Measuring the distribution of household income and outlays within a

national accounts framework

Crown copyright © See Copyright and terms of use for our copyright, attribution, and liability statements.

Disclaimer The results in this paper are not official statistics.

Citation Stats NZ (2018). Measuring the distribution of household income and outlays within a national accounts framework. Retrieved from www.stats.govt.nz.

ISBN 978-1-98-852879-3

Published in August 2018 by Stats NZ Tatauranga Aotearoa Wellington, New Zealand

Contact Stats NZ Information Centre: info@stats.govt.nz Phone toll-free 0508 525 525 Phone international +64 4 931 4600

www.stats.govt.nz

Background

The motivation for this work The statistics contained in this release reflect the convergence of two independent, but linked, lines of work:

a) The focus on household well-being

The System of National Accounts (SNA) provides a framework for the compilation and analysis of statistics on household income, consumption and wealth. However, the accounts provide aggregate or per capita measures only and depict conditions for an “average” household. Average measures of household income per person or per household do not give any information about the differences between households or how available resources are distributed, analyses which are often critical for the design of economic and social policies.

This point was emphasised in Recommendation 4 of the 2009 Report by the Commission on the Measurement of Economic Performance and Social Progress1,

“Recommendation 4: Give more prominence to the distribution of income, consumption and wealth: Average income, consumption and wealth are meaningful statistics, but they do not tell the whole story about living standards. For example, a rise in average income could be unequally shared across groups, leaving some households relatively worse-off than others. Thus, average measures of income, consumption and wealth should be accompanied by indicators that reflect their distribution. Median consumption (income, wealth) provides a better measure of what is happening to the “typical” individual or household than average consumption (income or wealth). But for many purposes, it is also important to know what is happening at the bottom of the income/wealth distribution (captured in poverty statistics), or at the top. Ideally, such information should not come in isolation but be linked, i.e. one would like information about how well-off households are with regard to different dimensions of material living standards: income, consumption and wealth. After all, a low-income household with above-average wealth is not necessarily worse-off than a medium-income household with no wealth. “

b) Reconciling macro and micro measures of household incomes, expenditure, saving and wealth

The National Accounts – and, more specifically the Household Sector Accounts – provide annual aggregate (macro) statistics on household incomes, outlays and saving2. Household distributional estimates (micro statistics) have been published for many years by Statistics New Zealand via the Household Economic Survey (HES), and these have, in turn, been the key data source for a large number of household income distributional studies3. While measures of the distribution of income and consumption across different household groups are provided by these micro-based data and studies, for many reasons such as differences in concepts, definitions and statistical practices, the

1 STIGLITZ, Joseph E., Chair, Amartya SEN, and Jean-Paul FITOUSSI (2009), Report by the Commission on the Measurement

of Economic Performance and Social Progress 2 Household balance sheets and financial accounts are also now published as part of a four-stage project to improve the

coverage of New Zealand’s national accounts. However, these series are still subject to methodological development and have not yet been integrated with the income and outlay accounts. The statistics contained in this release are confined to distributional analyses of the income and outlay accounts. 3 Refer for example to: Perry, Brian (2017) Household Incomes in New Zealand: trends in indicators of inequality and hardship 1982 to 2016. Ministry of Social development; Ball, C. and J. Creedy, Inequality in New Zealand 1983/84 to 2013/14. NZ Treasury Working Paper 15/06, June 2015.

micro data can yield results that diverge from macro aggregates. This means that measures created using these data sources may not be consistent with the figures in the national accounts. These differences, or inconsistencies, have been, and still remain, points of dispute between analyses based on either a macro or micro perspective.

Studies in a number of countries have shown that household distributions consistent with SNA aggregates can be produced using the information available in micro sources. To meet growing policy demands for improved distributional data, and in direct response to the Stiglitz et al report, in 2011 the OECD and Eurostat launched a joint Expert Group for measuring disparities within a national accounts framework. The Group’s aim was to develop a robust and internationally comparable methodology to produce measures of disparities across different household groups that are consistent with national accounts concepts and totals using existing micro data sources. Statistics New Zealand was a member of the Expert Group, whose main report was published in 20134.

The statistics contained in this release build on the initial work that was done as a member of the Expert Group. Included are:

• Distributional analyses of the Household Income and Outlay Account (HHIO), presenting results by income quintile, main source of income and household-type for the March years 2007, 2010, 2013 and 20165

• A macro-micro reconciliation of the 2007 HHIO variables with their equivalent from the 2007 Household Economic Survey.

