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Monthly Archives: October 2012

Can statistics make you happy? Maybe if you’re a stats geek like me, but seriously can the path to happiness, or at least some insights into the path to follow be found through the use of statistics? The Office for National Statistics (ONS) have been engaged in work to create a quantitative measure of our national well-being and despite some criticisms in the mainstream media, along the lines that in many cases it was either pointless, or a case of stating the obvious, the work, carried out on a large scale, has produced some interesting results and points to a number of areas for future research. As the well-being data has been collected through the Labour Force Survey the analysis so far seems particularly revealing about the work we do, or indeed don’t do  (though this is perhaps also a weakness as the data reveals less about other non-work factors) and in this post I will be dealing primarily with these issues of work status, occupation type and their relationship with life-satisfaction.

Is working less the key to happiness? The ONS well-being statistics suggest this may well be the case. Image http://www.moderntoss.com

The most immediately striking thing about the well-being stats is the affect of age. According to the ONS figures well-being follows a ‘u’ shaped curve according to age as shown by this graph referring to responses to the question ‘Overall, how satisfied are you with your life nowadays? in which respondents were asked to give a rating on a scale of 0 -10 with 0 being not at all and 10 being completely.

Source: Annual Population Survey (APS) – Office for National Statistics. Data Collected between April 2011 and March 2012

What could be the reasons for this similarly high ratings given by members of the older and younger age group? One thing both groups also have in common is their level of satisfaction of the amount of free time they have. When asked  ‘if they were satisfied with the amount of time to do the things that they like doing’ both younger and older people reported the highest levels of satisfaction.

Source: Opinions Survey, Office for National Statistics. Data collected in October 2011, February and June 2012.

It is quite likely that these higher levels are chiefly because both groups are less likely to have family commitments, or to be engaged in paid-work; For younger people this is because they are still in education, whilst for the older age group many of those aged 65+ will be retired from paid-work.

As a slight aside an interesting observation is that whilst in general life satisfaction appears to increase with age, this actually declines for the 80+ age group. There may be a number of possible explanations for this; one being poor health, which is in the ONS analysis also implicated in lower levels of well-being, or an alternative may be that social capital declines at this age leading to isolation.

So in terms of work is it that not working the answer to happiness? Well, not quite. The well-being statistics also showed that unemployment has a significantly detrimental impact on the level of well-being reported by an individual with 45% of unemployed people rating their life satisfaction as low (5-6), or very low (0-4). This compares with a much lower figure of 20% for people in employment whilst for economically inactive people this figure is 27.1% However, this latter category includes  people who are caring for a relative, looking after children, or who are unable to work due to illness/disability along with students, the idle rich and retired people, who we may possibly expect to have higher ratings – indeed more people in the economically inactive group, some 30.5% also rated their life satisfaction as high (9-10) compared with 24.4% of employed people, and 16.3% of unemployed people suggesting this is a rather bifurcated group.

Because of this wide variation among the ‘Economically Inactive’  group it is particularly hard to reach any conclusion without breaking down the category and though we can clearly see that being employed is clearly far more preferable from a well-being perspective to being unemployed – something which may well be due to the stigma and low status of being in the unemployed group – an interesting comparison to make would be between the unemployed and the idle rich.

Annual Population Survey (APS) – Office for National Statistics. Data from April 2011 to March 2012.

But can the type of work we do have an effect on well-being – yes, according to the ONS analysis which states that in terms of the ‘life satisfaction’ ratings, and sthe ‘how happy did you feel yesterday’ question.

higher scores appear to be given by the occupational groups who tend to have more responsibility and control over their work, as well as higher incomes (which were not controlled for in this analysis)

This seems to align the findings of the Whitehall II study,  based on a sample of 10 308 civil servants, which found that low levels of control at work had a particularly detrimental  effect on health. A (2004) pamphlet explaining the results of the research, which was carried out between 1985 and 1998, states:

While it is common for demands to increase as the occupational hierarchy is ascended, degree of control over work decreases with lower position. Whitehall II provides ample documentation of this: the lower the grade of employment, the less control over work. This combination of imbalance between demands and control predicted a range of illnesses. The evidence from Whitehall II suggested that low control was especially important. People in jobs characterised by low control had higher rates of sickness absence, of mental illness, of heart disease and pain in the lower back.

