Body mass index and occupational attainment

Journal of Health Economics 25 (2006) 347–364

Body mass index and occupational attainment

Stephen Morris∗Tanaka Business School, Imperial College London, London SW7 2AZ, UK

Received 1 May 2004; received in revised form 1 May 2005; accepted 1 September 2005Available online 14 November 2005

Abstract

In this paper I investigate the impact of body mass index (BMI) on occupational attainment in England.Using pooled cross-sectional health survey data for 1997 and 1998 I find using OLS that, conditionalon a comprehensive set of individual and area covariates, BMI has a positive and significant effect onoccupational attainment in males and a negative and significant effect in females. Subsequent analyses withdifferent covariates show considerable variation in the results for males, while for females the effect ofBMI is significant and negative irrespective of the covariates used. IV coefficients on the BMI measures areinsignificant in all models, though I am unable to identify any endogeneity problems with respect to BMI.© 2005 Elsevier B.V. All rights reserved.

JEL classification: I10; I12

Keywords: Body mass index; Obesity; Occupational attainment; Instrumental variables

1. Introduction

The prevalence of obesity in England has risen in recent years, and is a growing cause forconcern. In 1980, 6% of males in England were classified as obese, compared with 8% of females.By 2003 the prevalence had trebled to 21 and 24%, respectively (Department of Health, 2003).This is worrying because as well as being an important and debilitating condition in its own right,obesity is also a prominent risk factor for a number of major diseases including coronary heartdisease, non-insulin dependent diabetes mellitus, osteoarthritis, hypertension and stroke (NHLBI,1998). It also significantly increases premature mortality risk (Ibid.). In 2001 over 34,000 deathsin England were attributable to obesity—approximately 7% of all deaths that year (House ofCommons Health Committee, 2004).

∗ Tel.: +44 20 759 49118; fax: +44 20 759 49184.E-mail address: [email protected].

0167-6296/$ – see front matter © 2005 Elsevier B.V. All rights reserved.doi:10.1016/j.jhealeco.2005.09.005

348 S. Morris / Journal of Health Economics 25 (2006) 347–364

Obesity imposes a substantial economic burden. Attributable treatment costs in the EnglishNational Health Service (NHS) were recently estimated to be in the range £990–1225 millionper annum, accounting for 2.3–2.6% of total NHS expenditure (Ibid.). The broader societal costswere estimated to be in the range £2.3–2.6 billion. The burden on employers is considerable. Forexample, in 2001, there were more than 15 million days of medically certified sickness absenceattributable to obesity (Ibid.).

There are additional social effects: obese individuals may suffer from social stigmatisationand discrimination (NHLBI, 1998). This has been documented in a variety of settings includinghealth care and employment (World Health Organization, 1998; Kaminsky and Gadaleta, 2002;Cossrow et al., 2001; Wadden and Stunkard, 1985). Stigmatisation against the obese has also beenfound among schoolchildren (McIntyre, 1998), in other educational settings (Puhl and Brownell,2001) and in on-the-job training in the labour market (Everett, 1990).

The aim of the paper is to examine at the individual level the impact of body mass index(BMI) on labour market achievement in England. The analysis is conducted using individuallevel data from the Health Survey for England. The measure of labour market achievement used isoccupational attainment, measured in terms of the hourly wage rate associated with an individual’soccupation. The measure is computed using a dataset of sex-specific mean hourly wages in eachoccupation based on data from the Quarterly Labour Force Survey (see Section4.2 for furtherdetails).

In the next section I review current evidence on the impact of obesity on the labour market,focusing on British studies. I then present the model underpinning the analysis. Following this Idiscuss the main statistical issues, and the data and variables used. The results and conclusionsfollow.

2. Previous research

The existing literature on BMI and labour market outcomes has focused on three broad areas:the impact of BMI on earnings (Register and Williams, 1990; Gortmaker et al., 1993; Loh, 1993;Hamermesh and Biddle, 1994; Sargent and Blanchflower, 1994; Averett and Korenman, 1996;Pagan and Davila, 1997; Sarlio-Lahteenkorva and Lahelma, 1999; Harper, 2000; Cawley, 2004;Baum and Ford, 2004); the impact on economic activity (Hamermesh and Biddle, 1994; Sarlio-Lahteenkorva and Lahelma, 1999; Cawley, 2000; Harper, 2000); the relationship between BMIand occupation selection (Hamermesh and Biddle, 1994; Pagan and Davila, 1997; Harper, 2000).The majority are US studies, with only two (Sargent and Blanchflower, 1994; Harper, 2000) usinga British sample.

Sargent and Blanchflower (1994)used the 1981 sweep of the National Child DevelopmentStudy (NCDS) to examine the impact of obesity on the earnings of young British adults. Theyfocused on obesity at age 11, 16 and 23 years and its impact on hourly earnings at age 23 years. Theimpact was estimated using OLS, regressing log hourly wages on indicator variables for obesity(defined as a BMI at the 90th percentile of the sample distribution or greater, and at the 99thpercentile or greater) controlling for area level unemployment, industry, marital status, educationalattainment, firm size, whether or not the respondent worked part-time, union membership and non-White ethnicity. The findings showed no evidence of a statistically significant relationship betweenobesity at all three ages and earnings at age 23 years in males. In females, obesity at each age exerteda statistically significant and negative effect on earnings. The negative impact remained aftercontrolling additionally for parental socio-economic status and general intelligence, measuredusing maths and reading ability test scores. Also, for females who were obese at age 16 years

S. Morris / Journal of Health Economics 25 (2006) 347–364 349

the negative effect was found to persist whether or not the individual remained obese at age23 years.

