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Working paper

Social Expenditure in New Zealand: Stochastic Projections

Authors: John Creedy and Kathleen Makale

Abstract

This paper presents stochastic projections for 13 categories of social spending in New Zealand over the period 2011-2061. These projections are based on detailed demographic estimates covering fertility, migration and mortality disaggregated by single year of age and gender. Distributional parameters are incorporated for all of the major variables, and are used to build up probabilistic projections for social expenditure as a share of GDP using simulation methods, following Creedy and Scobie (2005). Emphasis is placed on the considerable uncertainty involved in projecting future expenditure levels.

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Acknowledgements

We are grateful to Christopher Ball, John Bryant and Nicola Kirkup for comments on an earlier version of this paper.

Disclaimer

The views, opinions, findings, and conclusions or recommendations expressed in this Working Paper are strictly those of the author(s). They do not necessarily reflect the views of the New Zealand Treasury or the New Zealand Government. The New Zealand Treasury and the New Zealand Government take no responsibility for any errors or omissions in, or for the correctness of, the information contained in these working papers. The paper is presented not as policy, but with a view to inform and stimulate wider debate.

Executive Summary

This paper reports projections of social expenditure in New Zealand, over the fifty-year period 2011 to 2061, which explicitly model uncertainty associated with a wide range of variables using a 'stochastic' approach.

The stochastic approach regards the parameters (fertility, labour force participation rates, age specific per capita expenditures, and so on) as being characterised by a distribution, rather than taking 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 generates a distribution of values in each year of the projection period, whose properties can be examined.

The first stage is the production of projections for the size of the population, together with its distribution by age and gender. This requires projections of trends in fertility, mortality and net migration. Projected labour force participation rates are then combined with age and gender specific unemployment rates to generate the size of the workforce. This is multiplied by average productivity per worker to obtain GDP. Social expenditures per capita are combined with population (by age and gender) to obtain total social expenditure on each of 13 categories for each age and gender group. The resulting total social expenditure is finally expressed as a share of projected GDP.

Focussing on 'pure ageing' results (whereby per capita social expenditure costs in each category grow at the same rate on average as productivity growth), the projections reveal considerable uncertainty regarding the ratio of total social expenditure to GDP as the time period increases.

In the benchmark case, the mean ratio of expenditure to GDP is projected to rise from its current level of 25 per cent to 28 per cent by 2061. However, the 5thand 95th percentiles are 22.5 per cent and 35 per cent.

A negligible part of the dispersion in this ratio is contributed by demographic uncertainty.

Much of the uncertainty was found to be contributed by uncertainty regarding future unemployment and labour force participation rates, and the rate of productivity growth. More optimistic assumptions regarding labour force participation and health costs among the aged produced lower average ratios of expenditure to GDP. A consistent finding was the tendency for the average expenditure ratio to fall slightly beyond around 2040, following the death of the post World War II baby boom generations.

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.

Notes

  • [1]See, for example, contributions collected in Creedy (1995) and Creedy and Guest (2007).
  • [2]For example, see Auerbach and Hassett (2000), and on tax smoothing see Davis and Fabling (2002).
  • [3]That paper produced the first stochastic demographic and expenditure projections for New Zealand and provides a discussion of alternative approaches and related literature.
  • [4]The use of a priori values is examined in detail in Creedy and Alvarado (1998). They obtained stochastic projections of social expenditure for Australia but did not, unlike Creedy and Scobie (2005) and the present paper, combine these with stochastic population projections.

2 The projection model

This section provides a brief description of the projection model: further details are given in Appendix A.[5] Exogenous age-specific and gender-specific rates are used and, as mentioned above, no allowance is made for possible feedback effects, which may for example be generated by general equilibrium changes in price and wage rates, or endogenous policy responses.

The sequence of calculations is set out in Figure 1, where the grey boxes represent input data. The first stage is the production of projections for the size of the population, together with its distribution by age and gender. This requires projections of trends in fertility, mortality and net migration. Projected labour force participation rates are then combined with age and gender specific unemployment rates to generate the size of the workforce. This is multiplied by average productivity per worker to obtain GDP.

Figure 1: The Structure of the Model

 

Figure 1: The Structure of the Model.

Social expenditures per capita are combined with population (by age and gender) to obtain total social expenditure on each of 13 categories for each age and gender group. These categories are listed in Appendix Tables 4 and 5. The resulting total social expenditure is finally expressed as a share of projected GDP.

In moving through the sequence of calculations, a random draw from the distribution of each variable is made, as explained below. This process is repeated 5000 times, to produce a distribution of the social expenditure ratio for each projection year. The process therefore also generates distributions of the population by age and gender, as well as for each category of social expenditure.

Consider a relevant variable, Χ, which could be, for example, an unemployment rate, a fertility rate for women of a given age, or an item of social expenditure. In cases where the variable may take positive or negative values, it is assumed to be normally distributed. Where a variable is necessarily positive, and the distribution is positively skewed, the distribution is assumed to be lognormal.[6]

Where a variable is normally distributed with mean and variance μ and σ2 respectively, Χ is distributed as N(μ2) If r represents a random drawing from N(0,1), a simulated value, χ, can be obtained using χ = μ + r σ. In the lognormal case, μ and σ2 refer to the mean and variance of logarithms. A random draw from a lognormal distribution is given by χ = exp (μ + r σ), where r is again a random N(0,1) variable.[7]

Each social expenditure projection is associated with its own demographic structure. The populations are necessarily derived using single-year age groups, but when calculating social expenditures and employment, some age grouping is necessary in view of the more limited data available for these variables.

