, and occupational mobility. Educational mobility was measured by the difference in the years of schooling between the father and the respondents. Three dummy variables were constructed: (1) upward educational mobility (respondents had more years of schooling than father); (2) stable with the same education (father and respondents had the same years of schooling); and (3) downward educational mobility (father had more years of schooling than respondents). Mother’s education was not included into the analysis because over 95% of respondents’ mother were illiterate.

Residential mobility was determined by respondents’ category of residence (rural vs. urban) in early childhood and older adulthood. Four dummy variables were constructed: (1) upward residential mobility (born in a rural area and currently lived in an urban area); (2) stable high residential status (born and currently lived in an urban area); (3) downward residential mobility (born in an urban area and currently lived in a rural area); and (4) stable low residential status (born and currently lived in a rural area).

Occupational mobility was measured by changes in the occupational status (professional job vs. non-professional job¹) between the father and the respondents. Four dummy variables were constructed: (1) upward occupational mobility (father had a non-professional job whereas respondents had a professional job); (2) stable high occupational status (father and respondents both had a professional job); (3) downward occupational mobility (father had a professional job whereas respondents had a non-professional job); and (4) stable low occupational status (father and respondents both had a non-professional job). Mother’s occupation was not included into the analysis because there was little variability for mother’s occupation in the study sample, as almost all the mothers of respondents had a non-professional job.

Covariates

The current study adjusted for respondents’ demographic characteristics, including age (in years) and marital status (married = 1; otherwise = 0). Individuals’ own education and spousal education were adjusted for, as years of schooling has been indicated to influence cognitive performance (Bento-Torres et al., 2017). Since income has been an important indicator of socioeconomic status, we also adjusted for respondents’ household income (in Yuan). Additionally, sleeping patterns have been suggested to impact cognitive performance (Loerbroks et al., 2010). We therefore adjusted for respondents’ sleep quality (1=very good/good; 2 =so so; 3=very bad/bad) in the analysis.

Statistical Analysis

Univariate comparisons between men and women were performed using t-tests for continuous variables and chi-squared tests for categorical variables. Logistic regression models were performed in men and women separately to assess the impact of socioeconomic mobility on the risk of cognitive impairment. The risk of cognitive impairment was predicted by each indicator of socioeconomic mobility and then by all the three indicators, where upward mobility was used as the reference group. The final regression models adjusted for respondents’ demographic characteristics, education, spousal education, household income, and sleep quality. The results were presented as odds ratios (ORs) and their 95% confidence intervals.

Results

Descriptive Results and Univariate Analysis

Table 1 presents the descriptive characteristics and univariate comparisons of the study variables. 46.53% of the study participants were men (N = 2367) and 53.47% were women (N = 2720). In the study sample, men had an average age of about 83 years and women had an average age of 87 years. Men attended school for an average of 3.91 years and women had an average of 1.15 years of schooling². The univariate analysis suggests that women were substantially older than men and had significantly lower educational level than men. On average, men and women had similar household income (about 35k Yuan). More men than women in the study sample were married and lived with their spouse. Men and women differed substantially in terms of educational and occupational mobility. Overall, men were more likely than women to experience upward educational and occupational mobility. The majority of men and women had a non-professional position as their father did. There were little gender differences in residential mobility. Whereas close to half of women were cognitively impaired, no more than one third of men were cognitively impaired.

Table 1.

Descriptive Characteristics and Univariate Comparisons of Study Variables by Gender, CLHLS 2014.

