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[{"id":"Emissions Reduction Plan 2 - Social Impacts.md","slug":"emissions-reduction-plan-2---social-impacts","body":"\nOur report provides advice on the Government's final decision about the second emission budget. In addition to a wide range of policies considered in our earlier (May) report, the current report further investigates the impact of: \n\n- alternative carbon pricing policies,\n- the Electrify NZ policy,\n- the EV charging network investment, and\n- the Carbon Capture, Utilisation and Storage (CCUS) and the Refrigerants Regulated Product Stewardship scheme assumptions.\n\nOur high-level results suggest that: \n\n- the high carbon price of the Fourth Pathway scenario leads to larger adverse economic and equity impacts.\n- a decrease in GDP is associated with lower household consumption, lower real wages and lower exports (volume). As will be discussed, the short, medium and long-term dynamics of these effects are important for households (as well as the emission and economic outcomes).\n- the emission targets are achievable, but there is a significant adverse impact on economic and equity outcomes. \n\nWe sensitivity tested the results for seven different Nationally Determined Contribution (NDC) scenarios. Our extensive modelling of the government policies provides a comprehensive database for various policy and investment assessments as well as the ESG planning.","collection":"publications","data":{"title":"Economic impact of New Zealand's second emissions reduction plan: Policy results","type":"Publication","tagline":"Macroeconomic and distributional impacts of New Zealand’s second emissions reduction plan (ERP2)","description":"Macroeconomic and distributional impacts of New Zealand’s second emissions reduction plan (ERP2)","pubDate":"2024-12-11T00:00:00.000Z","authors":["Dr Eilya Torshizian"],"cardImage":{"src":"/_astro/tobias-keller-73F4pKoUkM0-unsplash.B7ydb18D.jpg","width":1920,"height":1280,"format":"jpg","fsPath":"/opt/buildhome/repo/src/images/tobias-keller-73F4pKoUkM0-unsplash.jpg"},"downloadPdf":"https://environment.govt.nz/publications/economic-impact-of-new-zealands-second-emissions-reduction-plan-policy-results/"}},{"id":"Regional Land Transport Demand Model.md","slug":"regional-land-transport-demand-model","body":"\n## Abstract\nThe National Land Transport Demand Model (NLTDM) was developed between 2011 and 2012. It was intended to be a tool for considering how transport demand may evolve, rather than a tool for providing point estimates of demand.\n\nThe model takes a hybrid approach to forecasting transport demand. This simplifies the relationship between transport demand and macroeconomic aggregates. However, it combines top-down relationships with additional details of behavioural parameters and often includes reduced (that is, simplified) forms of conventional regional transport models.\n\nWith policy discussions focusing more on equity, emissions and inclusion, there is an increasing demand for more flexible policy models and tools. The Regional Land Transport Demand Model (RLTDM) provides a useful framework for current policy discussions. However, it was developed using the MATLAB software package, which is rarely used by practitioners. New Zealand Transport Agency Waka Kotahi (NZTA) commissioned Principal Economics Limited to recode the RLTDM in Stata, using its matrix language (Mata), and provide further notes for practitioners on how to use the model. \n\nThe hybrid approach means the NLTDM can be manipulated by people with differing degrees of modelling expertise. This enables researchers and policy advisors to further investigate transport-demand factors, which is useful given the inherent uncertainty with transport modelling. The model evaluates transport-demand scenarios 30 years into the future, taking account of mega-trends in:\n\n - population growth\n - spatial demography\n - technology\n - income and economic growth\n - industrial composition\n - policy and prices in relevant areas\n\n This report provides a technical tagline: of the Regional Land Transport Demand Model (RLTDM), which is a hybrid approach to forecasting transport demand across New Zealand regions. The model’s outputs include deterministic and stochastic forecasts of a wide range of economic and transport series. We re-coded the model in Stata and Mata. ","collection":"publications","data":{"title":"Regional Land Transport Demand Model","type":"Publication","tagline":"Technical notes for practitioners","description":"test","pubDate":"2024-06-01T00:00:00.000Z","authors":["Dr Eilya Torshizian","Alina Fehling"],"cardImage":{"src":"/_astro/auckland_spagetti.B5JCimxY.avif","width":2070,"height":1380,"format":"avif","fsPath":"/opt/buildhome/repo/src/images/auckland_spagetti.avif"},"downloadPdf":"/public/assets/012-regional-land-transport-demand-model-technical-notes-for-practitioners.pdf"}},{"id":"Default