CHALLENGING THE SPIRIT LEVEL: IS THEREREALLY A RELATIONSHIP BETWEENINEQUALITY AND OBESITY?

Darshan Zala*

AbstractThis paper examines the proposition, set out in The Spirit Level, that inequality is associated with high levels of obesity.It reviews existing literature and uses British Household Panel Survey data to study how district-level and regional-levelincome inequality is related to obesity in the UK. This is likely to be the first study of its type that uses individual-leveldata that is representative of the UK population.We find little evidence to support policies that reduce income inequalitywith the aim of reducing obesity levels in the UK.

JEL codes: I12, I14, I18.Keywords: Gini; inequality; obesity; psychosocial; stress.

1. Introduction

The recent government-commissioned report by Foresight (2007) argued that obesity hasbecome a societal challenge. Obesity is considered a public health issue because it is believedto be entangled with a variety of serious ailments including hypertension, angina, stroke,cardiovascular disease and a variety of cancers (WHO 2002). If obesity rates continue to rise atthe same pace as they rose in the late twentieth century, it has been predicted that by 2050around 60 per cent of men and 50 per cent of women in the UK will be obese, with additionalcosts to the nation of 45.5 billion per year.

The popular book The Spirit Level: Why More Equal Societies Almost Always Do Betterclaims that inequality is like a spirit level which we can hold up to compare the conditions ofdifferent developed societies (Wilkinson and Pickett 2009). Inequality is alleged to beresponsible for many social ills including obesity. In this paper the contention that inequality isa cause of obesity is subjected to theoretical analysis and empirical testing.

The empirical work uses British Household Panel Survey data to examine how district- andregional-level income inequality is associated with obesity.

2. How could obesity be related to inequality?

2.1. BMI and weight status: The basics

Obesity is typically measured by body mass index (BMI), which is an individuals weight inkilograms divided by the square of his or her height in metres. At the level of biology we can

*The author is an independent researcher with degrees from Warwick and Sheffield. In 2012 he spent some time at theInstitute of Economic Affairs. Email: darshanzala@hotmail.co.uk. The author is grateful for the helpful feedback andsuggestions, in particular from the editor. Please contact the author for fuller details of the literature search/review andmethodology.

2013 The Author. Economic Affairs 2013 Institute of Economic Affairs. Published by John Wiley & Sons Ltd

clearly characterise the factors of which BMI is a function (Maennig, Schicht and Sievers2008):1 energy consumption, energy expenditure, genetic factors, sex and age.

Medicine is aware of around 200 genetic factors quantitative trait loci which interactwith other genes and environmental factors to determine BMI (Pomp, Allan and Wesolowski2004; Snyder et al. 2004). These factors can be involved in a variety of processes and influencevarious things including our sense of taste and insulin sensitivity. Such factors vary by sex(Herskind et al. 1996). Age also has a significant impact on BMI (Hoffmeister et al. 1992), forexample through reduced metabolic activity in old age.

We must keep in mind that any causal sequences will end in either a change in energyexpenditure or consumption, so as to determine BMI.

2.2. The theorised inequality and weight status relationship

In their now well-known book, Wilkinson and Pickett (2009, p. 95) write that stress canincrease our food intake and change our food choices, a pattern known as stress-eating oreating for comfort.

In general, almost all the inequality studies surveyed below see the question as closelyrelated to stress inducement through considerations of status, a relative concept; and incomeinequality has a close connection to status. Thus The Spirit Levels argument contains twoelements, an inequalitystatus hypothesis and a statusBMI hypothesis, which we showdiagrammatically in Figure 1.

Some observers have posited a connection between the way in which animals respond tofood uncertainty by putting on weight and the way anxiety and uncertainty in modern life maylead to similar outcomes in humans (Smith 2009; Smith, Stoddard and Barnes 2009). In relationto the connection between stress and eating, some believe that, to compensate for physiologicalchanges due to chronic stress, we may comfort-eat (Dallman et al., 2003).

There is evidence that we consume more when stressed (e.g. Torres and Nowson 2007;Greeno and Wing 1994). There is also evidence that the additional calories we consume understress are harmful to our health: Oliver, Wardle and Gibson (2000) find that our preferencesare skewed to high energy-density foods, and Laitinen and Ek (2002) find that we are morelikely to eat unhealthily and consume more alcohol.

Clearly such opinions and evidence mainly concern the social statusBMI hypothesis. Theliterature review in Section 3 summarises studies testing the relationship between inequalityand weight status, which are intending to capture both the inequality-status and status-BMIhypothesise.

3. Existing research

3.1. Aggregate and cross-country studies

Most research into obesity at the individual level consists largely of the effects ofsocioeconomic status (SES) on BMI. SES includes indicators like education and income,

Inequality and/or relative income concerns BMI

Psychosocial effects Diet and exercise choices

Figure 1: The inequalitystatus and statusBMI hypotheses.

233economic affairs volume 33, number 2

2013 The Author. Economic Affairs 2013 Institute of Economic Affairs. Published by John Wiley & Sons Ltd

which are absolute concepts (e.g. Maennig, Schicht and Sievers 2008; Cantarero and Pascual2007; Nikolaou and Nikolaou 2009).

