Our house implemented cap and trade (i.e. “You must impose at most X risk” instead of “You must pay $X per unit of risk.”).
Both yield efficient outcomes for the correct choice of X, so the question is just how well you can figure out the optimal levels of exposure vs. the marginal cost of exposure. If costs are linear in P(COVID) then the marginal cost is in some sense strictly easier (since the way you figure out levels is by combining marginal costs with the marginal cost of prevention) which is why you’d expect a Pigouvian tax to be better.
But a cap can still be easier to figure out (e.g. there is no way to honestly elicit costs from individuals when they have very different exposures to COVID, and the game theory of finding a good compromise is super complicated and who knows what’s easier). Caps also allow you to say things like “Look the total level of exposure is not that high as long as we are under this cap, so we can stop thinking about it rather than worrying that we’ve underestimated costs and may incur a high level of risk.” You could get the same benefit by setting an approximate cost and then revising if the total level goes above a threshold (and conversely in this approach you need to revisit the cap if the marginal cost of prevention goes too high, but who knows which of those is easier to handle).
Overall I don’t think our COVID response was particularly efficient/rational, due to a combination of having huge differences in beliefs/values and not wanting to spend much time dealing with it. We didn’t trade that much outside of couples. Most of our hassle went into resolving giant disagreements about the riskiness of activities (or dealing with estimating risks). I don’t think that doing slightly more negotiation to switch to a tax would have been the most cost-effective way to spend time to reduce our total COVID hassle.
Overall I still think that Pigouvian taxes will usually be more effective for a civilization facing this kind of question, but the costs and benefits of different policies are quite different when you are 7 people vs 70,000 people (since deliberation is much cheaper in the latter case). I expect cap and trade was basically fine but like you I’m interested in divergences between what looks like a good idea on paper and then what actually seemed reasonable in this tiny experiment. That said, I think the object-level arguments for implementing a Pigouvian tax here are much weaker than in typical cases where I complain about related civilization inadequacy because the random frictions are bigger.
I am curious about how different our cap ended up being from total levels of exposure under a Pigouvian tax. I think our cap was that each of us was exposed to <30 microcovids/day from the house (i.e. ~1%/year). I’d guess that the efficient level of exposure would have been somewhat higher.
If costs are linear in P(COVID) then the marginal cost is in some sense strictly easier (since the way you figure out levels is by combining marginal costs with the marginal cost of prevention) which is why you’d expect a Pigouvian tax to be better.
Yeah, that.
here is no way to honestly elicit costs from individuals when they have very different exposures to COVID, and the game theory of finding a good compromise is super complicated and who knows what’s easier
I’m definitely relying on some level of goodwill / cooperation / trying to find the best joint group decision, or something like that. (Though I think all systems rely on that at least somewhat.)
I think the object-level arguments for implementing a Pigouvian tax here are much weaker than in typical cases where I complain about related civilization inadequacy because the random frictions are bigger.
I guess you mean the random frictions in figuring out what system to use? One of the big reasons I prefer the Pigouvian tax over cap-and-trade is that you don’t have to trade to get the efficient outcome, which means after an initial one-time cost to set the price (and occasional checks to reset the price) everyone can just do their own thing without having to coordinate with others.
(Also, did most people who set a cap / budget then also trade? Seems pretty far from efficient if you neglect the “trade” part)
I am curious about how different our cap ended up being from total levels of exposure under a Pigouvian tax.
I just checked, and it looks like we had ~0.3% of (estimated) exposure over the course of roughly a year. I think it’s plausible though that we overestimated the risk initially and then failed to check later (in particular I think we used a too-high IFR, based on this comment).
Our house implemented cap and trade (i.e. “You must impose at most X risk” instead of “You must pay $X per unit of risk.”).
Both yield efficient outcomes for the correct choice of X, so the question is just how well you can figure out the optimal levels of exposure vs. the marginal cost of exposure. If costs are linear in P(COVID) then the marginal cost is in some sense strictly easier (since the way you figure out levels is by combining marginal costs with the marginal cost of prevention) which is why you’d expect a Pigouvian tax to be better.
But a cap can still be easier to figure out (e.g. there is no way to honestly elicit costs from individuals when they have very different exposures to COVID, and the game theory of finding a good compromise is super complicated and who knows what’s easier). Caps also allow you to say things like “Look the total level of exposure is not that high as long as we are under this cap, so we can stop thinking about it rather than worrying that we’ve underestimated costs and may incur a high level of risk.” You could get the same benefit by setting an approximate cost and then revising if the total level goes above a threshold (and conversely in this approach you need to revisit the cap if the marginal cost of prevention goes too high, but who knows which of those is easier to handle).
Overall I don’t think our COVID response was particularly efficient/rational, due to a combination of having huge differences in beliefs/values and not wanting to spend much time dealing with it. We didn’t trade that much outside of couples. Most of our hassle went into resolving giant disagreements about the riskiness of activities (or dealing with estimating risks). I don’t think that doing slightly more negotiation to switch to a tax would have been the most cost-effective way to spend time to reduce our total COVID hassle.
Overall I still think that Pigouvian taxes will usually be more effective for a civilization facing this kind of question, but the costs and benefits of different policies are quite different when you are 7 people vs 70,000 people (since deliberation is much cheaper in the latter case). I expect cap and trade was basically fine but like you I’m interested in divergences between what looks like a good idea on paper and then what actually seemed reasonable in this tiny experiment. That said, I think the object-level arguments for implementing a Pigouvian tax here are much weaker than in typical cases where I complain about related civilization inadequacy because the random frictions are bigger.
I am curious about how different our cap ended up being from total levels of exposure under a Pigouvian tax. I think our cap was that each of us was exposed to <30 microcovids/day from the house (i.e. ~1%/year). I’d guess that the efficient level of exposure would have been somewhat higher.
Yeah, that.
I’m definitely relying on some level of goodwill / cooperation / trying to find the best joint group decision, or something like that. (Though I think all systems rely on that at least somewhat.)
I guess you mean the random frictions in figuring out what system to use? One of the big reasons I prefer the Pigouvian tax over cap-and-trade is that you don’t have to trade to get the efficient outcome, which means after an initial one-time cost to set the price (and occasional checks to reset the price) everyone can just do their own thing without having to coordinate with others.
(Also, did most people who set a cap / budget then also trade? Seems pretty far from efficient if you neglect the “trade” part)
I just checked, and it looks like we had ~0.3% of (estimated) exposure over the course of roughly a year. I think it’s plausible though that we overestimated the risk initially and then failed to check later (in particular I think we used a too-high IFR, based on this comment).