4 Fesseau, Maryse and Mattonettit, Maria I. (2013), Distributional measures across household groups in a national

accounting framework – Results from an experimental cross-country exercise on household income, consumption and saving, OECD Statistics Working Paper series. 5 These years align with the three-yearly Household Economic Survey that collects both income and expenditure data.

Results and tables This chapter describes the data tables this report is accompanied by.

The Household Unit In presenting the distributional results, the focus is on the household as the statistical unit. This is consistent with the national accounts and also with the HES – while the latter collects income data on a person basis, this can be aggregated to the household level using person-household relationship links. HES expenditure data is only collected at the household level.

The underlying logic of using the household as the statistical unit is that it is assumed that income is pooled and shared within the household, and that certain types of goods (eg food, housing, types of transport and communications etc.) are consumed collectively by the household, benefiting from economies of scale6.

To adjust for differences in consumption needs for households of different size and composition, the ranking of households in the inequality tables is based on equivalised disposable income. As the size of households increase with each additional person, due to economies of scale the needs of the household do not increase in the same proportion. Equivalence scales assign a value to each household member in proportion to their needs, which leads to each household being assigned a number of equal consumption units. Household level income and consumption variables can then be divided by this number of consumption units to arrive at comparable measures across different households, ie equivalised income and consumption results.

While the data in the core tables are presented on a per household basis, the household ranking is based on equivalised income. Full data tables on a per consumption unit basis can also be produced if required (see below).

Household Groups The objective of this work is to break down the HHIO included in the national accounts into more detailed household groups. There is no pre-determined set of household groups that might be used, and different groupings may be appropriate for different uses. Of course, the merits of other and more granular breakdowns would be dependent on the availability of the underlying data and the robustness of the methodology that brings together these macro and micro sources.

For this exercise, the following household groups have been chosen:

• by equivalised disposable income quintile (5 groups)

• by main source of income (4 groups)

• by household composition (8 groups)

In addition, a number of the tables are cross-classified with additional information on housing tenure status provided.

Refer to the Methodology section for a definition of these groups.

6 Using the person as the statistical unit would require information on income transfers made within the household, many of them in-kind. This would be very difficult. Using the household as the statistical unit removes such problems, as these transfers net out.

National Accounts benchmarks Benchmark table 1: Household Income & Outlay

Benchmark table 2: Household Final Consumption Expenditure by Purpose

These tables are a re-presentation of the HHIO tables 2.12 Households and 2.13 Household final consumption expenditure (FCE) that were published on 24 November 2017 as part of the National Accounts (Income and Expenditure): Year ended March 2017 release. These provide the benchmark accounts that are broken down by household groups.

Benchmark table 1 is simply a more summarised version of Table 2.12.

Benchmark table 2 differs from Table 2.13 as follows:

• Household FCE is made up of two components: (a) resident household spending in the domestic market; and (b) resident household spending abroad. In Table 2.13 the purpose (COICOP)7 breakdown is applied to both (a) and (b). In Benchmark table 2, the COICOP break down is applied only to (a), with (b) presented as a single expenditure item. This structure aligns better with the detailed expenditure codes used in the HES.

• Table 2.13 has an item Imports of low value goods purchased directly by households which is not broken down by COICOP. Benchmark table 2 includes an estimated breakdown, and this item is spread across a number of the COICOP categories.

• Table 2.13 suppresses values for COICOPs Health and Education, due to quality concerns. These concerns relate more to the pre-2000 years than the post-2007 years covered here. In addition, as these items are of some interest in distributional analyses, estimates8 have been included.

Distributional Results and Tables The following section provides a selection of the types of analyses that can be obtained from the various tables. The methods used to derive the distributions are explained in the Methodology section. In the commentary, the year refers to the year ended 31 March.

Table 1 Household Income, Consumption and Saving, by household distributional group, $millions

These could be regarded as the “core” tables. They show the distribution of each of the benchmark income and expenditure items across the various household groups.

Table 2 Household Income, Consumption and Saving, by household distributional group, per cent share of all households

The $m values in Table 1 are expressed as the percent share of the total all households value.

