00:01 for palm oil and in substitutes.
00:04 Now that's that first issue.
00:06 Second,
00:06 there's going to be this commitment problem,
00:08 and here's where the dynamics kick in.
00:10 OK,
00:10 so
00:11 once those forests are cut down,
00:13 ex post,
00:13 there will now be this temptation to reduce tariffs.
00:16 Why?
00:16 Because after the forest is gone,
00:18 those emissions are sunk.
00:20 But on the supply side,
00:22 farmers are thinking about their future palm oil revenues.
00:25 Again,
00:25 so then the problem is this.
00:26 Without commitment,
00:28 future tariffs are low.
00:29 And so if I'm a farmer,
00:30 I'm just going to keep on deforesting today,
00:32 even if
00:33 today's tariff happens to be high.
00:35 Why?
00:35 Because I'm thinking about the future mostly.
00:38 So,
00:39 the dynamics are really going to matter
00:41 economically here.
00:42 Uh,
00:42 they're also going to turn out to matter quantitatively.
00:45 But dynamics,
00:45 of course,
00:46 are going to make estimation much harder computationally.
00:49 So I'm also going to make a small methodological contribution here,
00:52 building on existing oiler techniques to
00:55 overcome
00:56 this challenge.
00:57 And then in terms of data,
00:58 satellite data are going to show me these
01:00 palm oil plantations and mills both over space and
01:04 where they're being developed over time.
01:07 So to preview results a little bit,
01:09 I'm gonna find that import tariffs are effective
01:11 relative to the domestic palm oil tax.
01:14 If it's the case that importers are coordinating
01:17 and they can commit to long-term policy.
01:20 Ah,
01:20 but here,
01:21 coordination and commitment are compliments,
01:23 OK,
01:23 and so we really need both of them.
01:25 If we don't have coordination,
01:27 we're going to have low coverage,
01:29 which leads to low,
01:30 uh,
01:31 which leads to leakage and therefore to low tariffs.
01:34 And then without commitment we can't uphold tariffs and
01:37 so we're going to have low tariffs tomorrow.
01:40 We therefore want to compensate with high tariffs today,
01:43 but we can't under leakage because high tariffs are
01:45 just pushing palm oil over to unregulated markets.
01:49 So then what do we end up with,
01:50 we end up with low tariffs tomorrow in addition to low tariffs today.
01:55 So then to touch briefly on contributions,
01:57 I have this new dynamic empirical framework for evaluating
02:01 emission-based trade policy,
02:03 and what I want to highlight here is
02:05 that by focusing on one particular important industry,
02:09 I'm going to have a pretty rich model of supply
02:12 that's going to capture very important features like the dynamics
02:15 and like the spatial heterogene.
02:17 Now,
02:18 second,
02:19 leakage and commitment are independently well
02:21 recognized and decently well studied,
02:23 of course,
02:24 in the very well studied,
02:25 of course,
02:26 in the environmental literature.
02:28 Uh,
02:28 I'm gonna combine them within a single framework and in doing so,
02:31 be able to show how they interact.
02:33 And that interaction is new,
02:35 especially in an empirical setting.
02:38 And of course speaking of empirics,
02:39 I'm going to have these empirical estimates for palm oil,
02:42 which is understudied,
02:44 but and I don't want to understate this,
02:46 a massive huge source of emissions
02:48 that is important in and of itself
02:50 if we want to hit our climate targets.
02:54 So
02:54 just to outline,
02:55 I'll start with the setting.
02:56 Then we'll go to the demand model which speaks to leakage,
02:59 then the supply model,
03:01 which is dynamic and speaks to commitment.
03:04 And then for supply,
03:05 let me highlight that the structural model captures
03:08 that these investment decisions are forward looking.
03:11 OK,
03:11 and so future prices matter here.
03:13 It's not going to be enough
03:14 for us to just regress palm oil supply today on the price today.
03:19 That's not all that farmers are thinking about.
03:21 And then last,
03:22 I'll run counterfactuals to quantify leakage
03:24 and commitment.
03:26 So maybe let me just spend 2 minutes going,
03:28 uh,
03:28 giving you highlights on the setting,
03:30 and then I'll pause for questions.
03:32 So,
03:32 palm oil is a perennial crop.
03:34 Once you grow the tree,
03:35 it's going to keep giving you this fruit,
03:37 OK?
03:37 And this is what the fruit looks like.
03:39 Uh,
03:39 that fruit is going to be taken to a nearby mill,
03:42 and it's going to become palm oil.
03:43 It's going to get milled into palm oil.
03:45 Of course,
03:46 when we set up plantations to grow that palm oil,
03:48 we're deforesting and destroying forest,
03:51 and in this region,
03:52 a lot of that forest is actually a special kind of forest called peatland
03:57 forest.
03:57 So here's a before and after,
03:58 left
03:59 and right.
04:00 Peatland forests are forests that sit on swamps that contain
04:04 deep layers of decomposing organic material
04:07 called peat,
04:08 and the trees would recover in about 100 years,
04:10 but that peat would take 10,000 years
04:12 to recover,
04:13 very much non-renewable.
04:15 And furthermore,
04:16 that peat has 10 times more carbon than the actual trees themselves.
04:21 Slash and burn is releasing all of that carbon into the air,
04:24 and that's why palm oil emissions are so,
04:27 so big
04:27 and so significant.
04:29 From there,
04:30 palm oil is going to be used in many familiar products,
04:32 both food like
04:34 cookies and margarine,
04:35 as well as non-food,
04:36 detergents,
04:37 soaps,
04:38 and even cosmetics like lipstick.
04:40 Now who's producing palm oil?
04:41 It's Indonesia and Malaysia.
04:43 They're producing a lot of palm oil
04:45 and they're doing it for export.
04:47 So they're 84% of world production
04:49 and 90% of exports.
04:52 For world consumption,
04:53 Indonesia and Malaysia consume 20% domestically.
04:56 And then
04:57 the top three importers,
04:58 the EU,
04:59 China,
04:59 and India,
05:00 consume another 35%.
05:02 So
05:03 notice here that China and India
05:06 don't really produce any,
05:07 don't produce any palm oil themselves.
05:10 So import tariffs here are really just a consumption tax with
05:14 no discrimination needed based on where
05:17 this palm oil is coming from.
05:19 And just to wrap up this kind of settings section
05:23 here I'm showing you how this industry is expanding over 3 decades over time.
05:28 So the dark blue here of course is palm oil plantations,
05:32 and what we see is as time unfolds,
05:36 you know,
05:36 as things unfold,
05:37 we're seeing this massive expansion
05:39 over time and over space,
05:42 and all of that land sort of
05:44 devastatingly is getting deforested.
05:46 All of that peatland is going up into the air.
05:48 In the form of emissions.
05:50 So what am I doing with uh counterfactuals?
05:52 I'm gonna roll back to 1988,
05:54 put in regulation in the form of import tariffs,
05:57 and then
05:57 I'm going to roll forward
05:59 and see
06:00 how things unfold
06:02 and change in response.
06:04 So let me pause briefly for,
06:06 for questions before we talk about the demand estimation.
06:15 Great,
06:16 if,
06:16 if there are no questions,
06:17 I'll just jump into the,
06:19 this is,
06:19 uh,
06:19 this is Tristan,
06:20 sorry,
06:20 I was waiting for the moderator,
06:21 but I'll go.
06:22 Uh,
06:23 so,
06:23 uh,
06:24 Article 2 of the Paris Agreement
06:26 recognizes that countries have differentiated responsibilities
06:30 and capabilities,
06:32 uh,
06:32 in addressing
06:34 climate change,
06:35 um,
06:36 and you know this case of Indonesia is one where clearly,
06:39 you know,
06:39 you have a,
06:40 a lower income country producing something that's carbon intensive.
06:44 And this is a policy of
06:46 rich countries,
06:48 uh,
06:48 to stop that.
06:49 Um,
06:50 I,
06:50 I guess how,
06:51 how would you account
06:52 for
06:53 an idea of differentiated responsibility,
06:56 ah,
06:57 in this model,
06:58 uh,
06:58 and you know,
06:59 is there something you can say about that?
07:01 Yes,
07:02 thank you for the question.
07:03 So
07:03 differentiated
07:04 ability is certainly
07:07 Part and parcel of the approach here.
07:08 This is kind of the motivation for thinking about these external external
07:12 policies like importers as opposed to
07:14 relying on the Indonesian government itself,
07:16 which may have enforcement issues,
07:18 uh,
07:19 you know,
07:19 at play.
07:20 However,
07:20 the differentiated responsibility,
07:22 ultimately I'm,
07:23 I will be able to speak to a bit.
07:25 In particular,
07:26 you're getting at this equity concern,
07:27 right,
07:28 where Indonesia and Malaysia happen to be
07:30 endowed with certain natural resources that are potentially theirs to exploit,
07:35 uh,
07:35 particularly Indonesia as a lower income country.
07:37 How can we be sensitive to that?
07:39 At the very end,
07:40 I'm going to show you
07:42 what those equity concerns.
07:43 Look like specifically because I have the full supply side specified,
07:47 so I can quantify that producer surplus loss for Indonesia,
07:51 for Malaysia
07:52 in response to any kind of tariff that we put in place in counterfactuals.
07:56 That kind of producer surplus loss is the kind of
07:59 loss,
08:00 the kind of damages that one would want to address
08:03 with
08:03 transfers in order to address this equity.
08:06 Um,
08:07 but you know,
08:08 and one can also think about waiting,
08:10 right?
08:10 So when tariffs are set,
08:12 they're set
08:12 in my baseline analysis,
08:14 waiting,
08:15 you know,
08:15 a dollar of producer surplus in Indonesia,
08:17 1 to 1
08:19 compared to consumer surplus in Europe,
08:21 which is maybe.
08:22 Unilevers $1 of consumer surplus,
08:24 OK,
08:25 uh,
08:25 but if you want to upweight Indonesian surplus
08:27 because maybe that's lifting people out of poverty,
08:30 then in that case,
08:31 maybe you want to upweight
08:33 producer surplus,
08:34 and,
08:34 and that would change the kind of tariff that is,
08:36 that is optimal.
08:39 Hi,
08:40 uh,
08:40 this is Govindra Timilna.
08:42 So it looks like you have a single commodity model,
08:44 right?
08:44 It's only for
08:45 palm oil,
08:47 but palm oil is used basically,
08:48 you know,
08:49 to produce the biodiesel.
08:51 For example,
08:51 even if there is an import tariff for palm oil,
08:55 Indonesia,
08:55 Malaysian farm,
08:56 they can produce biodiesel,
08:58 and they can export biodiesel,
09:00 right?
09:01 So in this case,
09:01 the leakage still would be there.
09:04 Yes,
09:04 so,
09:04 so the simple policy response is
09:07 to also put
09:08 the carbon tax on biodiesel,
09:10 right?
09:10 So not just palm oil,
09:12 raw palm oil,
09:12 but rather
09:13 palm oil content.
