Perspectives from Africa on policy responses to Covid-19

There is a very interesting set of interviews by Folashadé Soulé and Camilla Toulmin giving perspectives from Africa on the policy responses to the Covid-19 pandemic. Two interviews are highlighted in this post that bring out issues that merit much more policy attention from governments who are currently narrowly focused on Covid-19 numbers - the deep debt distress and the disproportionate negative impact on women have long term implications for the resilience of societies and economies. 

Pr Benno Ndulu, the former Governor of the Bank of Tanzania, flags the severe debt distress that the Covid-19 pandemic is accentuating in many African countries - "There has been a huge change in the composition of African debt: it’s shorter in maturity, and commercial to a large extent which means reputational risk is a much bigger issue than it has been in the past. That’s where the rating agencies come in. Unfortunately, rating agencies have tended to act in procyclical way: when things are bad, that’s when they pronounce you to be in really bad shape. African countries now have also to deal with a creditor coordination problem. It used to be the Paris Club and everything was settled there, but now there are new donors like China and the real question is how does one get coordination among all the creditors to deal with this issue."

He also raises the need for countries to get digitally ready and inclusively, pointing to the increasing role of technology in improving productivity and formalisation as well as globalisation through tele-migration taking "jobs to where people are rather than having to force migration of people from the rural areas into the cities in order to be able to offer their services."

Takyiwaa Manuh, Emerita Professor of African Studies at the University of Ghana, brings out the impact on women: "We’ve been very concerned about the gendered impacts of COVID-19. Government statements hardly mention gender, but mainly focus on the economy. It is expected for instance that GDP growth will fall from more than 6% this year to 2% and this will affect tax revenues, production, trade and commodity prices. Government speaks of these things as if there are no people who lie behind the changes, but just disembodied activities. But when you begin to disaggregate, say the tourism sector, which has been badly affected, you find the hotel industry, food chains, restaurants and catering establishments are run mainly by women, so there’s a disproportionate impact."..."even when there is a focus on gender it tends to be on the economic aspects, and not on the human rights and bodily integrity of women, which are essentially considered as private matters."  

Responsible modelling in times of uncertainty

Axel Leijonhufvud's piece on economists, "Life Among the Econ" did a brilliant takedown of what he termed "modls" as in "recognized status has come increasingly to be tied to cleverness in modl-making." Raising model-making to such high status has created considerable scepticism towards the discipline. There is now a new brilliant paper by 22 researchers - a manifesto with five principles to ensure that mathematical models best serve society (June 2020 Nature 582(7813):482-484).

1. Mind the assumptions: The results of models are only as good as their assumptions, which are often not adequately assessed. Solutions from one model therefore do not fit alternative situations. The recommendation is to undertake global uncertainty and sensitivity analyses - that is, allowing all that is uncertain — variables, mathematical relationships and boundary conditions — to vary simultaneously as runs of the model produce its range of predictions. Many a times the uncertainty in predictions is substantially larger than originally asserted, there should be transparency about the analysis to provide real value and applicability.

2. Mind the hubris: Researchers must resist the attraction of adding complexity to a model without reason. The authors note the lack of accountability leads to such hubris: "Whereas an engineer is called to task if a bridge falls, other models tend to be developed with large teams and use such complex feedback loops that no one can be held accountable if the predictions are catastrophically wrong."

3. Mind the framing: This is a critical principle that many forget to factor into their work. The example given of cost-benefit analysis is a common one we run up against when projects are being evaluated.
"Modellers know that the choice of tools will influence, and could even determine, the outcome of the analysis, so the technique is never neutral. For example, the GENESIS model of shoreline erosion was used by the US Army Corps of Engineers to support cost–benefit assessments for beach preservation projects. The cost–benefit model could not predict realistically the mechanisms of beach erosion by waves or the effectiveness of beach replenishment by human intervention. It could be easily manipulated to boost evidence that certain coastal-engineering projects would be beneficial7 . A fairer assessment would have considered how extreme storm events dominate in erosion processes."
"Modellers should not hide the normative values of their choices."
The authors therefore recommend that there should be international guidelines for a set of social norms that would include how to produce a model, assess its uncertainty and communicate the results.

4. Mind the consequences: The authors take examples from the ongoing Covid-19 crisis to show how quantification can backfire towards giving precisely wrong results and spurious predictions cause significant harm. The recommendation therefore is to avoid opacity, and give full disclosure on the uncertainty - this alone will build trust and utility of modelling.

5. Mind the unknowns: Acknowledging ignorance is no crime, in fact it builds accountability and trust. "Experts should have the courage to respond that “there is no number-answer to your question”, as US government epidemiologist Anthony Fauci did when probed by a politician."

These principles have been set for responsible modeling in all disciplines, but will mean the most towards setting appropriate policies in developing economies.