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Pandemics through the lens of occupations

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Anand Chopra discusses a macro-pandemic model where individuals can select into working from home or in the market.

Image courtesy of interviewee. May 29, 2024

Generating Workspace

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This video discusses a research initiative stemming from early pandemic discussions among members of the Vancouver School of Economics, public health officials, and the British Columbia Center for Disease Control. The research aims to understand the impact of individual actions, particularly regarding remote work, on the spread of COVID-19 and its economic implications.

Key points include:
1. **Differences in Occupation Risk**: The research emphasizes significant differences in the ability to work remotely and the risk of infection across various occupations. Health professionals face much higher exposure to infection compared to those in professions like investment banking.

2. **Macroeconomic Models**: The project incorporates heterogeneity in work-from-home capabilities and infection risks into macroeconomic models, blending them with SIR models to analyze the pandemic's dynamics.

3. **Impact of Work-from-Home Decisions**: The findings indicate that allowing individuals to work from home can lower infection rates but can also lead to increased output losses.

4. **Heterogeneity and Income Effects**: The research highlights the importance of understanding the occupational differences in the context of pandemic policies, which has implications for income distribution and consumption patterns.

5. **Data and Methodology**: The study used data from the Canadian census and collaborations with other research efforts to analyze earnings gaps and infection risks among occupations.

6. **Conclusions and Future Research**: The results emphasize that even without technological changes, the structure of work from home has substantial macroeconomic effects. It suggests future directions for research to explore long-term implications of remote work and its impact on the economy, housing prices, and production processes.

Overall, the research contributes to understanding how personal and occupational factors regarding remote work influence macroeconomic outcomes and public health during the pandemic.

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X-TIMESTAMP-MAP:LOCAL=00:00:00.000,MPEGTS=900000

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The starting part of the project that, you know,

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led from the initial discussions between some of the members

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of the Vancouver School of Economics

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where the PhD student at that time

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and the British Columbia Center for Disease and Control

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and other public health officials during the early onset

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of the pandemic during the initial days

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of the pandemic policy because were trying

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to understand the effect of individual actions on the spread

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of the infection and also the macroeconomy.

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But a key point that kept coming up basically was

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how there were differences in the ease to go remote work

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for some individuals and also

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how there was differences in the likelihood

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of some individuals catching

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or spreading the infection at their workplace.

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So for example, the exposure of health professionals

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to infection at their workplace is almost three

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to four times higher say than

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for professionals working in your investment banking

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or say national and applied sciences at the same time.

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Um, it's also, you know, there was a study conducted

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by professors Jose Maria Barro, Nicholas Bloom

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and Steven Ds in the us which also said

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that individuals working in those professions almost spent

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two and a half days of the week working from home compared

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to, you know, say professionals in the industry

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or manufacturing retail

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who only ever ended up spending less than a day working

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in these occupations.

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So clearly there were differences in both the ease

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to work in certain occupations

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and also the likelihood

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of catching an infection in certain occupations basically.

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And so this is where our project came in in order to kind

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of take this heterogeneity much more seriously into account

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of the ease of remote work

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and the transmission of risk internal understand the effects

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of work from home in a macro pandemic model.

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So there was an explosion of research, you know,

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at the start of the pandemic analyzing the link

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between the ecological models and the macro economy.

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But let me highlight a few key papers

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and I think are the closest to us.

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So first, looking at the effect

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of the work from home on the macro economy,

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I think our work is closest

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and compliments those of Martin Sergio Rebello

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and not Vermont title, the macroeconomics of pandemics.

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So they analyze in their paper the interaction also

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between the economic decisions

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and the spread of the epidemic using the general

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equian macro model.

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So we also employ a macroeconomic genal model,

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but relative to theirs, our kind

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of additional key contribution is to inherit

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and explore this heterogeneity in the occupational structure

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and ease of working and the infection risk.

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And I'll talk about more

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of the results in some in a few minutes.