7 COICOP, the Classification of Individual Consumption by Purpose, is the classification used to analyse FCE into the different purposes of the expenditure. 8 The term ”estimate” is used as the series are not officially published statistics. However, these “estimates” are considered to be of good quality, and are produced as part of the methodology used to balance the Supply-Use Tables which underpin the published GDP and Expenditure series.

Figure 1

In 2016, total household gross disposable income (GDI) was $146.6m and adjusted gross disposable income (AGDI) was $178.8m. The difference between the two is that AGDI includes social transfers in kind and can be regarded as a more comprehensive income measure, better reflecting the maximum value of goods and services that households can afford to consume from current income sources. The share of GDI and AGDI of the highest income quintile was 39.6% and 35.8% of the totals respectively, whereas the similar shares of the lowest quintile were 6.7% and 9.4%.

These shares have shown little change from 2007 to 2016.

Figure 2

Figure 3 presents the quintile shares of selected income items in 2016.

Figure 3

As can be seen, with the exception of the social benefits category, the highest quintile has the largest share of the income items shown. The shares of compensation of employees, entrepreneurial income, gross operating surplus and interest and dividends all increase as one moves up the quintiles. The reverse is almost observed for social benefits, which are more evenly shared across the quintiles. The shares of social benefits going to the lowest quintile (23.3%) and second quintile (27.5%) are higher than those of the later quintiles - nevertheless, the fourth and highest quintiles still receive sizeable social benefits from Government, approximately 15% of the total each. What does stand out are the highest quintile’s very high shares of investment income (67.9% of interest and dividends) and private pension fund benefits (94.4%).

Figures 4 and 5 illustrate the pattern of household saving by quintile.

Figure 4

Figure 5

In New Zealand, the household gross saving rate is quite low, as shown below:

Year % Saving Rate 2007 -0.9 2010 4.3 2013 3.4 2016 2.0

Household gross saving was negative in 2007, rising in 2010 to 4.3% but subsequently falling away. As figure 5 shows, the first three quintiles have been dissaving during this period. The saving of the fourth quintile, while positive, is quite small, with almost all saving occurring in the highest quintile. The OECD DNA study referred to earlier includes a cross country comparison using the standardised methodology, which showed that New Zealand’s pattern of dissaving by quintiles one to three was not unique. All countries in the study bar one showed negative saving for the lowest quintile, with a number also recording dissaving in quintiles two and three.

Table 3 Household Income, Consumption and Saving, per household, by household distributional indicator, $, current prices

Table 3 presents the Table 1 information on a per household basis. The $million values in Table 1 are divided by the number of households in each of the distributional categories, producing average values per household. Figures 6, 7 and 8 show average household disposable income, final consumption expenditure and saving for reach of the household distributional categories.

Figure 6

Figure 6 confirms the picture presented in figure 4, with households classified to quintiles one to three dissaving on average. For example, in 2016 the average disposable income of households ranked in quintile three was $77,200, while their household expenditure was $84,600, resulting in dissaving of $7,300. In contrast, quintile five households saved, on average $52,300.

Figure 7

Figure 7 provides information on the same variables for households grouped by main source of income (MSI). For households reliant on central government for their main source of income, i.e. households classified to MSI Government benefits and MSI Government pensions, average expenditures exceeded average income and these households dissaved. Average household saving was positive for the other two MSI categories.

Figure 8

Figure 8 provides further insight on the saving / dissaving households. Figure 8 groups households by eight categories based on household composition. As might be expected, on average, single person households over 65 are dissaving, possibly running down savings in their retirement. Similarly, single parent households are also shown to be dissaving. This pattern is found across all four years. The big savers appear to be households with two adults under 65 without children and the “other” category which is likely to include multiple adults.

Table 3 data can also be used to analyse the average expenditure levels and patterns of expenditure for households classified to the different groups. Figure 9 shows the average household expenditure on dwelling rentals – actual rents and imputed rents. The tenure shift from rented to owned dwellings as households move from the lower to the higher quintiles is clear.

Figure 9

Figure 10 illustrates the different levels of average household expenditure on health and education services. Figure 11 takes this a step further and shows average household actual consumption of these services, which shows the impact of adding the free health and education services provided by government (as social transfers in kind).

Figure 10

Figure 11

In figure 10, which shows average household consumption expenditures on these services, it can be seen that households classified to quintiles one to three have relatively low average expenditures on health compared to quintiles four and five. However, when health transfers in kind are added – and these are quite significant for all quintiles – the average levels of health services actually consumed is much more evenly spread across the quintiles. (Note that the Y axis scales in the two charts are different.)