The growth rates and standard deviations used in Creedy and Scobie (2005) were based on considerable information about past trends and the variability in several hundred fertility rates, mortality rates, migration rates, male/female birth ratios, labour force participation rates, unemployment rates and major categories of social expenditure. Their growth rates and standard deviations were estimated from the following regression:[8]

     log γt = α + β + ut       (1)

Where γτ is per capita expenditure in the relevant category at time τ and ut is an error term. This specification implies an estimated constant growth rate of

 

. Furthermore, Var(log γt) =

 

, so the standard deviations are derived from the estimated standard error of the regression,

 

. The log-linear specification was found to provide a good fit to the historical data.[9]

 

In producing the projections reported below, data were obtained for 2010 relating to male and female populations, inward and outward migrants by single year of age, along with male and female unemployment rates by five-year age groups. The details along with data sources are given in Tables 1-3 in Appendix B. The 2010 social expenditure costs per capita, again within 5-year age groups, were also obtained for 13 categories and are reproduced in Tables 4 and 5 of Appendix B. The standard deviations for demographic components of the model were adapted from Creedy and Scobie (2005), in view of the lack of more recent data.

Notes

  • [5]This section borrows heavily from Creedy and Scobie (2005).
  • [6]The assumption of lognormality was also made, for example, by Alho (1997) and Creedy and Alvarado (1998).
  • [7]One limitation of the approach is the assumption of independence, whereby in any year the random draw for any variable/age is independent of draws for other variables/age groups. For example, it does not allow for changes which may systematically affect mortality rates of all age groups. Positive correlations would tend to increase the confidence intervals beyond those obtained below.
  • [8]This form is a simplified form of the more general Box-Jenkins type of time series specification used by Lee and Tuljapurkar (2000).
  • [9]The use of past variability to reflect future uncertainty is of course just one possible approach. The same model could be used with a priori assumptions about the distributions, based on a combination of past information and a range of considerations concerning views of the future; see Creedy and Alvarado (1998).

3 Benchmark results

This section presents the benchmark projections for the distribution of social expenditure as a proportion of GDP. The essential features are that all social expenditures are assumed to grow in real terms at 1.5 per cent per year, the same rate as labour productivity. The results therefore refer to a ‘pure ageing' assumption. Immigration is based on the average over recent years of 14,500 net immigrants each year (with total annual immigration of 82,500). Changes in mortality rates are assumed to continue (at values given in Table 1 of Appendix B) for 15 years, after which they remain constant. Changes in labour force participation rates, taken from Creedy and Scobie (2005), are assumed to apply for 10 years. Fertility rates are assumed to change for 10 years, after which no further changes in these rates are projected. The standard deviation of productivity growth is assumed to be 0.02, reflecting a high degree of uncertainty about this variable.

For the social expenditure categories, the standard deviations (in each age, gender and expenditure category) were set at 0.05 for each category and age group and gender.

Figure 2 shows the projected population pyramids for ten-year intervals over the 50-year projection period (for plotting purposes, the results are arranged into 5-year age groups). The figures actually show the arithmetic mean values of the various distributions. As expected, the population projections are associated with a relatively small degree of uncertainty, in that the confidence intervals around the mean values are very small. For this reason they are not shown here.

Figure 3 shows the time profile of various measures of the distribution of the projected ratio of total social expenditure to GDP from 2010 to 2060.[10] In addition to the mean, the profiles of upper and lower quartiles, and 5th and 95th percentiles are shown.[11] This diagram may be compared with Figure 3 of Creedy and Scobie (2005), which covers the period 2001 to 2051. The latter, as expected, starts from a similar base, but the present projections display slightly smaller ‘spreads' in the profiles over time. Nevertheless the most striking feature of Figure 3 is the increasing uncertainty regarding the social expenditure ratio.

As with earlier projections, the arithmetic mean ratio of total social expenditure to GDP increases relatively sharply from around 2020, as a result of the movement of the post World War II baby boomers into retirement and old age. In Creedy and Scobie (2005) the projected profile of this ratio becomes stable by around 2040. In the present case the mean ratio falls very slightly after this date. Given the assumption that mean per capita growth rates of social expenditure are the same as that of (mean) productivity growth, the profiles are affected largely by the changing age composition over time. The slight reduction in the projected mean expenditure ratio in later years therefore seems to be explained by the fact that the baby boom generations will have all died by that time. However, given the large degree of uncertainty (the high dispersion in the distribution of the expenditure ratio), these reductions cannot be treated as statistically ‘significant'.

The generally lower average social expenditure ratio in the present case is not explained by the much higher annual value of net immigration, compared with the earlier results (which were based on a long term average of only 5,000 (associated with gross immigration of 60,000), a value that has been substantially exceeded since 2001).[12] Higher immigration has very little effect, although it must be recognised that in the present model migrants are assumed to acquire existing New Zealand mortality, fertility and labour force participation characteristics as soon as they arrive (along with entitlements to benefits). And although the average age of immigrants is slightly lower than that of the New Zealand population, there are of course substantial numbers of migrants in the older age groups.[13]

Figure 2: Population Projections by Age Groups and Gender 2010-2060

 

Figure 2: Population Projections by Age Groups and Gender 2010-2060.
Figure 3: Projected Social Expenditure as a Share of GDP: 2010-2060 (Benchmark Case)

 

Figure 3: Projected Social Expenditure as a Share of GDP: 2010-2060 (Benchmark Case).

To illustrate how the assumed standard errors of the per capita growth rates of social expenditures translate into standard errors of the costs, the projected growth in health and education costs per capita, for males and females separately, are shown in Figures 4 and 5. In each case the mean is plotted, along with two standard deviations either side of it. Health includes the five health categories aggregated; these are personal health, public health, mental health, DSS older, and DSS under 65. Education includes the two categories, primary and tertiary education.[14] The largest degree of uncertainty relates to unemployment benefits, since in this case the uncertainty also includes the age and gender-specific unemployment rates. Unemployment costs per capita are illustrated in Figure 6.

Figure 4: Health Expenditure per Capita: 2010-2060

 

Figure 4: Health Expenditure per Capita: 2010-2060.
Figure 5: Education Expenditure per Capita: 2010-2060

 

Figure 5: Education Expenditure per Capita: 2010-2060.
Figure 6: Unemployment Benefit Expenditure per Capita: 2010-2060

 

Figure 6: Unemployment Benefit Expenditure per Capita: 2010-2060 .