09:15 And,
09:16 and so that's going to prevent that margin of leakage.
09:19 Um,
09:19 but the,
09:19 the other thing I'll point out,
09:20 and we'll see this in demand is,
09:22 yes,
09:22 this is one commodity,
09:23 of course,
09:24 but I'm not thinking about it purely in isolation.
09:26 Right,
09:27 because on the European side,
09:28 for example,
09:29 uh,
09:29 palm oil is going to be a substitute with soybean oil,
09:32 rapeseed oil,
09:33 both of which the European Union produces a lot of,
09:36 particularly rapeseed oil.
09:38 And so demand,
09:39 of course,
09:40 is going to need to account for that,
09:41 right?
09:41 It's,
09:41 it's not as if palm oil is the one and only good
09:44 that people
09:45 use and,
09:46 and can't substitute to anything else.
09:50 Go ahead.
09:53 Please,
09:55 please.
09:56 Yes,
09:56 so,
09:57 uh,
09:58 regarding the single commodity model,
10:00 Indonesia clear field not just for palm oil.
10:04 They clear field for all other crops as well.
10:07 And how do you account for that?
10:08 Are you going to say all the deforestation is all due to palm oil plantation,
10:14 or there's some other way
10:16 that you're going to measure
10:17 specific for palm oil plantation?
10:20 Thank you.
10:21 So I,
10:22 to be very upfront,
10:23 I am not accounting for that in the
10:27 kind of estimates that I'll show you today.
10:28 And indeed this is going to be a source of carbon bias because,
10:31 you know,
10:32 you put in
10:32 palm oil tariffs,
10:33 palm oil production goes down,
10:35 but if that land is still being slashed and burned for acacia,
10:39 that's the number 2 crop in Indonesia,
10:41 then there's going to be huge leakage on that margin,
10:43 and then my
10:44 carbon estimates will be,
10:45 will be wrong.
10:46 They'll be biased.
10:47 Now the The reason I didn't put that into the model
10:50 explicitly is because I can actually check that in the data.
10:52 So I have data on acacia plantations,
10:55 and I can look at substitution from palm oil to acacia,
10:58 and that substitution turned out to be much smaller
11:00 than I expected.
11:01 I thought I was going to have a nested model where first
11:04 we choose as a farmer between all of these different crops.
11:07 Then if I choose palm,
11:08 it's this exact model.
11:09 If it's Acacia,
11:10 then within that nest,
11:11 it's again my exact model,
11:13 and I thought it was going to have this cross nest substitution,
11:15 but that substitution ended up being small,
11:17 and the reason I think is that
11:19 those other crops are all much less profitable
11:21 than palm oil.
11:22 The reason why basically all of the
11:24 deforestation in Indonesia and Malaysia is happening
11:26 for palm oil is because everyone is growing palm
11:29 oil because it's the most profitable crop by far.
11:31 And so part of this is just that,
11:33 you know,
11:34 Acacia is 7 times less profitable than palm oil.
11:37 That means that not everyone who's no longer
11:39 producing palm oil would move to Acacia,
11:41 and so that's why that substitution,
11:43 I measured to be small.
11:46 Otherwise I could have had this more complicated model,
11:48 but the substitution was small in the data,
11:49 so I just didn't,
11:50 didn't have that margin.
11:52 Uh,
11:53 I,
11:53 I,
11:54 I'm Malaysian.
11:55 I must say that I'm very surprised that you
11:58 associate all those,
12:00 uh,
12:00 development in Malaysia to
12:02 palm oil plantation,
12:04 which I know is going down,
12:05 not going up in Malaysia.
12:07 But this is what you,
12:08 you show on the map,
12:09 which is what
12:10 much of my surprise.
12:13 Yes,
12:14 so in,
12:14 in Malaysia,
12:14 well,
12:15 what's happened is it's been capped,
12:17 um,
12:17 and so the,
12:17 the Malaysian government says you can't,
12:19 you can't do more.
12:20 Of course,
12:20 Malaysia has,
12:21 you know,
12:22 it has more ability to enforce than than the Indonesian government.
12:26 Um,
12:27 but then also,
12:27 you know,
12:28 you can,
12:28 you can put a cap,
12:29 but then once you cut down the forest,
12:30 it's all gone already anyway,
12:31 right?
12:31 So it's,
12:32 it's,
12:32 uh,
12:32 there's no kind of
12:34 going back on the peatland that,
12:35 that has been destroyed.
12:37 Um,
12:37 but yes,
12:37 I,
12:38 I thought that there was going to be much more substitution.
12:40 It,
12:41 it turned out not to be,
12:42 um,
12:43 but that's,
12:43 you know,
12:44 That's in the data that that I observed.
12:46 In some counterfactual worlds,
12:48 maybe,
12:48 you know,
12:49 uh,
12:50 you don't have the economies of scale in palm oil,
12:51 and now Acacia takes off,
12:53 uh,
12:53 in that world,
12:54 then I'm not going to quite be capturing all of that.
12:56 Um,
12:57 for that though,
12:58 I would need a model
12:59 of
12:59 the Acacia industry,
13:00 which isn't quite what I have here.
13:03 But,
13:03 but that's how we would,
13:04 that's what you would need is this multi-industry.
13:07 And again,
13:08 I measured the substitution to be small.
13:11 OK,
13:11 so I'll just jump into demand.
13:13 So
13:14 for demand,
13:15 I have an almost ideal demand system.
13:17 I'm going to move through relatively quickly here just because I want to
13:20 flag that the richness really is going to be on the supply side.
13:23 But nonetheless,
13:24 let's jump in.
13:25 This is typical two-stage budgeting.
13:27 First,
13:27 consumers are choosing how much to spend on the vegetable oil category.
13:31 That's that top line
13:32 with the log log specification.
13:34 And then second,
13:35 consumers are allocating that spending between palm oil and other vegetable oils.
13:40 I want to flag here that these substitutes,
13:42 and this point came up a little bit before,
13:44 these substitutes are not associated with big carbon emissions.
13:48 So I'm not that worried about carbon bias,
13:50 bias in my carbon estimates from this margin of leakage.
13:54 Now the asterisk here is that South American soybean oil.
13:57 does have some deforestation associated with it,
13:59 with it,
14:00 but I think that that bias is going to be small even there because one,
14:03 it's only South American soybean oil.
14:05 Second,
14:06 Amazonian deforestation is really driven more directly by cattle than soybeans.
14:10 And third,
14:11 it's not peatland destruction.
14:13 So we're also talking about an order of magnitude,
14:15 order of magnitude less
14:17 emissions than what we're talking about with palm oil.
14:20 So then on the second line,
14:21 omega is the palm oil expenditure share,
14:24 Omega IT.
14:25 It's a function of 1,
14:27 a sort of secular oil specific time trend,
14:29 2,
14:30 the prices of each oil product.
14:32 OK,
14:32 so I'm going to get on price elasticities as well as
14:35 cross price elasticities,
14:37 and then 3,
14:38 the category budget.
14:40 So what I want to flag here is that this is a product
14:42 space demand system that's going to allow me to be very flexible on
14:46 the cross-product substitution patterns.
14:48 Now,
14:49 capturing that substitution is precisely the advantage
14:51 here relative to a reduced form approach,
14:54 where,
14:54 for example,
14:54 if I just regressed palm oil demand
14:57 on prices,
14:57 even of course if I use instruments,
14:59 then I wouldn't be fully accounting
15:01 for that switching to these alternative products
15:03 and therefore the demand elasticities that I would get would be,
15:06 would be biased.
15:07 So again,
15:08 to think about leakage,
15:09 I want those market specific demand elasticities.
15:12 I'm going to get them by estimating
15:15 demand by market.
15:15 And then in terms of data,
15:16 I have annual consumption by oil product and country.
15:20 And then annual prices by oil.
15:23 So of course you know prices are endogenous,
15:25 price endogenity on the demand side.
15:26 What do I want?
15:27 I want a supply shifter.
15:29 For that supply shifter,
15:30 I'm going to use growing season rainfall shocks for foreign oil producers.
15:33 For example,
15:34 I'm going to instrument for US palm oil prices with rainfall shocks in Indonesia,
15:39 which produces palm oil.
15:41 So the bottom line here
15:42 in terms of results is going to be that the
15:45 importers,
15:46 that is the dark blue lines,
15:47 have relatively elastic demand.
15:50 OK,
15:50 and so what this means is that leakage is a problem.
15:53 If China is unregulated,
15:54 then because Chinese demand
15:56 is relatively elastic,
15:57 it means that it is going to expand and therefore it will offset
16:01 EU tariffs.
16:01 OK,
16:02 leakage is an issue.
16:03 On the other hand,
16:04 actually,
16:05 if importers coordinate,
16:06 then leakage
16:07 is not so bad.
16:09 Leakage is not so bad because
16:12 what do import tariffs kind of always miss,
16:14 even if importers all coordinate?
16:16 They're going to be missing domestic consumption in Indonesia and Malaysia,
16:19 which
16:20 isn't exported.
16:21 However,
16:22 this domestic demand turns out.
16:23 To be
16:24 pretty inelastic.
16:25 That's the light blue line.
16:26 And,
16:27 and so leakage on that margin isn't that bad.
16:29 And here I think it's,
16:30 it's inelastic
16:31 just because
16:32 Indonesia and Malaysia produce so much palm oil that really palm oil is the main
16:36 thing that is used.
16:37 They're not really using too much olive oil,
16:39 and so there's just less substitution available
16:42 and therefore a lower
16:43 demand elasticity.
16:45 And then I can also aggregate up to give you the,
16:49 I can also aggregate up to give you the world demand curve.
16:51 So the 2015 curve is going to be farther to the right than the 1990 curve,
16:56 that is to say,
16:57 more demand
16:57 at all prices,
16:59 and I can trace out that intercept to give
17:01 you the path of the rightward shift over time.
17:04 And that's the graph on the right.
17:05 So demand is rising as palm oil gets widely adopted,
17:09 and note that this is in logs.
17:11 So demand is rising
17:12 fast
17:13 over this period of time.
17:15 This is why plantations have expanded so much and so quickly,
17:19 as we saw on the map before.
17:21 This is also why without regulation,
17:23 because demand is going up and that's putting upward pressure on prices,
17:26 this is why we would continue to keep expanding in the absence of regulation.
17:32 Uh,
17:32 so that's an answer.
17:34 I,
17:34 I take a quick pause for questions.
17:35 Yes,
17:36 yes,
17:37 uh,
17:37 David McKenzie,
17:38 please go ahead and then Tom,
17:40 uh,
17:40 yes,
17:40 so,
17:40 so this was,
17:41 uh,
17:41 uh,
17:41 just a quick clarification.
17:42 When it's,
17:43 when you're looking at the domestic consumption,
17:45 is that picking up then,
17:47 um,
17:47 the use of palm oil as an intermediate input
17:50 into these other products?
17:51 And so the reason that it's inelastic may be because of the
17:55 sort of export of
17:56 some of these,
17:57 these other products that are using palm oil.