The consumption of education services is similarly significantly affected by education transfers in kind. As figure 10 shows, the average expenditure on education services is highest for households classified to quintile five. However, the average actual consumption of education services is quite

different, as shown in figure 11. Not only are the actual consumption levels more even across quintiles, but quintile one is now shown to be consuming (on average) the highest level of education services. While the health transfers in kind are more evenly spread across quintiles, for education a higher proportion of these government-funded services are consumed by households classified in the lower income quintiles. (Refer also to Table 2 that shows the percent distributions across quintiles.)

As noted earlier, while Table 3 presents all of the series per household, similar tables can be produced that show the series per consumption unit. The number of consumption units in each of the household distributional categories is provided in Table 6 Socio-demographic information. Per consumption unit series can be derived by dividing the $million values in Table 1 by the relevant number of consumption units from Table 6.

Table 4. Household Income, Consumption and Saving, ratio to the average, by household distributional indicator

Table 4 provides a simple but useful indicator of disparity between the different household groups. The ratio to the average is the ratio of the value for each household group relative to the average of all households.

Figure 12

Figure 12 plots the ratio to the average of disposable income and adjusted disposable income by quintile for 2016. It shows that the disposable income of households classified to the highest quintile is 2 times the average, while the lowest quintile is 30% of the average. The similar ratios for adjusted disposable income are 1.8 times and 50% respectively, illustrating how the addition of social transfers in kind reduces the income disparities.

Figure 13

Figure 13 plots the ratio to the average of disposable income by quintile for 2007 and 2016. The difference between the two lines is negligible suggesting that, by this particular disparity measure, there has been little or no change across the nine years.

The charts also show that the median disposable income, approximated by the average income of the third quintile, is lower than the average income. This holds for all of the four years analysed, with the median varying between 80-90% of the average over this period.

Another disparity indicator is the ratio of the highest to lowest. This is the ratio of the value for the highest household group (quintile five) to the lowest household group (quintile one). These ratios can be obtained from Table 3. Figure 14 shows these ratios for disposable income and adjusted disposable income for 2007 to 2016.

Figure 14

The figure clearly shows the moderating impact of social transfers in kind. In 2016, the average disposable income of households in quintile five is 5.9 times the quintile one level: for adjusted disposable income this falls to 3.8 times. The chart also shows that while the ratios did not change between 2007 and 2016, supporting the figuret 13 conclusion, in 2013 there was an increase but this was temporary, as the ratio subsequently returned to the earlier level by 2016.

The above charts have focused on income disparity measures. When examining inequality, it can be

argued that household consumption measures may provide a better picture of well-being. For

example, incomes may fluctuate, while consumption can be maintained through borrowing or

running down assets. Or, income measures may fail to capture the services that durable goods

continue to provide, regardless of current income levels. figure 14 contrasts the two different

measures. It compares the ratio to average of disposable income and of final consumption

expenditure by quintile for 2016.

Figure 15

As can be seen, the consumption disparity measure is much lower than the income measure. While the disposable income of households in the highest quintile is 2 times the average, the level of final consumption expenditure for the same group of households is only 1.4 times the average. For the lowest quintile, similar figures are 30% and 70% respectively. Using a consumption ratio significantly lowers the measured level of inequality.

Methodology

Overview

The methodology used to distribute the national accounts values across different household groups largely follows the step-by-step approach recommended by the OECD Expert Group on Disparities in a National Accounts framework (EGDNA)9. Figure 16 provides an overview of the approach.

Figure 16. A step-by-step approach for the estimation of distributional information

Step 1 –Adjust national accounts totals.

Step 2 –Determine relevant distributional variables from micro data sources

in relation to the national accounts variables. Impute or model micro

distribution variables for missing elements.

Step 3 –Scale the micro data to the adjusted national accounts totals and

produce a HHIO unit record dataset.

Step 4 –Cluster households and derive the distribution tables.

The Statistics NZ approach involves reclassifying and regrouping the HES unit record dataset to match HHIO variables definitions, supplementing these where necessary with additional unit record fields to account for “gaps” in the HES coverage. These unit records (actual or proxies) are then scaled so that their weighted values match the published HHIO variables. Essentially, the HES unit record data set is converted to a HHIO unit record dataset, from which various household distributions consistent with the HHIO can be derived.