	Men (N = 2367)	Women (N = 2720)	p Value
Continuous Variables	Mean (SD)	Mean (SD)
Age (years; range: 60–117)	83.29 (9.37)	87.01 (10.78)	<.001***
Education (years; range: 0–20)	3.91 (3.81)	1.15 (2.57)	<.001***
Spousal education (years; range: 0–18)	1.58 (2.91)	2.46 (3.47)	<.001***
Household income (yuan; range: 0–100k+)	35,484.03 (32,581.82)	35,826.19 (32,318.93)	.711
Categorical Variables	n (%)	n (%)
Intergenerational educational mobility			<.001***
Upward educational mobility	1467 (28.84%)	523 (10.28%)
Stable	770 (15.14%)	1916 (37.66%)
Downward educational mobility	130 (2.56%)	281 (5.52%)
Intergenerational residential mobility			.137
Upward residential mobility	964 (18.95%)	1159 (22.78%)
Stable in urban areas	233 (4.58%)	223 (4.38%)
Downward residential mobility	33 (.61%)	29 (0.57%)
Stable in rural areas	1139 (22.39%)	1309 (25.73%)
Intergenerational occupational mobility			<.001***
Upward occupational mobility	259 (5.09%)	66 (1.30%)
Stable in professional positions	41 (.81%)	15 (.29%)
Downward occupational mobility	33 (.65%)	56 (1.10%)
Stable in non-professional positions	2034 (39.98%)	2583 (50.78%)
Cognitive impairment
Not cognitively impaired	1776 (34.91%)	1451 (28.52%)	<.001***
Cognitively impaired	591 (11.62%)	1269 (24.95%)	<.001***
Marital status			<.001***
Married	1353 (26.60%)	630 (12.38%)
Otherwise	1014 (19.93%)	2090 (41.09%)
Sleep quality			<.001***
Good/Very good	1544 (30.35%)	1549 (30.45%)
So-so	611 (12.01%)	769 (15.12%)
Bad/Very bad	212 (4.17%)	402 (7.90%)

Notes: MMSE = Mini-Mental State Examination.

* p < .05; ** p < .01; *** p < .001.

Intergenerational Socioeconomic Mobility and Cognitive Impairment in Men

Table 2 shows the odds ratios of cognitive impairment associated with intergenerational socioeconomic mobility in men. All the models adjusted for participants’ demographic characteristics, socioeconomic conditions, and sleep quality. Model one assessed the effect of educational mobility on cognitive impairment and found no significant impact. Similarly, Model two found no significant impact of residential mobility on cognitive impairment. Model three examined the impact of occupational mobility on cognitive impairment. According to Model 3, men who were stable with non-professional jobs across generations had a higher risk of cognitive impairment than their counterparts with upward occupational mobility. The same pattern was observed in Model 4, where the three indicators of socioeconomic mobility and all the covariates were included. According to Model 4, men who were stable in non-professional occupational status across generations were 78% more likely to become cognitively impaired than their counterparts who experienced upward occupational mobility (p < .05). There was also modest evidence that men who stayed in urban areas in both childhood and later adulthood had a higher risk of cognitive impairment than those who moved from rural areas to urban areas (upward residential mobility). Having more years of education was associated with a lower risk of cognitive impairment in men. Yet, spousal education had no impact on the risk of cognitive impairment. There is also evidence that being married and living with a spouse protected men from being cognitively impaired.

Table 2.

Odds ratios and 95% CI of intergenerational social mobility on cognitive impairment in men (N = 2311), CLHLS 2014.

Variable	Model 1	Model 2	Model 3	Model 4
Variable	OR (95% CI)	OR (95% CI)	OR (95% CI)	OR (95% CI)
Intergenerational educational mobility
Upward educational mobility	1			1
Stable	1.04 (.78, 1.40)			1.05 (.78, 1.42)
Downward educational mobility	1.06 (.66, 1.71)			1.04 (.64, 1.69)
Intergenerational residential mobility
Upward residential mobility		1		1
Stable in urban areas		1.46 (.99, 2.14)		1.48 (1.00, 2.17)*
Downward residential mobility		.74 (.24, 2.23)		.74 (.24, 2.26)
Stable in rural areas		1.07 (.86, 1.35)		1.06 (.85, 1.33)
Intergenerational occupational mobility
Upward occupational mobility			1	1
Stable in professional positions			1.42 (.50, 4.02)	1.37 (.48, 3.92)
Downward occupational mobility			2.35 (.94, 5.89)	2.39 (.95, 6.03)
Stable in non-professional positions			1.76 (1.12, 2.75)*	1.78 (1.13, 2.80)*
Age (years)	1.09 (1.08, 1.10)***	1.09 (1.08, 1.10)***	1.09 (1.08, 1.11)***	1.09 (1.08, 1.11)***
Married (vs. otherwise)	.73 (.59, .92)**	.74 (.59, .92)**	.74 (.59, .93)**	.74 (.60, .93)*
Education (years)	.92 (.87, .96)***	.91 (.88, .94)***	.92 (.89, .96)***	.93 (.88, .97)**
Spousal education (years)	.96 (.91, 1.01)	.96 (.91, 1.01)	.97 (.92, 1.02)	.96 (.91, 1.02)
Income (yuan)	1.00 (1.00, 1.00)	1.00 (1.00, 1.00)	1.00 (1.00, 1.00)	1.00 (1.00, 1.00)
Sleep quality
Good/very good	1	1	1	1
So-so	1.77 (1.40, 2.23)***	1.75 (1.39, 2.21)***	1.74 (1.38, 2.20)***	1.73 (1.37, 2.19)***
Bad/very bad	2.02 (1.43, 2.86)***	2.02 (1.43, 2.87)***	2.01 (1.42, 2.84)***	2.01 (1.42, 2.85)***
–Log pseudo-likelihood	1097.60	1095.61	1094.02	1091.80

