Series
Business Financial Data
en-NZBFD; Business data collection; BDC
en-NZStats NZ
en-NZBusiness Financial Data is a quarterly collection of key business financial values, designed to cover most of New Zealand’s market economy. It provides short term economic indicators from financial variables of the businesses in New Zealand.
en-NZIt provides indicators of sales, purchases, salaries & wages, and operating profit. This data feeds into the current quarterly measures of Gross Domestic Product (GDP).
en-NZNational Accounts (Quarterly GDP), economic analysts and forecasters.
The industries in the Business Financial Data collection (BFD) are published using New Zealand Standard Industrial Output Classification (NZSIOC) division level 1 and 2. There are several industries that are not included within the scope of the collection. These are industries where our administrative data source (GST) is not an appropriate measure of market activity, where the National Accounts framework use a different indicator (for example Agriculture), industries where sales and purchases are not a significant part of operating surplus (for example Financial and Insurance Services), and industries that are entirely dominated by government sector units (for example Local Government Administration) or non-for-profit businesses.
General government institutions and Non-profit institutions serving households are also identified by the Statistical Classification for Institutional Sectors code (SCIS), and removed from the population, where appropriate.
The majority of the data in the BFD begins on the June 2016 quarter. The Retail Trade and Accommodation industries are collected as part of the Retail Trade survey and then later published in the BFD, with data beginning from the September 2017 quarter. Though the data available in the Retail Trade survey collection dates back to September 1995. The Manufacturing, Wholesale trade and several select industries are collected as part of the Manufacturing, Wholesale Trade and Selected services surveys respectively, and also included in the BFD. These surveys are also published in their own right at the same time to provide more detailed data on specific parts of the economy.
See table below for more detail on variables, industries and institutional codes covered in the different collections:
| Collection | Data origin | Variables included | NZSIOC’s covered | SCIS covered |
| Business financial data | June 2016 | Sales, purchases, salaries & wages and operating profit | AA-RS (inclusive of industries from the other collections); excluding AA1, KK, LL121 and RS213 | Excludes SCIS codes of 3 (Govt institutions) & 4 (Non-profit institutions serving households) except where it is included in other collections |
| Economic survey of Manufacturing | December 1992 | Sales, purchases, raw material stocks and finished good stocks | CC | All |
| Wholesale trade survey | March 1995 | Sales and stocks | FF | All |
| Selected services survey | June 2004 | Sales | MN111, MN112, MN114, RS211 and RS212 | All |
| Retail trade survey | September 1995 | Sales and stocks | GH | All |
Data updates in the business financial data release can occur when new or updated information is received from businesses. Data updates can also occur when modelled and imputed data is reviewed against financial accounts or other data sources when they become available. When these occur, the data updates to the BFD are generally only considered for the previous 4 quarters unless there is a significant impact on the time series of data. In quarters where data updates do occur, they are noted in the information releases and downloadable content.
en-NZ2024 - In the September 2024 quarter, we changed the seasonal adjustment methodology to automatically detect additive outliers for special treatment. This is a more systematic approach for seasonal adjustment that is being implemented across Stats NZ following the impact of COVID-19. This treatment is being applied from the March 2023 quarter onwards. As a result of this change, trend series are now available.
We also implemented several improvements in our treatment of various series as follows:
Initiated or discontinued seasonal adjustment based on the presence of emerging seasonal patterns
Applied indirect seasonal adjustment to certain aggregate composite series
Applied complex treatments like level shifts to series impacted by the closure of Marsden Point refinery.
Implemented a synthetic bridge in series affected by COVID-related disruptions to facilitate a smooth transition back to seasonal adjustment.
Update to seasonal adjustment methodology for Business financial data has more details.
2023 – In the September 2023 quarter, we updated the data sources for several economically significant businesses and backdated the changes to the beginning of the series publication. These caused relatively significant changes to the Electrical, waste and water services industry and the Transport, postal and warehousing industry for all their financial indicators published in the business financial data collection. This new methodology improves the quality of the data in these industries and better represents the level of financial activity in the given quarter.
In the March 2023 quarter, in late January and mid-February 2023, Aotearoa New Zealand was affected by tropical cyclones Hale and Gabrielle. The adverse weather and resulting flooding caused loss of life, significant damage, and disruption, particularly across the East Coast and upper North Island. Some activity of businesses in affected regions was impacted by the cyclones, and we're confident that given our collection and quality assurance practices the statistics are fit for purpose. In addition, in the March 2023 quarter there was an update to the linking methodology between PAYE tax records and the businesses in the Stats NZ Business Register. This caused a minor improvement in the measure of salaries and wages paid and derived operating profit in the business financial data collection.
2022 – In the June 2022 quarter, petroleum refining activity ended in New Zealand. This caused level shifts in both manufacturing and wholesale trade sub-industries and totals due to classification changes. See Business financial data: June 2022 quarter for more information.
2020 – In December 2020, we published the first official release of the Business Financial Data collection. See the Business financial data: September 2020 quarter for the first publication. This used updated methodology from the previously experimental business data collection.
