Productivity in New Zealand decreased to 126.29 points in the fourth quarter of 2023 from 126.81 points in the third quarter of 2023. This dataset provides - New Zealand Productivity - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Key information about New Zealand Labour Productivity Growth

• New Zealand Labour Productivity dropped by 0.50 % YoY in Sep 2024, compared with a drop of 0.39 % in the previous quarter
• New Zealand Labour Productivity Growth data is updated quarterly, available from Jun 1988 to Sep 2024, averaging at 1.00 %
• The data reached an all-time high of 16.40 % in Jun 2021 and a record low of -10.70 % in Jun 2020

New Zealand: GDP per hour worked in current USD, PPP adjusted: The latest value from 2023 is 55.2 USD per hour worked, an increase from 54 USD per hour worked in 2022. In comparison, the world average is 75.9 USD per hour worked, based on data from 37 countries. Historically, the average for New Zealand from 1971 to 2023 is 25.2 USD per hour worked. The minimum value, 5 USD per hour worked, was reached in 1971 while the maximum of 55.2 USD per hour worked was recorded in 2023.

NZ: Multifactor Productivity: Capital Stock Output Ratio: % Point Contribution to Labour Productivity Growth data was reported at 2.365 % in 2021. This records an increase from the previous number of 0.504 % for 2020. NZ: Multifactor Productivity: Capital Stock Output Ratio: % Point Contribution to Labour Productivity Growth data is updated yearly, averaging 1.401 % from Dec 1987 (Median) to 2021, with 35 observations. The data reached an all-time high of 4.970 % in 1988 and a record low of -5.660 % in 2008. NZ: Multifactor Productivity: Capital Stock Output Ratio: % Point Contribution to Labour Productivity Growth data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s New Zealand – Table NZ.OECD.PDB: Multifactor and Capital Productivity: OECD Member: Annual.

Graph and download economic data for Unit Labor Costs: Early Estimate of Quarterly Unit Labor Costs (ULC) Indicators: Labor Productivity: Total for New Zealand (ULQELP01NZQ661N) from Q2 1987 to Q2 2023 about New Zealand, productivity, and production.

New Zealand GHG Productivity: Production Based GHG Productivity:(GDP) Gross Domestic Productper Unit of Energy Related CO2 Emissions data was reported at 2.600 USD/kg in 2020. This records an increase from the previous number of 2.480 USD/kg for 2019. New Zealand GHG Productivity: Production Based GHG Productivity:(GDP) Gross Domestic Productper Unit of Energy Related CO2 Emissions data is updated yearly, averaging 1.825 USD/kg from Dec 1995 (Median) to 2020, with 26 observations. The data reached an all-time high of 2.600 USD/kg in 2020 and a record low of 1.280 USD/kg in 1995. New Zealand GHG Productivity: Production Based GHG Productivity:(GDP) Gross Domestic Productper Unit of Energy Related CO2 Emissions data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s New Zealand – Table NZ.OECD.GGI: Environmental: GHG Productivity: OECD Member: Annual.