18:00 Yes,
18:00 in this,
18:01 in,
18:01 in terms of the data,
18:02 this USDA data,
18:03 it's,
18:03 it's measuring all of the consumption of the raw good,
18:06 whether it be used ah directly ah for a final consumer product,
18:10 as a final consumer product,
18:12 or whether it be used as an input to other,
18:14 other
18:15 industries.
18:16 Yes.
18:22 Thanks for that.
18:22 Please proceed.
18:24 Great.
18:25 Um,
18:25 so,
18:26 I will jump now into
18:27 supply.
18:29 So for supply,
18:30 I have this dynamic model
18:31 with sunk investment.
18:33 So I'm going to divide land into sites,
18:36 OK,
18:36 and a site is a block of land that can invest in palm oil.
18:40 And empty sites then are going to be potential entrants.
18:42 OK,
18:42 this is how I'm defining potential entrants in this market,
18:45 and note that,
18:45 you know,
18:46 you can only have so many potential entrants
18:48 because there's only so much land in Indonesia and Malaysia.
18:52 Now,
18:52 active sites are going to be operating,
18:54 they're gonna be producing palm oil,
18:56 they're gonna have one mill
18:57 and some set of plantations that are up,
19:00 up and running and online.
19:02 So this is going to be a Hoppenheim style
19:03 entry investment game with a dynamic competitive equilibrium.
19:07 That is to say that sites are going to invest in
19:09 deforest today in order to set up their palm oil trees,
19:12 and then those trees are going to produce revenues in every future period.
19:16 Now there's no exit here because,
19:18 you know,
19:18 once you've already paid the cost to set up the tree,
19:21 marginal costs at that point are pretty low,
19:23 so there's not really a big reason to exit anyway.
19:25 Also,
19:26 even if you exit,
19:26 deforestation has sunk at that point,
19:28 and that's really what I care about.
19:30 And then where do tariffs enter?
19:32 What's the point of tariffs?
19:33 Well,
19:33 tariffs reduce future revenues,
19:35 and that in turn is going to reduce the incentive
19:37 to invest and deforest in the first place today.
19:41 Now,
19:42 the structural model here,
19:43 what is it buying for us?
19:44 What is it capturing?
19:45 It's capturing that future prices matter because these are forward looking
19:49 investment decisions.
19:50 So again,
19:51 we don't just want to regress palm oil supply today on
19:53 prices today because prices today are not the one and only thing
19:56 that farmers are basing their investment decisions on.
20:00 And furthermore,
20:00 these are future prices,
20:01 so it's really expected future prices that matter,
20:05 not realized future prices,
20:07 and the structural model is going to help
20:08 me deal with those expectations in a flexible way
20:12 and kind of specify the assumptions that I need to make on them.
20:15 Now,
20:16 in terms of the timeline,
20:17 let me just show it to you graphically.
20:19 This timeline is gonna summarize the choices.
20:21 OK,
20:21 again,
20:21 we have the discrete and continuous elements here.
20:24 So,
20:25 so what are they?
20:26 Well,
20:26 we are an
20:27 empty site,
20:28 OK,
20:28 that's,
20:28 let's start on the left.
20:30 Uh,
20:30 we're gonna choose whether to build a mill or not.
20:33 That's a discrete yes or no binary choice.
20:36 If no,
20:37 then we're in the lower branch.
20:38 OK,
20:38 and we're gonna have the same choice next period.
20:41 If yes,
20:42 then we're in an upper branch,
20:43 in the upper branch,
20:44 and then what we're going to do is we're going to pay that
20:47 mill investment cost because we've chosen yes to build the mill.
20:51 Then once you are there and you've built that mill,
20:55 then you get to choose how much land to develop into plantations.
20:59 On the intensive margin,
21:00 and that's going to be a continuous choice.
21:02 Should we develop 456,
21:05 6.5,
21:06 you know,
21:06 6.25,
21:07 whatever
21:08 hectares of land into plantation.
21:10 And of course we're going to pay that plantation investment cost.
21:12 Big plantation,
21:14 lots of land,
21:14 bigger cost.
21:15 That's
21:17 now,
21:17 you know,
21:18 what's the point of having all of this investment costs that we're paying?
21:22 Well,
21:22 now starting in the next period,
21:24 we're going to be
21:24 producing palm oil and getting revenue.
21:27 And then at a later point,
21:28 we can also choose to expand our plantation.
21:31 We don't need to develop all of the land at once.
21:34 So a few comments here.
21:35 First,
21:36 these costs are going to be subject to logit cost shocks
21:40 that we're going to realize before we make the decision,
21:42 and we're going to base the decision on.
21:44 Uh,
21:45 Second,
21:45 again,
21:45 these are future revenues,
21:46 so we need to be dealing with expectations.
21:49 And then third,
21:51 these two margins of spatial dependence,
21:54 uh,
21:54 these two margins are going to capture that spatial dependence
21:57 between a mill and the land around it,
21:59 OK,
21:59 and,
22:00 and also that big fixed cost
22:01 for building the mill in the first place.
22:03 So it's not like every hectare of land has
22:05 its own cost function and its own cost shock.
22:08 No,
22:09 you know,
22:09 there are groups of land that are working together as one unit,
22:12 that's what these two margins are going to help capture.
22:16 Now for the state variables,
22:17 we have aggregate supply and aggregate demand which are observed
22:21 and of course they're going to affect world prices,
22:23 which in turn affect the revenues that firms are going
22:26 to get and therefore affect the incentive to invest.
22:29 So for supply,
22:30 very simply,
22:31 you know,
22:31 many plantations online,
22:33 high supply,
22:34 low prices.
22:35 And then for demand,
22:36 demand curve far out to the right,
22:38 high demand,
22:39 high prices.
22:40 So
22:41 in terms of spatial he
22:43 heterogeneity,
22:44 which is very important here,
22:45 sites are going to be spatially different in observed and unobserved ways.
22:51 So there's observed heterogeneity here in palm oil yields,
22:54 how much you can grow on any given plot of land,
22:56 and also cost factors like distance to a port,
22:58 which are important,
22:59 a major port,
23:00 which are important for
23:01 transport costs.
23:03 So that is to say that sites can be observably different.
23:06 And why is this important?
23:07 This is important because it's going to capture directly
23:10 that
23:11 this idea of the good land being developed first and therefore
23:15 the supply elasticity of the land that is left is going
23:17 to depend on what kind of land that is left,
23:20 and probably it's the worst land if it's,
23:22 it's
23:23 the kind of land that's been left
23:25 for a later period to develop.
23:27 But there's a restriction here,
23:28 OK,
23:28 the restriction is that there is no unobserved
23:32 site level heterogeneity
23:34 above and beyond the logic cost shocks,
23:36 which,
23:36 you know,
23:37 that's heterogeneity,
23:38 of course,
23:38 but it has a distributional assumption on it,
23:40 so it's restricted.
23:42 There's no site level unobserved heterogeneity,
23:44 like a,
23:45 a site fixed effect,
23:46 if you want to think of it that way,
23:48 because in this model,
23:49 once a mill is built,
23:50 it's built.
23:51 So I don't observe
23:52 this site level decision again and again over time.
23:56 And so I can't identify that site level fixed that.
23:59 Uh,
23:59 Instead,
24:00 what I need to do is I need to aggregate across space instead of over just over time.
24:04 And therefore I'm restricting unobserved heterogeneity here
24:07 to enter only at the regional level.
24:10 Uh,
24:10 so what that means is that I'm gonna
24:11 have regional fixed effects and regional time trends.
24:14 Uh,
24:14 but let me note that that's still
24:16 going to capture some very important unobservables,
24:19 like,
24:19 for example,
24:20 political economy,
24:22 where,
24:22 you know,
24:22 plantations in certain corrupt regions might be
24:25 cheaper because the bribes are cheaper there,
24:27 for example.
24:28 I don't see that,
24:29 but that's going to be absorbed
24:30 by this
24:31 regional unobserved heterogeny.
24:34 And then in terms of data,
24:35 I'm going to see these plantations and these mills,
24:39 these plantation and mill investments over time
24:41 and over space in the satellite data.
24:44 I'm also going to see revenues.
24:46 So I see world prices over time,
24:48 and I'm going to have quantities from the yields data.
24:50 So for yields,
24:51 I'm using an agronomy model developed specifically for palm oil.
24:55 This is a Hoffman et al.
24:56 2014,
24:58 and then I'm going to be able to feed
25:00 in climate data and get much more fine-grained than,
25:02 for example,
25:03 the FAO's agricultural suitability data,
25:06 which,
25:06 you know,
25:06 many people use,
25:07 but that's not quite what I'm using because it's not fine-grained enough
25:10 for,
25:10 for my purposes.
25:12 Now,
25:12 what I want to point out here in terms of the
25:14 quantity decision is that here active plantations are just producing their yield
25:18 in every period.
25:19 So on the,
25:20 the,
25:21 the quantity decision,
25:22 that is to say,
25:22 isn't
25:23 a matter of how much to produce on this given plot of land.
25:27 Instead,
25:28 that given plot of land is just producing its yield.
25:30 OK,
25:30 so the quantity decision instead is
25:32 over how much land to develop in the first place,
25:35 and again,
25:35 I see that by satellite.
25:38 And also
25:39 cost factors that vary over space.
25:41 So again,
25:41 you know,
25:42 the port distance thing,
25:43 but also being close to major roads
25:46 or major urban areas might be better because it lowers transport costs.
25:49 Uh,
25:50 and then I'm also gonna
25:51 estimate how carbon stocks enter firms' cost functions.
25:55 So what I'm gonna show you in a bit is that firms don't find
25:58 emissions privately costly.
26:00 That is to say that without regulation,
26:02 they're just gonna keep on emitting because they don't care
26:04 about those emissions.
26:05 It doesn't enter their cost function.
26:08 Uh it's a question by.
26:10 Please go ahead,
26:11 Mr.
26:11 Alan.
26:12 So the question is
26:14 why,
26:15 so I don't know if the government does it,
26:16 but wouldn't it be natural for the Indonesian government to levy an export tax
26:21 just to extract monopoly rents?
26:24 Yes,
26:24 so I,
26:24 I am going to show you that counterfactual at the very end actually of the export tax.
26:28 Ah,
26:28 maybe we think that it's administratively
26:31 easier to administer,
26:32 you know,
26:32 easier to administer because
26:34 now you can just impose it at the port
26:36 instead of,
26:37 you know,
26:37 1000 different auditors across all of the forests of Indonesia.
26:40 So I am going to show you that.
26:42 Yes,
26:43 Indonesia,
26:44 because it has market power,
26:45 will have this incentive to put in
26:47 some kind of tax to,
26:48 you know,
26:48 a monopolist wants to reduce supply in order to
26:50 drive up prices because they're a price maker.
26:53 It turns out that that motive will
26:55 impose some kind of tariff.
26:57 That tariff is not going to be nearly big enough
27:00 to match the size of the carbon externality that we're talking about today.
