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Expenditure aggregates

Based primarily on the HES, estimates are obtained for total annual expenditure of private households in each capital city for each of the 101 expenditure classes in the CPI. As these estimates relate to the expenditure of households in aggregate, they are referred to as ‘expenditure aggregates’.

While these expenditure aggregates are derived for well defined categories of household expenditure (e.g. bread), they are still too broad to be of direct use in selecting price samples. For this purpose, expenditure aggregates need to be subdivided into as fine a level of commodity detail as possible. As the HES is generally not designed to provide such fine level estimates, it is necessary to supplement the HES data with information from other sources such as other official data collections and industry data. The processes involved are illustrated below by reference to a stylised example for the Bread expenditure class of the CPI.

Suppose that, based on information reported in the HES, the annual expenditure on bread by all private households in a particular city is estimated at $8m. Further, suppose that there exists separately some industry data on the market shares of various types of bread. In combination these two data sources can be used to derive expenditure aggregates at a much finer level of detail than that available from the HES alone. The results are shown in the following table.

					Derived
			Market share	HES data	expenditure aggregates
		Type of bread	%	$’000	$’000
	1	White, sandwich, sliced	30	—	2 400
	2	White, sandwich, unsliced	2	—	160
	3	White, high fibre	20	—	1 600
	4	White, high top	3	—	240
	5	Wholemeal	10	—	800
	6	Multigrain	15	—	1 200
	7	Bread rolls	15	—	1 200
	8	Specialty	5	—	400
	Total breads		100	8 000	8 000

Elementary aggregates must have a price sample

The items for which it is decided to construct specific price samples are referred to as ‘elementary aggregates’. (There are approximately 1,500 elementary aggregates for each of the eight capital cities, or approximately 12,000 price samples at the national level.) The expenditure aggregates for the items that are not to be explicitly priced are reallocated across the elementary aggregates in such a way as to best preserve the representativeness of the price samples. In this example, this would be done in two stages. First, the expenditure aggregate for item 2 would be allocated to item 1 and the expenditure aggregate for item 4 would be allocated to item 3. In the second stage, the expenditure aggregates for items 7 and 8 would be allocated, on a proportional basis, across the four elementary aggregates. This process is illustrated in the following table.

		Expenditure aggregates			.
	Bread	Initial	Stage 1	Stage 2
	type	$’000	$’000	$’000		Elementary aggregate
	1	2 400	2 560	3 200		White sandwich
	2	160	—	—
	3	1 600	1 840	2 300		White high fibre
	4	240	—	—
	5	800	800	1 000		Wholemeal
	6	1 200	1 200	1 500		Multigrain
	7	1 200	1 200	—
	8	400	400	—
	Total	8 000	8 000	8 000

Determining outlet types

Having settled on the items for which price samples are to be constructed, the next step is to determine the outlet types (respondents) from which prices will be collected. In order to accurately reflect changes in prices paid by households for bread, prices need to be collected from the various outlets from which households purchase bread. Data are unlikely to be available at the individual elementary aggregate level by type of outlet. It is more likely that data will be available for bread in total. Suppose industry data indicates that supermarkets accounted for about 80% of bread sales and hot bake outlets the remainder. A simple way to construct the price sample for each elementary aggregate that is representative of household shopping patterns is to have a ratio of four prices from supermarkets to every hot bake price.