Letter to Editor

Abstract

This article evaluates the effectiveness of the 'hard' policy implemented by the Governments (labelled mrNBI by Authors) of eight Countries during the first wave of the COVID contagion spread and concludes that these policies had a negligible effect if compared to other, less restrictive ones (labelled lrNPI by Authors).A series of mistakes and imprecisions in the methodology cannot be ignored. I am going to illustrate them.

Authors Response to Letters to the editor regarding: ‘Assessing mandatory stay‐ At‐ Home and business closure effects on the spread of COVID‐ 19’

“…Thus, each regression model includes two countries-one with and one without more restrictive measures-each represented by the 'c' index that Chini found objectionable. 3 We do not pass a strong verdict on the role of parallel trends assumptions for causal identification here, but note that if it were indeed critical, that would invalidate most assessments of NPI effects that use similar econometric approaches, since the baseline trends are unique and highly nonlinear in each subnational unit. 6 We note that, by mistake, we cumulated the case counts for the Netherlands twice.…”

“…The variable construction is equivalent to interacting the policy indicator with a 'post' time variable for each policy. 3 We implement panel regression models where the coefficients on the Policy pcit variables identify 'breaks' in case growth patterns in each subnational unit following the implementation of each NPI identified by the specific Policy pcit variables, rather than a difference-in-difference as suggested by Chini. 3 We analyse every pair of countries with more and less restrictive measures.…”

“…3 We implement panel regression models where the coefficients on the Policy pcit variables identify 'breaks' in case growth patterns in each subnational unit following the implementation of each NPI identified by the specific Policy pcit variables, rather than a difference-in-difference as suggested by Chini. 3 We analyse every pair of countries with more and less restrictive measures. For each regression, we include a full set of district-level binary dummy variables (θ 0,ci ) for both countries in each model.…”

Challenges in determining causality: An ongoing critique of Bendavid et al’s ‘Assessing mandatory stay‐at‐home and business closure effects on the spread of COVID‐19’

“…Surprisingly, then they write that they 'implement panel regression model where coefficient on Policy {pcit} variables identify "breaks" [the quotes are by authors] in case growth patterns in each sub-national unit following the implementation of each NPI identified by specific Policy {pcit} variables rather than a difference-indifference as suggested by Zanetti Chini'. 4 The so-called 'breaks' (defined as 'structural breaks' in econometric literature to distinguish a break that produces perduring effects in the path of the time series under investigation from other ones that can be explained by cyclical oscillations or pure noise) cannot be identified by the coefficient of Policy {pcit} . This is a discrete-choice model for panel data, not a model for structural breaks.…”

“…Given the subject matter impacts on lives across the globe, we are pleased to have the opportunity to continue this worthwhile discussion. While the authors have written a response 2 to our initial concerns, [3][4][5] we feel that it falls short in a number of key ways, and thus, the paper still does not propose a useful assessment of the efficacy of Non-Pharmaceutical Interventions (NPIs) against COVID-19.…”