27:03 So yes,
27:04 that incentive is there,
27:05 but it's small relative to the carbon externality.
27:08 But then that tax is the entry point for the EU instead of trying to import.
27:13 Why don't you give an incentive for the government to increase the tax?
27:16 Yeah,
27:17 so the very last counterfactual,
27:18 I will show you this exact,
27:20 um,
27:21 this exact counterfactual,
27:22 and then I'll show you why
27:23 actually the,
27:24 the Indonesian government,
27:25 uh,
27:25 isn't happy with that,
27:27 that export tax as well.
27:28 Once I have the graph up,
27:29 it'll be a little easier to,
27:30 to,
27:31 to come to.
27:31 So,
27:32 so if I,
27:32 I,
27:32 I'll,
27:33 uh,
27:33 remember to bring this back up,
27:34 but if I don't,
27:35 please remind me.
27:35 It's on the last slide.
27:37 So,
27:37 uh,
27:38 Say also had a question.
27:40 Hi,
27:41 yes,
27:41 um,
27:41 so,
27:42 couple of things.
27:43 So,
27:43 uh,
27:44 first,
27:44 so should we,
27:45 you know,
27:46 most of the production then of palm oil in Indonesia is,
27:48 is,
27:49 uh,
27:49 you know,
27:49 individual meals,
27:51 or are there com you know,
27:52 multinational,
27:53 or are there companies basically that have multiple meals and so.
27:55 So,
27:56 so what,
27:56 what share of the production is,
27:58 is coming from,
27:59 you know,
28:00 individual,
28:00 individual mills.
28:01 And then,
28:02 so,
28:02 I mean,
28:02 I guess related to that,
28:03 I mean,
28:03 should we think about these producers as
28:06 price takers,
28:07 uh,
28:07 you know,
28:07 given that
28:08 most of the palm oil is produced in,
28:10 in Indonesia
28:11 and Malaysia,
28:12 again,
28:12 if,
28:13 if there's,
28:13 you know,
28:13 a large firm,
28:14 then,
28:14 you know,
28:15 the,
28:15 you know,
28:15 it's unclear that there would be.
28:17 Uh,
28:18 price takers,
28:19 and then,
28:20 you know,
28:20 sorry,
28:21 one last
28:21 point.
28:22 So,
28:23 so should we,
28:23 should we think about,
28:25 um,
28:25 so I guess,
28:26 so there's,
28:27 you know,
28:27 in terms of production,
28:28 there's no pests or other,
28:30 uh,
28:31 or,
28:31 or,
28:31 you know,
28:32 is that,
28:32 is that subsumed into,
28:34 into the,
28:34 into the climate shock,
28:35 I guess,
28:36 you know,
28:36 if there's,
28:36 you know,
28:37 if there's,
28:37 you know,
28:37 uh,
28:38 pests and stuff that might affect production,
28:40 um,
28:41 you know,
28:41 as well,
28:42 you know,
28:43 yeah.
28:44 Uh,
28:44 so is it the case that climate is going
28:46 to change production directly and are producers internalizing that?
28:50 Um,
28:51 that,
28:51 no,
28:52 because,
28:52 uh,
28:52 it's happening over such a long margin.
28:54 Also,
28:55 palm oil is pretty hardy a crop,
28:56 so temperatures can go up a bit and it's,
28:58 it,
28:58 it will actually be fine.
29:00 Um,
29:00 but then are there pests,
29:01 are there pests,
29:02 and,
29:03 you know,
29:03 uh,
29:04 that,
29:04 that,
29:04 that might,
29:05 might,
29:06 you know,
29:06 yeah,
29:07 might,
29:07 might.
29:09 You know,
29:09 that damage the production of palm oil.
29:12 There
29:13 probably there are some.
29:14 I'm sure I don't know the name of the specific pests that palm oil farmers deal with,
29:18 to be upfront.
29:20 So I don't,
29:20 I'm not controlling for any of that directly,
29:22 but that is going to be
29:24 picked up when I estimate the cost structure of the industry,
29:27 part of the costs of developing
29:29 a given plot of land into
29:31 plantation,
29:32 operational plantation is going to be inclusive of pesticide,
29:35 for example,
29:36 to deal with these pests.
29:37 Fair enough,
29:38 fair enough.
29:38 And then to get to your point on market power and market concentration.
29:43 There are
29:44 multinationals that have multiple mills.
29:46 There are about 1200 mills
29:48 in the data,
29:49 but you know,
29:50 um,
29:53 not Sinar Mas,
29:54 Wilmar
29:55 International is a huge company,
29:56 OK,
29:57 one of the biggest palm oil traders.
29:59 They actually only have 8 mills.
30:00 OK,
30:00 so they have 8,
30:01 it's more than 1,
30:02 but it's not
30:03 800.
30:04 And so I'm assuming everyone is a price taker,
30:06 and this is why
30:07 we see
30:08 that this market concentration here is pretty low.
30:11 So the,
30:12 the top.
30:12 One producer
30:14 in
30:15 the world is
30:16 FGV Burhad FGV Holdings Burhad.
30:20 This is a Malaysian company.
30:22 It's only 4% of world production.
30:24 The top
30:25 20 producers are 27%.
30:27 So it's a pretty unconcentrated market.
30:29 This is going to be important because I have a
30:32 substantive
30:33 assumption that I need to make to have estimation be tractable,
30:37 which rules out this,
30:38 this market power.
30:39 So that's,
30:39 that's also why I'm just assuming everyone is a price taker.
30:43 Um,
30:45 in terms of,
30:46 sorry,
30:46 sorry,
30:47 there are a couple more questions,
30:48 Alan,
30:48 uh,
30:48 Aaron,
30:49 then,
30:50 uh,
30:50 Ariana,
30:51 then Harris.
30:52 Try to both the questions and the answer is
30:55 a little brief in the interest of time.
30:58 Sure,
30:59 so I'm going to like ask very briefly.
31:00 So
31:01 I think in the upper branch of the decision tree,
31:03 you,
31:03 you observe like planting more or
31:06 like
31:06 not planting every year,
31:07 right?
31:08 If that's the case,
31:09 you can,
31:10 you can
31:11 estimate the unobserved heterogeneityity based on that like
31:13 the Harold Sucher's bus engine replacement problem,
31:16 right?
31:16 So is that correct?
31:18 That's correct.
31:18 But then the problem is going to be that I
31:20 don't see that until the mill is actually built.
31:23 So for many places,
31:24 I,
31:24 you know,
31:24 I have 30 years of data,
31:26 but if the mill didn't get built until 2005,
31:28 then actually I only have the tail end.
31:31 So I do see my decisions,
31:32 but it's not,
31:33 and it's not balanced across all of the,
31:35 all the mills.
31:36 And so that's,
31:37 that's why.
31:37 And then at the,
31:38 that's the intensive margin,
31:39 the upper branch,
31:40 but then for the mill itself,
31:42 it's you can't do it at all.
31:43 That's why I,
31:44 yeah,
31:44 that's why I just aggregated it to be regional for both.
31:48 Thanks.
31:50 I was wondering,
31:52 yeah,
31:52 I was wondering whether you can comment on on location specific production.
31:57 Uh,
31:57 so from what I understood,
31:58 from what I understand,
31:59 you said,
32:00 the issue is the specific,
32:02 uh,
32:03 environmental conditions in Malaysia and Indonesia.
32:05 And,
32:06 and so I'm,
32:06 I'm interested to understand whether the policy
32:08 should be targeting a reduction in those settings
32:10 and,
32:11 and potentially an increase in other settings such as
32:14 Africa,
32:15 Gabon,
32:15 and other places that are interested in,
32:17 uh,
32:17 in this area,
32:18 or should we view it simply as an
32:21 absolute reduction in production of.
32:25 Yes,
32:25 this is a very important point that you bring up.
32:27 I,
32:27 let me first say that I I am focusing on just
32:29 Indonesia and Malaysia because that's where I have very rich data,
32:32 and also they're 90% of production anyway,
32:35 so I just
32:36 call it a day there.
32:37 Uh,
32:37 but we can also think about kind of
32:39 supply leakage to West Africa,
32:41 South America.
32:42 There is some production there.
32:43 Nigeria,
32:43 for example,
32:44 is 3%
32:45 of world production.
32:46 But then
32:47 even within Indonesia,
32:49 there is this
32:50 question of whether all palm oil is equally bad,
32:53 and there the answer is no.
32:54 So palm oil produced on peatland forest is 10 times worse
32:57 than palm oil produced on non-peatland forest.
33:00 Not all forest in Indonesia
33:02 and Malaysia is peatland forest.
33:04 And so then what we realized then is that this import tariff that I'm talking about,
33:08 which treats
33:09 in my baseline,
33:10 I'm just going to treat all palm oil the same,
33:12 good and bad palm oil.
33:13 That's a blunt tool,
33:14 right?
33:14 Ideally,
33:15 and that's not a Peruvian thing.
33:16 Really,
33:17 the Peruvian thing would be to differentiate between the
33:19 good palm oil and the bad palm oil.
33:21 So I do run that counterfactual.
33:23 That is an important counterfactual.
33:24 I don't
33:25 set that as my baseline because when we're talking about import tariffs,
33:29 actually it's much easier to just treat all palm oil the same,
33:32 because otherwise,
33:32 You need to know for this specific unit of palm oil,
33:35 I need to trace back its production history over
33:38 time and figure out where it came from,
33:39 and I need to either do it myself or trust a certifier to do it correctly.
33:44 Also,
33:45 they're,
33:45 you know,
33:45 in the unregulated market,
33:47 good and bad palm oil is a perfect substitute.
33:49 So you have switching,
33:50 you know,
33:50 I'll take all of the good palm oil from China and send
33:52 it to Europe because Europe says they want good palm oil.
33:55 So it's not a kind of
33:56 fix everything,
33:57 but,
33:57 but of course it will be better because it's targeted,
33:59 and I do run that counterfactually.
34:03 OK,
34:03 final question for Harris,
34:04 and then I think we should
34:06 move on in the interest of time.
34:08 OK,
34:08 hi.
34:09 So I have a question regarding how firms access the land.
34:12 I mean,
34:12 how much do they pay in order to obtain a lease?
34:16 It's something that seems to be a little bit hidden in your model,
34:18 maybe through the production costs.
34:19 I mean there's no explicit
34:20 modeling of the price of the land.
34:22 I mean,
34:22 there's been a lot of debate.
34:24 About whether governments somehow squander the land by giving you very
34:28 favorable access to our producers.
34:31 So
34:31 isn't there a possibility of
34:33 having some policy through making land more expensive
34:37 for producers rather than tax imports or exports.
34:40 Yes,
34:41 absolutely.
34:41 So this would be a surprise.
34:42 Well,
34:42 I,
34:43 I hear actually two things that you're saying.
34:44 One is access to export markets.
34:47 There,
34:48 I'm just assuming that
34:49 there's direct access and the world price is the world price.