The step-by-step approach Step 1: Adjust national accounts totals

The household sector in the national accounts includes New Zealand resident households that live in either private or non-private (institutional) dwellings. Unlike the EGDNA exercise which is only concerned with the distribution information for private households, for this exercise the intention is to provide distributional information for the total household sector, thereby matching the coverage of the published household income and outlay accounts.

9 Refer Zwijnenburg, J, S. Bournot and F. Giovannelli (2017), Expert Group on Disparities in a National Accounts Framework: Results from the 2015 Exercise, OECD Statistics Working Papers, 2016/10.

The main operational issue that arises is that the scope of the Household Economic Survey (HES), which will be the major data source used to derive the distributional factors, is confined to private households only10. This raises the question: how to deal with the resident population usually resident in non-private dwellings?

The initial intention was to: (a) deduct the transactions of the non-private households from the household income and outlay (HHIO) values; (b) use the HES to distribute the private households data recorded in the HHIO; (c) produce a separate distribution of the non-private households; and (d) combine the two for a total HHIO distributional analysis.

In practice this was not done, and the HES data has been applied to the total HHIO account, ie no attempt has been made to split the HHIO into its private household (PD) and non-private household (NPD) components. The implication of this is that the HES distributional factors are assumed to apply equally to both household populations.

While this is an acknowledged weakness in the study, it is not thought that this will have a noticeable effect on the results. The NPD population omitted from the HES is minor: approximately 1.7% of the estimated resident population in 2007, and 1.8% in 2013. Based on the income statistics collected in the 2006 Population Census, it is estimated that the income of the NPD population is approximately 1.1% of the PD population income.

While the NPD impact on the DNA results is thought to be minor, it is acknowledged that there will be some impact. The population, income and expenditure profiles of the two groups can be expected to differ slightly. For example, Figure 17 below

Figure 17

illustrates the difference in income bands, based on the 2006 Population Census data. The age structure of the populations also differs, with the NPD population having a higher percentage of persons over 60.

10 The target population of the HES is the usually resident population of New Zealand aged 15 years and over living in private dwellings.

Step 2: Determine relevant distributional variables from micro data sources in relation to the national accounts variables. Impute or model micro distribution variables for missing elements.

There are two stages in this step:

(i) Concord the HES variables to the relevant HHIO variable.

(ii) As the HES data does not align with all of the HHIO variables, fill the gaps by developing micro-based methods so that all HHIO variables can be distributed.

(i) Concord the HES variables

In this step, the variables from the HES (Income) and HES (Expenditure) surveys were concorded to the matching or appropriate NZSNA variable. This concordance was done at the detailed HES item code level, and, at the end of the exercise all of the HES codes had been assigned an appropriate national accounts transaction code. It should be noted that the HES captures data on transactions that appear in a number of different accounts in the NZSNA, eg current purchases and sales of goods and services; purchases and sales of produced and financial assets; the incurrence of financial liabilities; payments to government classified as direct taxes; current and capital transfers; and so on. The point is, the scope of the transactions covered in the HES is wider than the scope of the transactions included in the HHIO, and it would be wrong to presume they have the same coverage. Unless appropriate adjustments are made to the HES scope, it can be quite misleading to directly compare the aggregate levels of HES income and/or expenditure with similar totals found in the HHIO.

Supplementary table 1 provides a summary of the macro-micro match, indicating the HES items (or groups of items) that have been classified according the NZSNA definitions and which have then been matched with the HHIO equivalent. Note:

• A number of items are unique to the national accounts and there will be no micro (HES)

equivalent, eg financial intermediation services indirectly measured (FISIM) and property

income attributed to pension holders. The distribution factors for these items are discussed

below.

• The “match” may not be exact. Either the HHIO variable has a coverage which may be

slightly different to the HES equivalent, or vice versa. The matching is done on a “closest fit”

basis.

Supplementary table 1 provides coverage ratios (HES variable/HHIO variable) which provide an approximate guide on the reliability one might place on using the HES variable to distribute the HHIO equivalent. If there is a high coverage ratio one might expect the resulting distribution to be of a higher quality than when the ratio is low. In general, the coverage ratios were considered adequate for the exercise and all of the matching HES variables have been used, with one exception (expenditure on, and winnings from, games of chance).

