Does social participation improve cognitive abilities of the elderly?

Journal of Population Economics (2021)Cite this article

Abstract

This paper examines the effect of social participation on cognitive performance using data from a longitudinal survey of the elderly in China. It addresses the problem of endogenous participation by exploiting the variation in changes in social participation that are driven by changes in community service for social activities. The results show that participating in social activities has significantly positive impacts on cognitive function among the elderly. The point estimates indicate that engaging in social activity raises cognitive scores by 29% of a standard deviation.

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Fig. 1

Notes

  1. 1.

    Maguire et al. (2000) found the posterior hippocampi, which are shown to be involved in spatial memory and navigation, of taxi drivers are significantly larger than those of the control subjects, and their volume is positively correlated with the amount of time spent as a taxi driver.

  2. 2.

    See Douglas et al. (2017) for a comprehensive review of the literature on gerontology investigating the association between social participation and health of the elderly.

  3. 3.

    The mental health of the rural elderly in China were even worse for those who were left behind when their adult children migrated to urban areas (Scheffel and Zhang 2019; Cai et al. 2020).

  4. 4.

    According to the World Population Prospects 2019 (United Nations 2019), the percentage of people aged 65 years or over will rise from 11.5 in 2019 to 16.9 in 2030, an increase of 5.4 percentage points, while the prediction on worldwide increase of the ratio of elderly people is 2.6 percentage points over 2019–2030.

  5. 5.

    According to the National Bureau of Statistics, the number of community services institute grew from 63,173 in 2009 to 104,237 in 2012, an increase of 65% (https://data.stats.gov.cn/english/easyquery.htm?cnC01). Accessed 21 December 2020.

  6. 6.

    CLHLS has been conducted every 3 years since 2002. To avoid more attrition in panel data over a longer period, I restrict the analysis to the two recent waves in 2011/2012 and 2014.

  7. 7.

    The Center for Health Ageing and Development Studies at Peking University collected these data from various statistical yearbooks and matched them with individuals surveyed by CLHLS. The data includes county-level demographic information in 2000 and 2010, and prefecture-level socioeconomic information in 2008 and 2011.

  8. 8.

    The Chinese version of MMSE was designed to make the questions easy to understand and practically answerable for subjects with normal cognitive function. For instance, in the orientation part, the survey asked “What is the month (Western or Chinese calendar) right now?” since some old person living in a rural area may not know the Western calendar. Respondents were asked to name as many kinds of food as possible within 1 min instead of reading and writing a sentence, due to the fact that the majority of very old Chinese subjects, particularly women, were illiterate (Zeng and Vaupel 2002).

  9. 9.

    Following the literature, I counted “unable to answer” as an incorrect answer (Fillenbaum et al. 1988; Herzog and Wallace 1997; Nguyen et al. 2003). Later, I also examine robustness of the main result to possible measurement error in cognitive scores.

  10. 10.

    The percentage of respondents with an answer belonging to the category “almost every day,” “not daily, but once a week,” “not weekly, but at least once a month,” “not monthly, but sometimes,” and “never” in wave 2011/2012 is 3.55, 2.33, 2.06, 8.72, and 83.34%, respectively. As a robustness check, I also examine the association between frequency of social participation and cognitive function. See footnote 18 for more details.

  11. 11.

    The results are similar if I use data from the wave 2014, which are available upon request.

  12. 12.

    Time-invariant explanatory variables are dropped in the fixed-effect estimation. These include dummies for male, occupation categories before age 60, and education categories.

  13. 13.

    For reasons of confidentiality, the data does not include identifier for prefecture or administrative levels below prefecture (including county and community). I infer respondents were from the same county if they were from the same province, and had the same information on prefecture-level GDP ratio of first industry in 2011 and county-level variables, including total area, number of male aged 6 years old and above in 2010, and number of female aged 6 years old and above in 2010. The inference may be imperfect due to missing values in some of the variables above and possible errors in statistical data.

  14. 14.

    The Kleibergen-Paap F-statistic of coefficient of instrument in the first-stage estimation declines from 85.7 to 40.2.

  15. 15.

    The respondents were asked to reflect their preference by the question “Whether you hope your community to provide services for social activities?”

  16. 16.

    The ADL comprise bathing, dressing, using the toilet, indoor transfer, continence, and eating. Individuals were classified as having difficulty in ADL if they were impaired in any of the six activities.

  17. 17.

    In China, the legal age of retirement is 60 for males, 55 for female cadres, and 50 for female workers. Given that the respondents in the sample were all aged 65 or above at the time of the survey, it is not surprising to observe such a high retirement rate.

  18. 18.

    I also examine the association between frequency of social participation and cognitive function by including four dummy variables (i.e. indicators of “almost every day,” “not daily, but once a week,” “not weekly, but at least once a month,” and “not monthly, but sometimes”) in the regressions. The OLS and FE estimates suggest that cognitive scores of all four groups are significantly higher than non-participants (the reference group). However, the coefficients of the four dummies are very similar in magnitude, indicating differences in cognitive function are mainly along the extensive margin of social participation. To identify the impact of different frequency of social participation on cognitive function requires exogenous variation in intensive margins of social participation, which are left for further research.

  19. 19.

    These results are consistent with predictions from Grossman’s (1972) health capital model. According to the model, the younger people may response more in terms of social participation to the provision of service for social activities by the community, as the depreciation rate of health stock may rise with age. The more educated people may also respond more, since they could be more efficient producer of health. The high-income group may lack the incentive to take part in social activities because of greater opportunity cost.

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Acknowledgements

I would like to thank the Editor, Klaus F. Zimmermann, and the three anonymous referees for their very helpful comments and suggestions. I am grateful to Xiaoyan Lei, Yi Zeng, Yaohui Zhao, and participants at the labour workshop of the China Center for Economic Research at Peking University and the 4th International Symposium on Contemporary Labor Economics at Xiamen University for their comments on a previous draft. I also acknowledge Huashuai Chen, Lingguo Cheng, and Faju Li for their assistance in using the data, and Wei Li for excellent research assistance. The financial support of the National Natural Science Foundation of China (71703058) is gratefully acknowledged. All remaining errors are my own.

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    Shu Cai

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Appendix 1

Appendix 1

Table 12 The Chinese version of the Mini-Mental State Exam (MMSE) in CLHLS
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Cai, S. Does social participation improve cognitive abilities of the elderly?. J Popul Econ (2021). https://doi.org/10.1007/s00148-020-00817-y

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Keywords

  • Cognitive function
  • Social participation
  • Ageing
  • Elderly

JEL classification

  • J14
  • I12
  • O53