<p>The Institute for Fiscal Studies has published research by Banks et al. entitled <em><a href="http://doi.org/10.1920/BN.IFS.2020.BN0281" rel="noreferrer">Recessions and health: the long-term health consequences
of responses to the coronavirus</a></em>, which investigates the ways existing literature and models on this topic can be applied to the current COVID-19 pandemic, with a specific focus on the UK. In particular, they attempt to apply the model of the impact of economic shocks on morbidity in Britain estimated in <em><a href="https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3560315" rel="noreferrer">Macroeconomic Conditions and Health in Britain: Aggregation, Dynamics and Local Area Heterogeneity</a></em> by Janke et al. According to the paper, although they note that:</p>

<blockquote>
  <p>The Janke et al. analysis looks at the prevalence of long-standing
  health conditions but does not examine the intensity or the duration
  of the condition within an individual’s life course.</p>
</blockquote>

<p>They state that</p>

<blockquote>
  <p>Quantitatively, Janke et al. estimate that a 1% fall in employment
  leads to a 2% increase in the prevalence of chronic illness.
  [...]
  Only about half this effect will be immediate: the full effect will
  not be felt for two years. The shock to employment from the
  coronavirus pandemic is likely to be much larger than this and so we
  may expect a larger rise in poor health.</p>
</blockquote>

<p>Given that the Office for Budget Responsibility (OBR)'s recent <a href="https://obr.uk/coronavirus-reference-scenario/" rel="noreferrer">coronavirus reference scenario</a> predicts a rise in unemployment from 4% to 10%, applying this rule would result in a 12% increase in the prevalence of chronic illness.</p>

<p>They also look at the conclusions of Van den Berg et al.'s <em><a href="https://www.aeaweb.org/articles?id=10.1257/000282806776157740" rel="noreferrer">Economic conditions early in life and individual mortality</a></em>, which looked at the effect of the state of the economy at birth on one's life expectancy, and concludes that being born in a recession reduces life expectancy by around 5%. They note that this is a result of many indirect effects - for instance:</p>

<blockquote>
  <p><a href="https://www.nber.org/papers/w13987" rel="noreferrer">Currie (2009)</a> documents the extensive evidence on the links between
  parental circumstances and child health, as well as the subsequent
  link between a child’s early-life health and their eventual
  educational and labour market outcomes. It is well documented that
  poor nutrition in early childhood, as well as in utero, will have a
  long-lasting impact on individuals, and there are many other examples
  where vulnerabilities and shocks in early life have long-term
  consequences.</p>
</blockquote>

<p>As well as physical health problems as a direct result of the shutdown measures, they also look at the potential indirect effects on mortality as a result of deteriorating mental health. This section again draws on the Janke model:</p>

<blockquote>
  <p>Estimates drawn from Janke et al. (2020) suggest that if the economic
  downturn were similar to that after the 2008 financial crisis, the
  number of people of working age suffering from poor mental health
  would rise by half a million.</p>
</blockquote>

<p>However, they note that using existing models to predict the effect on mental health is particularly tricky in this case:</p>

<blockquote>
  <p>To add to this, social distancing in itself is likely to have complex
  and nuanced effects on individuals’ social isolation and mental health.</p>
</blockquote>

<p>Another issue they identify that will make the effect of the economy on mortality more difficult to model is the disproportionate effect of the lockdown conditions on specific industries, for example, the tourism &amp; hospitality sectors. Again using the Janke model, they note that:</p>

<blockquote>
  <p>Janke et al. (2020) find heterogeneous morbidity responses to economic
  shocks across local areas. Those areas that are hit hardest are those
  that are the most deprived and have older populations and older
  industrial structures, which are precisely the kind of areas least
  able to withstand negative shocks.</p>
</blockquote>

<p>It is therefore imperative that any accurate model must factor in the specific demographics in the communities which most rely on the specific sectors most at risk from the lockdown conditions.</p>