34:52 I'm abstracting away from that.
34:53 But then at the level of,
34:55 you know,
34:55 land cost and production costs,
34:58 that's all getting picked up.
35:00 when I estimate the cost structure of the industry,
35:03 one point that can be made is,
35:04 you know,
35:04 when I run my counterfactuals,
35:05 I'm holding that cost structure fixed.
35:08 OK,
35:08 so if in response to palm oil tariffs,
35:10 the Indonesian government is responding
35:12 by changing the cost structure,
35:13 I'm going to miss that.
35:14 Although I can't get that without a model
35:15 of how Indonesia kind of sets cost structure.
35:18 But then also sort of,
35:20 what about this policy of bidding up?
35:22 What about Indonesia just bidding up the cost of land,
35:25 for example,
35:26 and reducing
35:27 things in that way.
35:28 There again,
35:28 that's going to rely on an Indonesian government that is both willing and able
35:32 to implement
35:33 that policy.
35:34 So one,
35:35 does the Indonesian government actually want to do that
35:37 given that palm oil is their number one agricultural export
35:40 and would be their top export,
35:43 full stop,
35:43 if it weren't for the fact that Indonesia is also a big natural gas oil producer.
35:47 Um,
35:48 but 2,
35:49 could the Indonesian government actually do it?
35:51 So they could put in a fine
35:53 for building in forested areas.
35:54 The central government could do that,
35:56 but then really because it's a decentralized government,
35:59 actually it's going to come down to enforcement by the local mayor
36:02 who,
36:02 you know,
36:02 I can bribe.
36:03 Ah,
36:03 and so there's an enforcement issue.
36:05 This is why I'm focused on the demand side.
36:07 These supply side policies are traditionally what environmental regulation
36:11 has focused on,
36:12 and I'm just pointing out that there's
36:13 this one other qualitatively different kind of approach
36:16 that might help in cases where
36:18 all of that
36:19 domestic regulation is very difficult and messy.
36:23 Actually,
36:23 we have another policy lever that we can use.
36:26 OK,
36:27 thanks for the clarification.
36:28 Thank you.
36:29 Great,
36:30 so I'll just talk a bit now about estimation.
36:32 So,
36:32 for estimation,
36:33 what I'm doing here is I'm combining classic continuous oiler methods
36:37 with newer discrete oiler CCP methods,
36:40 and both are going to be using short-term perturbations.
36:43 So,
36:43 I'm comparing investing today,
36:45 which is what actually happens in the data,
36:47 to delaying investment by one period,
36:50 and by revealed preference,
36:51 uh,
36:52 that
36:52 delayed investment is suboptimal because I didn't actually do it,
36:56 and that delay is going to help me pin down the sub
36:59 sub-optimality of that delaying is going to help me pin down the payoff parameters.
37:03 So notice here in both cases,
37:05 the investment is up and running by period T + 2.
37:08 And so some under some assumptions,
37:10 the continuation values are going to align and difference out.
37:14 Ah,
37:14 so differencing out the continuation values helps us a lot
37:17 computationally because now I don't need to compute them.
37:20 And that's the usual challenge.
37:21 Also,
37:22 future expectations are getting differenced
37:24 out alongside the continuation values.
37:26 That is to say,
37:26 this is the sense in which I can accommodate those expectations
37:30 without needing to specify exactly what they are,
37:32 of course,
37:33 subject to an assumption that I'm going to
37:35 talk about in just
37:36 one second.
37:37 Um,
37:37 so,
37:37 what am I assuming?
37:38 I'm assuming first that site owners are actually choosing between today or delay.
37:44 That's gonna put some restrictions on,
37:46 for example,
37:46 property rights or credit constraints or uncertainty,
37:49 although I can speak to each of those a little bit.
37:52 Now
37:52 I'm also assuming that sites are atomistic,
37:54 and this gets at Xavier's comment from before,
37:57 where this is going to rule out market power,
37:59 OK,
38:00 but this is a place where this is a setting where market concentration is low,
38:03 maybe that's not that bad,
38:05 but it's also gonna rule out spatial interaction,
38:07 uh,
38:07 which,
38:08 you know,
38:08 if I am
38:10 a site that is neighboring you,
38:12 another site,
38:12 and now because I've developed my.
38:14 Oil land into plantations.
38:17 Now I'm attracting a local labor market,
38:18 for example,
38:19 and that's making it cheaper for you
38:21 to produce.
38:22 That's a very realistic,
38:24 natural spatial interaction that I'm not going to be able to accommodate.
38:27 Why?
38:28 Because it's going to end up being hard to relax,
38:30 one,
38:31 absent a model of local labor markets and absent data on local labor markets,
38:35 which I don't have.
38:36 But two,
38:37 It's going to be hard to relax because if I'm a big player and I delay,
38:40 then others are going to respond
38:42 to that delay,
38:43 that I,
38:44 my action,
38:45 and that's going to change how the
38:47 economy is evolving and where the state of the economy is going.
38:50 That is to say,
38:51 now the continuation values are no longer going to align
38:54 and difference out,
38:55 and then we're going to have computational issues.
38:57 Now,
38:57 lastly,
38:58 I'm going to be assuming rational expectations,
39:00 which by the way,
39:01 does mean that expectations can be wrong today.
39:04 They just need to be correct over time.
39:06 Of course,
39:07 still a very strong assumption,
39:08 but it's going to be much weaker than needing
39:10 to specify what exactly expectations are in every period,
39:14 like we would do
39:15 with a conventional full solution approach.
39:19 Now,
39:19 the upshot of this is we're going to be
39:20 able to estimate the model with a linear regression,
39:23 and so I can talk about endogeneity in instruments in familiar ways,
39:27 and furthermore,
39:28 Euler methods are going to accommodate the non-stationarity of the problem.
39:31 So I actually can't use typical CCP methods like BBL or POB
39:36 because those require stationarity.
39:38 And
39:39 if,
39:39 you know,
39:39 BBL and POB are just letters to you,
39:41 then
39:41 you can ignore that sentence,
39:42 not a,
39:42 not a,
39:43 not central.
39:44 OK.
39:45 So let's start with uh estimation,
39:47 uh,
39:48 what,
39:48 what I'm estimating,
39:49 and let's start with the continuous choice on the intensive margin.
39:53 Now,
39:53 uh,
39:54 if
39:55 this is the plantation development decision,
39:57 that continuous twist.
39:59 This slide I want to flag is pretty standard.
40:01 It's just like the consumption,
40:02 consumption savings problem in,
40:04 you know,
40:04 grad school macro 10 1.
40:06 So I'm gonna go through quickly,
40:07 but I,
40:08 I,
40:08 I'm still gonna go through because the intuition is useful.
40:11 So,
40:11 in this top line,
40:12 the Euler equation is capturing that trade-off between investing today
40:16 or delaying.
40:17 Now,
40:17 this is a first-order condition.
40:19 If I invest today,
40:20 then revenues are higher because I'm going to start producing earlier,
40:23 but at the same time,
40:24 costs are also higher because I have to pay them today
40:27 instead of later when they're discounted.
40:30 So this is going to specialize to this second line.
40:32 I'm going to develop more plantations today,
40:34 AIT,
40:36 uh,
40:36 relative to tomorrow,
40:37 AIT plus 1,
40:38 for action,
40:39 if the extra revenues are high or the extra costs are low.
40:43 Now,
40:44 note that that top line has expectations,
40:46 but they don't show up in the
40:48 second line because I'm substituting them out
40:50 for realized values
40:52 plus expectational errors.
40:54 This is the usual trick.
40:56 Uh,
40:56 now,
40:56 under rational expectations,
40:58 those errors are mean zero conditional on anything in the period T
41:03 information set.
41:03 Note,
41:04 of course,
41:04 that we're talking here about
41:05 uh RT + 1
41:07 and not RT,
41:09 OK?
41:09 And so we need to be a little bit careful.
41:10 RIT + 1,
41:11 not RIT.
41:12 So we need to be a little bit careful here.
41:14 I need to use the lags.
41:16 So,
41:16 let's then talk about this linear regression that we get,
41:20 uh,
41:20 and let's talk about identification.
41:22 First off though,
41:22 what are the data
41:24 here?
41:25 Uh,
41:25 the left-hand side is data.
41:26 OK,
41:27 so I see in the satellite data how much plantation development A
41:31 or action there is in each site I and in each period.
41:35 AIT AIT plus 1,
41:36 I see it.
41:37 Beta discount factor,
41:38 generically unidentified,
41:40 you know,
41:40 Manac Desmar,
41:40 uh,
41:42 you know.
41:43 Not identified,
41:44 I'm just gonna set it.
41:45 I can play around with it though and show you robustness.
41:48 OK,
41:48 so the left-hand side,
41:49 I see everything.
41:50 And then
41:51 prices P
41:52 yields Y and cost factors like road distance,
41:55 urban distance X.
41:57 I see all of those,
41:58 those are data.
41:59 So then what am I actually estimating here?
42:02 The goal is to estimate these parameters data of the payoff function.
42:07 And then the error term is going to have
42:09 two kinds of errors that we need to worry about.
42:11 One,
42:12 the unobserved cost shocks epsilon,
42:14 as well as two,
42:15 those expectational errors.
42:17 Actually,
42:18 both are going to cause us problems.
42:19 So let's start with the epsilons.
42:21 Those are going to cause price endogeneity for the following reason.
42:24 Low costs today
42:26 mean high entry today,
42:27 so high supply tomorrow,
42:29 and therefore low prices tomorrow.
42:32 Now,
42:32 the atas are also correlated for the reason I said before.
42:34 This is PT plus 1,
42:36 not PT.
42:37 So for the instruments,
42:38 I need to use lags.
42:40 Um,
42:40 OK,
42:41 so price endogeneity on the supply side,
42:43 what do we want?
42:43 We want a demand shifter.
42:45 So I have that demand shifter.
42:46 I'm going to get that demand shifter
42:48 from
42:49 demand estimation,
42:50 that's that right word shift over time in the
42:52 demand curve that I plotted for you before.
42:54 Of course,
42:54 that's just time series variation at the end of the day.
42:56 OK,
42:57 so here what it's going to do is it's going to interact
42:59 with that cross sectional variation in yields,
43:02 which I can instrument for with exogenous factors like sunlight.
43:06 So here's the intuition.
43:07 When prices go up,
43:08 it's going to increase revenues for sites that can actually grow palm oil.
43:12 It's not going to help very much for a site that just can't grow palm oil to begin with.
43:16 Why?
43:16 Because,
43:17 you know,
43:17 revenue is price times quantity,
43:19 so if quantity is zero,
43:20 then revenue is always zero.
43:22 Price going up doesn't matter.
43:23 So that is to say that suitable sites
43:25 are going to be more likely than unsuitable sites
43:27 to develop land when prices rise,
43:30 and that difference is going to give me the supply elasticity
43:34 that I'm after.
43:35 And
43:36 then there's then there's a question about.
43:39 I'm good.