Whilst there is likely to be a correlation between level of control and remuneration  as this was not controlled in the analysis it is not possible to tell form the data if higher levels of pay alone can increase well-being. One interesting finding however, is that those in the occupational group ‘caring, leisure and other service occupations’ as well as those in ‘professional occupations’ achieved the highest average response to the question ‘overall, to what extent do you feel the things you do in your life are worthwhile’ at 8 out of ten. the lowest averages were reported by ‘sales and customer service occupations’, ‘process plant and machine operatives’, and ‘elementary occupations’ who all averaged 7.5.

This would seem to suggest that work which involves a level of altruistic motivation, or else purports to serve a higher purpose, is beneficial for overall well-being, compared to work in which financial reward is the primary, or sole motivation. This finding is particularly relevant to questions into the role of social purpose, or public service in work and in organisations in general. Could it be that individual well-being is enhanced within organisations such as social enterprises, or other voluntary organisations where there is a strong sense of social mission?

Overall:

In terms of the ONS well-being data it would appear that both younger and older persons, groups who due to their respective ages tend not to be engaged in full-time paid work, report both higher levels of life satisfaction, and higher levels of satisfaction with the time to do things. Though it is not necessarily the case that it is simply full-time work  that results in lower levels of satisfaction in both measures for the other age groups, and it may certainly be the case that other commitments such as looking after children also play a role, it does seem possible that full-time work, can result in dissatisfaction with the time a person has to do things and this in turn may well be linked to the corresponding lower levels of reported well-being for these age groups. Certainly this is an area where future research may be directed as the ONS analysis does not include any details on the relationship between well-being and hours worked, or with a persons satisfaction, or not, with the hours they currently work.

Finally It is also important to note that the LFS does not collect data on voluntary work. It may be the case that, particularly among the retired group, voluntary work may play a key role in well-being. A 2005 Joseph Rowntree Foundation paper Volunteering in Retirement, suggests that voluntary work can be particularly beneficial for the health of retired people. As mentioned the ONS analysis seems to suggest that work which performs a higher purpose is beneficial to well being therefore it may be interesting in particular to analyse the role of voluntary work, or more widely make a comparison of well-being levels between workers in the voluntary, or public sector and the private sector.

References:

Ferrie, J. (2004) Work, Stress and Health: the Whitehall II study CCSU/Cabinet Office

Office for National Statistics (2012) First ONS Annual Experimental Subjective Well-being Results 24th July 2012

Office for National Statistics (2012) Measuring National Well-being –
Measuring young people’s well-being 10th October 2012

Smith & Gay (2005) Active Ageing in Active Communities; Volunteering and the Transition to Retirement Bristol: The Policy Press


This graph was produced using the statistical software package SPSS and shows the relationship between life expectancy at birth and income inequality using data published in UN Human Development Reports ranging from 2004 to 2008.  It was initially created for a presentation on the assertion made within The Spirit Level, an influential book first published in 2009, that once countries pass a certain point of development, measured in terms of national income, the relationship which had hitherto been observed between increases in income and increases in life expectancy breaks down and among these countries it is income distribution within them which becomes a better predictor of life expectancy and a range of other health and wellbeing indicators (Wilkinson and Pickett 2010 p.6-11)

Research by Wilkinson and Pickett (2010),  based on two data sources: International data from 2004 UN HDR and US data from the year 2000, showed a strong correlation between life expectancy and income inequality. For the international data r = -0.44 p-value 0.04 whilst for the US data r = -0.45 p-value = <0.01. This research however, has been criticised for not using the latest available data and for excluding countries based on population in an attempt to exclude tax-havens resulting in the loss of countries with low populations which were in fact not tax havens (Snowdon 2010 p.13)

Conducting his own tests addressing these issues and using data from 2006 UN HDR Snowdon finds R₂ =0.026 whilst based on data from 2009 UN HDR R₂  is reported as = 0.0478 (Snowdon 2010 p.28-29). The graph follows Snowdon’s methodology, using the latest available life-expectancy data from 2011 from which it would appear that the observations do not support Wilkinson & Pickett’s suggestion that higher levels of income inequality are associated with lower levels of life expectancy. In particular Hong-Kong and Singapore which both have a high GNI per capita, but high levels of inequality also have high life expectancies whilst the Czech and Slovak Republics, towards the lower end of the income range both have lower life expectancies despite having lower levels of income equality- Indeed R₂ = 0.059 with a significance of only 0.214 therefore is neither significant at the 1% nor 5% level.