Price-quality Paths for Electricity Distribution Businesses.md","slug":"default-price-quality-paths-for-electricity-distribution-businesses","body":"\n The Commerce Commission is in the process of the 2025 reset of the electricity default price-quality path in a time of uncertainty and high-inflation. The Commission tasked Principal Economics to provide a solution for dealing with supply chain and economic uncertainty for regulating $25 billion of assets of the electricity distribution businesses over the DPP4 (2025-2030) period. For that work, we used a combination of methods, including stakeholder engagement, CGE analysis (for the impact of climate policy on cost categories), econometric analysis and forecasting. The work included significant stakeholder engagement and inputs from the electricity distribution businesses from their submissions (to the Commission). The outputs are adopted in the Commission's latest decision and are available here. \n","collection":"publications","data":{"title":"Default Price-quality paths for Electricity Distribution Businesses","type":"Publication","tagline":"Report to the New Zealand Commerce Commission","description":" The Commerce Commission is in the process of the 2025 reset of the electricity default price-quality path in a time of uncertainty and high-inflation. The Commission tasked Principal Economics to provide a solution for dealing with supply chain and economic uncertainty for regulating $25 billion of assets of the electricity distribution businesses over the DPP4 (2025-2030) period. For that work, we used a combination of methods, including stakeholder engagement, CGE analysis (for the impact of climate policy on cost categories), econometric analysis and forecasting. The work included significant stakeholder engagement and inputs from the electricity distribution businesses from their submissions (to the Commission).","pubDate":"2024-05-01T00:00:00.000Z","authors":["Dr Eilya Torshizian","Ruyi Jia","Alina Fehling"],"cardImage":{"src":"/_astro/power_poles.Cgevsarj.webp","width":2744,"height":3863,"format":"webp","fsPath":"/opt/buildhome/repo/src/images/power_poles.webp"},"downloadPdf":"https://comcom.govt.nz/__data/assets/pdf_file/0031/353983/Default-price-quality-paths-for-electricity-distribution-businesses-from-1-April-2025-Draft-reasons-paper-29-May-2024.pdf"}},{"id":"Emissions Reduction Plan 2.md","slug":"emissions-reduction-plan-2","body":"\nThe Emissions Reduction Plan 2 (ERP2) delineates Aotearoa New Zealand's strategy to attain its emissions reduction objectives for the 2026-2030 period, alongside setting a path towards achieving long-term emissions reduction objectives. ERP2 aims to reduce annual average emissions from 72.5 MtCO2e to 61 MtCO2e. The Ministry for the Environment (MfE) engaged Principal Economics Limited, the Centre of Policy Studies, and Infometrics Limited to evaluate the comprehensive impact of the proposed policies. This includes: \n\n - Assessing the comprehensive economic repercussions of emissions mitigation policy packages within ERP2. \n - Estimating and understanding of secondary or indirect consequences.\n - Carrying out distributional analysis of these ramifications. \n \n\n**The critical policies investigated in our report include:**\n - Increasing renewable energy through Electrify NZ\n - Targeting 10,000 public EV chargers\n - Lowering agricultural emissions\n - Investing in resource recovery\n - Improving public transport\n - Investigating carbon capture, utilisation and storage. \n \n\n**Cite this article:** \nTorshizian E, Adams P, Stroombergen A. 2024. Economic Impact of New Zealand’s Second Emissions Reduction Plan. Report to Ministry for the Environment by Principal Economics Limited in collaboration with the Centre for Policy Studies and Infometrics Limited.","collection":"publications","data":{"title":"Economic Impact of New Zealand's Second Emission Reduction Plan","type":"Publication","tagline":"ERP2 assessment the economic effects of key initiatives","description":"The Emissions Reduction Plan 2 (ERP2) targets reducing New Zealand's emissions from 72.5 MtCO2e to 61 MtCO2e by 2026-2030. This assessment examines the economic effects of key initiatives including increasing use of renewable energy and improving public transport.","pubDate":"2024-05-01T00:00:00.000Z","authors":["Dr Eilya Torshizian","Adams P","Stroombergen A."],"cardImage":{"src":"/_astro/stream.B7GOPqir.png","width":947,"height":500,"format":"png","fsPath":"/opt/buildhome/repo/src/images/stream.png"},"downloadPdf":"https://environment.govt.nz/assets/Economic-impact-of-Emission-Reduction-Plan-2-Principal-Economics-Limited-230524-Final-Preliminary-Report-1.pdf"}},{"id":"Economic