Cognitive & affective wellbeing: Commonly cited events had little effect on wellbeing (promotion, being fired, friends passing), others had profound impacts (financial loss, death of partner, childbirth)

The differential impact of major life events on cognitive and affective wellbeing. Nathan Kettlewell et al. SSM - Population Health, Volume 10, April 2020, 100533. https://doi.org/10.1016/j.ssmph.2019.100533

Highlights
• We study the effect of 18 major life events on wellbeing.
• We use a large population-based cohort and fixed-effect regression models.
• Effects on affective and cognitive wellbeing are compared.
• Effects generally smaller when conditioning on other events.
• Events sometimes have different impacts on affective versus cognitive wellbeing.

Abstract: Major life events affect our wellbeing. However the comparative impact of different events, which often co-occur, has not been systematically evaluated, or studies assumed that the impacts are equivalent in both amplitude and duration, that different wellbeing domains are equally affected, and that individuals exhibit hedonic adaptation. We evaluated the individual and conditional impact of eighteen major life-events, and compared their effects on affective and cognitive wellbeing in a large population-based cohort using fixed-effect regression models assessing within person change. Several commonly cited events had little, if any, independent effect on wellbeing (promotion, being fired, friends passing), whilst others had profound impacts regardless of co-occurring events (e.g., financial loss, death of partner, childbirth). No life events had overall positive effects on both types of wellbeing, but separation, injury/illnesses and monetary losses caused negative impacts on both, which did not display hedonic adaptation. Affective hedonic adaptation to all positive events occurred by two years but monetary gains and retirement had ongoing benefits on cognitive wellbeing. Marriage, retirement and childbirth had positive effects on cognitive wellbeing but no overall effect on affective wellbeing, whilst moving home was associated with a negative effect on cognitive wellbeing but no affective wellbeing response. Describing the independent impact of different life events, and, for some, the differential affective and life satisfaction responses, and lack of hedonic adaptation people display, may help clinicians, economists and policy-makers, but individual's hopes for happiness from positive events appears misplaced.

Keywords: Life eventsAffective wellbeingCognitive wellbeingHedonic adaptation

Discussion

The present study confirms what people know; that not all life events are equal and many are concurrent with other events. In some respect, this may seem to be a self-apparent conclusion to anyone who has ever lived but epidemiological research often ignores this by using summed checklists to assess impact, or just evaluates the impact of one event (Dohrenwend, 2006; Gray et al., 2004; Wethington et al., 1997). Our results also quantify the difference and allow us to infer the average effect in the population. Other studies have noted differences between events in the magnitude or duration of effect on wellbeing (Frijters et al., 2011; Luhmann et al., 2012), however we focus on the total impact (both magnitude and duration). Previous longitudinal studies following individuals across time also indicate health shocks (the duration of disability) (Lucas, 2007), and separation (divorce) (Lucas, 2005; Lucas, Clark, Georgellis, & Diener, 2003) have long-term negative effects but unlike Lucas (2005), we found that the impact of the death of a spouse seemed to diminish by 2 years. The evidence for long-term effects of marriage and unemployment is mixed, with some studies showing that they continue to influence wellbeing long after they have occurred (Lucas, Clark, Georgellis, & Diener, 2004), while others report adaption to these same events (Clark et al., 2008; Frijters et al., 2011) as we found. Fig. 5 provides a comparison of the total impact (magnitude and duration) of each event on wellbeing. For instance, on average the impact of a major financial loss on both types of wellbeing was the greatest whilst health shocks, losing a loved one (widowed), separation or divorce tended not to have as much negative impact on both. Conversely, getting married, a major financial gain, retirement and childbirth had positive effects on cognitive wellbeing with little overall positive effect on affective wellbeing. These data demonstrate that the practice of treating life events as comparable is untenable.

The impact of some events is negligible after accounting for the impact of concurrent events. In general, the conditional effects of life events were a little closer to zero than the unconditional effects, but in almost all cases this was minimal, reflecting how uncommon co-occurrence actually was. However the unconditional positive effect of pregnancy on cognitive wellbeing was all but reversed once concurrent events (childbirth) were accounted for.