Notes

  • [10]These summary values were produced for ten-year intervals rather than each year of the projection period, to reduce computer run-times.
  • [11]As in the previous analysis, the mean and median were found to be similar.
  • [12]Comparisons with earlier results are not exact because the 14 social expenditure categories used by Creedy and Scobie (2005) are not precisely the same as the 13 categories used here, in view of data limitations.
  • [13]The question of whether higher net immigration can to some extent substitute for higher fertility is examined in detail in the context of Australia by Creedy and Alvarado (1998b), who allow for ‘assimilation' to take several generations. They found relatively small effects.
  • [14]Early childhood education is excluded here, although recent policy changes have increased its importance.

4 Sensitivity Analyses

This section explores the impact on the projected levels of social expenditure of variations to the assumptions used in the benchmark case. In order to concentrate on the effects of the anticipated demographic transition, the sensitivity analyses retain the use of a ‘pure ageing' assumption (whereby average growth rates of the social expenditure categories are equal to average productivity growth).

First, it is of interest to examine the implications of having a higher age of eligibility for NZ Superannuation. The full effects cannot be modelled explicitly, but suppose that the age of eligibility is raised, for males and females, to aged 70.[15] To reflect this increase, the NZS costs per capita were changed: for males and females in age groups from 60 to 69 these were reduced to zero. For the age group 70-74 the annual per capita costs were changed to 10000 and 12000 for males and females respectively. Associated with these changes, the labour force participation rates for males in age groups 55-59, 60-64, 65-69, and 70-74 were changed to 0.9, 0.9, 0.75 and 0.1 respectively. For females in the corresponding groups the rates were increased to 0.8, 0.8, 0.75 and 0.1.

A related modification is a change to the assumed length of time over which mortality declines. This was changed from 15 to 30 years. The age-related health costs (DSS Older) for age groups from 50 to 64 were also reduced to zero. The modifications to labour force participation and health costs were, for simplicity, assumed to operate immediately. This modification from the benchmark case is thus one in which people continue to live longer, but this extra length of life is associated with improved health and hence also higher labour force participation. The extra longevity ultimately leads to higher stocks of retired individuals, though this is mitigated to some extent by the assumed higher labour force participation, which raises GDP.

The projected distribution of the ratio of social expenditure to GDP is shown in Figure 7. It is clear that these more optimistic assumptions imply a downward shift in the distribution, although the spread of values (between the 5th and 95th percentiles) remains similar to that of the benchmark case.

Figure 7: Projected Social Expenditure as a Share of GDP: 2010-2060 (Increased Longevity and Higher Labour Force Participation of Those Aged over 55)

 

Figure 7: Projected Social Expenditure as a Share of GDP: 2010-2060 (Increased Longevity and Higher Labour Force Participation of Those Aged over 55).

Clearly a wide range of alternative assumptions could be examined, as discussed in Creedy and Scobie (2005), but in view of the present emphasis on uncertainty it is of interest to consider alternative assumptions about the standard errors of a number of the variables. First, the elimination of any uncertainty regarding demographic elements has very little effect, as expected; the resulting diagram (not shown here) is difficult to distinguish from the benchmark of Figure 3.

Two more cases are reported here. In the first variant, the standard deviations of expenditure categories in all age groups were set to zero. The uncertainty is thus attributed to demographic, labour market and productivity variations. In the second variant, the standard deviations of the unemployment and participation rates, and that of the productivity growth rate, were set equal to zero. The resulting projected distributions of the social expenditure to GDP ratio are shown in Figures 8 and 9. Comparison of these two figures shows that spread of the distributions arising from labour market and productivity variations is substantially higher than that arising from the uncertainty with regard to per capita social expenditures (as reflected in the observed variability over earlier years).

Figure 8: Projected Social Expenditure as a Share of GDP: 2010-2060: No Uncertainty Regarding Growth of all Social Expenditure Categories

 

Figure 8: Projected Social Expenditure as a Share of GDP: 2010-2060: No Uncertainty Regarding Growth of all Social Expenditure Categories.
Figure 9: Projected Social Expenditure as a Share of GDP: 2010-2060: No Uncertainty in Participation and Unemployment Rates, and Productivity Growth Rate

 

Figure 9: Projected Social Expenditure as a Share of GDP: 2010-2060: No Uncertainty in Participation and Unemployment Rates, and Productivity Growth Rate.

Notes

  • [15]This is raised from the first year, rather than being gradually phased in. The structure of the model makes it difficult to introduce a selective gradual change of this type to the participation rates over time.

5 Conclusions

This paper has projected social expenditures in New Zealand over the fifty-year period 2011-2061, based on a stochastic approach using 13 categories of social spending, decomposed by age and gender. By allowing for uncertainty about fertility, migration, mortality, labour force participation and productivity, and all categories of social spending, it has been possible to generate projections with accompanying confidence bands.

Focussing on ‘pure ageing' results (whereby per capita social expenditure costs in each category grow at the same rate on average as productivity growth), the projections reveal considerable uncertainty regarding the ratio of total social expenditure to GDP as the time period increases. In the benchmark case, the mean ratio of expenditure to GDP is projected to rise from its current level of 25 per cent to 28 per cent by 2061. However, the 5th and 95th percentiles are 22.5 per cent and 35 per cent.

A negligible part of the dispersion in this ratio is contributed by demographic uncertainty. Much of the uncertainty was found to be contributed by uncertainty regarding future unemployment and labour force participation rates, and the rate of productivity growth. More optimistic assumptions regarding labour force participation and health costs among the aged produced lower average ratios of expenditure to GDP. A consistent finding was the tendency for the average expenditure ratio to fall slightly beyond around 2040, following the death of the post World War II baby boom generations.

