Research Article |
Corresponding author: Ksenia V. Rozhkova ( kvrozhkova@gmail.com ) © 2024 Ksenia V. Rozhkova.
This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Citation:
Rozhkova KV (2024) Does personality predict health? Non-cognitive skills, health behaviours, and longevity in Russia. Population and Economics 8(1): 132-155. https://doi.org/10.3897/popecon.8.e108813
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Non-cognitive skills have recently gained much attention as an explanation for various social outcomes, including health inequalities. This paper explores the relationship between non-cognitive skills measured as the Big Five and locus of control, health behaviours such as physical activity, smoking, and drinking, and the resulting measures of health. A set of binary and multinomial logit models, as well as Cox proportional hazard models for longevity, are estimated on rich panel RLMS-HSE data for the years 2011-2021. Conscientiousness from the Big Five and internal locus of control show a significant and consistent positive association with self-assessed health and objective longevity in both genders, which is only partly mediated through health behaviours. Gender-specific differences are also present, with neuroticism increasing the risks of mortality for males, and openness decreasing them for females. Openness, conscientiousness, extraversion, neuroticism, and internal locus of control all show a statistically significant link with self-assessed health. Policies, aimed at the formation of positive non-cognitive skills during early stages of socialisation, may be a promising instrument for improving individual health.
Big Five, health behaviours, locus of control, longevity, non-cognitive skills, personality, survival analysis
The World Health Organization defines health as a state of total physical, psychological, and social well-being, implying an objective lack of illnesses, but not necessarily limited to it (WHO 1946). From a microeconomic perspective, health is a core part of human capital which affects individual productivity, acquisition of skills, and, consequently, determines individuals’ earning potential (
Why do some people live longer and healthier lives than others? First, apart from genetically inherited diseases, the health risks are heavily dependent on health behaviours which individuals pursue. Heavy smoking and drinking, lack of physical exercise, unhealthy diet, and other actions all contribute to morbidity and mortality. Second, lack of access to quality medical care limits individual involvement in preventive and curative measures, which may significantly reduce longevity. Both reasons, at least partly, can be explained by socioeconomic status (SES) and education, which serve as the primary focus of health economics research. Individuals with lower SES are, on average, more inclined to bad health (
Another possible explanation for health inequalities might lie in individual psychological differences, which in economic research are usually referred to as non-cognitive skills. Non-cognitive skills are defined as a stable pattern of thoughts, feelings, and behaviours, which determine individual responses to certain circumstances (
Psychologists have long been exploring personality as a determinant of individual health. Personality serves as a cause of various illnesses, including cardio-vascular and inflammatory diseases (
This study is one of the first papers analysing the relationship between non-cognitive skills, health behaviours and the resulting longevity for Russia on rich longitudinal data. Using duration analysis design, as well as binary and multinomial logit models, this study establishes a statistically significant association between non-cognitive skills, measured as the Big Five or internal locus of control, and self-assessed health, health behaviours, and longevity. The results suggest that there exists a direct effect of non-cognitive skills on health, not mediated through individual health behaviours or acquired education.
The theoretical framework of this research is given by Grossman’s demand for health model (1972). Health is an important part of human capital, with higher levels of health implying higher individual productivity. Everyone is born with a certain innate stock of health, which suffers from amortisation, but can be enhanced with health investments. Health investment is a set of actions, including regular doctor visits, following medical prescriptions, conducting a healthy lifestyle with regular physical activities, healthy nutrition, and avoiding harmful addictions like smoking and drinking alcohol. Although health investments require financial resources and time, they also extend the time horizon, during which an individual receives benefits from other components of human capital. This makes health complementary to other forms of human capital and determines one’s ability to acquire other skills (
From the psychological perspective, there are several theories explaining the link between personality and health inequalities. One of them is the Health belief model (
Empirical literature investigating the relationship between non-cognitive skills and health generally relies on the well-known psychological concepts. The first concept is the Big Five, suggesting that everyone can be described from the viewpoint of five personality categories: openness to experience (defined as being imaginative and inventive), conscientiousness (comprised of diligence and hard work), extraversion (defined as being sociable and assertive), agreeableness (includes altruism, cooperation, and friendliness), and neuroticism (or the reverse of emotional stability). An alternative measure, frequently used in literature, is locus of control, which reflects the individual tendency to explain various life events with external forces (external locus of control) or with one’s own actions and behaviours (internal locus of control).