The same trend was found byHarper (2000)using a later (1991) round of the same survey.The author estimated the impact of obesity (plus general physical appearance, attractiveness andheight) at age 23 years on log hourly earnings at age 33 years. Obesity was defined by a BMI inthe 80–89th percentiles and the 90–100th percentiles of the sample distribution. The covariatesincluded social class, health, race, education, part-time employment, firm tenure, work experience,training received, trade union membership, marital status, firm size, occupation and industry, andregion of residence. As with the earlier study the results show a statistically significant andnegative effect of obesity on earnings for females, and insignificant effects for males. Harper alsotests for general employer discrimination versus occupation-specific effects by adding interactionterms for obesity and occupation sector. He argues that the bulk of the earnings differential is notoccupation-specific because the interaction terms are on the whole insignificant, which indicatesgeneral employer discrimination. There is evidence that obese females experience a pay penalty incraft occupations, which is attributed either to occupation-specific discrimination or productivityeffects.

Both studies take advantage of their cohort sample to reduce the likelihood of simultaneitybias, using BMI measured at a younger age and assessing its impact on current earnings. Whilereverse causality may not be a problem with these data endogeneity bias might arise from omittedvariables or measurement error.

In this paper I make a number of contributions to the literature. First, I consider the impactof body mass index on occupational attainment. As far as I am aware this is the first attempt tomodel this relationship using British data. Second, I present the first results for a British samplethat investigate the endogeneity of BMI using IV methods. Third, I consider males and females ofnormal working age rather than young adults, who have been the focus of previous British studies.Fourth, I separate the impact of BMI into two effects: the direct effect, plus indirect effects viathe correlation with other variables that impact occupational attainment. Fifth, the data are morerecent than those in earlier British studies, covering the period 1997–1998. This is useful becauseas the prevalence of obesity increases over time the impact of BMI on labour market outcomesmay change so that previous studies become out of date. Additionally, since the election of theLabour Government in May 1997 obesity has become an increasingly prominent policy issueand there have been a number of recent developments to address the health and social effects(Department of Health, 2004). The results presented in this study provide a baseline to assess theimpact of these policies on the occupational attainment of the obese.

3. Model

Suppose occupational attainmentY is a linear function of BMIB and other variablesX:

Yi = a0 + a1Bi + a2X1i + a3X

2i + ui (1)

whereu is an error term,i indexes individuals andX = (X1, X2) is a vector of variables thataffect occupational attainment, including health and non-health human capital variables. Someof the variables contained inX will be affected by BMI, and so BMI exerts an indirect effecton occupational attainment via these variables. The total effect can be obtained by excluding thevariables that are affected by BMI from the model. DefineX1 as a vector of variables affectedby BMI, including health status and other human capital variables that are influenced by health


status.X2 is a vector of variables not affected by BMI, such as age, sex and ethnicity. SupposeX1

depends linearly on BMI plus the other factors:

X1i = b0 + b1Bi + b2X

2i + ei (2)

wheree is an error term. Substituting(2) into (1) gives:

Yi = a′0 + (a1 + a2b1)Bi + (a3 + a2b2)X2

i + u′i (3)

Yi = c0 + c1Bi + c2X2i + vi (4)

wherea′0 = a0 + a2b0 andu′ = u + a2e. The indirect effect of BMI on occupational attainment is

a2b1, whereb1 is the impact of BMI onX1 anda2 is the impact ofX1 on occupational attain-ment. Comparing(4) with (1), the total effect is obtained by excludingX1 from the occupationalattainment equation.

If X contains comprehensive measures of health status, non-health human capital and otherfactors that explain occupational attainment thena1 < 0 is evidence for discrimination against theobese.

The indirect effect of BMI on occupational attainment arises from the relationship betweenBMI and health and other human capital measures. Individuals with a BMI outside the healthyrange may be less productive due to the higher incidence of ill health because BMI is a risk factorfor disease. BMI might also affect occupational attainment indirectly through non-health humancapital accumulation. This may be due, for example, to discrimination in the accumulation process(Everett, 1990), or the impact of BMI health effects on human capital accumulation.

4. Data and variables

4.1. Data sources

The data for this study came from a number of sources. Individual level data are pooleddata from two rounds (1997 and 1998) of the Health Survey for England (HSE) (Sproston andPrimatesta, 2003). The HSE is a nationally representative survey of individuals aged 2 years andover living in England. A new sample is drawn each year and respondents are interviewed on arange of core topics including demographic and socio-economic indicators, general health andpsychosocial indicators, and use of health services. Additionally, there is a follow up visit by anurse at which various physiological measurements are taken, including height and weight.

To construct the dependent variable I used data pooled from seven quarters of the UK QuarterlyLabour Force Survey (QLFS) (ONS, 2005). The QLFS collects nationally representative data from61,000 randomly selected households every quarter. Data are collected on individual, householdand family characteristics as well as on economic activity, education and training, health, and,income.

The area level data come from three sources. First, I used the Allocation of Resources toEnglish Areas (AREA) dataset for comprehensive data on deprivation, health and accessibility tohealth care services at the local authority ward level across England. The dataset was assembledfor a project that examined the determinants of the utilisation of hospital and community healthservices and general practice prescribing for a review of the resource allocation formulae forEngland (Sutton et al., 2002; Gravelle et al., 2003). Local authority level data on crime rates were


obtained from the Neighbourhood Statistics branch of the Office for National Statistics,1 and dataon house prices were obtained from the Land Registry.2 The area level data were converted tothe health authority level. England is divided into 95 health authorities with a mean populationof 515,517 residents (range 168,873–1,050,626). Mean values of the variables for each healthauthority were computed based on the proportion of each local authority’s/local authority ward’spopulation resident within each health authority, which is available in the AREA dataset. Thehealth authority data were then linked to the individuals in the HSE sample via their recordedhealth authority of residence.