Note. Cognitive Impairment: MMSE <25; OR = odds ratio; CI = confidence interval.

*p < .05; **p < .01; ***p < .001.

Intergenerational Socioeconomic Mobility and Cognitive Impairment in Women

Table 3 presents the odds ratios of cognitive impairment associated with intergenerational socioeconomic mobility in women, adjusting for all the covariates. Consistent with Table 2, the first three models in Table 3 each included only one of the indicators of socioeconomic mobility, and the last model included all the indicators of socioeconomic mobility. According to the four models, there is no evidence that intergenerational socioeconomic mobility (educational mobility, residential mobility, and occupational mobility) had an impact on the risk of cognitive impairment. Having more years of schooling and being married were associated with a lower risk of cognitive impairment among women. Yet, spousal education did not affect the risk of cognitive impairment in women.

Table 3.

Odds ratios and 95% CI of intergenerational social mobility on cognitive impairment in women (N = 2653), CLHLS 2014.

Variable	Model 1	Model 2	Model 3	Model 4
Variable	OR (95% CI)	OR (95% CI)	OR (95% CI)	OR (95% CI)
Intergenerational educational mobility
Upward educational mobility	1			1
Stable	1.33 (.92, 1.91)			1.30 (.89, 1.89)
Downward educational mobility	.95 (.62, 1.45)			.97 (.63, 1.49)
Intergenerational residential mobility
Upward residential mobility		1		1
Stable in urban areas		.87 (.61, 1.25)		.86 (.60, 1.23)
Downward residential mobility		1.11 (.47, 2.62)		1.11 (.47, 2.64)
Stable in rural areas		1.12 (.93, 1.35)		1.11 (.92, 1.34)
Intergenerational occupational mobility
Upward occupational mobility			1	1
Stable in professional positions			2.09 (.50, 8.73)	1.99 (.47, 8.38)
Downward occupational mobility			.59 (.22, 1.59)	.70 (.26, 1.91)
Stable in non-professional positions			.99 (.46, 2.10)	1.02 (.47, 2.20)
Age (years)	1.09 (1.08, 1.11)***	1.10 (1.08, 1.11)***	1.10 (1.08, 1.11)***	1.09 (1.08, 1.11)***
Married (vs. otherwise)	.77 (.60, .97)*	.77 (.60, .98)*	.77 (.61, .98)*	.77 (.60, .97)*
Education (years)	.91 (.86, .98)**	.89 (.85, .93)***	.88 (.84, .93)***	.91 (.85, .98)*
Spousal education (years)	.99 (.96, 1.02)	.99 (.96, 1.02)	.99 (.96, 1.02)	.99 (.96, 1.03)
Income (yuan)	1.00 (1.00, 1.00)	1.00 (1.00, 1.00)	1.00 (1.00, 1.00)	1.00 (1.00, 1.00)
Sleep quality
Good/very good	1	1	1	1
So-so	1.29 (1.05, 1.57)*	1.29 (1.06, 1.58)*	1.29 (1.05, 1.58)*	1.30 (1.06, 1.59)*
Bad/very bad	1.50 (1.16, 1.94)**	1.51 (1.17, 1.95)**	1.48 (1.15, 1.92)**	1.51 (1.17, 1.95)**
–Log pseudo-likelihood	1492.08	1493.84	1493.36	1489.70

Note. Cognitive Impairment: MMSE <25; OR = odds ratio; CI = confidence interval.

*p < .05; **p < .01; ***p < .001.