In September 2020, we published the experimental initiative, business data collection. This expanded on the existing surveys and collections (Manufacturing, Wholesale trade, Retail trade, and Selected services) to cover most market industries in the New Zealand economy. See Business data collection for more information.
2017 – In the September 2017 quarter, the retail trade survey was redesigned, and it started to capture more information for the business financial data collection. See Retail Trade Survey Quarterly – Current for more information on the design and collection activities specific to the retail trade industry.
2016 – In the June 2016 quarter, the business financial data collection was expanded to cover most market industries in the New Zealand economy.
The quarterly manufacturing survey was redesigned, and it started to capture more information for the business financial data collection. See Economic Survey of Manufacturing - Stats NZ DataInfo+ for more information on the design and collection activities specific to the manufacturing industry.
The wholesale trade survey was redesigned, and it started to capture more information for the business financial data collection. See Wholesale Trade Survey - Stats NZ DataInfo+ for more information on the design and collection activities specific to the wholesale trade industry.
en-NZQuarterly
Data source and response rate information
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Data source and response rate information en-NZ |
Studies
Coverage
Business Financial Data Collection
Methodology
Population
The target population is all kind-of-activity units (KAUs) on Statistics NZ’s Business Register (BR) that are operating in New Zealand, are economically significant, and are classified to Australian and New Zealand Standard Industrial Classification 2006 (ANZSIC06).
Statistical design
The BFD is a combination of direct survey for large businesses and complex business groups and GST data for the remaining businesses.
We supplement the GST data for each series with survey data for large and complex businesses that meet the following criteria:
- a $100 million significance rule – if an enterprise, or group of enterprises linked by ownership, have an annual GST turnover of more than $100 million
- For Retail businesses, the significance rule is set to $50 million for an enterprise or a group of enterprises linked by ownership
- a 3 percent industry dominance rule – if an enterprise makes more than a 3 percent contribution to annual total income for an industry in the Annual enterprise survey collection
- all enterprises that have a significant level of activity across multiple industries.
A breakdown of survey and administrative data contribution to the total published data and response rates are published here: Business Financial Data source contribution and response rate - Stats NZ DataInfo+.
Non-response imputation
Survey data imputation
Although we attempt to achieve a 100 percent response rate, in practice this does not occur. We estimate values for these non-responding businesses using methods that include:
historic imputation
ratio imputation
mean imputation.
Historic imputation involves multiplying the unit's response in the previous period by a forward-movement factor. The non-response factor is the average movement over the quarter for similar businesses.
Ratio imputation involves estimating the variable of interest from the unit's administrative data (GST sales), based on the relationship shown by similar businesses.
Mean imputation involves estimating a value for a unit by using the average value for a set of similar businesses.
Tax data imputation
In the administrative data (GST) IRD can have late filers of tax information, which are not received in time for publication. We impute the GST data using the historic and mean methods described above.
We also use median imputation for these small number of units, where we take a median response from a unit's previous GST history.
Seasonally adjusted and trend series
For any series, the survey estimates can be broken down into three components: trend, seasonal, and irregular. While seasonally adjusted series have the seasonal component removed, trend series have both the seasonal and irregular components removed. This Seasonal adjustment removes seasonal variation from a statistical series. By removing seasonal effects from BFD series, we can better understand turning points and the underlying economic activity. Examples of seasonal variation in economic activity are milking and lambing seasons, Christmas shopping, and peak periods for visitors to New Zealand.
We re-estimate seasonally adjusted and trend values quarterly when each new quarter’s data becomes available. Figures are therefore revised, with the largest changes normally occurring in the latest quarters. The seasonally adjusted and trend series are produced using the X-13ARIMA-SEATS package developed by the U.S. Census Bureau.
Direct and indirect seasonal adjustment methods
The level at which a series is seasonally adjusted is important, since it has the potential to affect the series' quality. The individual component series of the main economic variables can be seasonally adjusted and then summed to derive totals. This is called an indirect seasonal adjustment. Alternatively, the main economic variables can be seasonally adjusted at the total level, independently of the seasonal adjustment of their components. The adjustment of the total of an aggregate series is called a direct seasonal adjustment. The indirect approach has the advantage of retaining additivity, but this applies only to the current price series. While the indirect approach conceptually also provides additivity for volume series.
The direct approach will often give better results if the component series show similar seasonal patterns. At the most detailed level, the irregular factor may be large compared with the seasonal factor and therefore may make it difficult to perform a proper seasonal adjustment. In a small country like New Zealand, irregular events can have a strong impact on particular data. However, if the component series show the same seasonal pattern, aggregation often reduces the effect of the irregular factors in the component series. This is relevant for New Zealand, where seasonal fluctuations in the primary industries heavily affect economic series.
Note: The level at which seasonal adjustment is applied to quarterly series may differ from other Statistics NZ collections (eg gross domestic product). These may contribute to differences in the aggregate seasonally adjusted series.
See Seasonal adjustment in Statistics New Zealand for more information.
en-NZ