Appendix A: The Projection Model

Population projections are obtained using a social accounting, or cohort component, framework.[16] There are N = 100 (single year) age groups. The square matrix of flows, fij, from columns to rows, has N - 1 non-zero elements which are placed on the diagonal immediately below the leading diagonal. The coefficients, aij, denote the proportion of people in the j th age who survive in the country to the age i, where pj is the number of people aged j and

     

 

      (A1)

 

where only the ai+1,i, for i = 1,..., N - 1 are non-zero. Males and females are distinguished by subscripts m and f, so that matrices of coefficients are Am and Af. Let pm,t and pf,t denote vectors of male and female populations at t, where the i-th element is the corresponding number of age i. The vectors of births and immigrants are b and m respectively. The forward equations:

     

 

      (A2)

 

     

 

      (A3)

 

In general the matrices Am and Af, along with the births and inward migration flows, vary over time.

Suppose that ci represents the proportion of females of age i who give birth per year. In general the ci values vary over time. Suppose that a proportion, δ, of births are male, and define the N-element vector τ as the column vector having unity as the first element and zeros elsewhere. Then births are:

     

 

      (A4)

 

     

 

      (A5)

 

where c′ is the transpose of the vector c. Equations (A2) to (A5) can be used to make population projections, for assumed migration levels.

The per capita social expenditures are placed in a matrix, S, with N rows and k columns, where there are k items of social expenditure and the i, j th element sij is the per capita cost of the j th type of social expenditure in the i th age group. Suppose the j th social expenditure is expected to grow in real terms at the annual rate ψj in each age group. Then define gt as the k-element column vector whose j th element is equal to (1 + ψj )t-1. Aggregate social expenditure at t,Ct, is thus:

     

 

      (A6)

 

Expenditure per person in each category and age differs for males and females, so that (A6) is suitably expanded.[17]

Projections of Gross Domestic Product depend on: initial productivity (GDP per employed person); productivity growth; employment rates; participation rates; and the population of working age. Total employment is the product of the population, participation rates and the employment rate. Employment is calculated by multiplying the labour utilisation rate by the labour force. If Ut is the total unemployment rate in period t, the utilisation rate is 1 - Ut. The aggregate unemployment rate is calculated by dividing the total number of unemployed persons, Vt, by the total labour force, Lt. The value of Vt is calculated by multiplying the age distribution of unemployment rates by the age distribution of the labour force, where these differ according to both age and sex.

Let vectors Um and Uf be the age distributions of male and female unemployment rates. If represents diagonalisation (a vector is written as the leading diagonal of a square matrix with other elements zero) unemployment in period t is:

 

 

      (A7)

 

The labour force, Lt, is:

 

 

      (A8)

 

If productivity grows at the rate, θ, GDP t is the product of the utilisation rate, 1 - Ut = 1 - Vt/Lt, the labour force, Lt, and productivity, so that:

     

 

      (A9)

 

Notes

  • [16]For further exploration of this model, see Creedy (1995).
  • [17]Care needs to be taken with the treatment of unemployment costs per capita, because unemployment levels are endogenous (depending on unemployment rates, participation rates and the age structure). The unemployment costs per unemployed person in each age and gender group therefore need to be converted into per capita terms in each year.