Impact of Accreditation.md","slug":"economic-impact-of-accreditation","body":"\nAccreditation is a crucial part of New Zealand’s quality infrastructure. The accreditation services provided by International Accreditation New Zealand (IANZ) increase the confidence of New Zealand and overseas consumers to purchase products that are produced or tested by accredited organisations. In this report we provide an independent assessment of the economic impact of IANZ. \n\nAccreditation services create an 8 percent price premium for exporters through reduced transaction costs, which leads to improved productivity and profitability. We used our extensive Computational General Equilibrium (CGE) model of the New Zealand economy and identified that IANZ’s accreditation services lead to:\n\n - a total increase in exports by $3.4b, consisting of an $8.2b increase in exports of industries using accreditation offset by a decrease in exports of industries not using accreditation,\n - 1.6 per cent higher real wages, which increases consumption by $3.3b,\n - $2.4 billion increase in annual GDP of New Zealand.\n\n","collection":"publications","data":{"title":"The Economic Impact of Accreditation","type":"Publication","tagline":"Report to the International Accreditation New Zealand (IANZ)","description":"Accreditation is a crucial part of New Zealand’s quality infrastructure. The accreditation services provided by International Accreditation New Zealand (IANZ) increase the confidence of New Zealand and overseas consumers to purchase products that are produced or tested by accredited organisations. In this report we provide an independent assessment of the economic impact of IANZ.","pubDate":"2023-09-01T00:00:00.000Z","authors":["Dr Eilya Torshizian","Eugene Isack","Alina Fehling"],"cardImage":{"src":"/_astro/ianz.BpaI3S6V.svg","width":548,"height":968,"format":"svg","fsPath":"/opt/buildhome/repo/src/images/ianz.svg"},"downloadPdf":"https://cdn.prod.website-files.com/61f09e22f47578bafd96e382/66861689ebb2cc05566223bb_Economic%20impact%20of%20IANZ%20-%20Principal%20Economics%20Limited%20-%2020231219%20(1).pdf"}},{"id":"Incorporating distributional impacts into the CBA framework.md","slug":"incorporating-distributional-impacts-into-the-cba-framework","body":"\nTransportation decisions can have large and varied impacts on travellers and their communities. It’s important to measure these effects and consider their impact on various groups when planning projects.\n\nWaka Kotahi uses a framework to decide which transport projects and programmes to pursue. The economic business case must contain a cost–benefit analysis (CBA). CBAs assess the economics of a proposal by valuing (monetising) the costs and benefits to all members of society. However, CBAs sum across a wide range of people and don’t calculate inequities between groups or individuals, or who ultimately benefits from the project.\n\nTransport equity discussions focus on social justice. Equity impact analysis helps policymakers to make good decisions for a wide range of people. Distributional impact analysis needs to be complemented with wider investment and planning considerations. This includes any comprehensive policy framework that accounts for the overlapping effects of transport, housing and taxing policies.\nCite this article\n\nTorshizian, E., Byett, A., Isack, E., Fehling, A., & Maralani M. (2022). Incorporating distributional impacts in the cost–benefit appraisal framework (Waka Kotahi NZ Transport Agency research report 700).","collection":"publications","data":{"title":"Incorporating distributional impacts into the CBA framework","type":"Publication","tagline":"","description":"Transport equity discussions focus on social justice","pubDate":"2023-09-01T00:00:00.000Z","authors":["Dr Eilya Torshizian","Eugene Isack","Alina Fehling"],"cardImage":{"src":"/_astro/transport_equity.DGUMFyX2.webp","width":1792,"height":1024,"format":"webp","fsPath":"/opt/buildhome/repo/src/images/transport_equity.webp"},"downloadPdf":"https://www.nzta.govt.nz/resources/research/reports/700/"}},{"id":"Climate change adaptation and investment decision making.md","slug":"climate-change-adaptation-and-investment-decision-making","body":"\nClimate change is increasing the frequency and severity of extreme weather events, creating deep uncertainty for decision-makers. To build climate resilience, flexible evaluation methods that account for a range of future scenarios. Adaptive Decision Making (ADM) provides this flexibility by enabling adaptive planning that evolves as new information emerges, ensuring more effective investment decisions in an unpredictable climate.