43:41 So yeah,
43:41 I have two questions.
43:42 Uh,
43:43 first is,
43:44 can,
43:44 can you store palm oil at all?
43:46 Uh,
43:47 so,
43:47 uh,
43:48 palm oil can be stored.
43:49 I don't have that either on the demand side or the supply side.
43:52 On the demand side,
43:53 side,
43:53 I actually see the storage of palm oil.
43:55 You might be worried about a stockpiling
43:58 concern and dynamic demand.
43:59 It turns out to be very small,
44:01 and,
44:01 and I,
44:01 I see that it's small because it's in the data.
44:03 On the supply side,
44:05 wait one second.
44:06 What does it mean it's small
44:08 storage is,
44:08 is,
44:09 is non-zero,
44:09 then,
44:10 then you have.
44:11 Then you have the hotening conditions kicking in.
44:14 Yeah,
44:14 but the thing is,
44:15 if you look at the kind of ketchup papers or the laundry detergent papers,
44:18 so Handel Nouveau,
44:19 but also
44:20 Michael Keene has work on this,
44:22 there you can see they kind of back out,
44:24 OK,
44:24 for a given amount of consumption.
44:27 Every day I'm consuming this much relative to consumption.
44:30 How much do I have stored at any given period of time?
44:32 And you see,
44:33 I mean,
44:33 we see this with olive oil that we just use at home,
44:35 right?
44:36 How much do we use today relative to how much is in the bottle?
44:38 We're storing,
44:39 you know,
44:40 3,000% of what we're consuming day to day.
44:43 Here,
44:43 because it's part,
44:44 and part of this is just because of the aggregation,
44:46 it's at the country level and it's year on year.
44:49 You see that of the amount that is consumed every year,
44:51 it's only like 10%,
44:53 it's something like 10%
44:55 being stockpiled at any given time.
44:57 So this is the sense in which,
44:58 you know,
44:59 that stockpiling can go from
45:00 10% to 15%,
45:02 but it's not going to buy us things in a way.
45:04 I understand,
45:04 but the fact that
45:05 that the stockpile is non-zero and and the fact it's only 10% can be the outcome of.
45:11 Of an economy which is driven by,
45:13 by,
45:15 by prices
45:16 in the hoteling world.
45:17 I,
45:18 I,
45:18 you know.
45:20 The issue is,
45:20 is as soon as there is a potential for speculative storage,
45:24 then your price equation becomes pinned down by,
45:27 by,
45:28 by a different equation.
45:29 Yeah,
45:30 I agree.
45:31 No,
45:31 I agree.
45:31 This is going to be a fundamental issue with
45:33 at the end of the day,
45:34 my demand is static demand,
45:36 not dynamic demand.
45:38 We can talk about making a dynamic demand.
45:40 I'm not sure at the end of the day it's going to give me some price,
45:43 some price elasticity,
45:44 demand elasticity that's going to speak to the leakage question.
45:47 We can talk about if dynamic demand would change that price.
45:50 Elasticity
45:51 from 0.7 to 7.
45:52 If that's the case,
45:53 OK,
45:53 it's super important.
45:55 Uh,
45:55 but if it's going to go from 0.7 to 0.8,
45:57 then it's not going to change qualitative conclusions by that much.
46:00 But yes,
46:00 I totally agree with this.
46:02 No,
46:02 I'm talking on the,
46:03 I'm talking on the supply side.
46:04 I'm talking not talking about demand in Europe.
46:06 I'm talking about Indonesia traders or the stockpiling in order to,
46:10 to sell strategically
46:12 given
46:12 the crisis that are going to happen.
46:14 I think that's the most,
46:16 most problematic problem because this is how you get your amplification.
46:21 So your PT +1,
46:22 YT plus one.
46:24 Fully hedges on the fact that there is no,
46:26 that,
46:27 that your price opportunity is from a market clearing condition,
46:29 not on the storage condition.
46:31 Yes,
46:31 yes,
46:32 so I,
46:32 I agree with that.
46:33 That,
46:33 that's,
46:34 that on the supply side,
46:35 I,
46:35 I am missing that storage margin.
46:38 Here again,
46:38 so I,
46:39 I,
46:39 I
46:40 don't have the,
46:40 the data off the top of my head,
46:42 but also this stockpiling
46:44 within Indonesia is also observed.
46:45 And so one track that I haven't done but should do,
46:48 and,
46:48 and thank you for Bringing it up is
46:49 to see the extent to which that stockpiling is responding to
46:53 the historical price variation,
46:54 and I can
46:55 start to at least speak to this more concretely in that way.
46:58 Yeah,
46:59 here,
46:59 when you produce it,
47:00 you have to sell it right away
47:01 and,
47:01 and therefore,
47:02 yes,
47:02 the,
47:02 the price variation is just coming from,
47:04 from,
47:05 uh,
47:06 you know,
47:06 the,
47:07 the,
47:07 the kind of price variation
47:08 period to period.
47:10 Um,
47:10 in counterfactuals,
47:11 so that's an estimation concern.
47:13 For counterfactuals,
47:13 I'm not as worried,
47:14 you know,
47:15 conditional on the estimation being right,
47:17 because here you might think that,
47:18 you know,
47:19 I try to tie,
47:20 I see a tariff coming into play,
47:21 and so I try to rush by selling all of my stockpile right now,
47:24 for example,
47:25 um,
47:25 or I try to rush into
47:27 putting,
47:28 setting up a farm to kind of time out the tariffs being put in place.
47:31 But here,
47:32 tariffs are being put,
47:33 are,
47:33 are,
47:33 are in place on consumption in every future period.
47:36 OK,
47:36 so there's kind of a limit to the extent.
47:38 To which you can kind of rush
47:40 and uh
47:41 preempt the tariff in the way that you can in,
47:43 in other environmental
47:45 uh settings.
47:47 No,
47:47 no,
47:47 I'm fine,
47:47 but
47:48 my only concern is,
47:49 you know,
47:50 when,
47:50 when you showed your,
47:51 your,
47:51 your price trend,
47:52 your log price that is very stable over time,
47:55 it does look like
47:57 a storage,
47:58 uh,
47:59 price pattern.
48:00 Yes,
48:01 I mean it's very constant returns.
48:02 So,
48:02 so that's why I,
48:03 I,
48:04 no,
48:04 no,
48:04 totally you make your reason not to have it,
48:06 but that's what I wanted to try.
48:07 That's it.
48:07 Yeah,
48:08 absolutely.
48:08 No,
48:08 I,
48:09 I agree with that.
48:09 Although the one kind of
48:11 other point here is that if demand is just going up over time,
48:15 as it is,
48:15 you know,
48:16 very quickly,
48:17 then actually if you,
48:18 if you
48:19 expected this storage component to be really,
48:21 really meaningful,
48:23 and if palm oil can be stored for 567 years with no problem,
48:26 as it can be.
48:27 Then you would expect that everyone would just stockpile everything all
48:30 of the time and never sell anything given that no,
48:32 no,
48:32 no,
48:32 no,
48:32 no,
48:32 no,
48:32 no,
48:33 that's what the hotel and coalition tells you is people
48:35 in every period are indifferent between selling and stockpiling.
48:38 So it doesn't have any prediction on the quantity of stocks,
48:41 but this pins down
48:42 instead of having market con what I'm,
48:44 my point is instead of having the market clearance condition to pin down your price
48:48 in,
48:49 in your early equation,
48:50 it's a hotening condition that pins down the price.
48:52 I see,
48:53 I see that's a completely different,
48:55 that's,
48:55 that's a completely different early equation,
48:57 yeah,
48:57 because it's,
48:57 it's,
48:57 it's off of the storage versus selling as
48:59 opposed to selling today versus selling tomorrow.
49:01 Yeah,
49:02 producing today and producing tomorrow.
49:04 Yeah,
49:04 I,
49:04 I've coupled producing and selling.
49:06 Your point is that
49:07 producing and selling are actually two,
49:10 when we decouple it,
49:11 it becomes
49:12 more complicated.
49:12 You have this extra condition.
49:14 Yeah,
49:14 I,
49:14 I agree with that.
49:15 OK,
49:15 great.
49:16 So let's try to
49:17 move on to the results,
49:19 uh,
49:19 in the interest of time because we have about 20 minutes left.
49:22 Great.
49:23 Um,
49:23 OK,
49:24 so let me talk to you about the extensive margin.
49:27 So here,
49:27 what's happening,
49:28 uh,
49:28 is that we have this,
49:30 uh,
49:30 mill being built.
49:32 OK,
49:32 so this is that discrete choice.
49:34 Uh,
49:34 so if
49:35 this were the only margin,
49:36 then we could just use the usual Euler CCP
49:39 methods directly.
49:40 So,
49:40 let me Give you that intuition first.
49:42 Either we can invest today or delay.
49:44 Either way,
49:44 the mill is being built by T + 2,
49:46 so the
49:48 continuation values align,
49:49 we have finite dependence.
49:50 Now,
49:50 I am imposing that the mill actually gets built in that delayed case.
49:54 That's potentially sub-optimal.
49:55 It's going to depend on the loaded shock.
49:57 So this correction term gamma is going to account for that.
49:59 Now my model is a little different because
50:01 we also have this continuous intensive margin.
50:03 The problem here is going to be that delaying the mill also delays the plantation.
50:07 If I delay and only plant AIT,
50:09 then the continuation values don't align.
50:11 And so instead,
50:12 we need to catch up to that original path
50:15 to get finite dependence.
50:16 Again,
50:17 I'm imposing a particular potentially suboptimal choice.
50:19 The correction term needs to be expanded to account for that.
50:22 So combining this continuous,
50:24 these continuous and discrete pieces is the
50:27 marginal methodological contribution here.
50:29 This is common,
50:30 a common setup.
50:31 Businesses make a discrete choice to enter
50:33 a market and then a continuous choice over how much to produce,
50:36 but I don't want to overclaim here because
50:38 CCP methods could already deal with both margins.
50:40 It's just
50:41 oiler CCP methods
50:42 that focused on the extensive margin,
50:44 and I'm relaxing that.
50:46 So
50:46 from here we're going to get a regression.
50:48 This is a two-step estimator,
50:49 just like the usual CCP methods.
50:52 Without going into detail,
50:53 what I'll just say
50:55 is that
50:55 this piece here is where the two margins hook together.
50:58 OK,
50:58 so this is where the intensive margin
51:00 estimates
51:01 come in.
51:02 And what's the intuition here?
51:03 It's just that I'm more likely to build
51:05 the mill in the first place if those plantations
51:08 would actually be good.
51:09 If the plantations will be terrible,
51:10 then why build the mill to begin with?
51:13 OK,
51:13 so now I'm going to have the cost structure of the
51:15 industry which I'm going to need of course for counterfactuals.
51:17 Here are those cost estimates,
51:19 estimates.
51:20 So
51:20 I find that on average to develop one hectare of land into plantations,
51:24 I get an average cost of
51:26 $10,000.