Appendix B: The Data

Appendix Table 1: Basic Demographic Data by Age and Gender
  Male Female
Age P I E M ΔM/M P I E M ΔM/M F ΔF/F
0 32590 402 433 0.00519 -0.01071 31140 393 478 0.00411 -0.0125 0.00000 0.00000
1 32650 557 539 0.00049 -0.01071 30410 535 497 0.00044 -0.0125 0.00000 0.00000
2 32910 471 543 0.00026 -0.01071 31140 457 465 0.00022 -0.0125 0.00000 0.00000
3 31630 486 521 0.00024 -0.01071 30190 462 426 0.00020 -0.0125 0.00000 0.00000
4 30240 419 483 0.00021 -0.05211 28950 454 417 0.00017 0.0169 0.00000 0.00000
5 29640 429 392 0.00018 -0.05211 28160 381 354 0.00015 0.0169 0.00000 0.00000
6 29800 415 400 0.00015 -0.05211 28400 418 377 0.00012 0.0169 0.00000 0.00000
7 29030 369 327 0.00013 -0.05211 27430 390 385 0.00010 0.0169 0.00000 0.00000
8 28550 418 346 0.00012 -0.05211 27510 375 356 0.00008 0.0169 0.00000 0.00000
9 29890 350 382 0.00012 -0.02425 28420 382 372 0.00008 0.0189 0.00000 0.00000
10 30480 361 354 0.00011 -0.02425 29130 370 367 0.00008 0.0189 0.00000 0.00000
11 29710 371 398 0.00013 -0.02425 28070 330 356 0.00009 0.0189 0.00000 0.00000
12 30060 375 367 0.00018 -0.02425 28670 375 369 0.00011 0.0189 0.00007 0.00349
13 30020 401 390 0.00023 -0.02425 28880 393 373 0.00016 0.0189 0.00081 0.00290
14 31310 442 343 0.00032 -0.02732 29450 437 316 0.00021 -0.0064 0.00384 -0.00220
15 31900 565 350 0.00042 -0.02732 30280 637 360 0.00027 -0.0064 0.01301 -0.00192
16 32150 733 373 0.00051 -0.02732 30510 818 402 0.00033 -0.0064 0.02620 0.00456
17 33070 737 500 0.00065 -0.02732 31480 670 586 0.00038 -0.0064 0.04251 -0.00434
18 33580 1351 852 0.00077 -0.02732 31710 925 904 0.00040 -0.0064 0.05582 -0.00647
19 34810 1761 775 0.00088 -0.02732 32900 1255 757 0.00042 -0.0289 0.06571 -0.00730
20 34170 1649 804 0.00096 -0.04083 32930 1289 798 0.00040 -0.0289 0.06891 -0.00217
21 33360 1640 1212 0.00101 -0.04083 31580 1247 1203 0.00039 -0.0289 0.07794 -0.00277
22 32470 1778 1389 0.00103 -0.04083 30690 1493 1493 0.00035 -0.0289 0.08446 -0.00196
23 31100 1787 1620 0.00104 -0.04083 29230 1605 1613 0.00033 -0.0289 0.08781 0.00251
24 30300 1793 1598 0.00103 -0.04083 29410 1642 1551 0.00030 -0.0113 0.09299 0.00660
25 29630 1766 1598 0.00100 -0.05603 29380 1770 1592 0.00028 -0.0113 0.10073 0.01462
26 29300 1698 1638 0.00098 -0.05603 29370 1645 1473 0.00026 -0.0113 0.11040 0.01180
27 29060 1592 1477 0.00096 -0.05603 29290 1645 1313 0.00027 -0.0113 0.11749 0.01484
28 27970 1437 1260 0.00093 -0.05603 28760 1507 1133 0.00030 -0.0113 0.12445 0.01960
29 27220 1314 1105 0.00091 -0.02415 28440 1359 962 0.00034 -0.0099 0.12917 0.02857
30 26590 1221 970 0.00089 -0.02415 28400 1243 788 0.00037 -0.0099 0.13165 0.03785
31 25970 990 817 0.00090 -0.02415 28380 937 808 0.00044 -0.0099 0.13489 0.04502
32 25330 730 701 0.00093 -0.02415 27480 743 647 0.00049 -0.0099 0.12298 0.04321
33 25640 670 623 0.00095 -0.02415 27850 699 608 0.00055 -0.0099 0.11295 0.04349
34 25800 608 558 0.00102 -0.00274 27890 653 546 0.00058 -0.0178 0.10306 0.05128
35 26580 589 595 0.00108 -0.00274 29250 589 497 0.00063 -0.0178 0.08739 0.03948
36 27410 582 570 0.00116 -0.00274 30540 517 552 0.00065 -0.0178 0.07237 0.05702
37 28650 583 523 0.00123 -0.00274 31700 558 497 0.00069 -0.0178 0.05626 0.05008
38 29980 482 547 0.00133 -0.00274 33110 495 518 0.00073 -0.0178 0.04427 0.14021
39 30370 476 513 0.00141 -0.01166 33700 528 506 0.00080 -0.0091 0.03028 0.18708
40 29850 477 491 0.00152 -0.01166 32710 462 449 0.00089 -0.0091 0.02220 0.18807
41 30180 453 441 0.00161 -0.01166 32970 411 431 0.00100 -0.0091 0.01256 0.40534
42 29760 400 451 0.00173 -0.01166 32490 357 387 0.00111 -0.0091 0.00738 0.40805
43 29590 347 405 0.00181 -0.01166 32200 344 398 0.00123 -0.0091 0.00359 0.68216
44 29950 356 342 0.00192 -0.01613 32030 328 340 0.00134 -0.0189 0.00231 0.00000
45 30270 310 373 0.00202 -0.01613 32310 281 345 0.00146 -0.0189 0.00090 0.00000
46 31340 320 361 0.00216 -0.01613 33230 317 375 0.00157 -0.0189 0.00032 0.00000
47 31670 269 379 0.00233 -0.01613 34070 295 361 0.00171 -0.0189 0.00021 0.00000
