1 Introduction

The aim of this paper is to produce stochastic projections of social expenditure in New Zealand over the fifty-year period 2011 to 2061. By their very nature, expenditure projections cannot possibly provide accurate information about future levels. A very large range of parameters are held fixed or are assumed to change according to simple trends over the projection period. In considering the question of, 'what if recent trends were to continue and there were no endogenous policy changes?', such projections can at best provide an indication of the kind of stresses that could arise. There will inevitably be responses to those changes, including 'general equilibrium' types of response arising, for example, from changes in wage rates resulting from labour market pressures. While they cannot therefore be treated as forecasts, projections can stimulate and inform further analyses, considering for example whether market responses may be expected to mitigate or exacerbate the anticipated pressures.

Particular concern has been expressed regarding the consequences of the demographic transition in progress in New Zealand as in many industrialised countries. This involves the ageing of the baby-boom generations and, more importantly, continued reductions in fertility and especially mortality, with the latter producing the phenomenon of the 'ageing of the aged'. The fact that most types of social expenditure are age related makes this category of government expenditure a particularly important area of investigation. Of course, the present transition is merely one stage in earlier extensive demographic transitions experienced by developed economies. Furthermore, there have been very large changes in tax and expenditure ratios that have been quite independent of demographic changes.[1]

In view of the uncertainty that is inevitably involved in making projections, it is important to provide some indication of the potential range of values which could arise. Indeed, in considering possible policy action, and in particular the timing of such intervention (which may include tax smoothing in anticipation of higher future government expenditure), it is important to have some idea of the probability of future contingencies as well as their possible size.[2]

One approach is to consider a number of alternative 'scenarios', characterised by, for example, high labour force participation or higher mortality rates. However, there is no way to attach probabilities to such alternatives, and in the present context there are many parameters to consider. The starting point of the present stochastic approach is to regard the parameters (fertility, labour force participation rates, age specific per capita expenditures, and so on) as being characterised by a distribution, rather than being fixed values. A large number of projections of the variables of interest (such as total social expenditure in relation to GDP) can thus be made, in each case taking a random draw from each of the specified distributions. This kind of ‘Monte Carlo' approach thereby generates a distribution of values in each year of the projection period, whose properties can be examined. The method used here is based closely on the earlier work of Creedy and Scobie (2005).[3]

In specifying the form of the distribution (along with, say, the mean and standard deviation) of each relevant parameter, Creedy and Scobie (2005) used information about its past variability, which clearly involved the collection and analysis of a great deal of data. This was helped by, among other things, the existence of the Long Term Data Series, which was compiled within the Treasury. As this data series has not been maintained and regularly updated in more recent years (having been transferred to Statistics New Zealand), the present paper makes use of the growth rates and standard deviations obtained by Creedy and Scobie (2005). However, as indicated below, these were modified to some extent, either by ‘rounding' a number of values or by using a priori assumptions.[4]

Section 2 briefly describes the framework of analysis: for details see Appendix A. Section 3 presents the benchmark results. Sensitivity analyses are reported in Section 4. Conclusions are in Section 5.