<p>Finally, on a more positive note, they observe that negative economic shocks have been shown to contribute to a fall in unhealthy habits such as drinking and smoking, and note recent reports, for example in <a href="https://www.nationalgeographic.com/science/2020/04/pollution-made-the-pandemic-worse-but-lockdowns-clean-the-sky/" rel="noreferrer">National Geographic</a>, that the lockdown conditions have contributed significantly to lower pollution levels.</p>

<p>Hopefully, this paper shows the level of detail which would have to be attained by any model with any hope of being remotely accurate, and as a result, proves its current infeasibility. As these lockdown conditions are unprecedented, it is impossible to predict with any certainty how, for example, mental health will be affected by an extended lockdown. </p>

<p>Furthermore, unlike the analysis in the model on direct deaths caused by the virus in your question, there is no data on the long-term economical effects of a lockdown specifically on which to test any such model, which contributes to the level of uncertainty.</p>

<p>Despite these difficulties, it does draw some fairly rudimentary conclusions using past research about how the economic effects of the strict measures on indirect mortality may be modelled, which allows some primitive conclusions to be drawn, such as the 12% increase in chronic illness, and the observation in Van den Berg's paper of the ~5% reduction in lifespan as a result of a recession.</p>

CC BY-SA 4.0
2020-04-15T16:29:35.817Z
2020-04-16T15:59:56.563Z
CDJB
<ul>
<li><p><a href="https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3561934" rel="nofollow noreferrer">"The Benefits and Costs of Using Social Distancing to Flatten the Curve for COVID-19"</a>, Thunstrom et al.</p>

<blockquote>
  <p>We examine the net benefits of social distancing to slow the spread of COVID-19 in the United States. Social distancing saves lives but imposes large costs on society due to reduced economic activity. We use epidemiological and economic forecasting to perform a rapid benefit-cost analysis of controlling the COVID-19 outbreak. Assuming that social distancing measures can substantially reduce contacts among individuals, we find net benefits of about $5.2 trillion in our benchmark case. We examine the magnitude of the critical parameters that might imply negative net benefits, including the value of statistical life and the discount rate. A key unknown factor is the speed of economic recovery with and without social distancing measures in place. A series of robustness checks also highlight the key role of the value of mortality risk reductions and discounting in the analysis and point to a need for effective economic stimulus when the outbreak has passed.</p>
</blockquote></li>
<li><p><a href="https://www.nber.org/papers/w26981" rel="nofollow noreferrer">"A Simple Planning Problem for COVID-19 Lockdown"</a> (NBER) Alvarez, Argente, and Lippi</p>

<blockquote>
  <p>We study the optimal lockdown policy for a planner who wants to control the fatalities of a pandemic while minimizing the output costs of the lockdown. We use the SIR epidemiology model and a linear economy to formalize the planner's dynamic control problem. The optimal policy depends on the fraction of infected and susceptible in the population. We parametrize the model using data on the COVID19 pandemic and the economic breadth of the lockdown. The quantitative analysis identifies the features that shape the intensity and duration of the optimal lockdown policy. Our baseline parametrization is conditional on a 1% of infected agents at the outbreak, no cure for the disease, and the possibility of testing. The optimal policy prescribes a severe lockdown beginning two weeks after the outbreak, covers 60% of the population after a month, and is gradually withdrawn covering 20% of the population after 3 months. The intensity of the lockdown depends on the gradient of the fatality rate as a function of the infected, and on the assumed value of a statistical life. The absence of testing increases the economic costs of the lockdown, and shortens the duration of the optimal lockdown which ends more abruptly. Welfare under the optimal policy with testing is higher, equivalent to a one-time payment of 2% of GDP.</p>
</blockquote></li>
</ul>

