Rob:	It's my pleasure to introduce Michael and paint the setting here. Among the students who are here, probably none who had class with Bernie Saffran or met him, so for that segment of the audience, I want to try to briefly describe the man, his accomplishments, and his great humanity.
	First, let me welcome and introduce to you, Bernie's daughter, Jenny Saffran, who is here. She's a professor of psychology at the University of Wisconsin and a major academic figure in her own right. In fact, Midwest Airlines, the one that serves hot chocolate chip cookies on its flight, had several years ago, her picture in their flight magazine. It was really an advertisement for the University of Wisconsin, trying to show off their best. Now admittedly that airline has had some financial troubles since. But still there was great honor in their recognizing her work.
	She in fact followed in the footsteps of her mother, who was a famous, cognitive neuropsychologist. It took a lot of neuros and a lot of psychology to have lived with Bernie all those years. Bernie came to Swarthmore in 1967 from the University of California, Berkeley. He was really the start of modern economics in the department at Swarthmore. Its intellectual center, its principal link to the greater economics profession, and above all, the heart of the department.
	Bernie spent a year on the staff of the council of economic advisors under President Nixon. Yeah, the guy who had a different set of plumbers from Joe the plumber. That affiliation didn't stop Bernie from taking huge interest and enjoyment from following the Watergate debacle. I've thought often of how much he would've enjoyed the unfolding of scandals and misguided economic policy over the last four years since his passing. 
	Bernie had great connections at the highest echalance of the economics profession. Connections that redounded to the benefit of his department colleagues and to the students. He was close friends with two Nobel Prize winners in economics. One of whom, Dan McFadden, rushed in to personally clean up Bernie's apartment just before Bernie arrived back in Minnesota with his new bride. Minnesota was where he and Dan McFadden were graduate students. I can't imagine why it had to be cleaned up, Bernie's apartment for his new bride. 
	A third Nobel Prize winner, Joe Stiglitz was a close acquaintance. That brings me to another connection with Nobel elements. Bernie had played a key role in the creation of one of the major journals sponsored by the American Economics Association, the Journal of Economic Perspectives. After it had begun and run for a year of so, the editors of the journal invited Bernie to write a column which would appear in every issue of the journal. The column was titled, "Recommendations for Further Reading." It played to one of Bernie's unusual traits. He loved to read widely, not just in economics, but in other social sciences and in the sciences. 
	This lead to the connection to the fourth Nobel Price laureate, one of the founders of modern, rigorous economics. A few years after Bernie began publishing the columns, that person, Paul Samuelson, wrote a letter to Bernie unsolicited, saying, "When the JEP" that's the Journal of Economic Perspectives "arrives, I turn first to your further reading column. Your summer 1998 version was a special winner. I appreciate how much work this has to involve. The generations of leaders will salute your extraordinary accomplishment. Best regards, Paul Samuelson."
	The Journal of Economic Perspectives is involved in another anecdote. A Ukrainian graduate student studying in Belgium, I'm not kidding, wrote a thesis, a part of which was devoted to compiling a list of those economists who had been the most prolific in publishing. Bernie finished on the list second in the world. Second only to Paul Krugman. Bernie's response to congratulations on this fact was, "That just shows you that for each one of us, there is some metric out there that will put you in second place in the world." 
	Bernie's lecturing or discussion leading skills were outstanding. He was demanding in an offhand way. Students loved him and he brought out the best in them. He said that he had fashioned his lecturing and teaching styles to Woody Allen. Who, like him, had lived a Jewish childhood in New York. That was true, it was a Woody Allen style and it worked. 
	All of us in the department turned to Bernie to evaluate research papers we were producing. He had such a wide range of technical skills, but also trenchant insights, quickly finding weaknesses, either quantitative or in the verbal analysis and arguments, and suggesting ways to strengthen our clarify a piece. He was the perfect economics colleague.
	Bernie was well known throughout the campus. When I was an undergraduate at Amherst, the poet Robert Frost gave a talk at which he said, "All of history is just remembered gossip." On that basis, Bernie was the best historian at Swarthmore College. All knew that if you wanted some piece of information, particularly a juicy information, spread rapidly around the campus, you would first go with it to Bernie.