48 31740 281 362 0.00251 -0.01613 33810 242 325 0.00184 -0.0189 0.00012 0.00000
49 31120 238 324 0.00277 -0.02617 33120 245 281 0.00200 -0.0341 0.00016 0.00000
50 30040 249 312 0.00307 -0.02617 31490 256 280 0.00215 -0.0341 0.00000 0.00000
51 29530 238 292 0.00340 -0.02617 30920 223 289 0.00235 -0.0341 0.00000 0.00000
52 28050 219 273 0.00376 -0.02617 29510 251 279 0.00254 -0.0341 0.00000 0.00000
53 27840 219 267 0.00415 -0.02617 28790 265 258 0.00278 -0.0341 0.00000 0.00000
54 27170 179 253 0.00453 -0.03079 28130 229 256 0.00303 -0.0330 0.00000 0.00000
55 25950 221 229 0.00492 -0.03079 27090 261 236 0.00332 -0.0330 0.00000 0.00000
56 25270 195 220 0.00534 -0.03079 26340 215 211 0.00365 -0.0330 0.00000 0.00000
57 24450 197 176 0.00581 -0.03079 25290 215 200 0.00399 -0.0330 0.00000 0.00000
58 24170 186 171 0.00632 -0.03079 24800 205 188 0.00437 -0.0330 0.00000 0.00000
59 23590 160 154 0.00692 -0.03787 24390 209 156 0.00478 -0.0332 0.00000 0.00000
60 23590 184 148 0.00759 -0.03787 24510 212 134 0.00523 -0.0332 0.00000 0.00000
61 23260 171 143 0.00832 -0.03787 23700 198 136 0.00572 -0.0332 0.00000 0.00000
Appendix Table 1: Basic Demographic Data by Age and Gender (continued)
  Male Female
Age P I E M ΔM/M P I E M ΔM/M F ΔF/F
62 23250 157 131 0.00914 -0.03787 24210 167 116 0.00626 -0.0332 0.00000 0.00000
63 23360 182 107 0.01002 -0.03787 24310 160 110 0.00687 -0.0332 0.00000 0.00000
64 19630 149 72 0.01096 -0.03996 20490 143 77 0.00754 -0.0270 0.00000 0.00000
65 18590 136 70 0.01197 -0.03996 19380 130 74 0.00832 -0.0270 0.00000 0.00000
66 17800 133 66 0.01309 -0.03996 18320 126 72 0.00916 -0.0270 0.00000 0.00000
67 15630 99 55 0.01430 -0.03996 16460 103 68 0.01014 -0.0270 0.00000 0.00000
68 17170 95 52 0.01569 -0.03996 18250 83 47 0.01120 -0.0270 0.00000 0.00000
69 16790 91 51 0.01728 -0.03966 17830 83 44 0.01241 -0.0261 0.00000 0.00000
70 15400 77 36 0.01910 -0.03966 16280 67 54 0.01374 -0.0261 0.00000 0.00000
71 13620 57 37 0.02122 -0.03966 14950 76 39 0.01523 -0.0261 0.00000 0.00000
72 13030 60 35 0.02360 -0.03966 14120 61 32 0.01684 -0.0261 0.00000 0.00000
73 12150 50 27 0.02635 -0.03966 13460 56 36 0.01861 -0.0261 0.00000 0.00000
74 11290 38 17 0.02942 -0.04095 12660 35 31 0.02055 -0.0293 0.00000 0.00000
75 10750 30 29 0.03284 -0.04095 11780 34 25 0.02274 -0.0293 0.00000 0.00000
76 10060 28 25 0.03664 -0.04095 11470 32 27 0.02524 -0.0293 0.00000 0.00000
77 9540 26 20 0.04076 -0.04095 11280 30 26 0.02816 -0.0293 0.00000 0.00000
78 9400 24 15 0.04524 -0.04095 10860 28 20 0.03156 -0.0293 0.00000 0.00000
79 9140 22 15 0.05010 -0.02316 10910 25 19 0.03556 -0.0208 0.00000 0.00000
80 8270 20 13 0.05563 -0.02316 10460 24 15 0.04022 -0.0208 0.00000 0.00000
81 7690 17 11 0.06220 -0.02316 9650 20 14 0.04566 -0.0208 0.00000 0.00000
82 7050 14 9 0.07016 -0.02316 9210 18 12 0.05196 -0.0208 0.00000 0.00000
83 6290 12 7 0.07988 -0.02316 8530 16 9 0.05923 -0.0208 0.00000 0.00000
84 5500 10 6 0.09171 -0.01992 8060 14 9 0.06755 -0.0094 0.00000 0.00000
85 4880 8 5 0.10579 -0.01992 7600 12 8 0.07714 -0.0094 0.00000 0.00000
86 4070 6 4 0.12161 -0.01992 6710 10 8 0.08806 -0.0094 0.00000 0.00000
87 3410 6 3 0.13853 -0.01992 5900 8 6 0.10057 -0.0094 0.00000 0.00000
88 2870 5 3 0.15596 -0.01992 5350 6 3 0.11478 -0.0094 0.00000 0.00000
89 2260 4 2 0.17329 -0.01365 4610 6 2 0.13085 -0.0054 0.00000 0.00000
90 1680 3 2 0.18977 -0.01365 3200 5 2 0.14865 -0.0054 0.00000 0.00000
91 1290 2 1 0.20513 -0.01365 2810 4 1 0.16809 -0.0054 0.00000 0.00000
92 1080 2 0 0.22344 -0.01365 2460 4 1 0.18894 -0.0054 0.00000 0.00000
93 880 2 0 0.24261 -0.01365 2060 3 1 0.21119 -0.0054 0.00000 0.00000
94 550 1 0 0.26324 -0.01365 1780 2 0 0.23453 -0.0054 0.00000 0.00000
95 380 1 0 0.28563 -0.01365 1510 1 0 0.25950 -0.0054 0.00000 0.00000
96 220 1 0 0.31011 -0.01365 900 1 1 0.28602 -0.0054 0.00000 0.00000
97 100 0 0 0.33761 -0.01365 640 1 0 0.31413 -0.0054 0.00000 0.00000
98 80 0 0 0.36607 -0.01365 420 1 0 0.34352 -0.0054 0.00000 0.00000
99 50 0 0 0.39900 -0.01365 230 0 0 0.37476 -0.0054 0.00000 0.00000