<p>Also </p>

<ul>
<li><p>"The Macroeconomics of Epidemics" by Eichenbaum, Rebelo, and Trabandt. <a href="https://www.nber.org/papers/w26882" rel="nofollow noreferrer">Published in NBER</a> in March when it got NYT coverage; there's an "April update" now. The abstract of that paper isn't very revealing (as it only describes one scenario), but they actually consider several scenarios in the paper, with various exogenous constraints like treatments being found effective or not, vaccines being discovered etc. And then they consider a lockdown sequence optimization problem [in each scenario] as a Ramsey problem; see section 5 in the paper... and then some "exit strategies" from each. (Even that can be formulated as an optimization problem, which they call "smart containment" but it requires knowing the immunity status of everyone--which is a bit unrealistic as they admit.) Teaser figure from the paper (I won't try to explain it here):
<a href="https://i.sstatic.net/K6zGd.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/K6zGd.png" alt="enter image description here"></a></p></li>
<li><p><a href="https://www.nber.org/papers/w27047" rel="nofollow noreferrer">"Macroeconomic Dynamics and Reallocation in an Epidemic"</a>, (NBER, also <a href="https://cepr.org/sites/default/files/news/CovidEconomics5.pdf" rel="nofollow noreferrer">in CEPR</a> with somewhat more copyediting) Krueger, Uhlig, Xie</p>

<blockquote>
  <p>In this paper we argue that endogenous shifts in private consumption behavior across sectors of the economy can act as a potent mitigation mechanism during an epidemic or when the economy is re-opened after a temporary lockdown. Extending the theoretical framework proposed by Eichenbaum-Rebelo-Trabandt (2020), we distinguish goods by their degree to which they can be consumed at home rather than in a social (and thus possibly contagious) context. We demonstrate that, within the model the "Swedish solution" of letting the epidemic play out without government intervention and allowing agents to shift their sectoral behavior on their own can lead to a substantial mitigation of the economic and human costs of the COVID-19 crisis, avoiding more than 80 [percent] of the decline in output and of number of deaths within one year, compared to a model in which sectors are assumed to be homogeneous. For different parameter configurations that capture the additional social distancing and hygiene activities individuals might engage in voluntarily, we show that infections may decline entirely on their own, simply due to the individually rational re-allocation of economic activity: the curve not only just flattens, it gets reversed.</p>
</blockquote></li>
<li><p><a href="https://www.nber.org/papers/w27059" rel="nofollow noreferrer">"Internal and External Effects of Social Distancing in a Pandemic"</a> (NBER) Farboodi, Jarosch, Shimer</p>

<blockquote>
  <p>We use a conventional dynamic economic model to integrate individual optimization, equilibrium interactions, and policy analysis into the canonical epidemiological model. Our tractable framework allows us to represent both equilibrium and optimal allocations as a set of differential equations that can jointly be solved with the epidemiological model in a unified fashion. Quantitatively, the laissez-faire equilibrium accounts for the decline in social activity we measure in US micro-data from SafeGraph. Relative to that, we highlight three key features of the optimal policy: it imposes immediate, discontinuous social distancing; it keeps social distancing in place for a long time or until treatment is found; and it is never extremely restrictive, keeping the effective reproduction number mildly above the share of the population susceptible to the disease.</p>
</blockquote></li>
</ul>

<p>Not strictly about Covid-19, but also recent paper from FRB members (and one MIT faculty):</p>

<ul>
<li><p>Correia, Luck and Verner. <a href="https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3561560" rel="nofollow noreferrer">"Pandemics Depress the Economy, Public Health Interventions Do Not: Evidence from the 1918 Flu"</a>, </p>