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But this heterogeneity as I'll show

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and discusses the key to understanding not only kind

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of the macroeconomic effects

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but also the kind of the distributional consequences,

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that varied along the income

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and the occupational temperature.

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And second, there's also a large empirical literature.

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Uh, at that time we tried to understand the effect

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of voluntary behavior through the work from home

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or social distancing again on economic

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and activity and the pandemic.

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But, and so these people generally rely on exploiting the

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variation, the timing or the intensity of the lockdowns

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to understand kind of, you know, the effect of basically

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personal decisions in the reduction

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of mobility vis-a-vis state home orders by the government.

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So papers by Chen and both authors or by Zi

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and Silverson typically found modest to large role

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of individual behavior relative

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to basically state home orders in affecting kind

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of mobility decisions.

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But coming back to our conversation with policy makers

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and academics, this kind

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of two-way interaction which is relevant

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for understanding both the pandemic

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and the economy was kind

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of missing for some of these papers.

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And this is where we try to understand of how this would,

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you know, clear work and also from like a more broader kind

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of long run perspective.

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Also trying to create a framework to just think

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of basically work from home

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and the heterogeneity in the occupational structure,

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which is I would, you know, come back

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and back is also relevant now as well.

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In our paper we formalize the macro CHRO model

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and blended with the standard kinda susceptible infectious

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recovery of the SIR model for pandemics basically.

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And relative to MA standard macroeconomic models,

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our structure has two main innovations.

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So first we allow individuals to choose whether

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to work from home or work at the marketplace

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or at the workplace in any given occupation.

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From an individual's point of view,

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this choice depends on two things in our framework.

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First,

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income working from home vis-a-vis the

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workplace could be different.

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Uh, and second, basically, the risk of getting infected.

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The workplace is much higher.

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So if you get infected then you would lose some income,

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even if you recovered

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but with some problem you might not recover.

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And so infectious individual phase is then the straight off

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of working from home with potentially lower income,

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but also protecting themselves from infection.

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Um, by working from the second innovation,

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in our broad framework is we conceptualize aggregate output

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as been the outcome

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of labor effort across different occupation basically.

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And each occupation there's a gap from working from home

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and also differences in the likelihood of getting infected.

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The SIR structure, the susceptible infectious

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and recovered structure, the model is fairly standard

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but with one key point in difference.

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So as a name suggests, an individual

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in this structure could be healthy

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or susceptible to the infection.

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Um, with some property they might get infected basically,

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and if they do it's you know,

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with a certain property they can recover

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or with some poverty it might eventually lead to that.

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The key disti distinction in our model is

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to allow basically the interaction,

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economic economic decisions of the individuals

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and infection risk to suscept to individuals in the model

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who get infected through either interactions,

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with other individuals

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through say meeting a neighbor or meeting a friend.

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They could also get infected

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with interactions at the workplace basically

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with other coworkers

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or they could get infection

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through also doing consumption activities, for example,

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going shopping, or retail trade basically.

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And so we allow basically three different types of ways

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to get infected, basically potentially the model

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and how you, these are connected in healthy

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with also economic decisions.

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So for example, in this model then if you want

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to choose from basically work from home,

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basically this might lead to, you know, having a lower risk

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of infection but also lower consumption, which would imply

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that in the macro economy then there's a lower demand

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for goods and services and also workers basically.

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And so this could lead to a recession.

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And moreover, basically we also allow

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for the possibility that individuals at the workplace have

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differences in how likely they would get infected.

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The setting in which we tried

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to quantitatively understand the importance

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of this two-way interaction was basically British Columbia.

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So first, as mentioned

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before, like kind of our project started

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with the broader initiative at the My course School

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of Economics to help basically kind

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of the British Columbia Center for Disease Control

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as well in 2020 to understand the economic

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effect of the pandemic.

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So this was kind of funny, you know, a first reason

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to focus on British Columbia.

