A method for estimating wage, using standardised occupational classifications, for use in medical research in the place of self-reported income

Publication type

Journal Article

Published in

BMC Medical Research Methodology


Tom Clemens and Chris Dibben

Publication date


Income is predictive of many health outcomes and is therefore an important potential
confounder to control for in studies. However it is often missing or poorly measured
in epidemiological studies because of its complexity and sensitivity. This paper presents
and validates an alternative approach to the survey collection of reported income
through the estimation of a synthetic wage measure based on occupation.
A synthetic measure of weekly wage was calculated using a multilevel random effects
model of wage predicted by a Standard Occupational Classification (SOC) fitted in
data from the UK Labour Force Survey (years 2001-2010)a. The estimates were validated
and tested by comparing them to reported income and then contrasting estimated and
reported income's association with measures of health in the Scottish Health Survey
(SHS) 2003 and wave one (2009) of the UK Household Longitudinal Study (UKHLS).
The synthetic estimates provided independent and additional explanatory power within
models containing other traditional proxies for socio-economic position such as social
class and small area based measures of socio-economic position. The estimates behaved
very similarly to 'real', reported measures of both household and individual income
when modelling a measure of 'general health'.
The findings suggest that occupation based synthetic estimates of wage are as effective
in capturing the underlying relationship between income and health as survey reported
income. The paper argues that the direct survey measurement of income in every study
may not actually be necessary or indeed optimal.








Medicine, Income Dynamics, Wages And Earnings and Health


Open Access article