<blockquote>
  <p>what are the economic costs and benefits of non-pharmaceutical interventions (NPI)? Using geographic variation in mortality during the 1918 Flu Pandemic in the U.S., we find that more exposed areas experience a sharp and persistent decline in economic activity. The estimates imply that the pandemic reduced manufacturing output by 18%. The downturn is driven by both supply and demand-side channels. Further, building on findings from the epidemiology literature establishing that NPIs decrease influenza mortality, we use variation in the timing and intensity of NPIs across U.S. cities to study their economic effects. We find that cities that intervened earlier and more aggressively do not perform worse and, if anything, grow faster after the pandemic is over. Our findings thus indicate that NPIs not only lower mortality; they may also mitigate the adverse economic consequences of a pandemic.</p>
</blockquote></li>
<li><p>A 2nd (recent) paper from a FRB author on the 1918 pandemic, albeit a bit more descriptive: <a href="https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3582671" rel="nofollow noreferrer">"What Happened to the US Economy During the 1918 Influenza Pandemic? A View Through High-Frequency Data"</a> (and the part most relevant to the question here is left in teaser form in the abstract...)</p>

<blockquote>
  <p>Interventions to hinder the contagion were brief (typically a month) and there is some evidence that interventions made a difference for economic outcomes.</p>
</blockquote></li>
</ul>

CC BY-SA 4.0
2020-04-15T17:13:33.077Z
2020-05-08T21:42:21.633Z
2020-05-08T21:42:21.633Z
fantastic peace and prosperity
<p>I have been wondering whether there are statistical models like <a href="https://www.imperial.ac.uk/media/imperial-college/medicine/sph/ide/gida-fellowships/Imperial-College-COVID19-NPI-modelling-16-03-2020.pdf" rel="nofollow noreferrer">this one, (which estimated the deaths caused by the virus itself)</a> that estimate the deaths caused by the economic turndown. There is an estimate of how much the pandemic would cost the <a href="https://www.timesofisrael.com/pandemic-will-cost-israel-nis-45-billion-wipe-out-growth-treasury-says/" rel="nofollow noreferrer">Israeli health system</a>, and just a general <a href="https://www.bbc.co.uk/news/business-51706225" rel="nofollow noreferrer">economic analysis of the impact of the pandemic.</a> I was wondering if there are for example models which show the impact of the strict measures(deaths caused by the economic crisis due to lockdown) and just models which show how much more deaths would occur if there was no strict lockdown (and hence the no economic crisis, or at least not as drastic as after complete nationwide lockdowns) than the economic crisis itself.</p>

<p>I am not particularly interested in any specific country, just examples if such exist.</p>

<p>I'm looking for a model that shows the consequences of lockdowns and its economic impacts. ie how much more/fewer people will die from economic collapse rather than the virus given there was no lockdown (but social distancing and mass testing involved).</p>

CC BY-SA 4.0
2020-04-16T09:05:34.560Z
Re "economic impacts. ie how much more/less people will die". Economists seldom measure "economic impact" only in terms of deaths (although they do attach a dollar value to life sometimes). The "economic impacts" to output/GDP are likely to be much more significant than simply those directly from deaths, because e.g. people don't go to work (either because of government-imposed lockdowns or because they fear the virus, in no-official-lockdown scenarios.)
fantastic peace and prosperity
<reference-request><covid-19-crisis>
Statistical models which represent the impact of strict measures vs no strict measures (and less economic impact), enforced due to Covid-19
user
# Statistical models which represent the impact of strict measures vs no strict measures (and less economic impact), enforced due to Covid-19

(Asked by: username_0 on 2020-04-15)

I have been wondering whether there are statistical models like [this one, (which estimated the deaths caused by the virus itself)](https://www.imperial.ac.uk/media/imperial-college/medicine/sph/ide/gida-fellowships/Imperial-College-COVID19-NPI-modelling-16-03-2020.pdf) that estimate the deaths caused by the economic turndown. There is an estimate of how much the pandemic would cost the [Israeli health system](https://www.timesofisrael.com/pandemic-will-cost-israel-nis-45-billion-wipe-out-growth-treasury-says/), and just a general [economic analysis of the impact of the pandemic.](https://www.bbc.co.uk/news/business-51706225) I was wondering if there are for example models which show the impact of the strict measures(deaths caused by the economic crisis due to lockdown) and just models which show how much more deaths would occur if there was no strict lockdown (and hence the no economic crisis, or at least not as drastic as after complete nationwide lockdowns) than the economic crisis itself.