The New Zealand National Pasture Productivity map is a multi-temporal approach to estimating pasture (dry matter) yield in New Zealand's grassland area. The approach uses a model generated from collected historical records of pasture yield in 21 locations around New Zealand.It was hypothesised that yield of a paddock planted with pasture species would correlate with some average of normalised difference vegetation index (NDVI) observed at that location. The temporal median of NDVI of vegetated images across New Zealand's grasslands was obtained by calculating the median NDVI of all observations for each pixel where vegetation (NDVI > 0) was observed. Paddock polygons were manually created to surround each of the locations where yields had been obtained, and the spatial means within these polygons of the median NDVIs were plotted against the measured yields. A linear relationship between these quantities was created, which was applied to paddock polygons at a national scale. The uncertainty of the model is +/- 2.2 t/ha/yr for a 70% confidence interval.The data presented in this geodatabase is a segmentation of New Zealand grasslands using an automated multi-temporal approach presented by North, Pairman, and Belliss (2019). The parameters of this segmentation process were selected to achieve minimal missed boundaries between true paddock units. As a side-effect, some boundaries were created between sub-paddock areas with differing spectral response, such as in areas of strip grazing, areas divided by ridges or gullies, and areas with different management patterns. While these artefacts may improve the spatial accuracy of the pasture yield, the result is not intended to provide a one-to-one mapping between true paddock areas and their average pasture yield.The model was applied to each of these polygons. Because the data used to develop the initial model had generally high yields, the model has only been applied to polygons representative of higher-producing grasslands. Where median NDVI for a polygon was outside the domain of the assessed polygons, the model was deemed unfit to estimate pasture yield. Polygons with low median NDVI therefore have an assigned yield of zero.A full list of attribute definitions can be found in the attached PDF.The data used to generate the model has been derived from various published sources, in combination with Sentinel-2 imagery. Complete sets of Sentinel-2 for five passes covering mainland New Zealand were assembled and cloud-cleared in an automated manner using TMASK techinques. The resulting valid data was used to create medians of vegetated images on a per-pixel basis.The segmentation of New Zealand grasslands was derived by initially taking selected Sentinel-2 passes which gave suitable coverage of New Zealand's agricultural land, and applying the automated multi-temporal approach to boundary delineation. Around 200 passes from January - November 2018 were used to achieve the segmentation. These polygons were overlaid with the union of polygons from LCDB v5.0 (https://lris.scinfo.org.nz/layer/104400-lcdb-v50-land-cover-database-version-50-mainland-new-zealand/) which were either High-Producing or Low-Producing Grassland in both 2012 and 2018, and had not changed class between those dates.

New Zealand GHG Productivity: Production Based GHG Emissions: Index data was reported at 93.570 2000=100 in 2020. This records a decrease from the previous number of 98.610 2000=100 for 2019. New Zealand GHG Productivity: Production Based GHG Emissions: Index data is updated yearly, averaging 97.640 2000=100 from Dec 1995 (Median) to 2020, with 26 observations. The data reached an all-time high of 105.260 2000=100 in 2003 and a record low of 93.570 2000=100 in 2020. New Zealand GHG Productivity: Production Based GHG Emissions: Index data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s New Zealand – Table NZ.OECD.GGI: Environmental: GHG Productivity: OECD Member: Annual.

New Zealand NZ: Environmentally Adjusted Multifactor Productivity Growth data was reported at -1.060 % Point in 2013. This records a decrease from the previous number of 2.030 % Point for 2012. New Zealand NZ: Environmentally Adjusted Multifactor Productivity Growth data is updated yearly, averaging 1.120 % Point from Dec 1991 (Median) to 2013, with 23 observations. The data reached an all-time high of 3.320 % Point in 2002 and a record low of -1.920 % Point in 1991. New Zealand NZ: Environmentally Adjusted Multifactor Productivity Growth data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s New Zealand – Table NZ.OECD.GGI: Environmental: CO2 Productivity: OECD Member: Annual.

New Zealand NZ: Capital Quality: Capital Stock Output Ratio: % Point Contribution to Labour Productivity Growth data was reported at 0.548 % in 2021. This records an increase from the previous number of 0.411 % for 2020. New Zealand NZ: Capital Quality: Capital Stock Output Ratio: % Point Contribution to Labour Productivity Growth data is updated yearly, averaging 0.614 % from Dec 1987 (Median) to 2021, with 35 observations. The data reached an all-time high of 0.911 % in 2000 and a record low of 0.219 % in 2018. New Zealand NZ: Capital Quality: Capital Stock Output Ratio: % Point Contribution to Labour Productivity Growth data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s New Zealand – Table NZ.OECD.PDB: Investment in Capital Stocks and Services: OECD Member: Annual.

Spatial productivity metrics for redwood (Sequoia sempervirens): Site Index, a measure of height growth potential, and 300 Index, a measure of volume growth potential. These metrics are used as inputs to a redwood growth model which is available in Forecaster, which can be licensed from Integral Ltd. Productivity surfaces are also licensed for use within the NZFFA TreeFarmer application . Date: June 2022 Version: v1

The average for 2022 based on 2 countries was 72.84 index points. The highest value was in Australia: 86.18 index points and the lowest value was in New Zealand: 59.49 index points. The indicator is available from 1970 to 2022. Below is a chart for all countries where data are available.

