My background

I’m a New Zealander (my father was born in Wellington from South Island stock) — and an Australian (my mother was born on a farm in country NSW). I grew up in Tasmania, and have worked in various places including Melbourne and the UK. I’ve called Wellington home since 2005.

Backing the wrong fruit

I studied computers in the 1970s and completed a B.Sc. in 1980. (That was well before geeks became fashionable.) As computer science graduates were in short supply, it led to great opportunities. At age 21 I was leading the software design team for a new personal computer in the UK. Unfortunately, history tells that I backed the wrong fruit – the Apricot.

Mixing conservation and tech start-ups

Over the following decade I bounced between conservation advocacy (e.g., being a park ranger, and a lobbyist and media spokesperson for the somewhat anarchic NGO the Wilderness Society) and the tech industry (including writing database software for Hewlett-Packard).

In the early 1990s I tried to combine them in Verdant – a software company embodying social and environmental principles. (Today it would be called a social enterprise, but the term didn’t exist back then.) While its first incarnation was a commercial failure, it led to the very successful tech start-up Echoview software. I wrote its original software for analysing underwater acoustic data, then led the team that made it the globally leading package for fish stock assessment and research – supporting responsible fisheries management in the world’s oceans, rivers and lakes.

Second time not so lucky

In the early 2000s I tried to repeat the underwater success on land. With venture-capital backing, I helped create an ambitious new software product for the visualisation and analysis of time-varying geospatial data. The product – Eonfusion – was ahead of its time, or at least its commercial success was beyond our resources and capabilities. And the global financial crisis didn’t help. But it did teach me that I needed better business management skills.

Back to school

It was time to start learning from others’ mistakes, not just my own. I enrolled in an MBA at Victoria University of Wellington … and inadvertently discovered a love for economics and public policy. Time for a career change! After my degree I became a research fellow at the Institute for the Study of Competition and Regulation, researching NZ railways, telecoms, electricity markets, and other fascinating stuff.

Ten years at the Productivity Commission

Hearing that NZ was about to get a Productivity Commission modelled on its Australian namesake, I thought that should be the next place for me. I joined in April 2011 – its first week in operation – and immediately started on my first of what turned out to be 8 inquiries: International freight transport services, Strengthening trans-Tasman economic relations, Boosting services sector productivity, More effective social services, New models of tertiary education, State sector productivity, Technological change & the future of work, and Growing the digital economy & maximising opportunities for SMEs. I found social services the most rewarding; the tech ones stood out too.

The PC developed a reputation for well-researched, thorough analysis, but expressed in somewhat long and dense reports. While doing my best to use Plain English when writing on complex and technical topics, I wanted our work to reach other audiences. I initiated and contributed to 2 PC blogs – FutureworkNZ and PandemicEconomics.

The PC was short on inquiry topics during the 2020 national lockdown. I ended up working on Covid policy, including a cost–benefit analysis (CBA) for Treasury to demonstrate how CBA could be applied to Covid lockdown decisions.

My other “careers”

Work at the PC could get full-on, leaving less time than I liked for my other interests. I took a 16-month break in 2017–18, mostly spent adventuring in Fiordland. In 2021 I left the PC, looking for a better balance with my other “careers”. These include volunteering on conservation projects and for search & rescue, exploring the mountains on ski and foot, and the odd trail ultra-marathon.

I also volunteer as the editor of Asymmetric Information, the newsletter of the NZ Association of Economists (NZAE), and sit on the NZAE Council.

Asymmetric Information

Newsletter of the New Zealand Association of Economists (NZAE). Subscribe for free to receive 1-2 posts each week with interesting takes on NZ and international economic topics.

By Dave Heatley

In between those career adventures, I created Sawtooth Economics in 2021.

Why Sawtooth Economics?

NZ mountain ranges often look like the sharp edge of a saw. Indeed, NZ has two Sawtooth Ridges, one in Westland on the South Island and another in the Ruahines on the North Island. Sawtooth reflects my love of the mountains, and it embodies the idea of “cut-through” on complex, contentious and difficult topics.