	Above all, this was a man with a big heart. Thoughtful, solicitous, and wise. All sought him out for his wisdom from the most technical to the most personal matters. For me, one measure of this was that my oldest daughter could not proceed with here marriage plans until Bernie had met and approved my prospective son in law. 
	Well, I could go on, but time is short, but I do have copies available to anybody who wants them of a two CD set of the recording of the memorial service for Bernie, which was really a marvelous ceremony. It gave much more than my words possibly can of a full picture of what Bernie was for all of us.
	But now I turn to my assignment, introducing Michael Greenstone. Michael graduated Swarthmore 1991 with high honors. A recent empirical study shows that over the last 15 years Swarthmore has produced more PhDs in economics per size of the student body than any other department in the country. I believe that none of the crowd has risen as high in the profession in such a short time as Michael.
	He was a graduate in 1991 and had by 2006, risen to a full professorship with an endowed chair in what is arguably the best graduate economics program in the country. Now it should be noted that the chair, which Michael holds is the 3M Professorship in Environmental Economics. I believe that 3M stands for Minnesota, Mining and Manufacturing, probably one of the major polluters directly or indirectly in the world. What were they buying with their professorship?
	A few personal perspectives on Michael's career. I remember Michael as a strong student with definite interests in how economics might be used to understand social issues in policies. Particularly when he was talking with me, issues pertaining to poverty. But, and this may be unfair to him, I also felt he did not press himself to the full extent of his academic potential. 
	I knew he was a great basketball player. I would see him down at the gym practicing alone or with the team for hours at a time. It paid off for him in Swarthmore. He scored 1,000 points, he was the all-time record rebounder for Swarthmore, et cetera, et cetera. I thought, and I've said this to others many times, here's a guy who assessed his priorities and while an undergraduate, he put all of his priority efforts into basketball, while at the same time not neglecting the academics, witness high honors.
	Then after leaving Swarthmore, he applied the same perseverance to build a career in economics that he had to polishing his basketball talents. I want to hold him up as a role model for students who are planning perhaps to go on to graduate school and a career in economics. In that he did not go immediately to graduate school, but got a job at the committee on economic development, an organization which develops policy briefs on current economic problems.
	As a research assistant there, he would not only peruse the literature, but would call up the leading economic experts on the given issue and ask questions and discuss the analysis. Great networking that provided. In the evenings, he took courses to cover those topics in mathematics which he hadn't studied at Swarthmore. He also came back to Swarthmore to act as an assistant basketball coach, but that was just a cover because he was going to Drexel doing more mathematics to fill in the holes to get ready for graduate school.
	He was admitted to Princeton and seemed to immediately latch on to and seemed to, from my perspective, immediately latch on to Professor David Card, known as the most generous with graduate students and who was a nationally known leader in labor economics. 
	Michael told me some years ago that as he was searching for his thesis topic, Professor Card said that he thought he should look for some topic outside of labor economics because the field of labor economics was getting too crowded with young, talented economists. This I believe was what set him on the path to doing environmental economics, starting just ahead of the curve that has led to a huge growth in interest in all things environmental.
	Michael lists fields of interest as environmental economics, public finance, labor economics, health economics. His publications are in the best journals and too numerous to begin even to mention. We'll put his five page CV up on the department site for all to peruse and I urge you to do so.
	Michael has been good to current Swarthmore students. Indeed, he spent this afternoon interviewing candidates for a two year research assistance position to replace the previous Swarthmore students that he's had working with him. Michael and Bernie were close, another indication of Michael's ability to find the best in whatever environment he was working. There were life events that each had that brought them even closer.
	Michael is a cheerful and decent man. A fitting speaker in Bernie's honor. Michael.
Michael Greenstone:	Well, they say it's hard to go home. I don't think it is today. I'm very happy to be back here. I think Swarthmore College buried deep inside my aspirations as Rob referred to, to become an NBA player, despite being six foot one and a half, slow, and not a particularly good jumper, was someone who was interested in ideas and learning. I think, had I gone to any other college, I think that would've remained dormant. I owe a lot- I was able to discover that in myself at Swarthmore and for that I will be eternally grateful to Swarthmore and many of the faculty here, including Rob.