4.2. Occupational attainment

The dependent variable is occupational attainment. There is no generally accepted definitionof this (Harper and Haq, 1997), and I adopt a measure used previously based on mean hourlyoccupational earnings. The approach has been used in other studies examining the relationshipbetween occupational attainment and a range of human capital variables (Nickell, 1982; Harperand Haq, 1997), drug use (MacDonald and Pudney, 2000) and alcohol consumption (MacDonaldand Shields, 2001). An alternative would be to use actual earnings but it has been argued that theseare a poor predictor of labour market success since they are negatively correlated with humancapital investment at younger ages (Harper and Haq, 1997). In any case individual level earningsare not recorded in the HSE. Total household income is reported as well as the McClementshousehold score for equivalised income, but equivalised household income is unsuitable sincehouseholds may consist of individuals with different levels of BMI. The main disadvantage ofthe measure I use is that, as with actual earnings, it ignores the non-pecuniary costs and benefitsassociated with different occupations. A simplification would be to restate the aim and focus onfinancial occupational attainment. An alternative would be to use more comprehensive measuresof occupational status such as the Goldthorpe–Hope scale (Goldthorpe and Hope, 1974), whichis not available in the HSE.Harper and Haq (1997)find a “high degree of correlation” betweenthe Goldthorpe–Hope scale and mean hourly occupational earnings (p. 639).

The method for computing the mean hourly wage associated with each occupation is similar tothat used in previous studies (Nickell, 1982; Harper and Haq, 1997; MacDonald and Pudney, 2000;MacDonald and Shields, 2001). I use data pooled from seven quarters of the QLFS, from Spring1997 to Autumn 1998. The time period is commensurate with that covered by the HSE sample.Individuals are included in the QLFS for five successive quarters or waves. Since Spring 1997 theincome section of the questionnaire is asked to individuals in either their first or fifth wave. FromSpring 1997 to Autumn 1998 the QLFS coded respondent’s occupation to the three-digit levelusing the Standard Occupation Classification introduced in 1990, which is the same classificationsystem as that used in the 1997 and 1998 rounds of the HSE. I selected only those individuals inthe QLFS who were in employment, in waves one or five, and of normal working age (18–65 yearsfor males and 18–60 years for females). This yields a sample of 122,804 observations with whichto compute the occupation mean hourly wage. For each observation I divided the gross weeklywage by the usual weekly hours of work to compute the mean hourly wage. These individual levelestimates were then collapsed by three-digit occupation category and sex to produce a dataset ofmean hourly wages in each occupation for males and females. For males (females) there were 368

1 http://www.neighbourhood.statistics.gov.uk/home.asp.2 http://www.landreg.gov.uk/propertyprice/interactive/pprualbs.asp.

http://www.neighbourhood.statistics.gov.uk/home.asp

http://www.landreg.gov.uk/propertyprice/interactive/ppr_ualbs.asp


(325) occupation categories across the QLFS sample of the 899 possible in the 1990 StandardOccupation Classification system, with a mean number of QLFS observations per category of166 (190). The dataset was then merged with the HSE data on sex and occupation category togive for each individual in the HSE sample the sex-specific mean hourly wage for the occupationin which they work. Individuals in the HSE sample are employed across 367 occupations. Sixindividuals were employed in occupations not covered by the QLFS sample and so were excludedfrom the analysis. Each occupation has a different mean hourly wage and I treat the occupationmean hourly wage as a continuous variable.

4.3. BMI measures

BMI is computed for each respondent from the height and weight values obtained during thenurse visit and reported in the HSE. One useful feature of the HSE is that the BMI values arenot based on self-reported height and weight, which may be reported with error. This reduces thelikelihood of bias being introduced via measurement error. The HSE records data on respondents’current height and weight. I therefore analyse the impact of current BMI, which has also beenthe focus of previous studies (Register and Williams, 1990; Loh, 1993; Pagan and Davila, 1997;Cawley, 2000). I use two BMI measures. The first is the natural logarithm of BMI, where BMI ismeasured as respondents’ weight in kilograms divided by their height in metres squared (kg/m2).This measure is used to capture non-linearities in the relationship between BMI and occupationalattainment, and was selected after experimenting with linear, logarithmic and power functions ofBMI. The second BMI measure captures the effect of obesity on occupational attainment, and isa binary variable taking the value 1 if the individual has a BMI over 30 kg/m2 and 0 otherwise.

4.4. Covariates

I include a number of other explanatory variables, grouped in five categories. The first categorycontains education variables, measuring educational attainment and years of schooling. In the HSErespondents are asked questions on the highest educational qualification they have attained basedon seven categories. Years of schooling is included as a continuous variable measured as the ageat which respondents finished their full time continuous education at school or college minus 4years. I also include years of schooling squared.

The second category contains health variables, which are covered comprehensively in theHSE. I include measures of self-reported general health, acute ill health, longstanding illnessand psychosocial health. Self-reported general health is a measure of subjective general healthmeasured in five categories from very good to very bad. Acute ill health is measured by the numberof days in the last 2 weeks the respondent had to cut down on the things they usually do becauseof illness of injury, grouped in five categories. In terms of longstanding illnesses respondents areasked whether they have an illness, disability or infirmity that has troubled them over a periodof time, and its type by broad disease code, in 15 categories. Limiting longstanding illness iscategorized by whether any of these illnesses limits the respondents’ activities in any way. Theimpact of comorbidities is measured by the number of longstanding illnesses (five categories).Psychosocial health is measured by GHQ-12 score across 13 categories, where higher valuesindicate more severe psychosocial problems.

The third category of variables contains the available job characteristics in the HSE. Thesedescribe first whether or not the individual is an employee or self-employed, and second thenumber of people employed at place of work (measured in four categories).