Note that the coverage ratios will reflect a number of different factors that may explain why the HES-sourced variable may not match the HHIO equivalent. Reasons might include: imperfect concordance; population scope differences; HES survey error; HHIO measurement error; different calendar timing (eg the annual HES captures expenditure data spread over a 24 month period); and different valuation principles. The approach taken is that the HHIO macro value is “correct”, and the aim of the study is to distribute these HHIO variables across different household groups using the

best data available. To the extent that the macro-micro data alignment is poor and/or the HES variable has a high sample error, then this needs to be born in mind when assessing the quality of the resulting distributions –although it is worth noting that the same sampling issues arise when compiling similar income distribution statistics based entirely on the HES data.

(ii) Fill the gaps by developing micro-based methods so that all HHIO variables can be distributed.

A number of different approaches were taken:

a) Using a related micro variable from the HES dataset. For example, employer’s contributions to employee superannuation schemes11 are assumed to be related to the employee’s contributions, which are captured in HES (Expenditure).

b) Creating a micro indicator based on non-HES data, but which can be related to the unit records in the HES dataset using HES socio-demographic data. For example, health social transfers-in-kind have been allocated using what is termed the “insurance approach”. Initial data comes from the cost weights used by the Ministry of Health in their population-based funding model. Separate weights are obtained per person by gender and age, averaged across Maori, Pacifica and Other groups, using Population Census data to derive the average values. These are then assigned to the relevant persons/gender/age in the HES sample, and provide the distribution weights. Each person is assumed to receive an average in-kind health service for their age/gender class, irrespective of whether or not an actual service is received.

c) Using a model to derive a micro indicator based on other HES unit record data fields. For example, the HES does not collect data on income tax payable. This is modelled at the unit record level based on the (calibrated) taxable incomes earned by the household, taking into account changes in tax rates. A similar approach is adopted to account for the HHIO transactions linked to owner-occupied dwellings. For each household in the HES sample residing in an owner-occupied dwelling, a notional rental cost is imputed. The actual housing maintenance costs incurred by that household as recorded in the HES are then deducted from the notional rent, to derive a gross operating surplus (GOS) residual. This notional rent and the GOS residual are then used as the micro indicator for the equivalent HHIO variables.

d) For a small number of HHIO variables, the above methods are not available. In these cases, the variable has been distributed evenly across the HES population (or sub-population) based on a simple criterion. For example, the net costs incurred from gambling and games of chance are evenly distributed across all persons aged over 18.

The micro-distribution methods are set out in Supplementary table 2.

Step 3 –Scale the micro data to the adjusted national accounts totals and produce a HHIO unit record dataset.

This is a straight forward process.

a) From Step 2, each household record now contains variables and/or indicators for all of the HHIO variables. These are weighted up to population totals. Note that the actual workings are done at a finer level than that published in the HHIO. For example, Government social assistance benefits in

11 This indirect method was used for 2007, 2010 and 2013. A question on employer’s contributions to superannuation schemes was included in the HES (Income) from 2016 and this HHIO variable is now distributed directly using the HES value.

cash are broken down into 16 types of benefits, and each is weighted up separately. Subsequent distributions are carried out at this detailed level.

b) The weighted population totals for each HES-concorded variable are compared to the matching HHIO values and a calibration factor obtained. So, for example, in 2007 the national accounts record household consumption expenditure on Communications of $2,929m. The equivalent weighted value recorded in the HES is $2,479m. This results in calibration factor of 2,929/2,479 = 1.18.

c) This calibration factor is then applied to the unit record HES-concorded variables. So, for example, if household #3200 in the HES sample reports spending on communication (as defined in Step 2) of $700 for the year, this would be “rated-up” to $826 = $700*1.18. This is done for all HES-concorded variables.

d) The result is a HES-based household unit record dataset that has expenditure and income fields that, when weighted, will match the published HHIO. So, it is effectively, a HHIO micro dataset.

Note that when calculating income tax payable, this calculation at household level is based on the calibrated values of the taxable income sources. This results in a household level equivalent of tax payable, which is then re-calibrated to match the HHIO variable Tax payable, using the method set out above.

There is a major assumption under-pinning this step, viz. that all households in the HES sample under (or over) report to the same extent. Hence the relative expenditure or income for each variable is the same across households, whether using the pure HES data or the calibrated values. While this is the same assumption made in income distribution studies based solely on HES data, the DNA work will produce different income distributional results. This is because the relative mix of income sources within households will change, as the calibration factors will differ for each item.

Step 4 –Cluster households and derive the distribution tables.