	I should also mention, there was a key moment in life when I was applying, seemed natural at the end of college to do something, and my mother was certainly encouraging that. So the logical choice for a liberal arts student of course is to apply to law school, which I did do. Then there came the moment to go or not and I sought Rob and Bernie's counsel and both very emphatically dissuaded me from doing that. I think without their words of wisdom, I would be much richer, have a nicer house, a nicer car, and all kinds of other things. But I think ultimately, much less happy. So as I said, happy to be back here and this college changed my life for sure.
	So I'm gonna talk tonight about the consequences of climate change on mortality in the U.S. and India. The U.S. work is a little more worked out. The India work's preliminary, but nevertheless, I think interesting. It's quite an honor to do that in honor of Bernie Saffran. So I thought I would come up with some quotes or lessons that I learned from Bernie.
	These are three things that he would inevitably say to me in every single conversation. Rob has basically already painted the picture for this one. "Why are you wasting time playing basketball?" There's not a conversation that I had with him, be it in front of all my classmates in a honor seminar or privately that he didn't share that with me. For those of you who knew him, you also can hear how he said it, in a kind of gentle yet pushing way.
	Second is, "Don't go to law school." And thankfully, I was able to hear that one. I was not able to hear the basketball one very well. I would've if, by some miracle, I had grown and been able to run faster and been able to jump higher, I certainly wouldn't have paid attention on number one either. 
	Then the third is, and actually I realized after I typed that, that's not quite right. It was always, "How are you? How is your family?" I think Bernie's special interest in me as a person and I don't think that I was unique in that regard, was incredibly endearing and as Rob referred to when I was in college, my father died and at the same time, one of Bernie's daughters died. I think there was a looking for a father figure and looking for an extra child and I think we formed a kind of- we never really admitted that to each other, but it seemed in hindsight that that's what was going on.
	I miss him now as I'm sure everyone else does. It was quite a loss. But in answer to that question that he always posed, I thought I should find some proof. So there's my wife Catherine and our daughter Jessica and our son William who, for a brief moment in time, was behaving. 
	So I'm gonna talk about climate change and temperature. To begin with just to get us warmed up, as they say. And this is, anyone who've seen Al Gore, this movie, knows that this graph, this is the famous hockey stick. This is the temperatures over time from 1,000 AD to roughly the present and there's some dispute about this, but I think basically the general pattern is not really disputed. Which is that there's a sharp change and it's coincident to the release of lots of greenhouse gasses through energy production.
	I think, you know it's funny, when I first started working climate change, I would say, it was a slightly softer statement then. There's a near consensus that emissions from human activity. I'd say now there's almost a consensus that emissions from human activity will lead to climate change. In particular, increased temperatures, increased precipitation, and higher variability. We can just barrel in on a shorter period of time where the data's more reliable and you have kind of the same pattern.
	So the question I'm gonna try and ask or answer tonight is, what are the consequences of these changes in temperature gonna be for human health? Climate change is gonna change all kinds of parts of the way that we live and I think some of those will be good. I think if you want to own land in Minnesota, now might be the time to buy land in Minnesota, might be time to sell land in Arizona and I think some of them are not gonna be so good. So this is part of a larger effort of mine to try and understand what the cost and benefits of climate change are gonna be.
	I just want to emphasize this that the cost of shifting to clean energy sources appears substantial. That's not a small statement. When I teach environmental economics to my undergrads, I try to be as obnoxious as possible about this part, which is that there's an element of the environment that has kind of a quasi religious component to it in discussions. That quasi religious component always has the flavor that, "Well, we should do it no matter what. It's good for the environment." 