Additional analysis of the total sample (not shown) suggested that participants whose educational level remained the same as their father were more likely to have cognitive impairment than those who experienced upward educational mobility. This pattern was not observed in either gender when we performed separate analysis in men and women. In the total sample, participants who were stable with non-professional jobs across generations had a higher risk of cognitive impairment than their counterparts who experienced upward occupational mobility. This is consistent with the associations observed in men.

Discussion

The prevalence of dementia in mainland China has risen dramatically in recent decades (Ding et al., 2014). Using a nationally representative sample of older adults in mainland China, our study examined the impact of intergenerational socioeconomic mobility on the risk of cognitive impairment. Three dimensions of socioeconomic mobility were investigated: educational mobility, occupational mobility, and residential mobility. Consistent with our first hypothesis, we found that participants who were stable with non-professional jobs across generations had a higher risk of cognitive impairment than their counterparts who experienced upward occupational mobility. This finding is consistent with the line of literature suggesting that upward occupational mobility is protective for health (Behrens et al., 2016; Hallqvis et al., 2004). Our results support the accumulation risk model, stating that exposure to social adversities gradually accumulate to increase health problems in later life (Ben-Shlomo & Kuh, 2002; Cohen et al., 2010). Previous research has identified various mechanisms that may help explain the strong impact of stable low occupational mobility on cognitive impairment, including financial strains and psychological stress (Korten et al., 2017; Rudisill & Edwards, 2002). The second hypothesis that downward socioeconomic mobility would be associated with a higher risk of cognitive impairment was not supported by the findings. This is contrary to previous literature documenting the detrimental impact of downward socioeconomic mobility on health (Behrens et al., 2016; Hallqvis et al., 2004).

Our study revealed gender differences in the relationship between socioeconomic mobility and cognitive impairment, which lends support to our third hypothesis that the impact of socioeconomic mobility on cognitive impairment would be more pronounced in men than in women. The most robust finding is that, men who were stable with non-professional jobs across generations had a higher risk of cognitive impairment than their counterparts with upward occupational mobility. There is no evidence that intergenerational socioeconomic mobility had an impact on the risk of cognitive impairment for women. Our results are in line with prior evidence from developed countries reporting significant relationships between socioeconomic mobility and health outcomes in men but not in women (Janicki-Deverts et al., 2011; Tiffin et al., 2005). Similar gender differences have been reported in other studies with a focus on the health and well-being of middle-aged and older adults in mainland China (Fu, 2021; Liu et al., 2019). Previous literature suggested that men might receive greater health benefits from upward occupational mobility than women (Janicki-Deverts et al., 2011). Moreover, men who experience unwanted job mobility or work in a disadvantaged occupational position might suffer from a loss of role identity and self-esteem, which may further affect their health and well-being (Tiffin et al., 2005). There is also evidence that for women, the health benefits associated with upward occupational mobility may only partially compensate for the stress experienced during this process (Janicki-Deverts et al., 2011).

This study contributes to the growing literature on the health consequences of socioeconomic mobility among older adults in developing countries. We found that socioeconomic conditions at different life stages could cumulatively influence cognitive function in later life. Our findings highlight the health gains of upward occupational mobility across generations in the context of cognitive aging. Although there was no evidence that educational mobility was associated with cognitive impairment in our study population, having more years of education seemed to have a protective effect on cognitive impairment in later life. These findings call for effective interventions to support vulnerable populations to gain access to higher education and career advancement opportunities.

Limitations and Directions for Future Research

Our results cannot be fully understood without considering the limitations of the study. First, the generalization of the findings might be limited due to potential self-report bias and sample selection bias. Some measurements of SES (e.g., father’s occupation, father’s education) relied on retrospective reports by participants or their family members, which could lead to potential misclassification due to recall errors and subsequently underestimated associations (Batty et al., 2005). However, previous researchers also indicated that retrospective studies could contribute valuable information (Hardt & Rutter, 2004). The majority of the participants in this study had very few years of schooling, such that our findings cannot be generalized to older adults with higher educational attainment. Moreover, socioeconomic mobility in this study was determined by the mobility from father to respondents and was measured as a categorical variable with three or four categories, which may have removed some meaningful variation in patterns of socioeconomic transitions over time. Another limitation of this study is that we could not adjust for potential confounding factors such as life-course traumatic events and environmental factors that might affect the psychosocial well-being of individuals (Richards et al., 2017; Zhao & Fu, 2010). This information was not available in the CLHLS data but might affect the patterns of cognitive function in later life.