Notes:

P = population; I = immigration; E = emigration; M = mortality; ΔM/M = proportionate change in mortality; F = fertility; ΔF/F = proportionate change in fertility.

Population data are from New Zealand Treasury Fiscal Strategy Model: http://www.treasury.govt.nz/government/fiscalstrategy/model

Immigration and emigration data are from Statistics New Zealand Infoshare Database: http://www.stats.govt.nz/infoshare/ViewTable.aspx?pxID=492172f2-ccc5-407a-97e4-671786685d49

The aggregated data provided a value for individuals aged 75+. This value has been apportioned across age years 75 - 99.

Mortality rates (2010 base year) are from New Zealand Treasury Long Term Fiscal Strategy Model: http://www.treasury.govt.nz/government/fiscalstrategy/model.

Fertility rates (2010 base year) from Statistics New Zealand Infoshare Database: http://www.stats.govt.nz/infoshare/ViewTable.aspx?pxID=ef4929f3-a56c-4ba9-9081-efefb05f4cfb

 

Appendix B: The Data (continued)

Appendix Table 2: Standard Deviations for Mortality, Fertility and Migration
        Migration         Migration
Age Male
Mortality
Female
Mortality
Fertility In Out Age Male
Mortality
Female
Mortality
Fertility In Out
0 0.10 0.10 0.00 61.00 70.00 50 0.07 0.05 1.00 45.00 14.00
1 0.10 0.10 0.00 61.00 70.00 51 0.07 0.05 0.00 45.00 14.00
2 0.10 0.10 0.00 61.00 70.00 52 0.07 0.05 0.00 45.00 14.00
3 0.10 0.10 0.00 61.00 70.00 53 0.07 0.05 0.00 45.00 14.00
4 0.10 0.10 0.00 61.00 70.00 54 0.07 0.05 0.00 45.00 14.00
5 0.10 0.10 0.00 61.00 70.00 55 0.07 0.05 0.00 45.00 14.00
6 0.10 0.10 0.00 61.00 70.00 56 0.07 0.05 0.00 45.00 14.00
7 0.10 0.10 0.00 61.00 70.00 57 0.07 0.05 0.00 12.00 14.00
8 0.10 0.10 0.00 81.00 70.00 58 0.07 0.05 0.00 12.00 14.00
9 0.10 0.10 0.00 81.00 70.00 59 0.07 0.05 0.00 12.00 14.00
10 0.10 0.10 0.00 81.00 70.00 60 0.07 0.05 0.00 12.00 14.00
11 0.10 0.10 0.50 56.00 70.00 61 0.07 0.05 0.00 12.00 14.00
12 0.10 0.10 0.50 56.00 70.00 62 0.07 0.05 0.00 12.00 14.00
13 0.10 0.10 0.30 56.00 135.00 63 0.07 0.05 0.00 12.00 14.00
14 0.10 0.10 0.30 56.00 135.00 64 0.07 0.05 0.00 12.00 6.00
15 0.10 0.10 0.08 56.00 135.00 65 0.07 0.05 0.00 12.00 6.00
16 0.10 0.10 0.08 56.00 135.00 66 0.07 0.05 0.00 12.00 6.00
17 0.10 0.10 0.08 116.00 135.00 67 0.07 0.05 0.00 7.00 6.00
18 0.10 0.10 0.05 116.00 135.00 68 0.07 0.05 0.00 7.00 6.00
19 0.10 0.10 0.05 116.00 135.00 69 0.07 0.05 0.00 7.00 6.00
20 0.10 0.10 0.05 116.00 135.00 70 0.07 0.05 0.00 7.00 6.00
21 0.10 0.10 0.05 116.00 135.00 71 0.07 0.05 0.00 7.00 6.00
22 0.10 0.10 0.05 116.00 135.00 72 0.07 0.05 0.00 7.00 6.00
23 0.10 0.10 0.05 116.00 135.00 73 0.07 0.05 0.00 7.00 6.00
24 0.10 0.07 0.05 116.00 135.00 74 0.07 0.05 0.00 7.00 6.00
25 0.10 0.07 0.05 116.00 135.00 75 0.07 0.05 0.00 7.00 6.00
26 0.10 0.07 0.05 116.00 135.00 76 0.07 0.05 0.00 7.00 6.00
27 0.10 0.07 0.05 116.00 135.00 77 0.07 0.05 0.00 18.00 6.00
28 0.10 0.07 0.07 116.00 135.00 78 0.07 0.07 0.00 4.00 6.00
29 0.10 0.07 0.07 116.00 135.00 79 0.08 0.07 0.00 4.00 6.00
30 0.10 0.07 0.07 116.00 135.00 80 0.08 0.07 0.00 4.00 6.00
31 0.07 0.07 0.07 116.00 135.00 81 0.08 0.07 0.00 4.00 6.00
32 0.07 0.07 0.07 116.00 135.00 82 0.08 0.07 0.00 4.00 6.00
33 0.07 0.07 0.07 116.00 135.00 83 0.08 0.07 0.00 4.00 6.00
34 0.07 0.07 0.07 116.00 80.00 84 0.08 0.07 0.00 4.00 0.88
35 0.07 0.07 0.07 116.00 80.00 85 0.08 0.07 0.00 4.00 0.88
36 0.07 0.07 0.07 116.00 80.00 86 0.08 0.07 0.00 4.00 0.88
37 0.07 0.07 0.07 116.00 80.00 87 0.08 0.07 0.00 4.00 0.88
38 0.07 0.07 0.07 116.00 80.00 88 0.08 0.07 0.00 0.80 0.88
39 0.07 0.07 0.07 116.00 80.00 89 0.08 0.07 0.00 0.80 0.88
40 0.07 0.07 0.07 116.00 108.00 90 0.08 0.08 0.00 0.80 0.88
41 0.07 0.07 0.15 116.00 108.00 91 0.10 0.08 0.00 0.80 0.88
42 0.07 0.05 0.15 45.00 108.00 92 0.10 0.08 0.00 0.80 0.88
43 0.07 0.05 0.15 45.00 108.00 93 0.10 0.08 0.00 0.80 0.88
44 0.07 0.05 0.30 45.00 108.00 94 0.10 0.08 0.00 0.80 0.88
45 0.07 0.05 0.30 45.00 108.00 95 0.10 0.08 0.00 0.80 0.88
46 0.07 0.05 0.30 45.00 45.00 96 0.10 0.08 0.00 0.80 0.88
47 0.07 0.05 0.50 45.00 45.00 97 0.10 0.08 0.00 0.80 0.88
48 0.07 0.05 0.50 45.00 45.00 98 0.10 0.08 0.00 0.80 1.75
49 0.07 0.05 0.50 45.00 45.00 99 0.10 0.08 0.00 0.80 1.75

Notes: Standard deviation values have been adapted from those in Creedy and Scobie (2002), and have been rounded to smooth out the variation between them.

Appendix Table 3: Means and Standard Deviations of Unemployment and Labour Force Participation Rates
  Unemployment Rate (%) Workforce Participation Rate (%)
Age Categories Male
Mean
SD Female
Mean
SD Male
Mean
SD Female
Mean
SD
0-4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
5-9 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
10-14 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
15-19 23.6 4.0 25.8 3.0 47.0 5.0 48.7 6.0
20-24 12.6 4.0 11.3 3.0 77.8 1.0 68.5 3.0
25-29 7.2 2.5 8.6 3.0 90.1 1.0 70.5 2.0
30-34 4.9 2.5 6.2 1.5 93.4 1.0 69.9 2.0
35-39 3.7 2.5 5.5 1.5 93.1 1.0 74.5 2.0
40-44 3.4 1.5 4.8 1.5 92.2 1.0 80.0 2.0
45-49 4.0 1.5 3.8 1.0 91.7 1.0 82.4 2.0
50-54 3.6 1.5 4.2 1.0 90.6 1.0 82.8 3.0
55-59 3.9 1.5 3.2 1.0 86.2 2.0 77.0 3.0
60-64 3.7 1.0 2.6 1.0 78.9 15.0 60.8 20.0
65-69 2.0 1.0 0.0 0.0 22.6 15.0 12.4 2.0
70-74 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
75-79 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
80-84 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
85+ 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Notes: (a) No data available for this value. It is assumed to be zero.

The labour force participation rates and unemployment rates are those for the base year 2010. Data are from Statistics New Zealand Infoshare Database: http://www.stats.govt.nz/infoshare/ViewTable.aspx?pxID=22810555-769b-4418-9f57-a62211a57972

Standard deviation values have been adapted from those in Creedy and Scobie (2002), and have been rounded to smooth out the variation between them.