	But I think it's really important to keep in mind that to clean the environment or reduce the rate at which the climate changes, there's gonna have to be serious reductions in the consumption of many goods. So the way I try to be obnoxious to my undergrads is to say, "Well, there's nothing that my son loves more than the NBA League Pass." Which is a cable thing where you can see every single NBA game. And should we really deprive my son, it's got to the thing he loves most in the world, of the NBA League Pass or some other consumption item, in exchange for reducing climate? 
	Of course, we should get rid of NBA League Pass and a million other things to prevent climate change, but the truth is, people don't really like to give up consumption. One important thing to do is to understand exactly what the cost will be if we don't do anything. That's the spirit in which I'm gonna try and proceed. Then if those costs appear large then we may want to give up the NBA League Pass, we may want to buy higher priced energy, and a series of other things.
	So what are the potential ways that we might go about coming up with the cost of doing nothing? So they would be the cost of business as usual. One is the Stern Report, which is kind of a compilation of, I think Nick Stern would acknowledge this, of kind of mediocre evidence on the different ways in which humans interact with the climate. 
	The other is computable general equilibrium models. Does anyone in this room know what that is? Even the people who know what it is, can't really explain it. They are these enormous modeling exercises that at the end, have the great feature that they will produce an answer. But no one knows what goes into them and no one knows what- well you do know what comes out. So that's another way in which people, including at MIT where I teach, try to come up with evidence on what the cost of climate change would be.
	So I'm gonna take a very different approach, which I'll describe in a minute. But the point I just want to keep emphasizing is that evidence on the magnitude of the impacts of climate change on human welfare are crucial if you want to set rational climate change policy. You might not want to, but if you did want to set rational climate change policy and you wanted to figure out, should we have concentrations of 450 parts per million or 550 or 650. So this is an effort to come up, this is part of an effort to come up with some estimates of what the cost would be again, from doing nothing. If we know the cost of doing nothing, then we should know how much we should be willing to spend to prevent it from happening.
	So the specific questions I'm gonna try and talk about are, what is the welfare loss caused by the health impacts of climate change? That's kind of the broad question. I'll do that in the first, very specific question is, what will be the direct impact of climate change on the United States mortality? I'll describe in a second exactly what parts of that I'll be able to answer and what parts I won't.
	The second part, which I want to try and I think it's a somewhat- for economists, it may not be that subtle, but I think in the broader population it can be a little subtle, which is can individuals engage in self protection and response to climate change versus any other bad thing that happens to them? Of course the answer's yes. 
	But the point I want to make is that if you want to come up with the full cost of climate change, or the full health cost, you have to know what the health losses were, how many extra people would die and all the money that people spend protecting themselves from climate change, which they wouldn't have had to spend in the absence of climate change. If you only look at the health part, you could miss a big part of that. That's gonna play out in what I'm gonna show you in a minute.
	Then I'm gonna have some preliminary evidence on what's the direct impact of climate change. Again, what will be the direct impact of climate change on mortality in India.
	So the basic approach is to use the past to learn about the future. So the idea is really simple. Which is, in the past, we have hot years we have cold years, some of those hot years were as hot as years were predicted to always be with climate change. So what I'm gonna try to do is to use the hot years to make predictions about what will happen once there is permanent climate change. There's some limitations to this approach, which I'll try to flesh out as we go along.
	Then I was eluding to a moment ago, I'm gonna apply one of the basic insights from economic theory, people will adapt and try and mitigate the direct health impacts and we'll try to come up with some estimates of that. So there's a series of statistical challenges to try- I'm gonna be emphasizing for this exercise, the relationship between heat and mortality.
	There's a series of statistical challenges associate with that. The first is, there's a dynamic relationship between temperature and mortality, so what does that mean in English? Well, I guess in English, it means harvesting. That's what the epidemiologists refer to when there's really sick people who are kind of walking along doing their normal thing and some bad shock happens and they just get pushed into the grave. They get harvested into the grave. The temptation is to count that as a death due to climate change or whatever the factor is that you're studying. But in fact, their loss of life expectancy was really quite low. So what I'm gonna try and do is come up with estimates that are purged of people who had really low life expectancies when they died. 
	Then there's also kind of the reverse issue on cold days, which is really cold days can cause people to become sick, but not die right away. So you have to find some way to take account of that. The details of that are probably best left for later. For question and answer afterwards.