The particular attention to the Big Five and locus of control as proxies for non-cognitive skills is motivated by their stability over time. Psychological literature reports a trend increase in neuroticism and a decrease in conscientiousness in older cohorts due to ageing, as well as a decrease in neuroticism, extraversion, openness and an increase in agreeableness and conscientiousness in younger cohorts due to their maturing (
Although economic research has been mostly relying on the Big Five and locus of control as the core measures of non-cognitive skills, the choice of measure is usually motivated by data availability. For instance,
Psychological literature consistently reports conscientiousness to be the most predictive personality trait for health-related outcomes (
In contrast to the health benefits of conscientiousness, neuroticism is generally associated with the reduced health and higher mortality risks (
Conscientiousness, agreeableness, and emotional stability are positively correlated with the overall good self-assessed health, although only neuroticism is significantly associated with an objective measure of the number of sick days (Bellman and Hübler 2022). On PIAAC data (Programme for the International Assessment of Adult Competencies) from Poland, The Big Five categories explain a larger portion of additional variance in self-assessed health (2.6%) than cognitive abilities (0.3%). While conscientiousness and extraversion are positively related to health, neuroticism implies a negative effect (
Locus of control is also consistently associated with health outcomes. Externality implies a significantly higher hazard rate ratio for mortality, although for women the effect is either small or insignificant after the adjustment for health behaviours (
An open question is why non-cognitive skills predict health-related outcomes and whether fostering positive skills will help the improvement of health. The main channels linking personality with longevity and other health outcomes are health behaviours such as nutrition, sleep, smoking, and drinking alcohol.
Gender differences in the effects also exist. While for men openness is associated with health-harming behaviours, for women the relationship is reversed (
An alternative pathway from non-cognitive skills to health is education.
Part of the relationship also runs through socioeconomic status and income (Öhman, 2015). Stronger associations between non-cognitive skills (both the Big Five and LOC) and health outcomes are observed for low-income earners (
For Russia, there exists only limited economic research on the determinants of health and longevity.
The link between non-cognitive skills, health behaviours and health outcomes, including longevity for Russia, remains largely unexplored. The probability of alcohol consumption and the amount of alcohol consumed are significantly affected by the Big Five. While only conscientiousness (a negative relationship) and extraversion (a positive relationship) are related to the probability of alcohol consumption, agreeableness and neuroticism are only related to the volume of consumption. Moreover, non-cognitive skills may mediate the relationship between education and alcohol consumption (
For this study, data from the Russian Longitudinal Monitoring Survey (RLMS-HSE) is used. The survey is conducted annually since 1994 on a large nationally representative sample of approximately 14,000 respondents from 5,000 households, and comprises a wide array of socioeconomic, psychological, and health-related questions. The data are retrieved from rounds 20-30 of the survey which were conducted in 2011-2021. The analysis is performed on a sample for all individuals aged 17-100 years. Additional models calculated on a limited sample aged 18-65 provide similar results. As health behaviours and the effect of non-cognitive skills on longevity might have gender specifics, analysis is performed for men and women separately.
The RLMS-HSE dataset contains several measures of non-cognitive skills. Wave 20, for 2011, provides questions related to individual locus of control. To construct a measure of internal locus of control, an average of 7 behavioural questions, assessed on a scale from 1 to 4, is used. Given the personality stability assumption, the 2011 measure is extrapolated to future rounds if the respondent participated in them. The final measure is standardised with a mean of zero and a standard deviation of one. The full list of questions and a basic distribution of responses are available in Table
In 2016 (round 24), a new block of 24 behavioural questions was introduced to the survey. These questions can be mapped into 5 categories: 3 questions are related to openness to experience, 7 to conscientiousness, 3 to extraversion, 4 to agreeableness, and 7 to neuroticism. The questions are assessed on a scale from 1 to 4. Each category is constructed as an average of the attributed questions and standardised with a mean of 0 and a standard deviation of 1. The Big Five is used separately from locus of control due to the time mismatch and loss of observations. Also, the longevity analysis with the Big Five only covers 5 years (from 2016 to 2021), while that with locus of control covers 10 years. Although this can be considered as a limitation, the results provide some interesting insights about the relationship between health and personality.