I am not particularly interested in any specific country, just examples if such exist.

I'm looking for a model that shows the consequences of lockdowns and its economic impacts. ie how much more/fewer people will die from economic collapse rather than the virus given there was no lockdown (but social distancing and mass testing involved).

**Tags:** reference-request, covid-19-crisis
> **Comments:**
> **username_1**: Re "economic impacts. ie how much more/less people will die". Economists seldom measure "economic impact" only in terms of deaths (although they do attach a dollar value to life sometimes). The "economic impacts" to output/GDP are likely to be much more significant than simply those directly from deaths, because e.g. people don't go to work (either because of government-imposed lockdowns or because they fear the virus, in no-official-lockdown scenarios.)  

---

## 2 answers

### Answer by username_1 on 2020-04-15. Score: 11

* ["The Benefits and Costs of Using Social Distancing to Flatten the Curve for COVID-19"](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3561934), Thunstrom et al.

  > We examine the net benefits of social distancing to slow the spread of COVID-19 in the United States. Social distancing saves lives but imposes large costs on society due to reduced economic activity. We use epidemiological and economic forecasting to perform a rapid benefit-cost analysis of controlling the COVID-19 outbreak. Assuming that social distancing measures can substantially reduce contacts among individuals, we find net benefits of about $5.2 trillion in our benchmark case. We examine the magnitude of the critical parameters that might imply negative net benefits, including the value of statistical life and the discount rate. A key unknown factor is the speed of economic recovery with and without social distancing measures in place. A series of robustness checks also highlight the key role of the value of mortality risk reductions and discounting in the analysis and point to a need for effective economic stimulus when the outbreak has passed.
* ["A Simple Planning Problem for COVID-19 Lockdown"](https://www.nber.org/papers/w26981) (NBER) Alvarez, Argente, and Lippi

  > We study the optimal lockdown policy for a planner who wants to control the fatalities of a pandemic while minimizing the output costs of the lockdown. We use the SIR epidemiology model and a linear economy to formalize the planner's dynamic control problem. The optimal policy depends on the fraction of infected and susceptible in the population. We parametrize the model using data on the COVID19 pandemic and the economic breadth of the lockdown. The quantitative analysis identifies the features that shape the intensity and duration of the optimal lockdown policy. Our baseline parametrization is conditional on a 1% of infected agents at the outbreak, no cure for the disease, and the possibility of testing. The optimal policy prescribes a severe lockdown beginning two weeks after the outbreak, covers 60% of the population after a month, and is gradually withdrawn covering 20% of the population after 3 months. The intensity of the lockdown depends on the gradient of the fatality rate as a function of the infected, and on the assumed value of a statistical life. The absence of testing increases the economic costs of the lockdown, and shortens the duration of the optimal lockdown which ends more abruptly. Welfare under the optimal policy with testing is higher, equivalent to a one-time payment of 2% of GDP.

Also

* "The Macroeconomics of Epidemics" by Eichenbaum, Rebelo, and Trabandt. [Published in NBER](https://www.nber.org/papers/w26882) in March when it got NYT coverage; there's an "April update" now. The abstract of that paper isn't very revealing (as it only describes one scenario), but they actually consider several scenarios in the paper, with various exogenous constraints like treatments being found effective or not, vaccines being discovered etc. And then they consider a lockdown sequence optimization problem [in each scenario] as a Ramsey problem; see section 5 in the paper... and then some "exit strategies" from each. (Even that can be formulated as an optimization problem, which they call "smart containment" but it requires knowing the immunity status of everyone--which is a bit unrealistic as they admit.) Teaser figure from the paper (I won't try to explain it here):
  [![enter image description here](https://i.sstatic.net/K6zGd.png)](https://i.sstatic.net/K6zGd.png)
* ["Macroeconomic Dynamics and Reallocation in an Epidemic"](https://www.nber.org/papers/w27047), (NBER, also [in CEPR](https://cepr.org/sites/default/files/news/CovidEconomics5.pdf) with somewhat more copyediting) Krueger, Uhlig, Xie