51:27 That's going to line up.
51:28 With the $7000 accounting cost,
51:31 different
51:31 economic objects,
51:32 of course,
51:32 but we're in the same ballpark,
51:34 so this is at least one way of
51:35 starting to validate
51:37 the estimates.
51:38 Here cost factors don't enter because at this point the mill has sunk.
51:40 You just kind of build the plantations around the mill.
51:44 So for mills,
51:45 I'm going to get a $23 million average
51:46 cost compared to $20 million in accounting costs,
51:49 and here the cost factors do matter.
51:51 So being close to major roads,
51:52 major ports,
51:53 major urban centers,
51:54 major urban centers is going to be less costly because you have
51:58 lower transportation costs.
52:00 Um,
52:00 now we see here that sites actually do try to avoid forest.
52:03 That's good.
52:04 Maybe it's just because then they need to pay a bribe if they need to.
52:06 Go and cut down all of these trees,
52:08 but we see here that there's no accounting at all for peat.
52:11 OK,
52:11 so cutting,
52:12 releasing peat emissions is completely
52:15 inconsequential and not costly at all to these firms in terms of private cost,
52:19 and that's where all of the big emissions are.
52:21 OK.
52:21 And so this is the sense in which private costs
52:23 are failing to line up with social costs,
52:25 and this is the externality that we want to force firms to internalize.
52:30 OK,
52:31 so let me put the pieces together for you now and we can talk about
52:33 counterfactuals.
52:34 So,
52:35 suppose the EU puts in tariffs tar,
52:37 conditional on supply,
52:38 sellers are now going to be profiting less from the EU
52:41 and so they're going to shift sales over to China.
52:43 Now the demand elasticities are going to determine how
52:46 much shifts over to China and therefore how much
52:49 world prices fall.
52:50 and lower world prices are going to translate into less supply given the supply.
52:55 Now I have data on the carbon stocks,
52:56 where the trees and the peat
52:58 are,
52:58 and so
52:59 when supply contracts in this region versus that region,
53:02 I'm going to be able to compute the
53:05 implications for carbon reductions directly from the data.
53:09 Now I am going to monetize
53:10 emission reductions with the $40 social cost of carbon.
53:14 And then,
53:14 and this is getting at uh the question from before,
53:16 so I'll not rehash everything.
53:18 I am assuming that
53:19 palm oil tariffs are not increasing
53:21 non-palm deforestation.
53:23 Acacia is the big threat there,
53:24 but I check
53:25 uh that substitution in the data,
53:27 and it's,
53:27 it's small anyway,
53:28 so I didn't augment the model to
53:30 account for that.
53:31 Uh,
53:31 otherwise,
53:31 it would have been important to,
53:33 to account for it directly.
53:34 Now,
53:36 the
53:37 other thing that I'll just point out here is that,
53:40 you know,
53:40 sometimes this gets a little lost.
53:41 This is an equilibrium model.
53:43 You know,
53:43 there's this leakage across markets.
53:45 Prices are set in equilibrium,
53:47 farmers are playing a dynamic competitive equilibrium,
53:50 but this is only within palm oil.
53:52 OK,
53:52 so I don't have effects via other industries
53:54 because that would require a multi-industry model.
53:57 And so these trade computable general equilibrium models are
54:00 going to do much better at capturing that cross-industry
54:03 kind of leakage and those cross-industry effects,
54:05 but the cost is going to be that
54:07 the
54:08 production functions within each industry and those models is going to be much
54:11 more simple and a little bit crude compared to what I have,
54:14 where I can really give you good detail within this one industry.
54:18 OK,
54:19 so in the baseline,
54:19 I'm gonna set tariffs to maximize global welfare.
54:22 I can relax that,
54:23 and,
54:23 and in that case,
54:24 the terms of trade effects
54:26 or the kind of
54:27 Indonesian market power effects would come in.
54:29 Uh,
54:29 I do do that in,
54:30 in robustness.
54:32 Here I'm also to get at Ariana's question,
54:34 I am
54:35 treating all palm oil equally,
54:36 again because it's much easier to administer,
54:38 but I can and do relax that assumption as well.
54:41 I can also differentiate between good palm oil and bad palm oil.
54:45 So for leakage,
54:46 I'm going to show that the
54:49 effects of different,
54:50 I'm going to show you the effects of different coalitions.
54:52 So
54:52 all importers,
54:54 that's the big coalition or just the EU alone,
54:56 that's the small coalition.
54:58 I'm not going to be modeling the coalition formation game,
55:00 but I am going to be able to quantify for you
55:02 the incentives
55:03 to defect.
55:04 Uh,
55:05 now for commitment,
55:07 full commitment is tariffs upheld forever.
55:09 There's no game here because the regulator moves once at the beginning
55:12 and then producers follow.
55:13 There's no back and forth.
55:15 Uh,
55:15 no commitment is just sequential static optimization.
55:18 And then lastly,
55:19 limited commitment is like a Chinese 5-year plan.
55:22 OK,
55:22 you set it for 5 years,
55:23 you do it,
55:24 and then you reset again,
55:26 uh,
55:26 after 5 years are over.
55:28 And so,
55:28 uh,
55:28 a 2-year
55:29 plan is very close to sequential static
55:31 optimization because you're updating a lot.
55:33 A 50-year plan is like
55:35 full commitment because you don't update that much at all.
55:38 So note here there is going to be this back and forth,
55:41 there is a game
55:42 because
55:42 the regulator moves and then the producers
55:44 respond and then the regulator moves again,
55:46 taking producer's past response as given.
55:49 So I'm going to need to solve by backward induction.
55:52 Um,
55:53 note here,
55:53 and this sort of came up a bit before,
55:55 but I'll just,
55:55 ah,
55:56 bring it up again to clarify,
55:58 producers are atomistic and competitive,
56:00 so there's no sense in which they're gonna
56:02 rush to clear land and push tariffs to zero,
56:04 because if you can cut down all of the land,
56:06 then tariffs
56:07 should go to zero optimally.
56:08 But they can't do that because they're,
56:10 they're atomistic.
56:11 Uh,
56:11 they also can't really rush to get in before the tariffs,
56:14 and that's,
56:15 and that's the green paradox because tariffs
56:17 are getting paid in every future period.
56:18 So you can rush and avoid tariffs for one period,
56:20 but you're still paying it
56:22 over the future horizon.
56:24 Now,
56:25 for the sake of time,
56:26 I'm gonna skip how I,
56:28 the technical details of how I actually solved the model.
56:31 Let me just say this one point.
56:33 So,
56:33 when I estimated the model,
56:36 I estimated it without actually solving it,
56:38 because the Euler methods allowed me to do so.
56:40 That meant two things.
56:42 Excuse me.
56:43 Number 1,
56:44 computation was much easier,
56:45 and number 2,
56:46 there were fewer assumptions that I needed to make
56:48 on expectations.
56:50 Here,
56:50 in counterfactuals,
56:51 I actually need to solve the model.
56:52 There's no getting around it.
56:54 And so that,
56:55 again,
56:55 means two things.
56:56 Number 1,
56:56 computation is gonna be harder.
56:58 I'm gonna need to use some computational tricks here.
57:00 And 2,
57:00 I am gonna need some more assumptions on expectations.
57:03 I'm gonna need to actually specify them.
57:05 Uh,
57:05 but we can talk about that in if there's time for questions.
57:09 So let me just get into the,
57:10 the counterfactuals,
57:11 the counterfactual results.
57:13 So the baseline result here is that coordinated committed tariffs are effective,
57:17 OK?
57:17 How do we see that here?
57:18 The Y axis is emission reductions,
57:20 the higher is better,
57:22 and the x axis is the commitment period.
57:25 The solid blue line is coordinated tariffs.
57:28 So committed coordinated tariffs are right over here,
57:30 and we see they're pretty close to that,
57:32 uh,
57:33 domestic,
57:34 socially optimal domestic tax,
57:35 which I'm thinking of as infeasible,
57:37 but will be our,
57:38 our benchmark.
57:39 So there are two caveats here.
57:41 Number 1,
57:41 there are going to be pretty big losses as coordination breaks down,
57:44 down to the EU alone.
57:46 So for the EU alone,
57:46 for example,
57:47 leakage concerns are going to kick in because the EU
57:50 is acting alone and because it has low coverage,
57:53 and that those leakage concerns are going to result in small tariffs.
57:56 So losses here are disproportionate because low coverage
57:59 is going to get compounded by low tariffs.
58:02 Now,
58:03 0.2 is that there are big losses as commitment breaks down,
58:07 and the starkest case here is no commitment,
58:09 where tariffs never get applied because of time to build.
58:12 So,
58:13 the intuition here is that tariffs today are going
58:14 to get set to zero because they can't produce,
58:17 uh,
58:17 they can't prevent new plantations,
58:19 because there's no new plantations aren't producing anything that's taxable
58:22 until tomorrow.
58:23 So those new plantations are really only thinking about tariffs tomorrow,
58:26 not tariffs today.
58:28 But then by the time we get to tomorrow,
58:30 those new plantations aren't going to,
58:32 uh,
58:34 by the time we get to tomorrow,
58:35 tomorrow's tariffs are again going to be set to zero because
58:38 from the perspective of tomorrow,
58:40 the plantations that were new today are already sunk.
58:43 OK,
58:43 and so tariffs tomorrow can't change
58:45 the fact that those plantations have been built,
58:47 and so again,
58:48 tariffs are going to be set to zero,
58:49 that is to say they're always going to be zero.
58:52 But there's a
58:52 positive side here.
58:54 So number 3,
58:55 all of these policies are pretty efficient relative to other
58:58 things like retrofitting power plants for carbon capture and storage.
59:02 So unilateral EU
59:03 action,
59:04 yes,
59:04 has a pretty small effect,
59:06 2 or 3% compared to,
59:07 you know,
59:07 65%.
59:08 percent
59:09 in the,
59:09 in that domestic intervention benchmark.
59:12 So 2 to 3% is small,
59:13 but first off,
59:14 2 to 3% is better than nothing.
59:16 And then number 2,
59:16 at that small scale,
59:18 it's only $10 of
59:20 surplus for gun
59:21 per ton of carbon averted.
59:23 OK,
59:23 so pretty efficient.
59:24 And then number 4,
59:25 coordination is going to help us alleviate also the commitment problem.
59:29 So let me rescale those blue lines for you.
59:32 Under full coordination,
59:34 that's that solid blue line,
59:35 we see that committing for 5 years at a time is actually enough to achieve 95%
59:40 of the full commitment outcome.
59:42 And 5 years is salient because that's the political cycle.
59:45 On the other hand,
59:45 if the EU is acting alone,
59:47 then it needs to commit for 20 years at a time to get to
59:50 that 95% efficiency relative to the,
59:53 the full commitment outcome.
59:55 OK,
59:55 and so this is the sense in which leakage and commitment interact.
59:59 That is,
59:59 as you make progress on coordination,
1:00:01 you're alleviating the leakage problem and
1:00:03 you're also alleviating this commitment problem.