Year of reported death | Frequency (All-cause mortality) | Frequency (Health-related mortality) | Male sample (Health-related mortality) | Female sample (Health-related mortality) |
2012 | 221 | 202 | 115 | 87 |
2013 | 220 | 195 | 103 | 92 |
2014 | 209 | 185 | 105 | 80 |
2015 | 180 | 156 | 78 | 78 |
2016 | 146 | 136 | 72 | 64 |
2017 | 143 | 129 | 64 | 65 |
2018 | 175 | 168 | 85 | 83 |
2019 | 146 | 134 | 71 | 63 |
2020 | 154 | 146 | 80 | 66 |
2021 | 155 | 144 | 70 | 74 |
Total | 1,749 | 1,595 | 843 | 752 |
Three different approaches are used to assess the association between non-cognitive skills and health, given the benefits of longitudinal data. We describe them below separately.
First, a multinomial logit model with standard errors clustered on the individual level is run to look at the non-cognitive predictors of self-assessed health. The model uses self-assessed health as the dependent variable. Self-assessed health is widely used in the research context as a valid predictor of the actual mortality (
(1)
where Y is the dependent variable taking value k from 1 to 5, which reflects five possible health states, X is the vector of explanatory variables, and β are the estimated coefficients, i refers to an individual. The model is estimated in two specifications. Specification one controls only for non-cognitive measures (either the Big Five or locus of control) and socio-demographic factors. Specification 2 also includes health behaviors (a set of binary variables for smoking, drinking, and physical activity). Socio-demographic factors include gender (a binary variable which equals to 1 for male and 0 for female), age, education (higher education, vocational degree, or lower as a reference category), marital status (a binary variable which equals to 1 if the respondent is officially married and 0, otherwise), presence of children (a binary variable), number of family members (a continuous variable), type of settlement (a binary variable which equals to 1 for urban area, and 0, otherwise), employment status (a binary variable which equals to 1 for being currently employed, and 0, otherwise). We additionally control for the year of observation (2016-2021 for the Big Five models, 2011-2021 for locus of control models)
Second, a block of logit models with standard errors clustered on the individual level is used to measure the link between non-cognitive skills and health behaviours such as smoking, alcohol consumption, and physical activity. These health behaviours are reportedly named among the most important determinants of individual health (
(2)
where Y is the dependent variable (smoking, alcohol consumption, or physical activity) taking value 1 or 0, X is the vector of explanatory variables, and β are the estimated coefficients, i refers to an individual. Smoking is a binary variable which is assessed with the following question: “Are you a smoker?”. Drinking is a binary variable which measures any alcohol consumption, including moderate, and is assessed with the following question: “Do you consume alcoholic beverages, including beer?”. Physical activity is a binary variable which is assessed with the following question: “During the past 12 months did you take part, at least 12 times, in some type of physical activities?”, with the list of activities including running, swimming, exercising in a gym, walking, cycling, aerobics, shaping, yoga, playing basketball, volleyball, badminton, tennis, boxing, martial arts, or others. Similar to Model 1, independent variables include gender, age, education, marital status, presence of children, number of family members, type of settlement, employment status, year of observation.
Health behaviours may serve as channels of non-cognitive skills, eventually linking them to health. Therefore, these variables are used as dependent for logit models and as independent variables for other health and survival models.
When it comes to the association between personality and health, reversed causality may arise. First, worse health may affect certain measures of personality traits, given that data are collected via self-assessment. For instance, bad health may increase neuroticism and lead to the externalisation of locus of control. Second, reversed causality may arise when considering health behaviours. Although excessive drinking and physical activity may shift personality, less evidence exists for smoking (
λ(t, X, β, λ0) = f(X, β)λ0(3)
where λ is the expected hazard at time t, λ0 is the base hazard, corresponding to f(.) = 1, f(X, β) = exp(Xʹβ), X is the vector of explanatory variables, and β are the estimated coefficients. The non-parametric Cox proportional hazard model allows for a flexible baseline hazard function. The model is estimated in two specifications. Specification one controls only for non-cognitive measures (either the Big Five or locus of control) and socio-demographic factors. Specification 2 also includes health behaviors. Independent variables include gender, education, marital status, presence of children, number of family members, type of settlement, and employment status.
The survival model uses the fact of the reported death of a respondent determined from the household survey. If the household is surveyed at least two rounds in a row, the head of the household reports the absence of the household members who took part in the survey during the previous round. The reasons of absence can be the following: 1) the household member moved to another address; 2) the household member formed a new household; 3) the household member died (with a cause of death collected from 2001); 4) other reasons. We divide causes of death into external and health-related. From 2012 to 2021 there were a reported 1,749 all-cause deaths among the survey participants, 154 of them were due to external causes and 1,595 were related to medical conditions. The distribution of the reported deaths across years and genders is presented in Table
Tables
Second, there is a positive association between health self-assessment and education on Russian data before and after controlling for health habits, which is observed only on the female sample. Higher education implies a higher probability of good health (approximately 2 percentage points in both the Big Five and locus of control before and after controlling for habits) and a reduced probability of bad (approximately 3 percentage points) and very bad health (approximately 1 percentage point). The size of the effect is slightly reduced after the introduction of health habits.