The fourth category contains home and family variables. Housing, marriage and family sizevariables are included in this group. The HSE collects information on respondents’ marital statusand housing tenure, both measured in five categories. I also control for the number of infantsliving in the household aged 0 or 1 year (three categories) and the number of children aged 2–15years living in the household (seven categories).

In the final category I include variables that affect occupational attainment and do not dependon BMI. I include in this group gender, potential work experience, ethnicity (in nine categories),rurality (in three categories), region of residence (eight categories) and HSE year (two categories).I estimate separate models for males and females. Potential labour market experience is computedas the respondent’s actual age minus the age they left full-time education.3 I also include years ofpotential labour market experience squared.

Also, in the final category I include 60 area based indicators to control for the impact oflocal area characteristics on individual occupational attainment. They fall into three categories:deprivation measures, health measures and health care supply measures. Area deprivation ismeasured using the Indices of Deprivation 2000 (ID2000) (DTLR, 2000). These are a set ofindicators that describe multiple deprivation across geographical areas in England, based onroutinely collected administrative data. The ID2000 comprises six domains, each reflecting a par-ticular aspect of deprivation, and constructed from a number of individual variables. The incomedomain captures the extent of income deprivation in an area, measuring the proportion of thetotal population who are on a low income. It is made up of counts of people and their familiesin receipt of one or more of five income-related state benefits. A child poverty domain scorewas also produced from the income domain score based on a subset of indicators that reflectthe proportion of children living in low-income households. The employment domain measuresenforced exclusion from the world of work through unemployment, sickness or disability, mea-sured using five indicators. The health deprivation and disability domain identifies areas withhigher than expected numbers of people whose quality of life is impaired by poor health anddisability or whose life is cut short by premature death, measured using five indicators. The edu-cation domain measures the key educational characteristics of the local area that contribute to theoverall level of deprivation, based on six indicators. The housing domain covers those in unsat-isfactory housing, including the homeless, measured using three indicators. The access domainmeasures the extent to which people have poor geographical access to certain key services (postoffice, large food shops, GP surgery and primary school). The overall index of deprivation scoreis constructed by combining the weighted, exponentially transformed ranks of each domain. Alsoincluded are additional deprivation measures based on the ID2000: the proportion of the popu-lation receiving job seekers’ allowance; the percentage of the population aged 17 years or overnot going to higher education; the proportion of attendance allowance claimants over 60 years;the proportion of income support claimants over 60 years; the proportion and standardised rateof incapacity benefit/severe disability allowance claimants; the proportion and standardised rateof attendance allowance/severe disability allowance claimants. Area deprivation is also measuredusing area house prices (measuring separately the mean area price of detached houses, semi-detached houses, terraced houses and flats) and area crime rates (separate rates for violent offences,sexual offences, robbery, burglary from a dwelling, theft of a motor vehicle and theft from a motorvehicle).

3 This variable effectively picks up the effect of age on occupational attainment and so is treated as an obesity-independentvariable.


The second set of area based indicators measures the health of the local population. In additionto the health domain score from ID2000 described above three measures of area mortality are used(the all-age standardised mortality ratio [SMR], the SMR among individuals aged 0–64 years andthe SMR among individuals aged 0–74 years), plus the number of births in the local area, and thepercentage of births that were of low birth weight.

The third category contains measures of health care supply. Twenty-nine indicators measureaccessibility to health care in terms of waiting times for hospital services (acute, maternity, mentalhealth, private health care, and outpatient services), the number of beds at local hospitals, distanceto local hospitals, number of staff at local hospitals and the distance to and supply of GPs in thelocal area.

5. Estimation

5.1. Statistical model

The baseline regression model is

yi = δBi + β1xi + µi (5)

wherey is occupational attainment measured as a continuous variable,x is a vector of exoge-nous variables andB is the BMI of the individual.µ is an error term, andβ and δ arecoefficients. One option is to estimate(5) by OLS. If it is reasonable to assume thatµ is uncor-related withB and x then OLS will produce an unbiased estimate of the impact of BMI onoccupational attainment. In this case, ifx = (X1, X2) then δ = a1 from (1). If x = X2 then δ = c1from (4).

BMI may not be exogenous for a number of reasons. First, there may be simultaneity bias.While BMI may affect occupational attainment (the hypothesis) it is also possible that occupationalattainment affects BMI. For example, individuals may be obese because they perform poorly inthe labour market (Pagan and Davila, 1997). Second, there may be omitted variable bias: theremay be unobserved factors that affect both BMI and occupational attainment. One such factormight be the rate of myopia, which might affect unobserved human capital measures (and henceoccupational attainment) and also BMI (Baum and Ford, 2004). Third, there may be measurementerror if BMI is based on self-reported height and weight, which may mis-measure actual BMI.Under these conditions, estimating(5) by OLS will yield unreliable estimates of the causal effectof BMI on occupational attainment.

Suppose BMI has the following reduced form:

Bi = β2xi + α1Zi + ε1i (6)

whereZ is a vector of variables (instruments) that are correlated withB, α a vector of coefficientsandε is an error term that is uncorrelated withµ. With Z andε so defined it is possible to test theendogeneity of BMI and then if required produce a consistent estimate ofδ using an IV regressionapproach. To test for exogeneity a common method is based on theHausman test (1978). Thisinvolves first running(6) and from this predicting the residuals,ε̂1. Then, run(5) adding to theset of regressors the residuals from the BMI equation:

yi = δεBi + β3xi + θε̂1i + µεi (7)


Under the null hypothesis that BMI is exogenous the coefficient on the residuals,θ, will be zero.An F-test of the significance of the residuals is a direct test. If the coefficient is significantlydifferent from zero the null is rejected and IV methods should be employed (Wu, 1973; Hausman,1978). If we fail to reject the null hypothesis then, assuming the instruments are valid, it is notpossible to identify any endogeneity problems with respect to BMI and the OLS estimates arepreferred because they have lower standard errors (Baum et al., 2003).