\n\nBased on scientific studies and recent climate events in New Zealand, climate is beginning to exacerbate extreme “one-in-100-year” events. Higher temperatures mean more evaporation and moisture in the atmosphere and stronger storms, droughts and heat waves. Climate resilience means recognising that extremes are not necessarily extraordinary, and effective project evaluation methodologies are needed to support the ability to efficiently select between project alternatives, to prepare, respond and recover quickly.\n\nIn this report, we identify the available methods for ADM in climate change and their pros and cons. We then provide suggestions for considerations of climate change adaptation within an investment decision making framework.","collection":"publications","data":{"title":"Climate change adaptation and investment decision making","type":"Publication","tagline":"Considerations of climate change adaptation in evaluation.","description":"Adaptive decision making for evaluating economic land transport activities within Waka Kotahi’s Investment Decision Making Framework","pubDate":"2023-01-01T00:00:00.000Z","authors":["Dr Eilya Torshizian","Eugene Isack","Alina Fehling"],"cardImage":{"src":"/_astro/phill-brown-CE719W4ZArk-unsplash.D9E9NqMu.jpg","width":1920,"height":1280,"format":"jpg","fsPath":"/opt/buildhome/repo/src/images/phill-brown-CE719W4ZArk-unsplash.jpg"},"downloadPdf":"/Climate_change_adaptation_and_investment_decision_making_Principal-Economics_January-2023.pdf"}},{"id":"Great Decisions are Timely.md","slug":"great-decisions-are-timely","body":"\nAotearoa New Zealand suffers from an infrastructure deficit. Without the key infrastructure needed now for our economy to thrive, we deprive future generations from significant economic prosperity. While transformational infrastructure projects necessitate time to be developed into sound technical solutions to our needs, many New Zealand projects are further delayed by policy decision and financing constraints. In this novel application of the infrastructure Wider Economic Benefits approach, we quantify the cost to society of these further delays for the first time, by using the example of the Waikato Expressway. We used our subregional CGE model to estimate the downstream benefits of the Expressway. At a high-level, results of our analysis quantify the annual benefits of having the Waikato Expressway in the economy. Without the expressway in function as early as possible, $334 million of economic benefits were forgone each year.\nCite this article\n\nPrincipal Economics. (2022). Great decisions are timely: Benefits from more efficient infrastructure investment decision-making. Report to Infrastructure New Zealand.","collection":"publications","data":{"title":"Great decisions are timely","type":"Publication","tagline":"Benefits from more efficient infrastructure investment decision-making","description":"test","pubDate":"2022-10-01T00:00:00.000Z","authors":["Dr Eilya Torshizian","Eugene Isack","Alina Fehling"],"cardImage":{"src":"/_astro/pocketwatch.f4JPuEV6.avif","width":1932,"height":1087,"format":"avif","fsPath":"/opt/buildhome/repo/src/images/pocketwatch.avif"},"downloadPdf":"/resources/great-decisions-are-timely"}},{"id":"Business Development Capacity Assessment for Dunedin City.md","slug":"business-development-capacity-assessment-for-dunedin-city","body":"\nDunedin City Council appointed Principal Economics to provide a comprehensive assessment of the sufficiency in development capacity of business land within Dunedin to fulfills requirements of the the National Policy Statement on Urban Development (NPS-UD 2020), including an investigation of:\n\n* the locational requirements of business including shape, size, access, reverse sensitivities, and other market factors;\n \n* the external pressures businesses are facing (such as the uncertainty of the COVID-19 pandemic, and the impact of coastal hazards);\n \n* impacts on business activities from reverse sensitivities; Locational accessibility to labour markets and customers; Changes in demand from population growth; changes in residential distribution;\n \n* infrastructure requirements for different business sectors.\n \n\nIn our assessment of demand and sufficiency we identified existing businesses across New Zealand and their locational attributes including but not limited to land size, shape, access, reverse sensitivities and other market-based factors. We use industries' revealed preferences to assess the features of land that they have determined as being suitable. This was then matched with the supply of business land in Dunedin City after applying a range of spatial analysis techniques.","collection":"publications","data":{"title":"Business Development Capacity Assessment for Dunedin City","type":"Publication","tagline":"An assessment of demand, supply and sufficiency of business land in Dunedin","description":"An assessment of demand, supply