Third, we observe a significant association between health and non-cognitive skills, proxied with different psychological instruments. From the Big Five, there is a beneficial association between health and conscientiousness, both before and after controlling for habits. Higher conscientiousness is positively associated with a better health assessment and negatively with worse. The size of the effect is only slightly reduced after controlling for physical activity, smoking, and drinking. In contrast, neuroticism is associated with the reduced probability of good health (for the male sample, one standard deviation increase decreases the probability of good health by 4 percentage point, respectively; for the female sample, by 3.7 percentage point) and the increase of bad and very bad health, which is also in line with the literature (
The other Big Five categories demonstrate fewer stable results and are less consistent across genders. Extraversion demonstrates an increased probability of good health, a reduced probability of average health, and mixed results for bad and very bad health in the female sample. Openness demonstrates a reduced probability of good health in both genders, but a higher probability of average health. Agreeableness shows a very small positive association with very good health and low significance on the female sample. Finally, internal locus of control is associated with better self-assessed health both before and after controlling for health-related habits. For both genders, a standard deviation increase in internality is associated with a 3-percentage point lower probability of bad health and 4 percentage point higher probability of good health. Figures
The relationship between self-assessed health and the Big Five for the male sample, marginal effects from multinomial logit regression1
Very bad | Bad | Average | Good | Very good | Very bad | Bad | Average | Good | Very good | |
Openness | -0.00166 | 0.000661 | 0.0202*** | -0.0185*** | -0.000638 | -0.00189 | -0.000547 | 0.0193*** | -0.0163*** | -0.000507 |
(0.00141) | (0.00372) | (0.00670) | (0.00604) | (0.00128) | (0.00142) | (0.00376) | (0.00676) | (0.00603) | (0.00127) | |
Conscientiousness | -0.00542*** | -0.0142*** | -0.00399 | 0.0185*** | 0.00508*** | -0.00592*** | -0.0141*** | -0.00547 | 0.0201*** | 0.00533*** |
(0.00149) | (0.00373) | (0.00682) | (0.00619) | (0.00132) | (0.00160) | (0.00379) | (0.00686) | (0.00617) | (0.00132) | |
Extraversion | -0.000813 | 0.00121 | -0.0108* | 0.00995* | 0.000448 | -0.00114 | 0.000235 | -0.0110* | 0.0115** | 0.000356 |
(0.00114) | (0.00337) | (0.00590) | (0.00531) | (0.00101) | (0.00116) | (0.00342) | (0.00597) | (0.00534) | (0.000992) | |
Agreeableness | 5.58e-05 | 0.000270 | -0.00653 | 0.00529 | 0.000918 | 0.000202 | 0.000530 | -0.00690 | 0.00508 | 0.00109 |
(0.00139) | (0.00359) | (0.00641) | (0.00585) | (0.00123) | (0.00148) | (0.00366) | (0.00649) | (0.00587) | (0.00124) | |
Neuroticism | 0.00148 | 0.0123*** | 0.0257*** | -0.0424*** | 0.00290*** | 0.00142 | 0.0127*** | 0.0256*** | -0.0425*** | 0.00278** |
(0.00117) | (0.00331) | (0.00591) | (0.00541) | (0.00112) | (0.00121) | (0.00342) | (0.00599) | (0.00541) | (0.00111) | |
Higher education | 0.00604** | 0.000363 | -0.00824 | 0.00243 | -0.000603 | 0.00463 | -0.00573 | -0.0121 | 0.0115 | 0.00171 |
