A bet ter beginning
ideally it would be possible to use longitudinal survey data to track families’ wage growth over time. However, sample sizes for the eligible group in the most appropriate such survey, the british Household Panel Survey, were too small to be meaningful. For this reason it was necessary to take a snapshot of earnings of the eligible group – some 297 observations representing almost 300,000 UK families – from the Family resources Survey and draw on other sources of information to establish how wages might change subsequently. Drawing on a snapshot sample of families at a point in time is likely to have a number of limitations. First, in applying average wage growth rates to the earnings of different people in the sample (differentiated by age and gender), we effectively ignore any outliers, such as main earners who might become permanently unemployed and cease paying into the scheme. this could make the scheme appear viable at more generous parameters than would be needed to cover its costs. On the other hand, variation in people’s earnings could mean that those observed to have low wages at a point in time might see faster wage growth in the future, for example, if they returned to full-time employment from part-time work. this kind of effect could mean that the model under-estimates the level of payments we might see from the cohort. On balance therefore, it is reasonable to assume that the model will over-estimate the payments of some families and underestimate those of others in roughly equal measure. Our basic model of contributions therefore gives a sense of the policy parameters necessary to make the scheme costless overall, but substantially more time and analytical resource would be necessary to refine these basic estimates. Furthermore, only a pilot scheme would enable us to test the behavioural assumptions on which the results of the modelling depend, such as the assumption that take-up is uncorrelated with income.
Modelling results the model considered how the outcomes of the policy were affected by changes to six variables, given the estimated wage levels and trajectories of the main earner in eligible households.
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