Tables

This page contains an areas file for 2019 geographic areas.

Area files are text files that classify meshblocks into larger geographic areas, such as statistical area 2s, territorial authorities, and regional councils. You can use the area files, which are also known as concordance files, to map the types of digital boundaries (see Examples of geographic boundary layers) within the geographic hierarchy, to see how the various geographic boundaries link together.

For each meshblock, there is a series of codes that link to any meshblock-defined geographic area. All areas are set as at 1 January of the specified year. Concordance files are available from different years, to enable data for the same area classification to be compared over time.

The areas files can be used in conjunction with the geographic boundary files available on Stats NZ's Geographic Data Service. For earlier files refer to Geographic area files.

The labour market statistics information release combines data from three surveys to present a broad picture of the labour market.

From the Household Labour Force Survey (HLFS) we provide a picture of New Zealand's labour force – these statistics relate to employment, unemployment, and people not in the labour force.

The Quarterly Employment Survey (QES) estimates the demand for labour by New Zealand businesses – the levels and changes in employment, total weekly gross earnings, total weekly paid hours, average hourly and average weekly earnings, and average weekly paid hours in the industries we survey.

The Labour Cost Index (LCI) measures changes in salary and wage rates for a fixed quantity and quality of labour input. It is a measure of wage inflation, reflecting changes in the rates that employers pay to have the same job done to the same standard.

The Retail Trade Survey measures the sales turnover and stock levels of a wide range of businesses that provide household and personal goods and services. Retailers include car yards, petrol stations, supermarkets, department stores, hardware retailers, cafes and restaurants, hotels and motels, and other store-based and non-store-based retailers. Retail Trade Survey statistics are published quarterly for the following variables: sales value of goods and services, by industry, and region; sales volume, by industry; value of stock held, by industry.

All data produced and managed by the National Accounts Quarterlies team for the production of quarterly GDP releases. QGDP outputs are: - the production-based measure of GDP in chain-volume measures expressed in 2009/10 prices GDP(P) - the expenditure-based measure of GDP in both current prices and chain-volume measures expressed in 2009/10 prices – GDP(E)

This dataset is the proportion of people in the 2018 Census dataset and data sources used to count them by territorial authority / local board (TALB) geography. Coverage compares the proportion of people counted in the census with the number of people that should have been counted on census night. It is usually expressed as a percentage. We are using interim coverage at this time, with the official post-enumeration survey (PES) coverage rates for the 2018 Census being published by March 2020. The dataset shows that whilst there is variation across territorial authority / local board areas in the proportions of people we counted (from 96.3 to >100%), the interim coverage rates for local areas are generally very high.

Understanding how we counted people will help you to use the census dataset because it provides more information about the data sources used and the composition of the data in terms of the proportion of:

1. census individual forms received.

2. partial census form responses (via the paper dwelling form or online household summary form but did not receive an individual form for the person).

3. admin enumerations (the use of administrative data to add people to the usually resident census population when a census response has not been received).

The higher the proportion of census forms received, the more robust the characteristic data, for example income, occupation, will be within the dataset for variables. This is especially the case where characteristic information cannot be provided from alternative sources such as historic census data, administrative data or imputation.

A csv version of this table is also available (attachment below).