(0.00294) | (0.00823) | (0.0144) | (0.0131) | (0.00303) | (0.00305) | (0.00849) | (0.0142) | (0.0128) | (0.00287) | |
College | 0.00332 | 0.00971 | -0.0154 | -0.000407 | 0.00278 | 0.00336 | 0.00737 | -0.0158 | 0.00160 | 0.00344 |
(0.00283) | (0.00841) | (0.0143) | (0.0128) | (0.00265) | (0.00297) | (0.00847) | (0.0144) | (0.0128) | (0.00265) | |
Physical | -0.0151*** | -0.0259*** | 0.00213 | 0.0344*** | 0.00449* | |||||
(0.00361) | (0.00797) | (0.0125) | (0.0111) | (0.00231) | ||||||
Smoke | 0.0000 | -0.000165 | 0.0226** | -0.0158 | -0.00663*** | |||||
(0.00233) | (0.00644) | (0.0113) | (0.0101) | (0.00214) | ||||||
Drink | -0.0113*** | -0.0509*** | 0.0731*** | -0.00942 | -0.00149 | |||||
(0.00239) | (0.00561) | (0.0111) | (0.0104) | (0.00225) | ||||||
N of observations | 20,057 | 20,174 | ||||||||
N of clusters | 4,299 | 4,302 |
The relationship between self-assessed health and the Big Five for the female sample, marginal effects from multinomial logit regression
Very bad | Bad | Average | Good | Very good | Very bad | Bad | Average | Good | Very good | |
Openness | 0.000785 | -0.00798** | 0.0152*** | -0.00916** | 0.00120 | 0.0000 | -0.0105*** | 0.0174*** | -0.00818* | 0.00125 |
(0.00135) | (0.00352) | (0.00554) | (0.00463) | (0.000781) | (0.00134) | (0.00357) | (0.00557) | (0.00461) | (0.000781) | |
Conscientiousness | -0.00390*** | -0.0158*** | 0.0102* | 0.00882* | 0.000683 | -0.00403*** | -0.0164*** | 0.0110** | 0.00885* | 0.000575 |
(0.00129) | (0.00360) | (0.00551) | (0.00467) | (0.000831) | (0.00129) | (0.00366) | (0.00556) | (0.00466) | (0.000832) | |
Extraversion | -0.00229* | 0.00575* | -0.0119** | 0.00834** | 0.000130 | -0.00235** | 0.00473 | -0.0112** | 0.00863** | 0.000153 |
(0.00119) | (0.00328) | (0.00486) | (0.00402) | (0.000608) | (0.00118) | (0.00331) | (0.00488) | (0.00399) | (0.000609) | |
Agreeableness | -0.000528 | -0.00360 | 0.00102 | 0.00177 | 0.00133* | -0.000625 | -0.00390 | 0.00142 | 0.00178 | 0.00132* |
(0.00131) | (0.00354) | (0.00510) | (0.00433) | (0.000780) | (0.00131) | (0.00359) | (0.00514) | (0.00433) | (0.000786) | |
Neuroticism | 0.00113 | 0.0184*** | 0.0164*** | -0.0370*** | 0.00105 | 0.000996 | 0.0186*** | 0.0166*** | -0.0373*** | 0.00101 |
(0.00121) | (0.00316) | (0.00483) | (0.00415) | (0.000753) | (0.00120) | (0.00321) | (0.00486) | (0.00413) | (0.000759) | |
Higher education | -0.0135*** | -0.0255*** | 0.0143 | 0.0232** | 0.00145 | -0.0160*** | -0.0338*** | 0.0220* | 0.0262*** | 0.00165 |
(0.00354) | (0.00813) | (0.0116) | (0.00942) | (0.00142) | (0.00366) | (0.00816) | (0.0115) | (0.00925) | (0.00139) | |
College | -0.00285 | -0.00772 | 0.0140 | -0.00407 | 0.000669 | -0.00360 | -0.0104 | 0.0165 | -0.00302 | 0.000601 |
(0.00221) | (0.00674) | (0.0105) | (0.00893) | (0.00160) | (0.00223) | (0.00680) | (0.0106) | (0.00892) | (0.00160) | |
Physical | -0.0178*** | -0.0301*** | 0.0368*** | 0.0102 | 0.000892 | |||||
(0.00336) | (0.00656) | (0.00912) | (0.00739) | (0.00135) | ||||||
Smoke | 0.00218 | 0.0265** | -0.0196 | -0.00938 | 0.000310 | |||||
(0.00403) | (0.0103) | (0.0136) | (0.0102) | (0.00174) | ||||||
Drink | -0.0122*** | -0.0469*** | 0.0546*** | 0.00480 | -0.000371 | |||||
(0.00226) | (0.00538) | (0.00788) | (0.00657) | (0.00114) | ||||||
N of observations | 32,255 | 32,411 | ||||||||
N of clusters | 6,619 | 6,620 |