  > In this paper we argue that endogenous shifts in private consumption behavior across sectors of the economy can act as a potent mitigation mechanism during an epidemic or when the economy is re-opened after a temporary lockdown. Extending the theoretical framework proposed by Eichenbaum-Rebelo-Trabandt (2020), we distinguish goods by their degree to which they can be consumed at home rather than in a social (and thus possibly contagious) context. We demonstrate that, within the model the "Swedish solution" of letting the epidemic play out without government intervention and allowing agents to shift their sectoral behavior on their own can lead to a substantial mitigation of the economic and human costs of the COVID-19 crisis, avoiding more than 80 [percent] of the decline in output and of number of deaths within one year, compared to a model in which sectors are assumed to be homogeneous. For different parameter configurations that capture the additional social distancing and hygiene activities individuals might engage in voluntarily, we show that infections may decline entirely on their own, simply due to the individually rational re-allocation of economic activity: the curve not only just flattens, it gets reversed.
* ["Internal and External Effects of Social Distancing in a Pandemic"](https://www.nber.org/papers/w27059) (NBER) Farboodi, Jarosch, Shimer

  > We use a conventional dynamic economic model to integrate individual optimization, equilibrium interactions, and policy analysis into the canonical epidemiological model. Our tractable framework allows us to represent both equilibrium and optimal allocations as a set of differential equations that can jointly be solved with the epidemiological model in a unified fashion. Quantitatively, the laissez-faire equilibrium accounts for the decline in social activity we measure in US micro-data from SafeGraph. Relative to that, we highlight three key features of the optimal policy: it imposes immediate, discontinuous social distancing; it keeps social distancing in place for a long time or until treatment is found; and it is never extremely restrictive, keeping the effective reproduction number mildly above the share of the population susceptible to the disease.

Not strictly about Covid-19, but also recent paper from FRB members (and one MIT faculty):

* Correia, Luck and Verner. ["Pandemics Depress the Economy, Public Health Interventions Do Not: Evidence from the 1918 Flu"](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3561560),

  > what are the economic costs and benefits of non-pharmaceutical interventions (NPI)? Using geographic variation in mortality during the 1918 Flu Pandemic in the U.S., we find that more exposed areas experience a sharp and persistent decline in economic activity. The estimates imply that the pandemic reduced manufacturing output by 18%. The downturn is driven by both supply and demand-side channels. Further, building on findings from the epidemiology literature establishing that NPIs decrease influenza mortality, we use variation in the timing and intensity of NPIs across U.S. cities to study their economic effects. We find that cities that intervened earlier and more aggressively do not perform worse and, if anything, grow faster after the pandemic is over. Our findings thus indicate that NPIs not only lower mortality; they may also mitigate the adverse economic consequences of a pandemic.
* A 2nd (recent) paper from a FRB author on the 1918 pandemic, albeit a bit more descriptive: ["What Happened to the US Economy During the 1918 Influenza Pandemic? A View Through High-Frequency Data"](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3582671) (and the part most relevant to the question here is left in teaser form in the abstract...)

  > Interventions to hinder the contagion were brief (typically a month) and there is some evidence that interventions made a difference for economic outcomes.


---

### Answer by username_2 on 2020-04-15. Score: 7

The Institute for Fiscal Studies has published research by Banks et al. entitled *[Recessions and health: the long-term health consequences
of responses to the coronavirus](http://doi.org/10.1920/BN.IFS.2020.BN0281)*, which investigates the ways existing literature and models on this topic can be applied to the current COVID-19 pandemic, with a specific focus on the UK. In particular, they attempt to apply the model of the impact of economic shocks on morbidity in Britain estimated in *[Macroeconomic Conditions and Health in Britain: Aggregation, Dynamics and Local Area Heterogeneity](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3560315)* by Janke et al. According to the paper, although they note that:

> The Janke et al. analysis looks at the prevalence of long-standing
> health conditions but does not examine the intensity or the duration
> of the condition within an individual’s life course.