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The second key reason also

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to focus on the British Columbia labor force was the fact

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that, you know, again, there were individuals in the British

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Columbia economy who were less likely

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to be employed in management occupations than say,

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compared to Canada broadly basically.

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Now, again, in these occupations, again,

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there's differences basically in how likelihood,

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in terms of ease of working

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and also getting infected basically.

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So in order to conduct our quantitative exercise,

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then we need data on two components basically.

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One is the earnings gap between working from home

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and the workplace, and the second being the, likelihood

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of getting infected across these detailed

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occupation categories.

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Basically. So to get data on wages

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and the proportional workers who work from home,

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we use the Canadian sensor for 2016.

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Um, and we use kind of

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for fatigue occupation categories classified using the

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national occupational classification in order to map

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how basically the differences in work from home

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and working from market.

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We make an assumption that this is proportional to the ease

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of working from home, which is measured by the fracture

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of individuals, doing remote work in the data.

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Second, to get more,

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information on the transmission risk

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by occupations we rely on.

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The other project was also happening in the Vancouver school

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economic involving professors, David Green,

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Nicole Fota and and others.

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So the normality of the measure they construct

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of transmission risk is

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that it uses both job characteristics.

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So for example, how like likelihood you have in order

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to contact infections, from meeting your coworkers,

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the physical proximity maybe or talking to your customers,

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but also worker characteristics like for example,

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individuals in those occupations, how likely they are

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to use public transport

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or you know, be living with a health worker

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or living in smaller occupations.

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And so again, coming back to kind of, you know,

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key thing was the public health experts always stress was

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that in the order to understand the transmission risk,

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one needed to account for both the job characteristics

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but also worker characteristics to understand

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how they would spread it in the economy basically.

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And so this measure basically was more novel in the sense

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of accounting for both of these.

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So even though our model explicitly does not have

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differences in family structure

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and workplace structures, the,

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I think using this particular method basically

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of infection risk allows us to take into some

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of those considerations into account.

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The model showed that the decision

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to work from home plays an important role in both the

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infection and the macroeconomic dynamics.

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So individual decision to work at home

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actually substantially reduces basic economy's peak

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infection rate, but also increases output losses compared

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to the economy where this decision is not allowed.

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So the endogenous choice to self isolate

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by working from home could actually reduce weekly infection

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data, it speed by two percentage points relative

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to when this option is not allowed basically.

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And at the same time it can also lead

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to almost a four percentage larger fallen

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consumption in output.

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Uh, during the economy these numbers are quite large,

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basically was in the baseline.

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The peak infection rate is only 7%.

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So these numbers of being 2% point different

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is actually quite large in symptoms.

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Now the key driver for this result is the degree

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of risk aversion that individuals have basically.

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So the more the individuals are concerned about the risk

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of infection, the stronger is the motive to work from home

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and the stronger is the reduction buying pair of infections.

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In fact, this peak differences

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between the peak infection at a working

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of home when this option is allowed

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or not completely go away in an economy

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where individuals are completely risk neutral

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to infection risk, also consistent with the prediction.

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The modern most importantly is a survey by bru Horton

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and co-authors who found that, in US states basically

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where participants chose

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to work from home more in those states.

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They also found lower cases of COVID-19, again, kind

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of a prediction of the model, which is kind

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of seen in the data as well.

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The second important finding is the heterogeneity,

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as we talked about in infection risk occur occupations even

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though it does not so much matter for the aggregate results,

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but has strong distribution implications

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that is differences in terms of

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who works from home infection rates

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and spending patterns by occupations.

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So the model shows that consistent intuition, individuals

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who switch to working from home are,

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when you know the pandemic hits are,

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are those whose occupation it's easiest to do.

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So support those in management or professional occupations.

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These are also the occupations in the Canadian context

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that experienced the lowest employment losses

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during the initial phase of the pandemic.