The relationship between self-assessed health and internal locus of control for the male sample, marginal effects from multinomial logit regression
Very bad | Bad | Average | Good | Very good | Very bad | Bad | Average | Good | Very good | |
Internal locus of control | -0.00730*** | -0.0278*** | -0.0107** | 0.0419*** | 0.00394*** | -0.00797*** | -0.0284*** | -0.0121** | 0.0442*** | 0.00431*** |
(0.00113) | (0.00284) | (0.00531) | (0.00495) | (0.000955) | (0.00119) | (0.00291) | (0.00535) | (0.00496) | (0.000972) | |
Higher education | 0.00219 | -0.00697 | 0.00503 | 0.00118 | -0.00142 | 0.000579 | -0.0109 | -0.00422 | 0.0139 | 0.000644 |
(0.00242) | (0.00747) | (0.0125) | (0.0116) | (0.00213) | (0.00254) | (0.00768) | (0.0124) | (0.0114) | (0.00200) | |
College | 0.00206 | -0.000779 | 0.0135 | -0.0151 | 0.000337 | 0.00188 | -0.00273 | 0.0109 | -0.0110 | 0.000942 |
(0.00252) | (0.00720) | (0.0122) | (0.0112) | (0.00208) | (0.00261) | (0.00726) | (0.0123) | (0.0112) | (0.00208) | |
Physical | -0.0173*** | -0.0128* | 0.00338 | 0.0239** | 0.00280 | |||||
(0.00364) | (0.00685) | (0.0108) | (0.00978) | (0.00180) | ||||||
Smoke | -0.00259 | 0.00494 | 0.0414*** | -0.0376*** | -0.00611*** | |||||
(0.00209) | (0.00556) | (0.00952) | (0.00861) | (0.00167) | ||||||
Drink | -0.0134*** | -0.0539*** | 0.0810*** | -0.0130 | -0.000684 | |||||
(0.00201) | (0.00485) | (0.00966) | (0.00919) | (0.00182) | ||||||
N of observations | 33,422 | 33,622 | ||||||||
N of clusters | 5,191 | 5,193 |
The relationship between self-assessed health and internal locus of control for the female sample, marginal effects from multinomial logit regression
Very bad | Bad | Average | Good | Very good | Very bad | Bad | Average | Good | Very good | |
Internal locus of control | -0.0107*** | -0.0335*** | 0.00642 | 0.0349*** | 0.00292*** | -0.0115*** | -0.0364*** | 0.00979** | 0.0350*** | 0.00303*** |
(0.00123) | (0.00294) | (0.00436) | (0.00356) | (0.000746) | (0.00129) | (0.00294) | (0.00436) | (0.00354) | (0.000757) | |
Higher education | -0.00558** | -0.0267*** | 0.0112 | 0.0205** | 0.000608 | -0.00728*** | -0.0349*** | 0.0170* | 0.0244*** | 0.000736 |
(0.00274) | (0.00716) | (0.00997) | (0.00806) | (0.00117) | (0.00278) | (0.00722) | (0.00993) | (0.00792) | (0.00115) | |
College | -0.000786 | -0.00942 | 0.0179* | -0.00572 | -0.00195 | -0.00117 | -0.0129** | 0.0199** | -0.00378 | -0.00204 |
(0.00198) | (0.00613) | (0.00918) | (0.00766) | (0.00135) | (0.00199) | (0.00621) | (0.00926) | (0.00767) | (0.00134) | |
Physical | -0.00774*** | -0.0289*** | 0.0290*** | 0.00623 | 0.00141 | |||||
(0.00252) | (0.00613) | (0.00812) | (0.00642) | (0.00102) | ||||||
Smoke | 0.00236 | 0.0214** | -0.00660 | -0.0181** | 0.000945 | |||||
(0.00357) | (0.00933) | (0.0117) | (0.00855) | (0.00142) | ||||||
Drink | -0.0128*** | -0.0497*** | 0.0674*** | -0.00651 | 0.00158 | |||||
(0.00191) | (0.00455) | (0.00668) | (0.00559) | (0.00106) | ||||||
N of observations | 55,442 | 55,723 | ||||||||
N of clusters | 7,960 | 7,962 |
Tables
Male sample | Female sample | |||||
Physical activity | Smoking | Drinking | Physical activity | Smoking | Drinking | |
Openness | 0.0335*** | -0.00754 | 0.00539 | 0.0505*** | 0.00566 | 0.0192*** |
(0.00540) | (0.00832) | (0.00672) | (0.00481) | (0.00488) | (0.00605) | |
Conscientiousness | 0.00513 | -0.0297*** | -0.0204*** | -0.000459 | -0.00535 | -0.00458 |
