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Professor John Gibson is an Economist at the University of Waikato. His recent paper, Cumulative excess deaths in New Zealand in the COVID-19 era: biases from ignoring changes in population growth rates, summarises the ease with which statistical errors are made and the importance of recognising potential errors and bias in statistical claims, particularly those made by politicians and their bureaucrats. His paper abstract states:

Accurate health and economic data are needed to evaluate policy responses to COVID-19. A potentially comprehensive health indicator is excess deaths. Local commentators highlight an excess deaths series that suggests negative cumulative excess mortality for New Zealand in the first three years of COVID-19 – in other words, fewer deaths than expected. This flawed measure ignores changes in population growth. Deaths rose in New Zealand from 2015 to 2019 as population grew at two percent per annum. Population growth came almost to a standstill after the border closed in March 2020. Methods of extrapolating from the past to predict future deaths, to ascertain if actual deaths exceed projections, must account for this sharp change in population growth rates. Rather than New Zealand being unique, in having negative cumulative excess deaths in the COVID-19 era, cumulative deaths are about four percent above expected deaths (through 2022) once population growth rate changes are accounted for.

His paper conclusion states:

Accurate health data are needed to evaluate responses to COVID-19. Local media, politicians and public health commentators have coalesced around a projection-based measure of excess deaths due to Karlinsky and Kobak (Citation2021) that seemingly shows negative cumulative excess mortality for New Zealand in the first three years of COVID-19. This measure poorly suits the New Zealand context because it ignores the sharp fall in population growth rates after the border was closed in March 2020. Deaths (but not death rates) had risen sharply pre-COVID, alongside rapid population growth, and the projection-based method simply extrapolates that trend forward. Hence, a rising number of ‘expected’ deaths makes the actual rise in deaths in the post-2020 period seem less remarkable. To correct this flaw in the projections, population is included as a predictor and this shows that cumulative excess mortality for New Zealand was roughly four percent over the first three years of COVID-19; approximately 4000 excess deaths. This is similar to what is shown by measures of excess mortality based on death rates (such as in Figure 1(c)) that are readily available online and that show that cumulative excess deaths for New Zealand had already moved well into positive territory by mid-2022.

My final conclusion concerns disciplinary differences in research culture. It is offered to prompt thinking on the why question, of public health commentators coalescing around K&K projection-based excess mortality estimates rather than HMD ones that use death rates and that clearly showed positive cumulative excess mortality from mid-2022 onwards, as Figure 1(c) shows. Peer review in economics is adversarial, conditioning economists to build in responses to likely criticisms from the outset. Public health seems more collegial; reviews are faster and lighter and author teams larger (internalizing different views). For example, Baker et al. (Citation2023) has 15 authors. If economists were more prominent in the public square in the COVID-era, it may more quickly have become apparent that claimed negative cumulative excess mortality was just an artefact of an inappropriate way to predict expected deaths in a country with a fluctuating population growth rate. With our obsession for robustness tests and sensitivity analyses, the odds of data from the same source (OWID) or an upstream source (HMD) that showed different patterns of excess mortality remaining undisclosed would have been lower and flaws in the K&K approach might have been brought to light more quickly.

These disciplinary differences usually don’t matter. No one discipline has a monopoly on the best way to find truth. But it matters this time because New Zealand lacks checks and balances. A few cabinet ministers, perhaps just the Prime Minister, can change the course of the country, with no subnational variation or second chamber debates to see (at least ex post) if the changes helped. For example, in the United States, Florida’s Surgeon-General advocates against mod-RNA vaccines due to their DNA contamination and the Texas Attorney General is suing Pfizer for misleading claims about the vaccines.Footnote¹⁰ Yet officials in other states, such as California, have strong views in the other direction, and so out of this contest we eventually may learn something (just as the differing state and county approaches to lockdowns yielded counterfactuals used by Gibson (Citation2022)). In contrast, policy making and implementation in New Zealand is much more monolithic, making it especially important to promote a contest of ideas from different disciplines before the decisions made by politicians are embedded

Relevant to this is a 2008 paper, The grand impact of the Gates Foundation: Sixty billion dollars and one famous person can affect the spending and research focus of public agencies. Bill Gates has admitted himself, to an interest in lying with statistics. Dr Pierre Kory articulates the issue of corrupted science well here.