IV regression usesZ to isolate the exogenous variation inB, thereby providing a way to estimateδ. First,(6) is estimated using OLS and from this the linear prediction ofB (B̂) is computed. Second,y is then regressed onx andB̂ yielding the estimatorδIV which gives a consistent estimate of thecausal effect of BMI on occupational attainment conditional onx:

yi = δIV B̂i + β4xi + µIVi (8)

The correct standard errors from(8), which account the two-step nature of the estimation, can thenbe obtained using the asymptotic covariance matrix given byWooldridge (2002, p. 95). Note thatδIV in (8) andδε in (7) will be the same, but the correct standard errors are more easily obtainedusing(8) (Wooldridge, 2002, p. 120).4

The main difficulty with the approach is to find suitable instruments forB; that is,Z-variablesthat are partially correlated with BMI once the other exogenous variables have been netted out(α �= 0|x), and are orthogonal to the error term in the second stage regression (Cov(Z, µ) = 0). Ifthe first condition does not hold the IV estimator will be inconsistent (Bound et al., 1995). Anempirical test for weak instruments is to test the (joint) significance ofZ in the first stage regressionusing anF-test. The second requirement is that the instrumental variable is independent of the errorprocess in the second stage. If an instrument is itself endogenous thenδIV is no longer a consistentestimate of the impact of BMI (Wooldridge, 2002, p. 101). It is not possible to test directly whetherthe instruments are exogenous (Wooldridge, 2002, p. 86), though in an overidentified model itis possible to test the conditional validity of the additional instruments under other maintainedassumptions.

I also experimented with adding a selection bias correction term to control for non-randomparticipation in the labour market. I first estimated a probit selection (participation) equation onthe sample of workers and non-workers and from this computed the usual inverse Mills ratio foreach observation. This was then added to the OLS and IV occupational attainment equations. Aswith previous studies (e.g.Harper, 2000), the estimates are left virtually unchanged, and so theresults are not reported here.

5.2. Instruments

I use two area based indicators to instrument BMI in the IV models. Area based measureshave been used as instruments for individual level variables in other studies (see, for example,Currie and Cole, 1993; Card, 1995; Grabowski and Hirth, 2003; Lo Sasso and Buchmueller,2004; Sloan et al., 2001). The first instrument is the mean BMI across individuals living in thehealth authority in which the respondent lives. The second is the prevalence of obesity in thehealth authority in which the respondent lives. The first requirement of an instrument is that it iscorrelated with BMI conditional on the other variables that affect occupational attainment. The

4 The IV models are estimated using the Stata command –ivreg2– written byBaum et al. (2003). This commandcomputes the correct IV standard errors internally.


main risk factors for obesity are excessive intake of high-fat and high-calorie foods and physi-cal inactivity (NHLBI, 1998). Environmental influences, which affect attitudes and behavioursto food intake and exercise, are a key determinant of obesity (James, 1995). Area BMI mea-sures provide a summary measure of obesity-affecting environmental influences. They capture,among other things, food intake and physical activity behaviours in the local population. AreaBMI is therefore expected to be a non-weak predictor of individual level BMI conditional onthe other covariates, and both measures are expected to be positively correlated with individualBMI.

The second requirement of an instrument is that it is not correlated with the error term inthe occupational attainment equation. For example, if area BMI measures are correlated withunobserved area effects that impact individual occupational attainment, then IV estimates areinconsistent. If area BMI is correlated with occupational attainment other than through its impacton individual BMI then plausibly this arises only via its correlation with socio-economic sta-tus and health. Given that I include a large and comprehensive set of individual and area levelcovariates measuring socio-economic status and health, plus a vector of regional dummy vari-ables, then area BMI measures are not a component of the error term in the occupationalattainment equation and do not give rise to a correlation between BMI and the error term.It is difficult to think of another way in which area BMI can affect occupational attainmentother than via its impact on individual BMI, and individual and area level health and/or socio-economic status. Hence, the instruments are not endogenous, and they pass the orthogonalityrequirement.

The instruments were constructed by collapsing individual level values of BMI and BMIgreater than 30 kg/m2 in the HSE sample across all non-pregnant individuals of working age(18,026 observations) by health authority of residence to produce a dataset of mean BMI andobesity prevalence at the health authority level. The mean number of HSE observations per healthauthority is 190 (range 47–405). This health authority level dataset was then merged with theindividual level HSE data on the respondents’ recorded health authority of residence to givefor each individual in the HSE sample the mean BMI and prevalence of obesity for the healthauthority in which they live. The correlation coefficient between the two instruments is 0.8235(p < 0.0001). After experimenting with using both instruments separately and together the meanBMI across individuals living in the health authority in which the respondent lives was a betterpredictor of log BMI conditional on the covariates, and the prevalence of obesity was a betterpredictor of individual BMI over 30 kg/m2. Hence, for both BMI measures results are presentedfor these instruments only.

To control for the distribution of BMI in the local population I also added to the set of area levelcovariates the number of HSE respondents used to generate mean BMI in each health authorityof residence, and the standard deviation of BMI in each health authority of residence among HSErespondents.

5.3. Modelling strategy

For both sexes, both BMI measures and both statistical methods (OLS and IV) I report sixregressions. The first includes all the covariates and measures the direct effect of BMI on occu-pational attainment. The next four regressions provide a measure of the indirect effect of BMIoperating via a range of excluded variables. I exclude in turn the education variables, the healthvariables, the job characteristics and the home and family variables and examine the coefficienton the BMI variables. The final model measures the total effect of BMI on occupational attain-


ment, excluding jointly all the factors that predict occupational attainment and may be affectedby BMI.