These results also challenge the notion of many of the identified life events as being intrinsically “stressful”, the implication of which is that they should have some negative effect on wellbeing. Holmes and Rahe's Social Readjustment Scale (Holmes & Rahe, 1967) weights marriage as the sixth most stressful event yet we found no negative impact on affective wellbeing and a profound anticipatory and subsequent positive effect on life satisfaction. Conversely people's wellbeing in the lead up to some positive events was impaired, the most notable being reconciliation which most likely demonstrates the effect of relationship difficulties just prior to the event.

The differential impact of events on the components of affective and cognitive wellbeing supports their distinction as separate constructs, although both show hedonic adaptation. A novel aspect of the present study is the comparative differences of the affective and cognitive wellbeing response to certain events. For instance, some positive events had a substantial impact on cognitive wellbeing while eliciting relatively little impact on affective wellbeing or “happiness” (e.g., Married, Retired, Childbirth, Pregnant). In contrast, negative events tended to have comparable and untoward effects on both cognitive and affective wellbeing, with the exception of Separated which again elicited a greater (negative) impact on cognitive wellbeing, and Moving which had no affective response but reduced life satisfaction. The differential impact of events on the components of affective and cognitive wellbeing supports the distinction between wellbeing components and their treatment as separate constructs. It also implies that, on average, hoping for happiness from positive events appears misplaced.

Limitations

A few general issues are worth discussing in large, longitudinal models and studies of this kind. Such studies preclude the use of the experience sampling method of assessing affective wellbeing which many consider the best method for assessing short term intra-individual variation in affective wellbeing. The fixed effects models exclude anyone who did not experience the event in the time window of interest. This means that in any particular event, such as marriage, average differences in subjective wellbeing between married people and unmarried people may be present, however these between-group differences will not be revealed by the fixed effects model which estimates within-subject changes in the sample of interest. As a result, these population estimates can reveal what to expect once an event has occurred, but cannot be used to predict whether an event such as marriage will increase or decrease wellbeing in any particular case. That is, the effects of marriage may be specific to the kinds of people who get married and should not be offered as evidence or a reason to get married.

We used an unbalanced panel, which means a slightly different set of individuals may contribute to the pre- and post-event coefficients (although there is considerable overlap). A balanced approach (Clark et al., 2008) only includes people with measurements before and after the event, which ensures the same cohort is followed over time. However, balancing reduces efficiency and risks inducing potential selection effects, so other researchers have taken a more liberal approach and included anyone with more than one consecutive observation, regardless of when those observations occurred (Frijters et al., 2011), which we follow in this study. In a sensitivity analysis we restricted the sample to a balanced panel observed pre- and post-event (see Balanced Models in Supplementary Materials) which did not materially change the overall results or inferences.

We also note some causes of potentially non-random measurement error inherent in any dynamic model of this sort. First, due to censoring issues we do not know at time $t=1$ if a life event occurred before that first year (e.g., 2002). Similarly, at time $t=T$ we do not know whether an event occurred after the final year (e.g., 2016). We do not expect this to significantly bias our estimates since many events occur infrequently and this only affects years close to the endpoints of our data. A similar issue arises in the case of missing life event information‚ either because the respondent did not complete that part of the questionnaire or because they are missing from the sample in a particular year. In both cases, we assumed no life event occurred in the missing year when constructing pre- and post-indicators. Again, we expect any bias to be small given that most life events are infrequent and more than 65 percent of people are responding year-to-year (see Table S2 in Supplementary Material). In a follow-up analysis (Uncontaminated Models in Supplementary Materials), we excluded from the sample any observations within three years of missing life event data to estimate an uncontaminated (as well as balanced) model. This means we only estimated effects for the years 2005–2012, and so after balancing and de-contamination this was our most restricted sample. As a result, our estimates became less precise and, while generally qualitatively similar to the main results, some effects became statistically insignificant (particularly for the positive events).