To the HHIO micro dataset calculated above are added “cluster” variables that indicate which of the household groups each household will be classified to. For this study, households have been distributed (clustered) by three criteria.

1. Equivalized household gross disposable income quintile Households are classified according to the level of their equivalized gross disposable income. The gross disposable income (GDI*) measure that has been used to rank households differs slightly from the HHIO gross disposable income aggregate, and is closer to the operational definition set out in the Canberra Group Handbook on Household Income Statistics12. While the two are very similar, the GDI* measure used excludes a number of (relatively minor) income and outlay items that would not normally be viewed by a household as a source of regular income from which consumption and saving can be financed (for example, the HHIO imputed income item Earnings attributed to insurance / pension policyholders.) In addition, some of the items (for example, losses / winnings from games of chance and insurance claims) are excluded as they may fluctuate and not be regarded as “regular” at the individual household level.13

12 UNECE (2011), Canberra Group Handbook on Household Income Statistics, second edition. See Table 2.1, p.11. 13 At the macro HHIO level they can be regarded as “regular” as they are a predictable, and matching, inter-household flow that nets out at the all household level.

The GDI* measure used to rank households can be defined as:

GDI as defined in the HHIO - imputed income earned on pension funds and non-life insurance + pension and non-life insurance premium supplements payable (these are a part offset to the imputed income, ie supplement equals imputed income less service charge. - insurance claims +/- gambling winnings/losses +/- FISIM, ie interest flows are not adjusted for FISIM.

= GDI* Note that the GDI* per household uses calibrated values. The Oxford-modified equivalence scale (also called the OECD-modified scale) is used to equivalize GDI* for each household. This scale assigns a value of 1 to the household head, of 0.5 to each additional adult member – aged 14 and over - and of 0.3 to each child – aged below 14. Households have been ranked according to the value of their equivalized GDI* and allocated to five equal groups (quintiles), each of them containing 20% of all households. 2. Main source of income Households are classified according to the main source of income for the household as a whole. The main source assigned has the highest contribution to the household’s GDI*. These income sources are calculated post-calibration. The four income sources identified and the income sources that define them are shown in figure 18 below. Figure 18: Definition of the Main Sources of Income Groups

MSI Income Sources

Income from employment • Compensation of employees

• Entrepreneurial income

Property income and pension fund benefits

• Interest and dividends excluding FISIM

• Pension fund benefits (from NZ and overseas funds)

Government pensions • NZ Superannuation

• Veterans pension

Government benefits and other transfers

• ACC benefits in cash

• Social assistance grants in cash (excl. NZ superannuation and veterans pension.)

• Miscellaneous current transfers (excl. net winnings from gambling and games of chance)

3. Household type Households are classified according to three criteria: the number of adults in the household; the age of the adults; and the presence of children living at home. Eight household sub-groups are distinguished:

• single person under the age 65

• single person aged 65 and over

• single parent with children living at home, whatever the age of the adult

• two adults both under the age of 65 and without children living at home

• two adults with at least one aged 65 and over, and without children living at home

• two adults with less than three children living at home whatever the age of the adults

• Two adults with at least three children living at home whatever the age of the adults

• Other household types. This includes households with more than two adults such as households where grandparents live with their children and grandchildren.

Methodological issues This work is still at the developmental stage and there remain a number of methodological issues under investigation. These include, but are not confined to:

• Determining how best to deal with the NPD population in the distributional results. Linked to

this is the need to obtain improved (linked) micro data on sub-populations that, while within

the HES scope, may not be well covered in the HES. This mainly relates to the growing

number of older persons living in retirement villages.

• Examining the feasibility of using different calibration factors for different (groups of)

households when calibrating the micro variables to the HHIO “benchmarks”.

• Linked to the above is the feasibility of using data from Statistics NZ’s Integrated Data

Infrastructure to enhance the HES database by adding new micro level variables and/or

amending existing ones. Improving the macro-micro match will reduce the impact of the

calibration phase. Using additional micro data may also improve the robustness of the micro

distributional factors, given the sampling errors in the HES

• Providing alternative household clustering for analyses.

• Adjusting raw HES data to remove or ameliorate the effects of outliers and/or households

reporting nil or little income but sizeable expenditures.

• Refining existing methods (Steps 2 and 3) by using data and applying the distributional

methods at a more granular level. In other words, how robust are the existing methods and

would “refinements” lead to noticeably different results?