and sufficiency of business land in Dunedin","pubDate":"2022-07-01T00:00:00.000Z","authors":["Dr Eilya Torshizian","Eugene Isack","Alina Fehling"],"cardImage":{"src":"/_astro/dunedin.D0HWLM2m.jpg","width":1920,"height":1280,"format":"jpg","fsPath":"/opt/buildhome/repo/src/images/dunedin.jpg"},"downloadPdf":"/Business-Demand-and-Capacity-Assessment-Principal-Economics-report-to-Dunedin-City-Council-Final-report-1.pdf"}},{"id":"Review of HBAs 2021.md","slug":"review-of-hbas-2021","body":"\nThe Ministry for the Environment (MfE) engaged Principal Economics and Urban Economics to review Housing and Business Development Capacity Assessments (HBAs) across councils, with the exception of Rotorua and Wellington, whose HBAs were unavailable at the time. \n\nOur review focused on compliance with the National Policy Statement on Urban Development 2020 (NPS-UD 2020) and identified key areas for improvement to guide future HBAs for both councils and the ministries (MfE and HUD).\n\nReviews that have been published online can be found below:\n\n- [Dunedin - Otago Regional Council and Dunedin City Council](https://www.dunedin.govt.nz/__data/assets/pdf_file/0005/853277/Final_Review-of-Dunedin-HBA_Principal-Economics_December-2021.pdf)\n\n- [Futureproof - Waikato Regional Council, Hamilton City Council, Waikato District Council and Waipa District Council](https://futureproof.org.nz/assets/FutureProof/Final-Review-of-Future-Proof-Partners-HBA_Principal-Economics_December-2021.pdf)\n\n- [Tasman - Tasman District Council](https://www.tasman.govt.nz/document/serve/Final_Review%20of%20the%20Nelson%20Tasman%20HBA_Principal%20Economics_December%202021.pdf?DocID=33106)","collection":"publications","data":{"title":"Review of the housing and business development capacity assessments 2021","type":"Publication","tagline":"National Policy Statement on Urban Development","description":"National Policy Statement on Urban Development","pubDate":"2021-12-01T00:00:00.000Z","authors":["Dr Eilya Torshizian","Eugene Isack","Alina Fehling"],"cardImage":{"src":"/_astro/michael-amadeus-W47UMydgshw-unsplash.Dxp1ShKV.jpg","width":1920,"height":1283,"format":"jpg","fsPath":"/opt/buildhome/repo/src/images/michael-amadeus-W47UMydgshw-unsplash.jpg"}}}]

[{"id":"Uneven Roads.md","slug":"uneven-roads","body":"\nThis article identifies and evaluates possible methodologies for estimating the capital value of New Zealand’s local road network. Local councils and central government agencies could use the findings to address the current inconsistencies in valuation approaches and enable better-informed decision-making for local road investment, maintenance, and user charges. The outputs will improve our understanding of the socio-economic and financial costs of providing and using the New Zealand transport system. The article discusses that the commonly used accounting-based valuation methods underestimate roads' (economic) value. Suppose the purpose of a valuation is to prioritise investment. In that case, an accounting-based approach prioritises costlier road linkages instead of those with the highest economic value. ","collection":"articles","data":{"title":"Uneven Roads","description":"Methodologies for estimating the capital value of New Zealand’s local road network","authors":["Ruyi Jia","Dr Eilya Torshizian"],"cardImage":{"src":"/_astro/lindis_pass.DECLoxbd.avif","width":2070,"height":1380,"format":"avif","fsPath":"/opt/buildhome/repo/src/images/lindis_pass.avif"},"downloadPdf":"/Moving-towards-value-based-local-road-valuation-approaches-Principal-Economics-Insight-Article-2410.pdf","type":"Article","tagline":"Addressing the Inconsistencies in Local Road Valuation Across New Zealand","pubDate":"2024-10-01T00:00:00.000Z"}},{"id":"SURF.md","slug":"surf","body":"## Synthetic data generation using Statistics NZ IDI microdata\n\nPolicy decisions increasingly require technical modelling approaches informed by granular data on business demographics, household, and urban environment features. Over the past decade, our team has conducted various data analyses using microdata projects. However, due to confidentiality protocols, this granular data is often unavailable outside Statistics NZ’s Datalab, limiting its use for monitoring and research.\n\nEffective analysis aims to generate actionable insights and guide informed decision-making. Microdata analysis reveals the intricate relationships between variables and establishes the foundation for sophisticated decision-making. However, lacking access to microdata outside secure environments for scenario testing, monitoring outcomes, and evaluating effectiveness can still restrict policymakers from achieving meaningful change.