The analyses of indirect effects rely on the maintained hypothesis that the instrument is uncor-related with the error term in the occupational attainment equation, even though I no longercondition on individual health and socio-economic status variables. Note, however, that I do stillinclude the full range of area level covariates in all models. Nevertheless, some caution shouldbe given to these results. Also, the analysis focuses on the impact of current BMI on currentoccupational attainment. The indirect effect incorporates the impact of BMI on the education,health, job characteristics and home and family variables. Current BMI is less likely to be rele-vant in explaining these factors than previous BMI. In this case I assume that current BMI is agood indicator of BMI at younger ages. There is evidence to support this view (see, for example,Whitaker et al., 1997).

5.4. Sampling issues

In the 1997 and 1998 rounds of the HSE the samples of children but not adults were deliber-ately boosted to include greater numbers of children. Since adult respondents were not over- orundersampled each observation has a weight of unity in the regressions.

It is possible that observations within areas are not independent; it is therefore nec-essary to adjust the standard errors in the regression models for within-area correlation(Moulton, 1990). I therefore control for clustering within primary sampling units (PSUs), usingHuber/White/sandwich robust variance estimators that allow for within-group dependence.

To maximise the sample size I impute missing values for all the covariates. For contin-uous variables missing values are imputed using the linear prediction from a regression ofthe variable on the other covariates. For binary and categorical variables missing values areassigned arbitrarily to the omitted category. To allow for the possibility that items are not miss-ing at random I include dummy variables for all imputed items to indicate item non-response.I use this approach in preference to other methods for dealing with missing data, such ashotdecking, because in the sample items may not be missing at random. If the dummy vari-able is insignificant non-responders’ occupational attainment is affected in the same way asthe responders by the imputed variable and the imputation has increased sample size withoutbiasing results. If the dummy variable is significant then responders and non-responders areaffected in different ways by the variable and inclusion of the missing item dummy variableenables estimation of an effect for responders that is not contaminated by the imputation fornon-responders.

The total sample size combining all HSE observations across 1997 and 1998 is 35,200. Exclud-ing pregnant females and individuals outside the normal working age reduces the sample to18,026. Twelve thousand eight hundred and forty-two of these individuals are in paid employ-ment and 12,215 (95%) have a valid BMI measurement from the nurse visit (256 respondents didnot complete the nurse visit and a further 371 respondents had an invalid BMI measurement;reasons for invalid BMI measurements were “Height/weight/BMI not useable”, 29 respon-dents, “Height/weight refused”, 84 respondents, “Height/weight attempted but not obtained”,107 respondents and “Height/weight not attempted”, 151 respondents). Seventy-eight of therespondents with valid BMI measures (<1%) have missing values for their occupation cate-gory, so cannot be assigned an occupation mean hourly wage and so are excluded. Hence, thefinal estimation sample comprises 12,137 individuals, of whom 6479 are male and 5658 arefemale.


Table 1Occupational attainment by BMI

BMI (kg/m2) Males (n = 6479) Females (n = 5658)

Occupation meanhourly wage (£)

Sample (%) Occupation meanhourly wage (£)

Sample (%)

<20 7.42 3 6.03 620–25 8.05 34 6.15 4625–30 8.33 47 6.01 3030–35 8.12 13 5.83 1235–40 7.82 2 5.87 4>40 7.37 1 5.38 2

>30 8.05 16 5.79 18>35 7.73 3 5.72 6

6. Results

6.1. Descriptive statistics

The sample distribution of BMI is inTable 1. Only 34% of males and 46% of females havea BMI within the range normally considered to be healthy (20–25 kg/m2), while 16 and 18%,respectively, are classified as obese (BMI over 30 kg/m2). For males the overweight category(25–30 kg/m2) has the highest proportion of the sample (47%), while for females the largestcategory is 20–25 kg/m2. Table 1also presents data on mean occupational attainment by BMIcategory. For males occupational attainment is highest in the overweight (25–30 kg/m2) category,while for females it is highest in the slimmer 20–25 kg/m2 category. The data suggest a non-linear relationship (an inverted U-shape) between BMI and occupational attainment. For malesmean hourly wages are lowest in the underweight and the morbidly obese categories—they bothearn around 12% lower mean wages than the highest category. For females the picture is slightlydifferent: underweight females earn only around 2% less than the highest category, while themorbidly obese earn 12% less.

The sample means and standard deviations of the variables used in the occupational attainmentequations are available from the author.

6.2. OLS results

The key regression results are inTables 2 and 3. The coefficients on the two BMI measuresand the relatedt-statistics/z-scores are reported. In the case of log BMI the elasticity of meanoccupation wage with respect to changes in BMI is computed by dividing the coefficient onlog BMI by the sample mean of the mean occupation wage.5 For the BMI over 30 kg/m2 modelsthe percentage average marginal effect is reported.6 The tables also report the explanatory powerof the OLS models.

5 The estimated occupational attainment equation has a semi-logarithmic functional form with respect to BMI:yi = δ ln Bi +βxi +µi. Differentiatingy with respect toB gives dy/dB = δ(1/B). The elasticity is (dy/dB)B/y = δ(1/B)B/y = δ/y.

6 The estimated model isyi = δBi +βxi +µi, whereB in this case is a binary variable taking the value 1 if the individualhas BMI over 30 kg/m2 and 0 otherwise. The linear prediction of the occupation attainment variable for each observation is

β̂xi + δ̂Bi. The percentage average marginal effect is (1n

∑n

i=1[ δ̂

β̂xi+δ̂Bi|B=0]) × 100, wheren is the number of observations.