Appendix B: The Data (continued)

Appendix Table 4: Social Expenditure per Capita: Males
Age Group Personal Health Public Health Mental Health DSS Older DSS Under 65 Education
(Primary
and Secondary)
Tertiary
Eduation
NZ Super DPB+WB IB+SB Family Assistance Accommodation Unemployment
Benefit
0-4 2788 127 6 0 99 0 0 0 0 0 0 0 0
5-9 793 161 93 0 150 3089 0 0 0 0 0 0 0
10-14 778 159 157 0 163 2955 0 0 0 0 0 0 0
15-19 800 152 271 0 173 3913 2895 0 0 291 7 167 52
20-24 718 153 420 0 194 0 3144 0 40 500 267 590 628
25-29 747 107 525 0 246 0 3718 0 68 468 735 463 609
30-34 834 105 525 0 289 0 3741 0 149 715 1162 331 441
35-39 905 103 473 0 289 0 3806 0 533 611 1619 431 232
40-44 1105 101 384 0 281 0 4177 0 103 1048 1373 237 235
45-49 1362 99 369 0 279 0 4282 0 161 1117 855 343 238
50-54 1758 98 265 27 283 0 4189 0 140 778 679 209 45
55-59 2302 97 212 59 355 0 4219 0 100 1509 196 251 530
60-64 3006 97 152 116 533 0 3928 30 80 1156 146 94 188
65-69 4215 97 138 326 0 0 0 12227 70 72 11 216 134
70-74 5220 97 150 739 0 0 0 14226 0 55 84 106 0
75-79 6202 97 168 1537 0 0 0 13764 0 42 0 163 0
80-84 6780 97 223 2801 0 0 0 14401 0 0 0 157 0
85+ 7190 96 235 5646 0 0 0 14044 0 0 0 302  

Notes: DSS = Disability Support Services; NZS = New Zealand Superannuation; DPB = Domestic Purposes Benefit; WB = Widow's Benefit; IB = Invalid's Benefit; SB = Sickness Benefit.

Health Expenditure data for categories one to five are from New Zealand Treasury Fiscal Strategy Model: http://www.treasury.govt.nz/government/fiscalstrategy/model

Aggregate Education (Primary and Secondary) Expenditure data are from New Zealand Treasury calculations based on Ministry of Education administrative data. These data were disaggregated by age and gender using a weighted average approach according to the basic population demographic data in Table 1 for the relevant ages (4 - 17).

Aggregate Tertiary Education Expenditure data are from Statistics New Zealand: http://www.stats.govt.nz/browse_for_stats/education_and_training/Tertiary%20education/StudentLoansandAllowances_HOTP10/Tables.aspx These data were disaggregated by age and gender using a weighted average approach according to the basic population demographic data in Table 1 for the relevant ages (17 - 64). The aggregated data provided a value for individuals aged 60+. This value has been apportioned to the 60-64 age category.

Data for social expenditure data categories eight to thirteen are calculated using New Zealand Treasury model TaxWell, based on HES 08/09 and HES 09/10.

Appendix Table 5: Social Expenditure per Capita: Females Social Expenditure per Capita: Females
Age Group Personal Health Public Health Mental Health DSS Older DSS Under 65 Education
(Primary and
Secondary)
Tertiary
Eduation
NZ Super DPB+WB IB+SB Family Assistance Accommodation Unemployment
Benefit
0-4 2411 127 4 0 73 0 0 0 0 0 0 0 0
5-9 697 161 35 0 81 2942 0 0 0 0 0 0 0
10-14 664 159 127 0 97 2811 0 0 0 0 0 0 0
15-19 1088 152 352 0 96 3717 2735 0 556 315 366 486 285
20-24 1352 155 278 0 149 0 2997 0 1474 288 900 735 346
25-29 1549 107 335 0 236 0 3772 0 1476 311 1441 837 446
30-34 1774 105 408 0 242 0 4049 0 1385 372 2357 632 188
35-39 1577 102 431 0 311 0 4214 0 1759 786 3059 772 252
40-44 1327 101 405 0 278 0 4543 0 1177 775 1998 556 399
45-49 1480 100 380 0 345 0 4568 0 617 990 1235 395 166
50-54 1741 99 298 42 352 0 4371 0 530 716 650 368 107
55-59 2138 98 275 76 448 0 4372 0 651 973 281 456 319
60-64 2686 97 261 152 627 0 4072 805 1037 1479 81 299 147
65-69 3588 98 213 378 0 0 0 13119 97 260 26 201 331
70-74 4380 99 195 838 0 0 0 15344 0 33 40 404 0
75-79 5104 99 212 1794 0 0 0 15297 0 0 7 267 0
80-84 5590 98 254 3985 0 0 0 15628 0 0 3 229 0
85+ 5965 98 217 9488 0 0 0 17510 0 0 0 296 0

Notes: DSS = Disability Support Services; NZS = New Zealand Superannuation; DPB = Domestic Purposes Benefit; WB = Widow's Benefit; IB = Invalid's Benefit; SB = Sickness Benefit.

Health Expenditure data for categories one to five are from New Zealand Treasury Fiscal Strategy Model: http://www.treasury.govt.nz/government/fiscalstrategy/model

Aggregate Education (Primary and Secondary) Expenditure data are from New Zealand Treasury calculations based on Ministry of Education administrative data. These data were disaggregated by age and gender using a weighted average approach according to the basic population demographic data in Table 1 for the relevant ages (4 - 17).

Aggregate Tertiary Education Expenditure data are from Statistics New Zealand: http://www.stats.govt.nz/browse_for_stats/education_and_training/Tertiary%20education/StudentLoansandAllowances_HOTP10/Tables.aspx These data were disaggregated by age and gender using a weighted average approach according to the basic population demographic data in Table 1 for the relevant ages (17 - 64). The aggregated data provided a value for individuals aged 60+. This value has been apportioned to the 60-64 age category.

Data for social expenditure data categories eight to thirteen are calculated using New Zealand Treasury model TaxWell, based on HES 08/09 and HES 09/10

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