\n\nThis article discusses the creation of a Synthetic Unit Record File (SURF) for disseminating publicly assessable microdata that emulates the real world while maintaining confidentiality. Our team recently applied this methodology to a large dataset of Motor Vehicle Registration, with a sample population of over 10 million, to extract anonymised data for broader organisational and research use. This article describes the purpose of adopting this approach and the methodology involved.\n\n\n\nAt Principal Economics, we rely heavily on data to conduct our analysis, drawing together data from disparate sources across government departments, open data providers, spatial data, generated data, simulated data, and we’ve used it. In our field, there’s always a balance to maintain. We’re tasked with addressing broad questions, yet answers are always layered with nuance. This is not too dissimilar to the type of data we work with. A relatively simple question posed to us may entail;\n\nWhat was the average travel distance by light vehicles in New Zealand in 2021? Easy enough, 19,730. This can be found as publicly available data. How many people were unemployed in 2021? Again, easy to answer, by the end of the December quarter it was 3.2%. What is the difference between the travel patterns of employed and unemployed people? Suddenly, providing an appropriate answer becomes far more complex. While aggregate statistics are readily available to the public, the challenge escalates when more detailed disaggregation is needed, especially when pulling from disparate data sources. We may consider using microdata de-identified individual unit record data. As trusted researchers, we are approved to access and conduct research using Statistics New Zealand’s Integrated Data Infrastructure, providing that the research meets access criteria and the outputs adhere to confidentiality requirements.\n\nOften aggregated data isn't enough for government departments striving to assess the nuanced effects of policies—such as how a policy might affect a rural community versus an urban one or how income groups experience public services differently. Using microdata, with its individual-level detail, we can offer these insights, and our team is experienced in and has undertaken this practice many times before. Just as policymakers want to make the right decision for their constituents, we want to provide the right information that captures the full spectrum of how individuals make decisions and all the factors that influence their choices.\n\nAccess to microdata enables researchers to derive invaluable multidimensional insights. Even still, there is tension between data accessibility and privacy protection amplified by an ever-increasing demand for detailed information. From simple queries to complex insights\n\n## Using administrative microdata for enhanced insights\n\nThinking back to our original question: What is the average travel distance by light vehicles in New Zealand in 2021? Is this different by age group? What about by region? Does income level change how much we travel? How about households with children? Indeed, our travel decisions vary based on the suburbs we live in, reflecting differences in accessibility, local services, and lifestyle. And while we’re at it, what type of vehicles are being driven? And how does this change over time? On that note, what is going on with electric cars?\n\nAll these factors are critical to understanding travel behaviours and, in turn, how we plan our cities, address infrastructure needs, and shape our policies. The more we delve into a seemingly simple question, disaggregating the nuances as we finally begin to grasp the data before us, new lines of inquiry inevitably arise. Yet, each additional breakdown of tabulated data heightens the risk of disclosure. And so, the value of highly granular, cross-tabulated data becomes apparent, while the limitations of aggregated data become increasingly clear.\n\n## A case study in the use of microdata\n\nIn our recent research report, The Geodemographics of VKT, we explored the application of synthetic data methodology to a range of datasets. Given the wide range of factors of VKT, it is crucial to explore granular data. For example, Age, income, geographic location, vehicle attributes, urban form, and public transport coverage all affect travel behaviour. Furthermore, how these factors interact with one another is only sometimes consistent. For example, someone who lives in a suburban neighbourhood with limited public transport options may drive significantly more than someone in a central urban location with easy access to multiple transport modes, even if their income or household structure is similar. Someone with a larger, less fuel-efficient vehicle may take shorter trips due to higher running costs; if you had an electric car, you might have completely different travel patterns.