They state that

> Quantitatively, Janke et al. estimate that a 1% fall in employment
> leads to a 2% increase in the prevalence of chronic illness.
> [...]
> Only about half this effect will be immediate: the full effect will
> not be felt for two years. The shock to employment from the
> coronavirus pandemic is likely to be much larger than this and so we
> may expect a larger rise in poor health.

Given that the Office for Budget Responsibility (OBR)'s recent [coronavirus reference scenario](https://obr.uk/coronavirus-reference-scenario/) predicts a rise in unemployment from 4% to 10%, applying this rule would result in a 12% increase in the prevalence of chronic illness.

They also look at the conclusions of Van den Berg et al.'s *[Economic conditions early in life and individual mortality](https://www.aeaweb.org/articles?id=10.1257/000282806776157740)*, which looked at the effect of the state of the economy at birth on one's life expectancy, and concludes that being born in a recession reduces life expectancy by around 5%. They note that this is a result of many indirect effects - for instance:

> [Currie (2009)](https://www.nber.org/papers/w13987) documents the extensive evidence on the links between
> parental circumstances and child health, as well as the subsequent
> link between a child’s early-life health and their eventual
> educational and labour market outcomes. It is well documented that
> poor nutrition in early childhood, as well as in utero, will have a
> long-lasting impact on individuals, and there are many other examples
> where vulnerabilities and shocks in early life have long-term
> consequences.

As well as physical health problems as a direct result of the shutdown measures, they also look at the potential indirect effects on mortality as a result of deteriorating mental health. This section again draws on the Janke model:

> Estimates drawn from Janke et al. (2020) suggest that if the economic
> downturn were similar to that after the 2008 financial crisis, the
> number of people of working age suffering from poor mental health
> would rise by half a million.

However, they note that using existing models to predict the effect on mental health is particularly tricky in this case:

> To add to this, social distancing in itself is likely to have complex
> and nuanced effects on individuals’ social isolation and mental health.

Another issue they identify that will make the effect of the economy on mortality more difficult to model is the disproportionate effect of the lockdown conditions on specific industries, for example, the tourism & hospitality sectors. Again using the Janke model, they note that:

> Janke et al. (2020) find heterogeneous morbidity responses to economic
> shocks across local areas. Those areas that are hit hardest are those
> that are the most deprived and have older populations and older
> industrial structures, which are precisely the kind of areas least
> able to withstand negative shocks.

It is therefore imperative that any accurate model must factor in the specific demographics in the communities which most rely on the specific sectors most at risk from the lockdown conditions.

Finally, on a more positive note, they observe that negative economic shocks have been shown to contribute to a fall in unhealthy habits such as drinking and smoking, and note recent reports, for example in [National Geographic](https://www.nationalgeographic.com/science/2020/04/pollution-made-the-pandemic-worse-but-lockdowns-clean-the-sky/), that the lockdown conditions have contributed significantly to lower pollution levels.

Hopefully, this paper shows the level of detail which would have to be attained by any model with any hope of being remotely accurate, and as a result, proves its current infeasibility. As these lockdown conditions are unprecedented, it is impossible to predict with any certainty how, for example, mental health will be affected by an extended lockdown.

Furthermore, unlike the analysis in the model on direct deaths caused by the virus in your question, there is no data on the long-term economical effects of a lockdown specifically on which to test any such model, which contributes to the level of uncertainty.

Despite these difficulties, it does draw some fairly rudimentary conclusions using past research about how the economic effects of the strict measures on indirect mortality may be modelled, which allows some primitive conclusions to be drawn, such as the 12% increase in chronic illness, and the observation in Van den Berg's paper of the ~5% reduction in lifespan as a result of a recession.


---