(0.00551) | (0.00842) | (0.00714) | (0.00472) | (0.00498) | (0.00603) | |
Extraversion | 0.00832* | -0.00160 | 0.0169*** | 0.00456 | 0.0196*** | 0.0230*** |
(0.00475) | (0.00749) | (0.00588) | (0.00412) | (0.00429) | (0.00525) | |
Agreeableness | 0.00531 | 0.00195 | -0.0116* | -0.00603 | -0.00574 | -0.00285 |
(0.00525) | (0.00801) | (0.00659) | (0.00428) | (0.00456) | (0.00565) | |
Neuroticism | -0.00726 | 0.0239*** | 0.00374 | -0.00664 | 0.00910** | 0.0135*** |
(0.00466) | (0.00738) | (0.00612) | (0.00411) | (0.00421) | (0.00522) | |
Higher education | 0.139*** | -0.217*** | 0.0369*** | 0.136*** | -0.116*** | 0.0414*** |
(0.0107) | (0.0169) | (0.0142) | (0.00942) | (0.0102) | (0.0122) | |
College | 0.0455*** | -0.0856*** | 0.00453 | 0.0445*** | -0.0499*** | 0.0166 |
(0.0118) | (0.0168) | (0.0143) | (0.00957) | (0.00851) | (0.0112) | |
N of observations | 20,344 | 20,332 | 20,229 | 32,626 | 32,605 | 32,482 |
N of clusters | 4,306 | 4,306 | 4,303 | 6,629 | 6,629 | 6,627 |
Health behaviours and internal locus of control, logit model marginal effects
Male sample | Female sample | |||||
Physical activity | Smoking | Drinking | Physical activity | Smoking | Drinking | |
Internal locus of control | 0.0211*** | -0.0420*** | 0.00304 | 0.0275*** | -0.00647 | 0.0320*** |
(0.00418) | (0.00691) | (0.00552) | (0.00340) | (0.00422) | (0.00470) | |
Higher education | 0.149*** | -0.224*** | 0.0319** | 0.141*** | -0.109*** | 0.0368*** |
(0.00907) | (0.0156) | (0.0129) | (0.00747) | (0.00974) | (0.0105) | |
College | 0.0523*** | -0.0865*** | 0.00566 | 0.0497*** | -0.0433*** | 0.0255*** |
(0.00969) | (0.0157) | (0.0129) | (0.00760) | (0.00832) | (0.00957) | |
N of observations | 33,946 | 33,936 | 33,740 | 56,146 | 56,111 | 55,884 |
N of clusters | 5,196 | 5,196 | 5,195 | 7,969 | 7,969 | 7,967 |
Finally, Table
Second, higher education reduces the risks of mortality in both genders, although in self-assessed health models it was only statistically significant for the female sample. In the locus of control models, higher education reduces the risks by 40 percentage points for males and by 32 percentage points for females before controlling for health behaviours. The effect is slightly reduced after their introduction. In the Big Five models, the size of the effect is similar but higher education loses its significance in the model with health habits. The difference in findings can be explained by a shorter period covered by the Big Five models.
Third, non-cognitive skills demonstrate a significant association with longevity. Conscientiousness from the Big Five appears to be the most consistent characteristic, reducing the probability of health-related mortality in both male and female samples. One standard deviation increase in conscientiousness reduces the risk of death by 20 percentage points for males and by 12 percentage points for females after controlling for health habits. One standard deviation increase of neuroticism increases the risks of mortality by 12 percentage points. At the same time, there is a positive effect of openness to experience for females: one standard deviation increase in openness increases the mortality risk by 13 percentage points. Finally, internal locus of control is associated with the reduced risks of mortality for both men (by 11 percentage points) and women (10 percentage points).