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Table 2Impact of BMI on occupational attainment with different covariates: results for males

OLS IV

Coefficient t Elasticity R2 Coefficient t Elasticity F-test instrument = 0[p-value]

HausmanF-test[p-value]

log BMI(1) Direct effect 0.666 2.8 0.08 0.33 4.941 0.9 0.61 13.38 [0.0003] 0.69 [0.4059](2) No education variables 0.331 1.3 0.04 0.16 0.025 0.0 0.00 13.79 [0.0002] 0.01 [0.9565](3) No health variables 0.614 2.7 0.08 0.32 4.002 0.8 0.49 15.25 [0.0001] 0.50 [0.4786](4) No job characteristics 0.622 2.7 0.08 0.31 3.433 0.7 0.42 13.41 [0.0003] 0.29 [0.5874](5) No home and family variables 0.823 3.6 0.10 0.32 4.552 0.9 0.56 13.38 [0.0003] 0.52 [0.4697](6) Total effect 0.309 1.2 0.04 0.08−3.682 −0.6 −0.45 15.90 [0.0001] 0.56 [0.4550]

Coefficient t Average marginaleffect (%)

R2 Coefficient t Average marginaleffect (%)

F-test instrument = 0[p-value]


BMI > 30 kg/m2

(1) Direct effect 0.077 0.9 1.00 0.33 1.364 0.5 18.17 8.74 [0.0032] 0.25 [0.6178](2) No education variables −0.092 −0.9 −1.15 0.16 1.141 0.4 14.71 8.61 [0.0035] 0.19 [0.6668](3) No health variables 0.037 0.4 0.48 0.32 1.106 0.5 14.60 10.29 [0.0004] 0.21 [0.6463](4) No job characteristics 0.067 0.7 0.87 0.31 0.770 0.3 10.07 8.62 [0.0035] 0.07 [0.7899](5) No home and family variables 0.095 1.0 1.22 0.32 0.748 0.3 9.78 8.99 [0.0028] 0.07 [0.7984](6) Total effect −0.205 −1.9 −2.54 0.08 −1.262 −0.4 −15.26 10.38 [0.0013] 0.16 [0.6890]

Notes: The number of observations in every model is 6479. The following covariates are also included in the direct effect models: years of full-time education and years offull-time education squared; educational attainment; self-assessed general health; days of acute sickness in last 2 weeks; whether or not the individual has a limiting longstandingillness; whether or not the individual has a specific longstanding illness; number of longstanding illnesses; GHQ-12 score; number of people at the place of work; whether ornot the individual is self-employed; marital status; housing tenure; number of infants aged 0–1 year living in the household; number of children aged2–15 years living in thehousehold; years of potential labour market experience and years of potential labour market experience squared; ethnic group; rurality; region of residence; month of interview;year; missing item dummy variables; a constant term. Additionally, 62 health authority level covariates are included measuring deprivation, health, the supply of health servicesand the distribution of BMI in the local area. In all models the standard errors are adjusted for clustering on PSUs. The instrument for log BMI is the mean BMI across individualsliving in the health authority in which the respondent lives. The instrument for BMI over 30 kg/m2 is the prevalence of obesity across individuals living in the health authorityin which the respondent lives.

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Table 3Impact of BMI on occupational attainment with different covariates: results for females

OLS IV

Coefficient t Elasticity R2 Coefficient t Elasticity F-test instrument = 0[p-value]


log BMI(1) Direct effect −0.259 −2.1 −0.04 0.40 3.080 1.1 0.51 11.61 [0.0007] 1.44 [0.2300](2) No education variables −0.434 −3.0 −0.07 0.15 0.943 0.3 0.16 11.53 [0.0007] 0.17 [0.6776](3) No health variables −0.273 −2.2 −0.05 0.40 2.389 1.0 0.40 15.24 [0.0001] 1.24 [0.2650](4) No job characteristics −0.269 −2.2 −0.04 0.40 3.078 1.1 0.51 11.54 [0.0007] 1.43 [0.2324](5) No home and family variables−0.247 −1.9 −0.04 0.39 3.425 1.2 0.57 11.82 [0.0006] 1.73 [0.1880](6) Total effect −0.552 −3.5 −0.09 0.06 0.359 0.1 0.06 15.49 [0.0001] 0.09 [0.7558]

Coefficient t Average marginaleffect (%)

R2 Coefficient t Average marginaleffect (%)

F-test instrument = 0[p-value]


BMI > 30 kg/m2

(1) Direct effect −0.083 −1.4 −1.44 0.40 1.245 1.0 22.58 13.12 [0.0003] 1.09 [0.2951](2) No education variables −0.153 −2.2 −2.58 0.15 0.908 0.6 15.82 12.73 [0.0004] 0.48 [0.4860](3) No health variables −0.096 −1.7 −1.67 0.40 0.975 1.0 17.48 17.03 [<0.0001] 0.94 [0.3327](4) No job characteristics −0.087 −1.5 −1.51 0.40 1.184 1.0 21.41 13.12 [0.0003] 0.99 [0.3195](5) No home and family variables−0.084 −1.4 −1.44 0.39 1.281 1.1 23.19 13.45 [0.0003] 1.15 [0.2836](6) Total effect −0.216 −2.9 −3.58 0.06 0.324 0.24 5.47 17.23 [<0.0001] 0.16 [0.6934]

Notes: The number of observations in every model is 5658. The covariates in the direct effect models are the same as inTable 2. In all models the standard errors are adjustedfor clustering on PSUs. The instruments are the same as inTable 2.