	The other thing is that cross sectional comparisons might reflect differences in underlying health of populations, rather than impact of climate. You would think if you looked at Florida as a perfect example, that you might have an exaggerated sense of the relationship between temperature and mortality there because there's a lot of old people there who are sick. So you have to find some way to purge that. 
	The other thing that is a challenge was that the relationship between temperature and health is highly non linear. So going form 70 to 72 degrees is not really a very big deal. In fact, I'll show you that in the data, but as you get to really high levels, the relationship can kind of turn upwards at a very quick pace. So the challenge is to find a statistical model that can capture those complexities.
	Then this is one that I've now mentioned a few times. The welfare impact of climate change on health should include the value of the health loss and the costs of whatever self protection people are engaging in.
	So I have until 8:15. So then we're gonna skip this, this is just math or not even math really. But this is just that is exactly this point that I was just making. Which is, people are going to- people care about their health, people care about their consumption, and to buy more health, they might give up some consumption of the NBA League Pass or bigger house or things like that. And that's what all that says with some algebra.
	The punchline of all that is that even with the zero impact on health, the welfare costs of climate change can be quite large. So it's very difficult to get measurable activities that people engage in to protect themselves. The one that I can gather data on from the United States pretty easily is energy consumption. That's just that when it gets hot, people turn on their air conditioning. That's partially to protect their health, but it's also for comfort reasons. So I'm gonna argue later that you'd want to count any increased energy consumption due to higher temperatures as part of the cost of climate change.
	So here are some problems with what I'm actually gonna do is the first is- I love mortality data and I've used it in several papers. The reason I love it is you don't have to argue about the outcome, it's pretty clear that that's bad. Most people don't want it to happen. The important thing is that this- by looking at mortality, we're not gonna be able to measure all the health impacts. But I think this is an important one that most people care about.
	I'm gonna have a limited range of the measures of adaptation that people undertake. In particular, as I mentioned, just energy consumption. So one thing that's really important to highlight is that these estimates that I'm gonna show you are gonna be overestimates of what the cost of climate change will be. 
	The reason is, I'm relying on these year to year comparisons from historical data and that's very different than permanent climate change because with permanent climate change, people will be able to do things like pick up their house and more from Arizona to Minnesota. Here, there's a much smaller range of adaptation to people being able to undertake. So in a hot year you can turn your air conditioning but you might not be able to install central air or something like that.
	So all this is leading to an estimated statistical model that relates annual mortality rates in each U.S. county to annual exposure to temperature in that county and it's gonna rely on the idea or the notion that year to year variation of temperature is random. These will be from models that allow the effects of temperature to vary by male and female or by gender and then by different age groups. We'll do the same for residential energy consumption.
	Having uncovered this relationship, which I'm gonna show you between temperature and mortality, the idea is then to take predictions about climate using kind of standard climate change models. And say, "Okay, now let's impose this new climate relative to the current one and see what the change in mortality is." The idea is really simple, we're gonna get more hot days and if hot days kill more people, then that's gonna lead to higher mortality. And we'll repeat the exercise for India.
	I should emphasize, if it wasn't clear, climate change is likely to affect human health through a lot of different channels, this is just I'm only measuring one, which is the direct impact, through the relationship between temperature and mortality. All right. So I'm missing a whole bunch of others that are interesting. All right. I think people understand that.
	So let me just tell you a little bit about the data that I used for the United States. There's something called the compressed mortality files. I have data on every single death certificate from 1968 to 2002. My wife can't quite understand why it's so great to have 73 million deaths on your computer, but I do. We're gonna use that to create annual mortality rates again by race, sex, age group, county, and cause of death.
	It turns out the United States weather service has weather stations throughout the country and we can use that to get annual measures of temperature for each country. The idea is if we relate these annual mortality rates at the county level to the annual temperature data. Then we'll take predictions on how climate will evolve from two very standard models, one is the Hadley Three Model and the other is Community Climate System Model. For both of those we're gonna use scenarios that are kind of business as usual scenarios. So they imply that there's no tax on carbon or no price on greenhouse gases. Okay, we can skip that.