Scion’s spatial projections of the volume productivity of coast redwood (Sequoia sempervirens) developed at a 25 m resolution. The displayed productivity is defined by the 300 Index which is the mean annual volume increment at age 30 normalised to a stand density of 300 stems/ha, with units of m3/ha/yr. Using a national dataset (n = 569 observations) a random forest model was used to predict 300 Index from environmental surfaces and this model was used to produce the displayed map.

Although the plots used for model development were reasonably well distributed throughout New Zealand there were few plots located on the west coast of the South Island. As this region has a unique climate, with very high rainfall and low fertility, predictions in this region should be treated with caution. The displayed spatial projection masked large lakes, major roads, contiguous areas of native forest, urban areas, and protected areas. Regions with a mean annual air temperature below 9°C were also masked, as it is unlikely the species will be planted in areas within these cooler climates. The presented surface is intended only as a guide for afforestation and growers should take into account the impact of microsite when choosing an appropriate species.

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This indicator measures the amount of phytoplankton in ocean water around New Zealand using satellite data. Phytoplankton are microscopic algae and primary producers, meaning they enable those higher up the food web to survive. Phytoplankton growth is affected by the availability of nutrients and light, which in turn are affected by the structure of the upper water column. Large-scale changes to climate and oceanographic conditions can change the water column structure and thus lead to changes in phytoplankton growth and primary productivity. Phytoplankton growth supports marine organisms throughout the marine environment, including fish, mammals, and seabirds (Pinkerton et al, 2019). We monitor the changes in phytoplankton by measuring chl-a concentration to provide an understanding of how marine ecosystems are changing. This affects the services we rely on for economic, cultural, and recreational purposes, such as fisheries (Nixon & Buckley, 2002).

More information on this dataset and how it relates to our environmental reporting indicators and topics can be found in the attached data quality pdf.

Opportunistic sightings and strandings of Caperea marginata (n=196) from the vicinity of Australia and New Zealand (1884 to early 2007) were used to relate geographic and temporal patterns to oceanographic and broad-scale climatic variability. Records were not uniformly distributed along the coast and more (69%) were from Australia than New Zealand. Data from Appendix 1 and 2 of the cited paper.

Scion’s spatial projection of the carbon productivity of radiata pine (Pinus radiata) at age 40 for stands at a medium stocking typical of a structural grade regime (650 stems/ha). The displayed surface has a 25 m resolution. Total carbon productivity, which includes the below ground component, is displayed in CO2 equivalents with units of tonnes CO2/ha. Predictions of carbon were made using a stand level carbon model (Forest Carbon Predictor Version 5.1) and C_Change which is a model that determines allocation of carbon to different biomass components. These predictions were then spatially scaled using air temperature and geospatial productivity surfaces (300 Index, Site Index). The displayed spatial projection masked large lakes and a forest located in the central North Island.

A description of the methods and results is given in Watt et al. (2022a) and the displayed carbon surface is included in this paper as Figure 2e. A more detailed description of the methods that includes a validation (but shows projections as elemental carbon rather than CO2 equivalents) is given in Watt et al. (2022b).

We are grateful to the forestry companies with radiata pine permanent sample plots who granted permission to use this dataset for constructing the displayed map. The New Zealand Strategic Science Investment Fund (SSIF) was used to fund this project.

The presented surface is intended only as a guide for afforestation and growers should take into account the impact of microsite when choosing an appropriate species. The surface was derived from permanent sample plot data, which typically comprise plots of well managed trees, that do not have any unstocked areas and are sited away from exposed ridges. Consequently, we recommend that presented values of total carbon are reduced by 15% when applied to standard forestry sites.

Watt, M.S., Kimberley, M. O. (2022a). Comparing regional variation in carbon sequestration for radiata pine and redwood throughout New Zealand. New Zealand Journal of Forestry, 67 (1) 12 – 21.

Watt, M.S., Kimberley, M. O. (2022b) Spatial comparisons of carbon sequestration for redwood and radiata pine within New Zealand. Forest Ecology and Management 513, 120190.

The average for 2022 based on 2 countries was 65.8 USD per hour worked. The highest value was in Australia: 77.5 USD per hour worked and the lowest value was in New Zealand: 54 USD per hour worked. The indicator is available from 1971 to 2023. Below is a chart for all countries where data are available.