The models in row (1) contain the full set of covariates and estimate the direct effect of BMIon occupational attainment given bya1 in Eq.(1). Subsequent rows report the effects of the BMImeasures on occupational attainment after excluding different sets of covariates. The differencebetween the results in row (1) and those in rows (2)–(5) is a measure of the indirect effect of BMIon occupational attainment operating via the excluded variables in that row. In row (6) the totaleffect of BMI on occupational attainment is reported after excluding all the covariates except theobesity-independent variables. The full results for the direct effect models, including the full setof covariates, are available from the author.

In terms of the OLS coefficients, in males (Table 2) log BMI has a positive and significantdirect effect on occupational attainment. The elasticity is 0.08, which means that a 10% increasein BMI leads on average to a 0.8% increase in mean occupation wage. BMI over 30 kg/m2 hasa positive and insignificant direct effect on occupational attainment. In females (Table 3), thedirect effect of BMI on occupational attainment is negative and significant in the OLS model.This suggests that a 10% increase in BMI leads on average to a 0.4% decrease in mean occupationwage. BMI over 30 kg/m2 has a weakly significant and negative direct effect in the OLS model,indicating that on average obese females have a 1% lower mean occupation wage than non-obesefemales.

In terms of the indirect effects, for both sexes the largest impact is achieved via the educationvariables. In males, relative to the direct effect in row (1) the marginal effect is less positive/morenegative when the education variables are excluded. A similar finding is achieved for females. Theresults are consistent with the view that education has a positive effect on occupational attainmentand BMI is negatively correlated with education.

In row (6) the total effect of the BMI measures on occupational attainment is reported afterexcluding all the covariates except the obesity-independent variables. The elasticities and per-centage average marginal effects measure the total effect of BMI on occupational attainment,picking up all the effects, both direct and indirect, that occur through the excluded variables. Inmales, log BMI has a positive and insignificant impact on occupational attainment. The total effectof BMI over 30 kg/m2 is negative and significant. This suggests that obese males have a meanoccupation wage that is on average 3% lower than that for non-obese males.

For females, the total effect of log BMI is significant and negative: a 10% increase in BMIleads to a 0.9% decrease in mean occupation wage. Females with BMI over 30 kg/m2 are onaverage paid 4% lower wages than those with BMI less than or equal to 30 kg/m2, and the effect isstatistically significant. For both BMI measures the total effect is more than twice the magnitudeof the direct effect.

6.3. IV results

F-tests for the significance of the instrument in the first stage regressions in the IV models arereported inTables 2 and 3. The results show in all models that, as expected, the relevant area BMImeasure is a significant predictor of individual BMI conditional on the covariates. In all modelsfor females and in the log BMI models in males theF-statistic exceeds the minimum value of 10suggested byStaiger and Stock (1997).

The IV regression results for males and females are similar: in all cases except for the totaleffects in males the coefficient on the BMI measures is positive, and, in none of the models is thecoefficient significantly different from 0 at the 5% level.

Using a Hausman test, also reported in the tables, I cannot reject the hypothesis that the OLSand IV coefficients are equal in any of the models. In other words, it is not possible to identify


any endogeneity problems with respect to BMI. Assuming the instruments are valid this suggeststhat the OLS estimates should be preferred.

7. Concluding remarks

In this paper I use Health Survey for England data to examine for the first time the impact ofBMI on occupational attainment in England. I present OLS and IV estimates, using area basedmeasures as IVs for BMI, which empirically are found to be non-weak predictors of BMI, andwhich are plausibly not themselves endogenous with occupational attainment.

Controlling for a comprehensive set of individual and area level covariates I find using OLS thatthe BMI measures have a positive direct effect on occupational attainment in males (significant forlog BMI and not significant for BMI over 30 kg/m2) and a negative impact in females (significantfor log BMI and weakly significant for BMI over 30 kg/m2). Subsequent analyses of indirecteffects using OLS show considerable variation in the results for males depending on the BMImeasure and covariates. The effect of the BMI measures is generally statistically significant andnegative in females.

The findings for males are consistent with recent North American (Baum and Ford, 2004;Cawley, 2004) and earlier British (Sargent and Blanchflower, 1994; Harper, 2000) studieswhich, taken together, find a range of effects of BMI on labour market achievement in malesdepending on the BMI measure used, the covariates and the characteristics of the sampleanalysed.

The findings for females are also consistent with these studies, which find that obesity measureshave a significant negative impact on labour market outcomes. The results suggest but do not provethe presence of obesity-related discrimination in the workplace. Based onBalsa and McGuire(2003)one might hypothesise that discrimination arises for a number of reasons. First, there maybe prejudice by employers, reflecting their distaste for obese workers and the psychological costsincurred when dealing with them (Moon and McLean, 1980). Second, there may be stereotypingby employers, arising from a belief that the obese are less productive (Everett, 1990). Third,discrimination may arise through uncertainty, or a lack of knowledge about the productivityof obese workers (Pagan and Davila, 1997). The presence of obesity-related discrimination is,however, only hypothesis, and a call for further research into the investigation of systematicdiscrimination against obese workers, as made in another recent study (Baum and Ford, 2004), isechoed here.

The IV coefficients on the BMI measures are insignificant in all 24 models. Using a Hausmantest it is not possible to identify any endogeneity problems with respect to BMI. This finding isconsistent with a recent study of obesity and wages in the US (Cawley, 2004) that also failed toreject the hypothesis that OLS and IV coefficients on obesity measures were significantly different.Assuming the instruments are valid this suggests that the OLS estimates should be preferred. Itshould be borne in mind, however, that this finding is also consistent with a lack of instrumentpower.

Acknowledgements

I am grateful to the Allocation of Resources to English Areas project for assistance with data.I would also like to thank Robert MacCulloch, and the Editor and two referees for their helpfulsuggestions.


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Body mass index and occupational attainment