Sawtooth can assist with your projects — simple or complex. For more information, or just to chat, email dave@sawtootheconomics.com or phone +64 273 545 672.

At Sawtooth, we strive for solid analysis, well-grounded in theory and data, and emphasise clear and concise communication of complex analysis and concepts to wide audiences.

This website features a selection of my recent work, including cross-post from Asymmetric Information (AI), the newsletter of the New Zealand Association of Economists. (Among other things, I am the Editor of AI.)

I work closely with Andrew (Andy) Sweet of Firecone Limited, and with Sue Rundle, an experienced statistician and policy analyst, the co-owner of Sawtooth.

Andy and I are accredited researchers with Stats NZ, and have conducted several projects that use the Integrated Data Infrastructure (IDI) — an amazingly detailed yet underutilised treasure trove of data about New Zealanders and NZ businesses. Our recent work in the IDI includes an analysis of NZ’s low-income households for the Infrastructure Commission.

Sawtooth can assist with your projects — simple or complex. For more information, or just to chat, email dave@sawtootheconomics.com or phone +64 273 545 672.

Let me introduce you to R_PE, a “quick & dirty” estimate of the effective reproduction number of COVID-19.¹

Today’s estimate of the effective reproduction number for COVID-19 in New Zealand is 0.11.

In a perfect world, the Government would be able to track the effective reproduction number (R_eff) of COVID-19 on a daily basis. R_eff is the average number of secondary infections per primary infection in an actual population given the policy measures in place at that time and the behavioural responses of that population.² Tracking R_eff allows the Government to both gauge the extent to which the virus is under control, and track the effects of changes to social distancing rules, or improvements in supporting measures such as testing, tracing and isolation.

R_PE (estimates reproduction number), selected countries, 28 March - 22 April 2020

But accurate estimation of R_eff requires complex modelling. So, I developed R_PE – an easily calculated and updateable proxy measure of R_eff (see below for the formula for R_PE). I’ve calculated this proxy measure for the past three weeks for ten countries (including New Zealand) and for the states of Australia.³

Te Pūnaha Matatini’s (Plank et al., 2020) reported estimates of R_eff for six countries provides a useful crosscheck. So, I’ve compared R_PE against R_eff estimates from their complex model, finding a close match for most jurisdictions.

The following table compares estimates of R_PE against Te Pūnaha Matatini’s estimates of R_eff. Te Pūnaha Matatini did not specify a reference date for their estimates. The last New Zealand data point in their graphs is 5 April, suggesting a reference date in early April. Their R_eff values are very close in most cases to R_PE for 1 April.

[* I have not calculated R_PE for NSW and the USA, as numbers of recovered individuals are not consistently available in the John Hopkins University dataset. Current R_PE values for NSW are likely to be near zero as there have been very few new cases over recent days.]

In New Zealand, R_PE fell quickly in response to the level 4 lockdown from 26 March. This fall is also apparent in all the comparator countries in this post, bar Singapore.⁴

An R_eff below 1 corresponds to a situation in which the pool of infected individuals is shrinking, or equivalently, virus spread is under control. Acknowledging the imprecision of R_PE, I propose a threshold of 0.8 rather than 1.

All countries in the graph bar Singapore are below the 0.8 threshold, that is, they have achieved effective control. How they have done so and how that might inform policy for New Zealand will be the subject of my next post.

I see value in R_PE, or an equivalent measure, that can be updated daily. It could help decision makers monitor the success of policy measures to manage COVID-19 spread. New Zealand and other countries have made significant progress over the past few weeks (see graph). This makes it important to base policy on up-to-date data. R_PE can help fill the gap between the less frequent, albeit more accurate, estimates of effective reproduction numbers for COVID-19 provided by the New Zealand Government’s modellers.

Calculation of R_PE

R_PE is calculated on a daily basis using the formula (new cases) / (active cases 5 days prior) * 10.

“5” in this equation is the assumed average number of days between infection and infectiousness (eg, Linton et al., 2020).

