ESRI: Lower-income groups gain most from budget
ESRI researchers find budget measures have been distributionally neutral in general
A budgetary policy which is neutral in distributional and macroeconomic terms would increase tax bands, tax credits and welfare payment rates in line with forecast wage growth. Incomes for all households would grow at the same rate. This is the neutral benchmark against which the ESRI measures the distributional impact of policy. Photograph: Frank Miller
Budget 2017 has been framed against a backdrop of strong economic growth. Both the ESRI and the Central Bank forecast that wages will grow by close to 2.4 per cent in 2017.
A budgetary policy which is neutral in distributional and macroeconomic terms would therefore increase tax bands, tax credits and welfare payment rates in line with this wage growth. Incomes for all households would then grow at the same rate. This is the neutral benchmark against which we measure the distributional impact of actual policy.
We have done an initial analysis of how different income groups are affected by Budget 2017, using Switch, the ESRI tax-benefit model. The model is based on the CSO’s Survey on Income and Living Conditions. For the first time we have pooled the two latest years of this survey (2013 and 2014) to increase the sample size to almost 8,000 households. This data is carefully calibrated to represent the Irish population in 2017, and we calculate the impact on each family unit and individual within these 8,000 households. We then summarise to provide a nationally representative picture of the impact of the budget on Irish households, which simply cannot be gained from selected example cases.
There are a number of issues relating to the timing of policy changes – particularly the suspension of water charges in March 2016, and the move to have welfare payment rates increase in March 2017. We take account of these by presenting two different perspectives.
We look first at the impact of policy as announced in Budget 2017 as compared with policy as announced in Budget 2016. We term this a “budget-to-budget” comparison.
The second view takes account of the fact some policies change within the calendar year. Here the focus is on comparing policies over the whole calendar year 2017 with those in force over the calendar year 2016. We refer to this as a “year-on-year” comparison.
Tax and welfare
Some new initiatives cannot be included at present, though future work will help to identify their likely effects. Work is already under way on including in the model the housing assistance payment and the affordable childcare scheme, which has both universal and means-tested elements. Neither of these, nor the help-to-buy scheme, can be included in the present analysis.
Similarly, the impact of the 50 cent rise in tax on cigarettes cannot be included, though it is hoped to do so in future work.
In order to summarise the distributive impact, we rank households by their income, adjusted for numbers of adults and children in the household. We then divide the households into five equal-sized groups or “quintiles”, from lowest income to highest income. The chart shows the percentage gain or loss for each of these quintiles for Budget 2017.
For most income groups, the impact of Budget 2017 changes is rather small. The greatest gains are seen in the lowest income quintile – the poorest section of the population. On a budget-to-budget basis, the gain is about three-quarters of 1 per cent, or about half of that level on a year-on-year basis.
The deferral of welfare rate increases to March 2017 also reduces gains in the year-on-year comparison.
Stepping back from the detail of these small changes, one could say that Budget 2017 has been close to distributionally neutral overall, but with some additional resources targeted towards those on the lowest incomes. The increase in welfare payment rates – the first for non-pension payments in several years – is clearly important in this respect.
Tim Callan, Caitriona Logue, Michael Savage, John R Walsh and Mark Regan are researchers at the ESRI