1:00:07 Now I can also zoom in on the division of surplus across markets,
1:00:10 and here that exercise is going to show us that actually,
1:00:13 unfortunately,
1:00:14 coordination and commitment are both difficult in practice.
1:00:18 So first off,
1:00:18 there's going to be this incentive
1:00:20 not to coordinate.
1:00:22 So suppose the EU,
1:00:23 China and India are all acting and all importers are and other importers here,
1:00:27 Nigeria,
1:00:28 Bangladesh,
1:00:28 the US,
1:00:29 etc.
1:00:29 are thinking about joining the coalition.
1:00:32 So if they do,
1:00:33 then they're going to sacrifice,
1:00:34 they're going to be sacrificing consumer surplus.
1:00:36 We see consumer surplus goes down.
1:00:38 They're happy to do so because it reduces emissions if they care about emissions.
1:00:42 If they don't care about emissions and only care about consumer surplus,
1:00:45 there's no point in sacrificing their consumer surplus
1:00:47 and therefore no point in imposing import tariffs themselves.
1:00:51 And furthermore,
1:00:51 here they're going to be able to free ride
1:00:53 because the tariff coalition in acting,
1:00:56 that is the EU,
1:00:57 China,
1:00:57 and India in acting,
1:00:58 are putting in tariffs and pushing down world prices.
1:01:01 And so now these other importers compared to other
1:01:04 worlds where no one is doing anything are actually even better off
1:01:07 because they're enjoying lower,
1:01:08 lower world prices.
1:01:11 Uh,
1:01:11 so this gap then is the transfer that we need to think about
1:01:14 in order to induce participation,
1:01:16 uh,
1:01:17 into the coalition by these actors.
1:01:20 So there's going to be a very similar incentive not to commit,
1:01:23 where,
1:01:24 you know,
1:01:24 if you commit for a long period of time,
1:01:26 that's increasing tariffs,
1:01:27 and the point of doing that is
1:01:28 more consumer surplus sacrifice
1:01:30 for bigger
1:01:31 carbon reductions.
1:01:32 But again,
1:01:32 that only makes sense if countries care about carbon reductions
1:01:36 and carbon emissions.
1:01:38 However,
1:01:38 note that under coordination,
1:01:39 actually this gap here is not really very big,
1:01:42 um,
1:01:43 but when coordination breaks down,
1:01:44 so here the other importers have left,
1:01:46 it's just the EU,
1:01:47 China and India left,
1:01:48 that gap we see is,
1:01:49 is bigger.
1:01:50 So again,
1:01:50 this interaction between
1:01:52 uh coordination
1:01:53 and commitment.
1:01:55 And then last,
1:01:56 I can show you those effects on Indonesian and Malaysian surplus.
1:02:01 So if the problems are only weak incentives for domestic regulation,
1:02:04 then actually here tariffs can help
1:02:06 a bit.
1:02:07 So suppose importers threaten Indonesia and Malaysia.
1:02:10 That is to say,
1:02:10 either you put in the large socially optimal domestic tax
1:02:14 or face tariffs.
1:02:16 What we see here is that in most cases,
1:02:18 you know,
1:02:19 Indonesia and Malaysia are better off just accepting those tariffs,
1:02:21 which are smaller and therefore hurt producer surplus
1:02:24 by less.
1:02:25 Uh,
1:02:25 they'll just accept them.
1:02:26 But if the threat on the other hand,
1:02:28 is coordinated tariffs,
1:02:29 then we see that actually Indonesia and Malaysia are better off just moving to
1:02:33 that socially optimal domestic tax,
1:02:35 which one isn't very much bigger than coordinated tariffs anyway.
1:02:39 Coordinated tariffs are already pretty big.
1:02:41 That is to say,
1:02:41 in moving to this domestic tax,
1:02:43 producer surplus is not going down by very much.
1:02:46 However,
1:02:46 now,
1:02:47 Indonesia and Malaysia are able to get that government revenue
1:02:49 from that tax that was before going abroad to Europe,
1:02:53 you know,
1:02:53 China,
1:02:54 India,
1:02:54 etc.
1:02:56 Uh,
1:02:56 now,
1:02:57 we might also think,
1:02:59 uh,
1:02:59 and this came up before,
1:03:00 that
1:03:01 an export tax is a smaller ask that's maybe easier to administer.
1:03:05 So administratively it's easier,
1:03:07 as I said before,
1:03:08 because now we only need 5 auditors at the ports
1:03:10 instead of 1000 auditors to cover all the forests.
1:03:13 Uh,
1:03:13 but you know,
1:03:14 again,
1:03:14 the counterpoint is that maybe companies now only need
1:03:16 to bribe 5 people instead of 1000 people,
1:03:18 maybe it's easier.
1:03:19 Um,
1:03:20 but either way,
1:03:21 uh,
1:03:21 we,
1:03:22 if we can put in the socially optimal export tax,
1:03:25 actually we're going to achieve exactly the
1:03:27 same emission reductions as these coordinated tariffs,
1:03:30 because in both cases we're covering all imports.
1:03:32 That is to say we're covering all exports out of Indonesia and Malaysia.
1:03:36 Uh,
1:03:36 but note,
1:03:36 however,
1:03:37 that
1:03:37 these
1:03:38 uncoordinated tariffs over here
1:03:40 are
1:03:41 also still not enough to push Indonesia and Malaysia to this export tax.
1:03:45 Uh,
1:03:46 and then also that export tax might increase,
1:03:48 uh,
1:03:48 as Orellana had brought up before,
1:03:50 this palm oil production outside of Indonesia and Malaysia,
1:03:53 in West Africa and South America,
1:03:55 which import tariffs would
1:03:57 kind of catch by default.
1:03:59 Uh,
1:03:59 so,
1:04:00 lastly,
1:04:00 note here,
1:04:01 and this gets to Tristan's equity concern from the very beginning.
1:04:05 OK,
1:04:05 note that all regulation hurts Indonesia
1:04:08 and Malaysia.
1:04:08 Everything is negative across all of these policies.
1:04:11 OK,
1:04:11 and so,
1:04:12 uh,
1:04:12 if we
1:04:13 have these equity concerns,
1:04:14 which we should,
1:04:15 of course,
1:04:16 then
1:04:16 at least one,
1:04:17 I'm going to be able to quantify those damages for you,
1:04:21 and therefore,
1:04:21 2,
1:04:22 we can talk about the transfers that might be needed to address
1:04:25 these equity concerns as,
1:04:26 as much as we can.
1:04:29 Uh,
1:04:29 so,
1:04:30 let me just take 30 seconds to conclude,
1:04:32 and then we'll have some time for questions.
1:04:34 Um,
1:04:35 OK,
1:04:35 so to summarize what we talked about today,
1:04:37 we've talked about how import tariffs can be effective if they're coordinated
1:04:41 and if they're committed.
1:04:42 And this is going to help when domestic regulation is infeasible,
1:04:45 including when those domestic issues maybe take time to change,
1:04:49 but we want to act on the climate today
1:04:51 and now.
1:04:52 And then as we're setting,
1:04:53 we look at the palm oil industry,
1:04:55 which accounts for a massive 5%
1:04:57 of global carbon emissions.
1:04:59 But I want to end on a,
1:05:01 on a positive note.
1:05:02 Even though that past deforestation is sunk and it's over,
1:05:06 there are still huge swathes of forests that remain intact,
1:05:09 especially in Papua and also to a lesser extent on Borneo.
1:05:13 And that's the scope of what we can still save if
1:05:16 we go ahead and act and implement good policy today.
1:05:19 Thank you.
1:05:22 OK,
1:05:23 that's great.
1:05:23 Thanks for sticking to the time,
1:05:25 Govinda.
1:05:26 Sorry,
1:05:26 I had another question.
1:05:28 Alan,
1:05:28 I'm just wondering about your CO2 reduction coefficient.
1:05:32 Uh,
1:05:33 the,
1:05:33 if the palm oil is used for biodiesel,
1:05:36 so in that case,
1:05:37 the biodiesel substitute the fossil fuel and
1:05:40 reduce the emission on the demand side.
1:05:42 So whether you have accounted for that reduction
1:05:45 in the emission in the supply side.
1:05:48 Yes,
1:05:48 so actually,
1:05:49 I,
1:05:49 I did that calculation.
1:05:50 When you have biofuels,
1:05:51 you're averting kind of fossil fuel emissions.
1:05:54 It turns out that those fossil fuel emissions averted
1:05:57 are very small compared to the kind of emissions needed to,
1:06:00 to grow palm oil,
1:06:02 uh,
1:06:02 on average.
1:06:02 Um,
1:06:03 those palm oil emissions are so big that
1:06:05 even if you account for that,
1:06:06 it's,
1:06:06 it's still,
1:06:07 you know,
1:06:07 it's on the order of 5%.
1:06:09 Yes,
1:06:09 you avert a little bit,
1:06:10 but not nearly enough to account for the peat that you've destroyed.
1:06:13 That's why Europe has removed,
1:06:15 they used to subsidize palm oil
1:06:17 for use in biofuels,
1:06:18 and,
1:06:18 and
1:06:19 just in the past couple of years,
1:06:20 they've removed those subsidies.
1:06:22 Uh,
1:06:22 and so it's just a little bit after my,
1:06:23 my sample,
1:06:24 but,
1:06:24 um,
1:06:25 they removed those subsidies because they realized,
1:06:27 OK,
1:06:27 actually,
1:06:27 we really should not be subsidizing biofuel,
1:06:30 uh,
1:06:30 palm oil for use in biofuels.
1:06:32 Not only that,
1:06:33 the EU already banned use of palm oil for the biodiesel production starting in 2020.
1:06:41 Yes,
1:06:41 although it's,
1:06:41 it's not a full-on ban,
1:06:42 it's,
1:06:43 it's phased out over time.
1:06:44 Originally it was a full-on ban,
1:06:46 and then,
1:06:46 you know,
1:06:46 Indonesia said we're going to cancel all of our Airbus,
1:06:49 purchases,
1:06:50 for example.
1:06:51 And
1:06:51 so there's diplomatic,
1:06:52 there's a bit of a diplomatic row,
1:06:54 and there was a WTO lawsuit,
1:06:56 and so they shifted it to,
1:06:57 I think it's by 2030 now.
1:06:58 Everything's going to get phased out.
1:06:59 Norway though,
1:07:00 however,
1:07:00 Norway's not an EU country,
1:07:01 of course,
1:07:02 but closely related.
1:07:03 Uh,
1:07:03 Norway just full-on banned and,
1:07:04 and,
1:07:05 and called it,
1:07:06 called it a day.
1:07:12 OK,
1:07:12 that's great.
1:07:13 If there are no further questions,
1:07:14 then let's conclude the seminar and,
1:07:16 uh,
1:07:17 thank you very much,
1:07:18 Alan.
1:07:18 It was very interesting.
1:07:20 Uh,
1:07:20 thank you for meeting both.
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