“10” is the average number of days an infected individual is infectious for. Following Siddarth and Weyl (2020), I assume individuals are no longer infectious 15 days after they became infected. This corresponds with the findings of Woelfel et al. (2020), who reported that people with COVID-19 but were not hospitalised had high levels of SARS-CoV-2 in their respiratory tract early on in their illness. The levels dropped off after four or five days of symptoms, and by the tenth day after they got sick, there was hardly any virus left. Zhou et al. (2020) reported that the virus was detectable in hospitalised patients in China for up to 20 days after symptoms started, but virus levels dropped off when symptoms did.

“Active cases” is the cumulative number of infected people less those recovered or died. To moderate the effects of data spikes, “new cases” and “active cases” are moving averages over three days.

High numbers of asymptomatic infectious individuals, if present, would bias R_PE downwards. This becomes more problematic for the model as the numbers decline and we get very few new positive cases.

References

Delamater, P. L., Street, E. J., Leslie, T. F., Yang, Y., & Jacobsen, K. H. (2019). Complexity of the Basic Reproduction Number (R0). Emerging Infectious Diseases, 25(1), 1-4. https://dx.doi.org/10.3201/eid2501.171901

Linton, N. M., Kobayashi, T., Yang, Y., Hayashi, K., Akhmetzhanov, A. R., Jung, S., Yuan, B., et al. (2020). Incubation Period and Other Epidemiological Characteristics of 2019 Novel Coronavirus Infections with Right Truncation: A Statistical Analysis of Publicly Available Case Data. Journal of Clinical Medicine, 9(2), 538. MDPI AG. http://dx.doi.org/10.3390/jcm9020538

Plank, M.J., Binny, R.N., Hendy, S.C., Lustig, A., James, A., & Steyn, N. (9 April 2020) A stochastic model for COVID-19 spread and the effects of Alert Level 4 in Aotearoa New Zealand. https://www.medrxiv.org/content/10.1101/2020.04.08.20058743v1.full.pdf
Siddarth, D., & Weyl, E.G. (2020). Why We Must Test Millions a Day. COVID-19 Rapid Response Impact Initiative,  White Paper 6. Edmond J. Safra Center for Ethics. https://ethics.harvard.edu/files/center-for-ethics/files/white_paper_6_testing_millions_final.pdf

Woelfel, R., Corman, V.M., Guggemos, W., Seilmaier, M., Zange, S., et al. (2020). Clinical presentation and virological assessment of hospitalized cases of coronavirus disease 2019 in a travel-associated transmission cluster. [Preprint]. https://doi.org/10.1101/2020.03.05.20030502

Zhou, F., Yu, T., Du, R., Fan, G., Liu, Y., et al. (2020). Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study. The Lancet. 395(10229), p. 1054-1062. https://doi.org/10.1016/S0140-6736(20)30566-3

Data

Data from John Hopkins University downloaded 13:30 17 April 2020 NZST. This includes NZ data up to 16 April. https://github.com/CSSEGISandData/COVID-19/tree/master/csse_covid_19_data/csse_covid_19_time_series

Notes

1. “PE” in R_PE stands for “Proxy Estimate”.

2. R_eff is the effective reproduction number at a point in time in an actual population, given actual control measures in place and behavioural responses by that population. It differs from the commonly citied reproduction numbers R₀ and R_e. R₀ is the number of secondary cases one primary case would produce in a completely susceptible population (Delamater et al., 2019). R_e is the average number of secondary cases per infectious case in a population made up of both susceptible and non-susceptible hosts. R₀, R_e and R_eff will typically be the same at the onset of a pandemic, but will diverge over time.

3. This graph could easily be extended to many more countries and further into the past. The calculation is only as good as the quality of the underlying data, making it less reliable for developing countries. R_PE assumes closed borders (or that the net movement of infected individuals across borders is small relative to the number of active cases). It does not make sense to apply the model to periods when a major source of infection is people arriving from other countries.

4. The countries in this note are based on the list in Plank et al. (2020). That paper presents estimates of R_eff in these countries drawn from a forthcoming paper by Binny et al. I added South Korea and Singapore as interesting edge cases – South Korea because they brought the number of new cases down to low numbers in March, and Singapore as its virus control performance has recently deteriorated.

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