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Furthermore, professor Racha in co-author showed

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that in the US context at least, spending recoveries,

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were faster in

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for those individuals in the lower income portal than

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the high income portal.

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One way to kind of rationalize this in the context

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of this model is that one good thing

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that individuals in this lower income group are also those

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individuals who have lower ability

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to work from home basically.

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And so they go to the workplace basically,

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and this leads to basically faster growth in their

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consumption patterns as well.

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And so this kind of heterogeneity in the occupational

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strategy is kind of understanding in differences in

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consumption, spending patterns as well.

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During the pandemic though, do like to point out,

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one caveat though is that this model is much better suited

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to understand the initial view of the pandemic, basically

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where individuals had to make decision about the place

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of work without kind of much knowledge about the

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how the pandemic would work and the effect of vaccine

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and lockdown policies by the governments.

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The main takeaway I would like, you know, for others

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to have our paper is kind of highlighting the importance

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of work from home and the heterogeneity

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of working from home across occupations for the macro

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and consumption inequality.

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So even without changing the nature of technology

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work from home, we shown this model can have large macron

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economic effects even in the short term.

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And so this leads to kind

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of the more obvious question I think for future research,

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which is to think about the policy implications,

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also from analyzing work from home effects in

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the long run, basically.

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So when actually when you allow basically work from home

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to change and how can that impact basically, you know,

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in terms of affecting both the work structure basically

13:21.345 --> 13:23.605
for individuals and also basically

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how can affect say housing price and acts.

13:26.025 --> 13:29.325
For example, the recent work by professors Morris Davis

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and GaN and Jesse gr showed

13:31.485 --> 13:35.205
that when work from home complement office work can lead

13:35.205 --> 13:36.565
to large gains in income,

13:36.625 --> 13:38.965
but increases in residential housing prices.

13:39.545 --> 13:41.405
On the other hand, Peter said, I can go also show

13:41.405 --> 13:43.725
that the surge in work from home opportunities can almost

13:43.725 --> 13:46.845
explain one third of the increase in firm entry post

13:47.165 --> 13:51.005
pandemic, but crucially, recent work by poor

13:51.725 --> 13:53.725
Nicholas Bloom and Steven Davis also show

13:53.995 --> 13:57.165
that work from home does not always is complimentary

13:57.165 --> 13:59.605
to office work in some cases can reduce individual

13:59.605 --> 14:03.445
productive data is also negative for young workers in terms

14:03.445 --> 14:04.485
of networking aspect.

14:04.625 --> 14:07.005
So I think it leads to kind of an obvious question.

14:07.225 --> 14:09.405
We just trying to think of kind of, you know, take

14:09.405 --> 14:10.925
of this idea of work from home

14:10.925 --> 14:13.405
and office work complementary much more seriously

14:13.665 --> 14:15.565
and understand its macroeconomic effects.

14:16.485 --> 14:18.505
And finally, let's try to point out one more thing

14:18.505 --> 14:20.465
that I put, I think also be kind of, you know, very useful,

14:20.925 --> 14:24.105
to think about basically is to understand kind

14:24.105 --> 14:26.145
of the nature of the production process basically,

14:26.445 --> 14:28.785
and the relationship between say upstream

14:28.785 --> 14:29.825
and downstream forms.

14:29.825 --> 14:32.905
Basically when thinking about work from home, basically.

14:32.905 --> 14:36.425
So has work from home changed basically how firms,

14:37.415 --> 14:39.665
with their kind of, you know, other people, firms

14:39.845 --> 14:42.025
who they kind of, you know, take more input from

14:42.285 --> 14:43.505
or give other input inputs.

14:43.505 --> 14:44.785
Has this changed basically,

14:45.125 --> 14:47.825
or basically the centrality of a firm, for example,

14:48.325 --> 14:50.785
if one firm is very important in the whole production

14:50.785 --> 14:52.925
Process, if they change to have workers

14:53.425 --> 14:54.485
who do work from home

14:54.585 --> 14:57.085
or don't, how much can that impact the,

14:57.625 --> 15:00.245
so I think these are kind of few areas of, I think,

15:00.285 --> 15:03.525
fruitful, economic research, that, you know,

15:03.525 --> 15:04.645
I think would be very exciting to see.