	So the entire empirical exercise can be explained in about three or four graphs, which I'm gonna do right now. So this is the distribution of annual daily mean temperatures between 1968 and 2002. The way you should think about this is, I've divided days. We'll just take a- this isn't Montgomery County, what is this? Delaware County. So we're gonna take Delaware County and we've got 365 days a year in Delaware County just like other counties. I'm gonna bin that data up. So what do I mean by that? I'm gonna count the number of days where the temperature is greater than 90, between 80 and 90, 70 to 80, all the way down to less than 10. Let me be precise, this is the mean temperature, which is just the average of the min and the max. So when they say today's high, I'm actually talking about the average of the min and the max. So greater than 90's actually pretty hot 'cause that means it's the high of 100, the low can only be 80.
	I'm gonna take that for 1968, 2002 for Delaware County and I'll do that for every other county and I'll take the average across the entire United States and I'll population weight it. So what this tells you is, this is the average number of days that the average person in the United States faces in each of these temperature bins. So you can see, the average person does not actually live in Delaware County, 'cause the model bin is about in the 60 to 70 range. I suspect that in Delaware County, it's probably down a little bit further.
	So this is the current distribution of temperature that a person faces in a year. I just want to point out a few things. The average person gets very few days here. All right. So now what I'm gonna do is, I'm gonna take these predictions on climate and I'll bin it up in the same way. So then I'll have predicted permanent climate at the end of the 21st century. I'm just gonna bin by bin, subtract one from the other and find out how many extra days, or how many fewer days we're gonna get in each bin. That's what's gonna be in the next figure.
	It's astounding. So we have no days essentially now in this bin, and under these two different climate models, we'll get 54 extra days with the Hadley Three model and with the CCSM Model we get about 22 extra days in the greater than 90 bin. You can see, it's all coming from the kind of middle of the distribution. 
	When I began this project, I thought, "Well, you know, climate change is gonna get rid of some of the really cold days and cold days are bad for health. It'll add some really hot days and the two might balance each other out." But what you can see here is we're just gonna get a whole bunch of extra hot days and not a real big relief on the number of cold days. Now you can probably guess the next step.
	The next step is, to find out what is the impact of an extra day in this bin on mortality or these two bins, relative to in this region. So if these days kill more people than these days, then we're gonna have an increase in mortality.
	So that's the weather and the climate change data. Then I also have data on energy consumption, which is measured in quad. So the average- this will be important in a minute, but the annual average U.S. energy consumption is 16.6 quadrillion BGs. Had to learn this, but it's 16.6 times 10 to the 15th. 
	So here's the first piece of evidence from the U.S. This is, the way you should read this on the Y axis, we've got, you should think of this as the impact on the annual mortality rate. So the number of deaths per hundred thousand people of a day in any of these bins relative to the 50 to 60 bin. So I'm just gonna make everything relative to that bin. So the exercise is, if I take one day out of this bin and add it here, then since that says 0.14 that means you'll get 0.14 extra deaths per hundred thousand population.
	The thing to note is that these days really are the worst days. The hottest days. So there you get almost one extra death annually per hundred thousand population. So this kind of has the you figure and you can see that when we take a whole bunch of days from here and shove them here, there's gonna be some increase in mortality. Although, for those of you who can figure out how many people in the United States, you'll see it's not actually that big of an increase. I'll show you that more systematically in a minute.
	So that's the evidence on mortality and I'll summarize it now, in one second. But first I want to show you the energy consumption model. So people basically use the same amount of energy throughout the temperature range until things get really hot. So here, one more day in this greater than 100 bin, for statistical reasons we're able to estimate this, while we weren't for the mortality, you get about a quarter of an extra quad. Since we're gonna get somewhere between 30 and 50 extra days, you can right away see the basic answer, which is there's gonna be about a three or four quad increase in energy consumption.