\n\nIn the IDI we keep the dataset disaggregated , we disaggregate the dataset to allow flexible analysis and linkages. This approach supports aggregating data for individual and household correlations, analysing specific vehicle attributes, and future potential for connecting with other datasets like health and employment records. It also enables longitudinal studies to track changes over time. This flexibility helps us address evolving research questions, uncover complex relationships, and model diverse factors affecting vehicle usage.\n\nWhile we can identify these correlations, and all the complex questions posed to us, the real challenge lies in determining how to effectively use this information. How do we translate these insights into actionable strategies, policies, or interventions that address the underlying issues? Understanding the data is only the first step—applying it to create tangible, positive outcomes is where the real impact lies.\n\nTo adequately evaluate how various factors and their interactions influence VKT, we use the Statistics NZ Integrated Data (IDI). This extensive database provides de-identified microdata on individuals and households from administrative sources. We assemble the dataset by analysing over 10 million odometer readings from multiple snapshots of the MVR to determine VKT for each vehicle and its owner. As odometer readings are not continuous, we calculate VKT by measuring the difference between readings to construct an annual vehicle usage profile. These profiles were then linked with the IDI Core and Experimental Administrative Population Census (APC) data to establish links between individual VKT and demographic variables.\n\nOur analytical dataset includes various vehicle attributes, such as age, engine size, fuel type, and body type.\n\n## Assessment of the impact of policy packages and their sequencing\n\nFollowing the trend of increasing granularity in analysis, digital twins (virtual replicas of In the IDI, we disaggregate the dataset to allow flexible analysis and linkages. This approach supports aggregating data for individual and household correlations, analysing specific vehicle attributes, and future potential for connecting with other datasets like health and employment records. It also enables longitudinal studies to track changes over time. This flexibility helps us address evolving research questions, uncover complex relationships, and model diverse factors affecting vehicle usage. While we can identify these correlations and all the complex questions posed to us, the real challenge lies in determining how to effectively use this information. How do we translate these insights into actionable strategies, policies, or interventions that address the underlying issues? Understanding the data is only the first step—applying it to create tangible, positive outcomes is where the real impact lies. The process behind synthetic data generation\n\nTo assist with checking and validating the Ministry of Transport’s Monty agent-based modelling outputs, we generated a SURF (Synthetic Unit Record File) of annual VKT for individuals. Synthetic datasets closely mimic the relationships and distributions found in the original dataset, preserving its statistical properties. To simplify the outputs, we aggregate and annualise the data by summing the VKT for all vehicles registered to an individual over a year. Household and individual attributes are aligned to a single point, ensuring consistency when linking demographic and vehicle data to travel behaviour.\n\nCreating a synthetic dataset involves balancing accuracy with privacy. The aim is to closely replicate the original data's characteristics while protecting sensitive information. Synthetic data are semi-realistic representations of the population, designed to respect only the maintained distributions, variables, and relationships. The process involves replicating the statistical relationships between variables without revealing any sensitive or identifiable information.\n\nAfter generating the synthetic dataset, we validate it to ensure it mirrors the original data’s statistical properties and analytical outcomes. This involves testing both univariate distributions and pairwise correlations to confirm that the synthetic data accurately reflects the original structure and relationships.\n\nWhile we primarily rely on Classification and Regression Trees (CART) models (often the default choice in synthetic data generation), we found that, at times, they would produce outcomes that diverged significantly from the actual data. Effective fine-tuning of the data synthesis process requires not only technical expertise in modelling methods but also domain expertise in understanding the relationships between variables.