00:00 - The starting part of the project that, you know, led from the initial discussions between some of the members of the Vancouver School of Economics where the PhD student at that time and the British Columbia Center for Disease and Control and other public health officials during the early onset

00:14 - of the pandemic during the initial days of the pandemic policy because were trying to understand the effect of individual actions on the spread of the infection and also the macroeconomy. But a key point that kept coming up basically was how there were differences in the ease to go remote work for some individuals and also how there was differences in the likelihood of some individuals catching or spreading the infection at their workplace.

00:38 - So for example, the exposure of health professionals to infection at their workplace is almost three to four times higher say than for professionals working in your investment banking or say national and applied sciences at the same time. it's also, you know, there was a study conducted by professors Jose Maria Barro, Nicholas Bloom and Steven Ds in the us which also said that individuals working in those professions almost spent two and a half days of the week working from home compared

01:07 - to, you know, say professionals in the industry or manufacturing retail who only ever ended up spending less than a day working in these occupations. So clearly there were differences in both the ease to work in certain occupations and also the likelihood of catching an infection in certain occupations basically. And so this is where our project came in in order to kind of take this heterogeneity much more seriously into account

01:29 - of the ease of remote work and the transmission of risk internal understand the effects of work from home in a macro pandemic model. So there was an explosion of research, you know, at the start of the pandemic analyzing the link between the ecological models and the macro economy. But let me highlight a few key papers and I think are the closest to us. So first, looking at the effect of the work from home on the macro economy,

01:53 - I think our work is closest and compliments those of Martin Sergio Rebello and not Vermont title, the macroeconomics of pandemics. So they analyze in their paper the interaction also between the economic decisions and the spread of the epidemic using the general equian macro model. So we also employ a macroeconomic genal model, but relative to theirs, our kind of additional key contribution is to inherit

02:18 - and explore this heterogeneity in the occupational structure and ease of working and the infection risk. And I'll talk about more of the results in some in a few minutes. But this heterogeneity as I'll show and discusses the key to understanding not only kind of the macroeconomic effects but also the kind of the distributional consequences, that varied along the income and the occupational temperature.

02:39 - And second, there's also a large empirical literature. at that time we tried to understand the effect of voluntary behavior through the work from home or social distancing again on economic and activity and the pandemic. But, and so these people generally rely on exploiting the variation, the timing or the intensity of the lockdowns to understand kind of, you know, the effect of basically personal decisions in the reduction of mobility vis-a-vis state home orders by the government.

03:07 - So papers by Chen and both authors or by Zi and Silverson typically found modest to large role of individual behavior relative to basically state home orders in affecting kind of mobility decisions. But coming back to our conversation with policy makers and academics, this kind of two-way interaction which is relevant for understanding both the pandemic and the economy was kind

03:32 - of missing for some of these papers. And this is where we try to understand of how this would, you know, clear work and also from like a more broader kind of long run perspective. Also trying to create a framework to just think of basically work from home and the heterogeneity in the occupational structure, which is I would, you know, come back and back is also relevant now as well. In our paper we formalize the macro CHRO model

03:54 - and blended with the standard kinda susceptible infectious recovery of the SIR model for pandemics basically. And relative to MA standard macroeconomic models, our structure has two main innovations. So first we allow individuals to choose whether to work from home or work at the marketplace or at the workplace in any given occupation. From an individual's point of view, this choice depends on two things in our framework. First,

04:22 - income working from home vis-a-vis the workplace could be different. and second, basically, the risk of getting infected. The workplace is much higher. So if you get infected then you would lose some income, even if you recovered but with some problem you might not recover. And so infectious individual phase is then the straight off of working from home with potentially lower income, but also protecting themselves from infection.

