Tax and Transfer Progressivity in New Zealand: Part 1 Methodology
Author: Benjamin Ching
This note is the first in a two-part series that seeks to improve our understanding of effective average tax rates (EATRs) in New Zealand.
This note seeks to:
- Explain why considering more comprehensive EATRs is valuable, and why microsimulation modelling is useful.
- Present our method for estimating more comprehensive EATRs, which uses a prototype-extension to the Tax and Welfare Analysis (TAWA) microsimulation model that can measure the effect of multiple tax and transfer types against broad definitions of income
Tax and Transfer Progressivity in New Zealand: Part 2 Results
Authors: Benjamin Ching, Chelsey Reid and Luke Symes
This note is the second in a two-part series that seeks to improve our understanding of effective average tax rates (EATRs) in New Zealand.
This note presents the results from our prototype extension to the Tax and Welfare Analysis (TAWA) microsimulation model that estimates these more comprehensive EATRs across the income and wealth distributions. The intent is for these results to measure the progressivity of the tax and transfer system when we consider a more comprehensive definition of income. We describe our experimental EATR measures as ‘more comprehensive’ because data limitations prevent us from including all taxes and income sources.
Trends in the household income distribution: 2007-2021
Author: Meghan Stephens
The distribution of household income in New Zealand is continually changing and is expected to significantly transform in the years ahead due to demographic trends and shifts in the labour market. This raises an important question: how can we ensure that our tax and transfer system can support future generations of New Zealanders, especially as the population ages and work patterns evolve in response to technological advancements and environmental challenges?
This note lays the groundwork for future research by presenting comprehensive data on household income distributions over the past 14 years. It goes beyond headline statistics on incomes, inequality, and poverty by using unit record data from Stats NZ to supplement existing outputs and reports, such as Perry (2019, 2022). Through visual representations, this note presents household income distributions for various types of households from 2007 to 2021, shedding light on trends and patterns. Additionally, the note delves into the impact of an aging population by creating a counterfactual distribution based on 2007 age demographics, allowing for further investigation into this important demographic shift. The detailed data and analyses presented in this note serve as a valuable foundation for future work in understanding household incomes in New Zealand.
Estimating the Distribution of Wealth in New Zealand
Authors: Benjamin Ching, Tayla Forward and Oscar Parkyn
This paper discusses alternative methods to estimate the distribution of wealth in New Zealand. It develops a taxable income capitalisation method for estimating the distribution of wealth in New Zealand that is based on the approach of Saez and Zucman (2016; 2022) and adapted for New Zealand data sources, which we term “the New Zealand capitalisation method”. Internationally, taxable income capitalisation has been found to be a high-performing method for estimating asset holdings that generate taxable income flows, particularly towards the top of the wealth distribution where household surveys often undercount wealth. The New Zealand capitalisation method combines Inland Revenue taxable income administration data with Stats NZ’s Household Balance Sheet to give new estimates of the distribution of New Zealand individuals’ wealth. We present results for 2010, 2015, and 2018, and compare these distributions with those recorded by the Household Economic Survey (HES) 2018. Our method also allows for wealth estimates of smaller groups than can be reliably obtained through HES, including estimates of the wealth held by the top 0.1% of the wealth distribution. The New Zealand capitalisation method suggests more wealth at the top of the distribution than estimated by HES, which is consistent with similar work internationally. Results also show that wealth shares at the top of the distribution fell between 2010 and 2018, while at the same time the greatest increase in average wealth went to the top of the distribution.
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