Many believe, however, that status must be a relative concept, and recently there has beenan explosion of research into the effects of relative income (or inequality) on health. Wilkinsonand Pickett (2009) typify this trend. In associated academic work (Pickett et al. 2005) theyconduct various cross-country regressions, the most advanced of which regress (for each sex)the percentage of obese people in a country on inequality measures, average daily per capitacalories and gross national income per capita. Their most convincing result suggests that risinginequality increases the number of obese women in the population.

Offer, Pechey and Ulijaszek (2010) conduct a similar country-wide set of regressions butwith a wider array of covariates. They find the opposite relationship to that of Pickett et al.(2005), although most results are statistically insignificant. Jones-Smith et al. (2011) focus onwomen in more than 30 developing countries. Unlike the other studies, theirs uses acountry-level random effects model. The authors find a perplexing relationship: being ahigher-GDP and higher-inequality developing country is associated with increased overweightprevalence among wealthy individuals; but high GDP and low inequality implies highoverweight prevalence among poorer individuals.

Offer, Pechey and Ulijaszek (2010) and Jones-Smith et al. (2011) represent a considerableimprovement on Pickett et al. (2005), which informs the claims advanced in The Spirit Level.But, clearly, with such country-level studies it is almost impossible to tease out any causalrelationships. Even at this aggregate level the choice of variables is hardly exhaustive and thestudies lack key controls for example, average calorie expenditure in the case of Pickett et al.(2005), or even averages concerning SES. Indeed, as Offer, Pechey and Ulijaszek (2010, p. 306)accept, the actual mechanisms require investigation at the level of individuals; and so we moveon to the more advanced individual-level studies.

3.2. Individual-level studies

Table 1 gives the details of key individual-level studies, numbered 19 for ease of reference.Most report at least some statistically significant effects, and almost equal numbers report apositive relationship (higher inequality implies higher weight status) and a negativerelationship.

Most studies use the Gini coefficient as their measure of inequality. There are manyformulas for the Gini coefficient, but it is most easily defined by the two steps of itsconstruction. To obtain the Gini coefficient we first take the mean of all possible incomedifferences between every pair of individuals in the population (or the sample), and second wedivide this by the average income.

Failing to control correctly for income (or family income) in regressions may simply resultin an effect being picked up based on the particular income composition of the society thus,income could be a confounding factor. Yet some studies 1, 6 and 2 from Table 1 seeminglyuse proxies based on averages. Studies 4 and 8 seem to have a very good range of controls.Such studies should include dummy variables concerning the area under study (district, statedummies, etc.) but 1 and 3 seem to lack these. Studies 7 and 9 do not control for education ormarital status. All the studies, with the possible exception of numbers 6 and 7, include

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2013 The Author. Economic Affairs 2013 Institute of Economic Affairs. Published by John Wiley & Sons Ltd

age-squared or, in a similar way, categorised age dummies. Kahn et al. (1998) is the only studythat controls for basic dietary and exercise variables, which seems appropriate given its focuson weight distribution.

Are any of these studies comparable to our own analysis? Only studies 4, 5, 6 and 9 examinepopulations of developed countries in modern times, and they only use data for the United Statesof America. Of these, study 6 is not comparable because it examines whether people have gainedweight at the waist, which concerns weight distribution. Study 9 controls only for income, age andrace, which contrasts with the variety of controls we use. Intriguingly, only studies 4 and 5 givestatistically significant results for women but show effects in different directions.

4. Data description

The British Household Panel Survey (Economic and Social Data Service)2 is a representativelongitudinal survey that began in 1991; it is household-based with interviews for every adult inthe household and is suitable for UK-wide research. It contains information on householdorganisation, employment, accommodation, income and wealth, housing, health, SES, maritaland relationship history, and individual and household demographics, among other things.

Table 1: Individual-level studies

Author(s)Outcome(s) ofinterest Covariate(s) of interest Main method Result

1. Carson (2010) BMI continuous State wealth Gini Quantile regression - (across BMI distribution)2. Subramanian,

Kawachi andSmith (2007)

BMI categorical State consumption Gini Logistic -/+ (odds ratio increase incase of under, pre-over,overweight and obese)

3. Kimhi (2003) BMI categorical Peasant association wealthcoefficient of variation(CV)

Probit/linearregression

- (increase probability ofunderweight, linear effectalso negative)

4. Chang andChristakis (2005)

BMI categorical Metropolitan household (HH)income Gini, CV, RobinHood, ratio measures

Logistic /linearregression

Insignificant (except forlower odds of overweightand obese for women,linear effect also negative)

5. Robert andReither (2004)

BMI continuous Community income Gini Linear regression + (only for women)

6. Kahn et al. (1998) yes/no waist-gain State HH ratio measures Logistic + (only for men)7. Ling (2009) BMI categorical, waist

circumferenceProvincial Gini, relative

deprivation measuresLogistic Insignificant (except Negative

effect on probability ofoverweight and high waistcircumference)

8. Chen andMeltzer (2008)

BMI categorical Community CV Linear ProbabilityModel

+

9. Diez-Rouxet al. (2000)

BMI continuous Gini, Robin Hood, ratiomeasures

Linear regression Insignificant (except forwomen with incomes