04:47 - by working from the second innovation, in our broad framework is we conceptualize aggregate output as been the outcome of labor effort across different occupation basically. And each occupation there's a gap from working from home and also differences in the likelihood of getting infected. The SIR structure, the susceptible infectious and recovered structure, the model is fairly standard but with one key point in difference. So as a name suggests, an individual

05:15 - in this structure could be healthy or susceptible to the infection. with some property they might get infected basically, and if they do it's you know, with a certain property they can recover or with some poverty it might eventually lead to that. The key disti distinction in our model is to allow basically the interaction, economic economic decisions of the individuals and infection risk to suscept to individuals in the model

05:39 - who get infected through either interactions, with other individuals through say meeting a neighbor or meeting a friend. They could also get infected with interactions at the workplace basically with other coworkers or they could get infection through also doing consumption activities, for example, going shopping, or retail trade basically. And so we allow basically three different types of ways

06:03 - to get infected, basically potentially the model and how you, these are connected in healthy with also economic decisions. So for example, in this model then if you want to choose from basically work from home, basically this might lead to, you know, having a lower risk of infection but also lower consumption, which would imply that in the macro economy then there's a lower demand for goods and services and also workers basically. And so this could lead to a recession.

06:28 - And moreover, basically we also allow for the possibility that individuals at the workplace have differences in how likely they would get infected. The setting in which we tried to quantitatively understand the importance of this two-way interaction was basically British Columbia. So first, as mentioned before, like kind of our project started with the broader initiative at the My course School of Economics to help basically kind

06:51 - of the British Columbia Center for Disease Control as well in 2020 to understand the economic effect of the pandemic. So this was kind of funny, you know, a first reason to focus on British Columbia. The second key reason also to focus on the British Columbia labor force was the fact that, you know, again, there were individuals in the British Columbia economy who were less likely to be employed in management occupations than say,

07:14 - compared to Canada broadly basically. Now, again, in these occupations, again, there's differences basically in how likelihood, in terms of ease of working and also getting infected basically. So in order to conduct our quantitative exercise, then we need data on two components basically. One is the earnings gap between working from home and the workplace, and the second being the, likelihood of getting infected across these detailed

07:39 - occupation categories. Basically. So to get data on wages and the proportional workers who work from home, we use the Canadian sensor for 2016. and we use kind of for fatigue occupation categories classified using the national occupational classification in order to map how basically the differences in work from home and working from market. We make an assumption that this is proportional to the ease

08:02 - of working from home, which is measured by the fracture of individuals, doing remote work in the data. Second, to get more, information on the transmission risk by occupations we rely on. The other project was also happening in the Vancouver school economic involving professors, David Green, Nicole Fota and and others. So the normality of the measure they construct of transmission risk is

08:24 - that it uses both job characteristics. So for example, how like likelihood you have in order to contact infections, from meeting your coworkers, the physical proximity maybe or talking to your customers, but also worker characteristics like for example, individuals in those occupations, how likely they are to use public transport or you know, be living with a health worker or living in smaller occupations. And so again, coming back to kind of, you know,

08:53 - key thing was the public health experts always stress was that in the order to understand the transmission risk, one needed to account for both the job characteristics but also worker characteristics to understand how they would spread it in the economy basically. And so this measure basically was more novel in the sense of accounting for both of these. So even though our model explicitly does not have differences in family structure and workplace structures, the,

09:18 - I think using this particular method basically of infection risk allows us to take into some of those considerations into account. The model showed that the decision to work from home plays an important role in both the infection and the macroeconomic dynamics. So individual decision to work at home actually substantially reduces basic economy's peak infection rate, but also increases output losses compared to the economy where this decision is not allowed.