\n\nTo address these issues we selected methods on a per-variable basis to best preserve statistical relationships. In addition, we apply stratification techniques to ensure subgroups, such as geographic regions are appropriately synthesised. These adjustments we necessary to ensure that the synthetic dataset remained both accurate and reliable for analysis both between variables and by geographic areas.\n\nThe synthesised dataset we created encompasses all MVR observations, allowing us to calculate VKT and establish linkages to demographic attributes. Comparing between the synthesised and actual data showed strong pair-wise utility scores well within recommended margins across all variables confirming its reliability for analysis. The extensive data synthesis and high utility scores suggest promising applications of synthetic data generation in other domains requiring detailed demographic and geographic analysis.\n\n## Concluding remarks\n\nThe purpose of any analytical process is to drive meaningful insights and informed decisions. Microdata analysis determines the relationships between variables, deriving the parameters needed for complex simulation modelling. Once we've understood the interconnected relationships, synthetic data enables us to simulate scenarios and assess potential impacts without direct access to the sensitive, often restricted, microdata. This is invaluable for monitoring and testing policy impacts.","collection":"articles","data":{"title":"Synthetic Unit Record Files","description":"test","authors":["Eugene Isack"],"cardImage":{"src":"/_astro/img1_synthetic_data.DYRU-vX9.webp","width":1315,"height":986,"format":"webp","fsPath":"/opt/buildhome/repo/src/images/img1_synthetic_data.webp"},"type":"Article","tagline":"Synthetic data generation using Statistics New Zealand IDI microdata","pubDate":"2023-09-01T00:00:00.000Z"}}]

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Without the key infrastructure needed now for our economy to thrive, we deprive future generations from significant economic prosperity. While transformational infrastructure projects necessitate time to be developed into sound technical solutions to our needs, many New Zealand projects are further delayed by policy decision and financing constraints. In this novel application of the infrastructure Wider Economic Benefits approach, we quantify the cost to society of these further delays for the first time, by using the example of the Waikato Expressway. We used our subregional CGE model to estimate the downstream benefits of the Expressway. At a high-level, results of our analysis quantify the annual benefits of having the Waikato Expressway in the economy. Without the expressway in function as early as possible, $334 million of economic benefits were forgone each year.","tagline":"Benefits from more efficient infrastructure investment decision-making","authors":["Principal Economics"],"cardImage":{"src":"/_astro/pocketwatch.f4JPuEV6.avif","width":1932,"height":1087,"format":"avif","fsPath":"/opt/buildhome/repo/src/images/pocketwatch.avif"},"iframe":"/cost_of_delay/index.html","downloadPdf":"/public/assets/Cost-of-delay-in-infrastructure-decisions-Principal-Economics-report-Oct-2022.pdf","pubDate":"2022-10-01T00:00:00.000Z"}},{"id":"Emissions Reduction Initatives.md","slug":"emissions-reduction-initatives","body":"\n<div class=\"hidden\">\n \n</div>\n","collection":"resources","data":{"title":"Emission Reduction Initiatives","type":"Resource","description":"Potential policy instruments suitable for transport emissions reduction.","tagline":"Potential policy instruments suitable for transport emissions reduction","authors":["Principal Economics"],"cardImage":{"src":"/_astro/wellington_tram.D6qLXfAw.avif","width":2148,"height":1611,"format":"avif","fsPath":"/opt/buildhome/repo/src/images/wellington_tram.avif"},"iframe":"/emission_reduction_initiatives/index.html","pubDate":"2022-07-02T00:00:00.000Z"}},{"id":"Drivers of House Price Growth.md","slug":"drivers-of-house-price-growth","body":"\n<div class=\"hidden\">\n \n</div>\n","collection":"resources","data":{"title":"Drivers of House Price Growth","type":"Resource","description":"In this report we assess the extent to which the Resource Management Act (RMA) and other environmental regulations have contributed to house price inflation in Aotearoa. To do this, we review the factors of housing price growth (HPG) in New Zealand and provide a simple description of the available data and existing research on the topic.","tagline":"Drivers of House Price Growth tagline","authors":["Principal Economics"],"cardImage":{"src":"/_astro/wellington_housing.Be3qK3AQ.avif","width":2070,"height":1380,"format":"avif","fsPath":"/opt/buildhome/repo/src/images/wellington_housing.avif"},"iframe":"/house_price_growth/Index.html","pubDate":"2021-07-01T00:00:00.000Z"}}]