Even when reverting Wilkinson and Pickett’s original methodology the 2011 life expectancy data do not show a strong relationship between life expectancy and income inequality, R₂ remaining a relatively low 0.038 whilst significance is 0.373 as this following graph shows:

Conclusions:

The 2011 life expectancy data, when using either Snowdon’s, or Wilkinson & Pickett’s methodology, do not appear to support claims that there is a strong relationship between income inequality and life expectancy. This is not to say however, that Wilkinson and Pickett’s original assertions were necessarily incorrect. Life expectancy for example is generally increasing, so it may well be that recent improvements in this area have led to the relationship Wilkinson & Pickett observed being eroded. This research was also only carried out on only one aspect of the spirit level thesis and therefore is not applicable to other areas covered by Wilkinson & Pickett, such as crime and obesity.


This image has been produced by software developed by Wolfram/Alpha and is based upon data on my friends and mutual friends it has accessed from my Facebook account .

Far more than a pretty graphic, which bears more than a passing resemblance to a map of a galaxy, this chart provides an example of how researchers can harness the power of new computing technology and social media to shed new light on areas of interest; This particular chart mapping my friends and mutual friends provides a great illustration of my ‘social capital’ – broadly speaking the social connections I have and groups I may belong to.

As a researcher I may be interested in how social capital varies by age, gender, ethnicity and employment status. It may also be possible to take a  longitudinal approach; how does my social capital change over time, what happens when I become a parent, emigrate, get a new job, become unemployed, or retire? charts such as this one can be used to make comparisons, or chart changes over a time period.

One issue however, is that Facebook is not wholly representative. There are, of course many people who do not have a Facebook account, particularly older people – therefore any data is likely to be skewed. Looking at a bar chart of the age range of my contacts, also by Wolfram/Alpha, it is interesting to see that the ages of my friends approximates a normal distribution around my own age, but its positive skew is likely to be a result of this age bias.

Age bias among Facebook users

To illustrate what the chart can show I have added my own annotations:

From the chart I can see that much of my social capital seems to be divided into distinct spheres; education, work, family and friends.

Education:

In terms of education my connections are strongest from middle school and secondary school.Though there is some overlap with middle school, friends from infant school, or even earlier pre-school do not feature on the chart. This is perhaps likely to be because at that young age we do not form the same type of friendships and connections which we begin to do once at middle school; Friendships in these early stages could well be much more fluid and transient.

Similarly my social capital relating to university seems to be rather sparse; my contacts and mutual connections both being particularly weak. This may be because I attended university in my home town and therefore was less part of the university ‘scene’, but perhaps more importantly university drew people from a very wide catchment reducing the number of mutual contacts compared to someone from school who grew up in the same area and went to work in the same area too. As many of my course-mates left the area following their studies the social network became dispersed.

This is even more so the case when it comes to the two years of my masters degree. As the people on this course came from an even wider area, and as the course covered a shorter time period.

Friends:

The biggest group in terms of the area it covers is my ‘friends’ group. This is the largest group, though my contacts are less clustered than elsewhere suggesting a more loosely connected web of ‘acquaintances’

Work: 

In terms of work my latest job is further away from the main clusters; this is as a result of my job being in a different town. Though not far in terms of distance it shows that, in general, peoples networks are closely bounded by geography.  My previous job I had obtained through old friends so it was much closer to my friendship cluster. As my previous jobs have been in very different organisations there is also no over-lap between my work clusters.

Family:

this cluster is as expected, however the previously mentioned age bias of social networking is perhaps undercounting this cluster more so than any others.

Wider implications:

Charts such as this one, using data held by social networking sites such as Facebook can provide an  understanding of how social capital is formed and what factors affect this. The relative strength of social capital from school may go in part to explain the enduring power of networks based on the ‘old school tie’. Similarly the intensity of connections related to work can show the importance of professional networks.