09:42 - So the endogenous choice to self isolate by working from home could actually reduce weekly infection data, it speed by two percentage points relative to when this option is not allowed basically. And at the same time it can also lead to almost a four percentage larger fallen consumption in output. during the economy these numbers are quite large, basically was in the baseline. The peak infection rate is only 7%.

10:06 - So these numbers of being 2% point different is actually quite large in symptoms. Now the key driver for this result is the degree of risk aversion that individuals have basically. So the more the individuals are concerned about the risk of infection, the stronger is the motive to work from home and the stronger is the reduction buying pair of infections. In fact, this peak differences between the peak infection at a working of home when this option is allowed

10:31 - or not completely go away in an economy where individuals are completely risk neutral to infection risk, also consistent with the prediction. The modern most importantly is a survey by bru Horton and co-authors who found that, in US states basically where participants chose to work from home more in those states. They also found lower cases of COVID-19, again, kind of a prediction of the model, which is kind of seen in the data as well.

10:59 - The second important finding is the heterogeneity, as we talked about in infection risk occur occupations even though it does not so much matter for the aggregate results, but has strong distribution implications that is differences in terms of who works from home infection rates and spending patterns by occupations. So the model shows that consistent intuition, individuals who switch to working from home are, when you know the pandemic hits are,

11:26 - are those whose occupation it's easiest to do. So support those in management or professional occupations. These are also the occupations in the Canadian context that experienced the lowest employment losses during the initial phase of the pandemic. Furthermore, professor Racha in co-author showed that in the US context at least, spending recoveries, were faster in for those individuals in the lower income portal than the high income portal.

11:52 - One way to kind of rationalize this in the context of this model is that one good thing that individuals in this lower income group are also those individuals who have lower ability to work from home basically. And so they go to the workplace basically, and this leads to basically faster growth in their consumption patterns as well. And so this kind of heterogeneity in the occupational strategy is kind of understanding in differences in

12:16 - consumption, spending patterns as well. During the pandemic though, do like to point out, one caveat though is that this model is much better suited to understand the initial view of the pandemic, basically where individuals had to make decision about the place of work without kind of much knowledge about the how the pandemic would work and the effect of vaccine and lockdown policies by the governments. The main takeaway I would like, you know, for others to have our paper is kind of highlighting the importance

12:43 - of work from home and the heterogeneity of working from home across occupations for the macro and consumption inequality. So even without changing the nature of technology work from home, we shown this model can have large macron economic effects even in the short term. And so this leads to kind of the more obvious question I think for future research, which is to think about the policy implications, also from analyzing work from home effects in

13:09 - the long run, basically. So when actually when you allow basically work from home to change and how can that impact basically, you know, in terms of affecting both the work structure basically for individuals and also basically how can affect say housing price and acts. For example, the recent work by professors Morris Davis and GaN and Jesse gr showed that when work from home complement office work can lead to large gains in income,

13:36 - but increases in residential housing prices. On the other hand, Peter said, I can go also show that the surge in work from home opportunities can almost explain one third of the increase in firm entry post pandemic, but crucially, recent work by poor Nicholas Bloom and Steven Davis also show that work from home does not always is complimentary to office work in some cases can reduce individual productive data is also negative for young workers in terms of networking aspect.

14:04 - So I think it leads to kind of an obvious question. We just trying to think of kind of, you know, take of this idea of work from home and office work complementary much more seriously and understand its macroeconomic effects. And finally, let's try to point out one more thing that I put, I think also be kind of, you know, very useful, to think about basically is to understand kind of the nature of the production process basically, and the relationship between say upstream

14:28 - and downstream forms. Basically when thinking about work from home, basically. So has work from home changed basically how firms, with their kind of, you know, other people, firms who they kind of, you know, take more input from or give other input inputs. Has this changed basically, or basically the centrality of a firm, for example, if one firm is very important in the whole production Process, if they change to have workers

14:53 - who do work from home or don't, how much can that impact the, so I think these are kind of few areas of, I think, fruitful, economic research, that, you know, I think would be very exciting to see.

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