Determinants of mortality, total adult sample, non-parametric Cox regression results, hazard ratios1
Male sample | Female sample | |||||||
Openness | 0.971 | 0.969 | 0.866* | 0.853* | ||||
(0.0686) | (0.0675) | (0.0703) | (0.0702) | |||||
Conscientiousness | 0.797*** | 0.780*** | 0.880* | 0.870** | ||||
(0.0637) | (0.0629) | (0.0592) | (0.0587) | |||||
Extraversion | 1.065 | 1.073 | 1.030 | 1.031 | ||||
(0.0907) | (0.0927) | (0.0566) | (0.0576) | |||||
Agreeableness | 1.039 | 1.034 | 1.076 | 1.073 | ||||
(0.0696) | (0.0669) | (0.0854) | (0.0851) | |||||
Neuroticism | 1.121* | 1.130* | 1.007 | 1.012 | ||||
(0.0726) | (0.0767) | (0.0842) | (0.0854) | |||||
Internal locus of control | 0.887** | 0.874*** | 0.903* | 0.888** | ||||
(0.0473) | (0.0426) | (0.0530) | (0.0529) | |||||
Higher education | 0.778 | 0.683** | 0.697*** | 0.601*** | 0.808 | 0.732* | 0.728* | 0.681** |
(0.139) | (0.122) | (0.0970) | (0.0888) | (0.137) | (0.137) | (0.137) | (0.126) | |
College | 0.849 | 0.804 | 0.877 | 0.835 | 0.940 | 0.915 | 0.924 | 0.908 |
(0.153) | (0.141) | (0.0987) | (0.0944) | (0.194) | (0.186) | (0.125) | (0.124) | |
Physical | 0.759 | 0.758 | 0.386*** | 0.565*** | ||||
(0.151) | (0.143) | (0.0973) | (0.118) | |||||
Smoke | 1.610*** | 1.876*** | 1.767* | 2.534*** | ||||
(0.206) | (0.192) | (0.526) | (0.590) | |||||
Drink | 0.810 | 0.722*** | 0.957 | 0.982 | ||||
(0.109) | (0.0685) | (0.157) | (0.138) | |||||
N of observations | 19,178 | 19,298 | 32,069 | 32,272 | 30,890 | 31,045 | 53,121 | 53,398 |
N of subjects | 4,282 | 4,285 | 5,168 | 5,169 | 6,594 | 6,597 | 7,926 | 7,929 |
N of failures | 266 | 268 | 527 | 531 | 266 | 266 | 460 | 461 |
Non-cognitive skills are a rapidly developing research topic in social sciences which might well be of education policy relevance. Previous research for Russia suggests that non-cognitive skills, primarily proxied by valid psychological concepts such as the Big Five and locus of control, are related to labour market outcomes (
First, non-cognitive skills are predictive of self-assessed health in both men and women. Higher conscientiousness and emotional stability from the Big Five demonstrate a consistent positive association with better health, which is in line with most of the existing empirical literature, both in the field of psychology (e.g.,
Second, non-cognitive skills are not only relevant for a subjective measure of health but also for objective ones, such as longevity. Conscientiousness and internal locus of control consistently reduce the risks of mortality in both genders.
Third, only a small part of the observed effect of non-cognitive skills is transmitted via health behaviour variables, such as physical activity and smoking. Openness to experience and internal locus of control in both genders are positively associated with the probability of regular sports activities, while conscientiousness (negative, in males), extraversion (positive, in females), neuroticism (positive, in both genders), and internal locus of control (negative, in males) have a statistically significant link with smoking. Controlling for health behaviours only slightly reduces the size of the effect on non-cognitive skills. Therefore, other transition mechanisms should be carefully reassessed. Adding non-cognitive skills into health analysis makes our idea about health investments more heterogeneous than it is commonly assumed. Non-cognitive skills should be carefully considered, especially when “education-health gradient” is assessed.
Fourth, a positive relationship between higher education and health, which is well-established in research literature, reveals itself based on the Russian data, although a statistically significant and noticeable effect only arises for the female sample. Highly educated individuals are more likely to occupy safer jobs, pursue healthy lifestyles (including higher probability of physical activity), have better access to qualified medical services, and to be future-oriented, demonstrating preferences towards long-term health investments (
This study has several limitations. First, there is no control for cognitive abilities which are often seen as a confounding factor in education-health gradient. However, studying the impact of education on health or vice versa is not the focus of this paper. In this analysis, we assume that controlling the level of education also absorbs the effect of cognitive abilities. Second, the time for which non-cognitive measures are available (especially the Big Five) is limited. This may affect the results of the survival analysis. Still, the obtained results provide valuable and rare insights for Russia, concerning the effect of personality on individual health inequalities. From an education policy perspective, promoting such non-cognitive skills as conscientiousness, internal locus of control, and emotional stability as part of early socialisation during the initial stages of education may positively affect health and longevity in the long run.
Data is openly available at https://www.hse.ru/en/rlms/
This research is part of a Strategic Project ‘Social Policy for Sustainable Development and Inclusive Economic Growth’ at National Research University Higher School of Economics (Moscow, Russia).
WHO (1946) Constitution of the World Health Organization // American Journal of Public Health: 36(11): 1315-23. https://doi.org/10.2105/ajph.36.11.1315
Rozhkova Ksenia – junior research fellow at Laboratory for Labour Market Studies, HSE University, Moscow, 101000, Russia. Email: krozhkova@hse.ru