The Computer System Design Services industry is experiencing robust growth, underpinned by ongoing digital transformation, cloud adoption, and rising capital investment in software and digital platforms. Both private enterprises and government agencies are prioritising technology to improve productivity, operational efficiency and service delivery, boosting demand for system design, integration and maintenance services. Major government-led cloud migration initiatives have catalysed demand for advanced cloud integration, cybersecurity and change management, positioning firms with cloud expertise to secure lucrative, long-term contracts. The rapid adoption of artificial intelligence further accelerates productivity gains. It creates new opportunities for AI-driven system design and support, especially for businesses that can help clients manage workforce transitions and ethically leverage automation. The industry’s resilience is evident in its ability to maintain steady growth despite economic uncertainty, supported by a diverse and expanding client base across sectors like agriculture, banking, retail and creative industries. However, the landscape is becoming increasingly fragmented and competitive as new entrants – primarily small firms and sole operators – drive service diversity and innovation, intensifying margin pressures. Sustainability is emerging as a key differentiator, with clients seeking energy-efficient IT solutions and providers responding with value-added services like carbon footprint reporting and green IT certifications. Industry revenue is expected to climb at an annualised 4.9% over the five years through 2025-26 to total $18.3 billion. This trend includes anticipated growth of 2.8% in the current year. In the coming years, the industry is poised for continued expansion, fuelled by accelerating cloud adoption, AI integration and diversification into high-growth areas like fintech, health tech and creative industries. Businesses and government agencies shifting to cloud infrastructure will underpin steady demand for system design, cybersecurity and managed services. Rising automation and AI adoption will support profitability and drive the need for skilled professionals in emerging technologies. The industry will benefit from increased software investment, improved connectivity and the rise of remote work, which will broaden the talent pool and client reach beyond urban centres, fostering regional economic development. However, the market will face challenges from intensifying competition, industry fragmentation and the growing threat of offshoring commoditised services to lower-cost markets. To thrive, firms must differentiate through niche specialisation and sector-specific expertise and deliver value-added, compliance-focused solutions that leverage local regulatory knowledge. Sustained growth will depend on continuous investment in talent development, innovation and strategic partnerships. Industry revenue is forecast to expand at an annualised 5.7% through 2030-31 to total $24.2 billion.

Land fragmentation is a growing issue in New Zealand, however, no consistent or regular national monitoring has been established. A methodology for assessing land fragmentation was applied nationally for the first time, revealing that the greatest proportion of fragmentation occurred on land used for diffuse rural residence (>0.40 to ≤2.0 ha) and small parcels (>2.0 to ≤8.0 ha) with a 128% and 73% increase, respectively, between 2002 and 2019. In New Zealand, the most highly productive land (Land Use Capability (LUC) class 1, 2 and 3) is most impacted by continued fragmentation with 38%, 28% and 17% of baseline area, respectively, occupied by medium sized parcels or smaller (≤40.0 ha) with a dwelling in 2019. Impacts were greatest for Auckland with 40%, 44% and 25% of the region’s LUC 1, 2 and 3 land, respectively, occupied by small sized parcels or smaller with a dwelling, increasing to 64%, 67% and 47%, respectively, when including parcels ≤ 40.0 ha. Protection of LUC class 1 and 2 land, particularly, requires national attention. This metric provides an opportunity to evaluate land fragmentation and development over time that could serve both the assessment of policy performance and environmental reporting at national and regional levels.

New Zealand GHG Productivity: Demand Based GHG Emissions: Tonnes of CO2-equivalent data was reported at 68.400 Tonne mn in 2020. This records a decrease from the previous number of 72.930 Tonne mn for 2019. New Zealand GHG Productivity: Demand Based GHG Emissions: Tonnes of CO2-equivalent data is updated yearly, averaging 70.470 Tonne mn from Dec 1995 (Median) to 2020, with 26 observations. The data reached an all-time high of 80.670 Tonne mn in 2005 and a record low of 61.710 Tonne mn in 1995. New Zealand GHG Productivity: Demand Based GHG Emissions: Tonnes of CO2-equivalent data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s New Zealand – Table NZ.OECD.GGI: Environmental: GHG Productivity: OECD Member: Annual.