Business Financial Data Collection

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: an annual GST sales significance rule – if an enterprise, or group of enterprises linked by ownership, have an annual GST turnover of more than $140 million. This applies from the June 2025 quarter onwards (previously $100 million). for Retail businesses, the significance rule is set to $70 million from the June 2025 quarter onwards (previously $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