Given these limitations, future studies could benefit by measuring socioeconomic mobility as a continuous variable or analyzing socioeconomic trajectories to capture the dynamic nature of SES over the life course. It will also be valuable for future research to consider adjusting for other confounders (e.g., traumatic experiences, geographic location) that might influence socioeconomic mobility and cognitive function. The mechanisms through which socioeconomic mobility shapes cognitive function over the life course needs to be further investigated as well. An additional future direction of research is to distinguish voluntary and involuntary socioeconomic mobility, especially for occupational mobility. Previous research reported that voluntary and involuntary job turnover might affect work characteristics and outcomes in different ways (Strully, 2009; Wu, 2010). It is therefore plausible to believe that voluntary and involuntary occupational mobility might exhibit different physiological and behavioral responses and as a consequence would influence cognitive function differently.

Implications for Policy and Practice

China has over one-fifth of the world’s older population aged 65 years or above and the oldest old individuals aged 80 years or above are the fastest growing segment of the older population (Ortman et al., 2014). The burden of dementia in mainland China is expected to increase dramatically (Chan et al., 2013), which calls for effective interventions to improve the cognitive function of Chinese older adults. Previous research has recommended both technology-based and human-based interventions to support people with cognitive impairment or dementia (Kemp et al., 2021; Lazar et al., 2018). Our findings further suggest that policies and interventions aimed at promoting upward occupational mobility for disadvantaged groups might improve their cognitive function in later life. To this end, more training programs for career advancement could be offered to individuals in early and middle stages of career. Such programs could focus on career consulting, professional networking, and leadership development (Szabó-Bálint, 2019; Womack et al., 2020). In addition, health professionals and policymakers should target more resources to disadvantaged older adults who experience involuntary occupational transitions.

The findings of this study also highlight the need for effective interventions to support vulnerable populations to gain access to fundamental and higher education. We found that respondents’ own educational attainment was a robust predictor of cognitive function in later life, regardless of father’s or spouse’s educational achievement. This is consistent with the fundamental cause theory, which frames education as a fundamental cause of health and disease (Ross & Mirowsky, 2010). The fundamental role of education in one’s cognitive health stresses the importance of receiving formal education. Therefore, as suggested by Ross and Mirowsky (2010), policy makers may consider investing in educators and schools rather than just doctors and hospitals, as educational attainment may directly help improve health conditions at the population level. In the long run, the government may consider providing more programs and opportunities for adult learning, especially for middle-aged and older adults (Xu & Fu, 2009).

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by a grant from the Committee on Teaching and Faculty Development at Siena College.

ORCID iD

Rong Fu

Notes

Table A1.

Mini-Mental State Exam (MMSE) Questions, CLHLS 2014

Item	MMSE Question	Score (Total= 30)
Orientation	1. What time of day is it right now (morning, afternoon, evening)?	1
	2. What is the month (western or Chinese calendar) right now?	1
	3. What is the date (Chinese calendar day and month) of the mid-autumn festival?	1
	4. What is the season right now, spring, summer, fall, winter?	1
	5. What is the name of this county or district?	1
Naming foods	6. Please name as many kinds of food as possible in 1 minute (1 point for each food and 7 points for those who name 7 or more foods)	7
Registration	7. Repeat the name of “table” at the first attempt	1
	8. Repeat the name of “apple” at the first attempt	1
	9. Repeat the name of “clothes” at the first attempt	1
Calculation	10. $20 - $3 = ?	1
	11. $20 - $3- $3 = ?	1
	12. $20 - $3 - $3 - $3 = ?	1
	13. $20 - $3 - $3 - $3 - $3 = ?	1
	14. $20 - $3- $3 - $3 - $3 - $3 = ?	1
Copy a figure	15. Draw a figure following a given example	1
Recall	16. Repeat the name of “table” a while later	1
	17. Repeat the name of “apple” a while later	1
	18. Repeat the name of “clothes” a while later	1
Language	19. Name “pen”	1
	20. Name “watch”	1
	21. Repeat the following sentence: “What you plant, what you will get.”	1
	22. Follow the interviewer’s instruction and take a piece of paper using the right hand	1
	23. Follow the interviewer’s instruction and fold the paper in the middle using both hands	1
	24. Follow the interviewer’s instruction and place the paper on the floor	1

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