	So let's just summarize it now. With the Hadley Three Model in the United States, by the end of the century, you would predict about a three percent increase in the annual mortality rate and this would be on the borderline of being statistically significant. With the CCSM Model, you get about a two percent increase in the annual mortality rate. I should emphasize, these are not very big numbers at all. One way to put them in context is, the annual mortality rate decreases by about one percent per year due to just technological progress in medicine.
	You could view this as by the end of the 21st century, we're just gonna have gotten rid of two years of medical innovation in this model and about three in that. Then the increase in energy consumption is larger. It's about 14% with this model and about 25% with that model. Oh okay. Well, this is interesting to talk about.
	So those are kind of the basic results from the U.S. Now one thing we might want to do is try and turn that into dollars. Since that's a common unit that all economists can deal with and is certainly the proper way to think about all problems, is with dollars. Another way to do that is to calculate the loss in life years between 2010 and the end of the 21st century. 
	The way you do that is you count up the extra deaths and you note their age and then you assign some number of remaining years. So this is a somewhat imprecise exercise, but with the Hadley Three model, we'd lose about 114 billion extra years of life by the end of the 21st century, U.S. would. And about 60 billion with the CCSM model. Now again, it's gonna prove to not be that big, but I guess it's in the eye of the beholder.
	Then with energy consumption, there'd be about 315 extra quads of energy consumption between 2010 and 2100 so that's over 90 years. And about 130 with the CCSM model. So if we're gonna start multiplying those by monetary figures, we can come up with an estimate of the loss. I think this, you could probably spend a good hour in a class discussing the different ways to make numbers look big or small. 
	One way to do that is to take the present discounted value of that with today's values of the loss of a life year, which one could probably spend five hours about how to calculate that. If you did that, you would say the present value of the full stream of losses that we're gonna get due to climate change is about 2.7 trillion dollars with this model and about 1.6 trillion dollars with the other model. These are kind of on the edge of being statistically significant.
	Alternatively, you could allow the price of energy to increase over time and you could allow the value of a statistical life year to increase over time and you can make these numbers much bigger. To put this in some context, so why is this important? Suppose this were all really true and this were the only impact of climate change. Then this is how much you would be willing to pay to prevent it from happening today. To avoid all the losses you're gonna get between 2010 and 2099. Again, to put that in some context, 2.7 trillion the current GDP is maybe 15 trillion and sinking, but it's- I don't know if that's large or small. But that's the number that comes out of this exercise.
	All right so now let me talk about- I'm gonna do a very similar exercise for India. The results are quite different than for the United States. So why I look at India, India's hotter than the United States and India's a lot poorer than the United States. So you might think that they're less, they're at the very margins of what's acceptable for living conditions, just due to temperature, plus they don't have the resources to protect themselves in the same way that we do. So they're not gonna ramp up air conditioning, at least under the current situation there.
	The great thing is, due- I don't know that it's great on net, but due to the Brits having colonized India, they have a tradition of data collection. So that means there's data we can use. So here's the analogous figure for India. This is the distribution of days faced by the average person in India between 1950 and 2000 and unfortunately it's in celsius so I'm not very good at doing that conversion. But the greater than 36 bin is like saying greater than 97, that's really, really hot. 'Cause again that's the average of the min and the max. So these are the number of days the average Indian faces.
	The important thing is though that distribution is shifted to the right. So they're facing many more hot days to begin with. But it has the same flavor. It starts hot and the hot days are really gonna pile on and so the greater than 97 bin with the Hadley Model will get about 30 extra days. With the CCSM model, it's nowhere near as extreme, about 10 extra days. But the key thing is, just like in United States, they're gonna be trading days in here and trading them for days here. So if mortality's higher at these high bins, just then that could lead to tremendous increases in predicted mortality.
	So here's the exact same response function that I showed you for the United States, except now for India. Again, the way to interpret this is, that I've changed the dependent variable and it's now the log of the annual mortality rate. The reference bin is gonna be 22 to 24 and what that means is, again, you should read each of these point estimates. Never mind standards. But if each of these point estimates adds- what happens if I take one day out of this bin and say put it in the greater than 36 bin? If you read that off the Y axis, that's like saying one extra day in this bin and remember, they're gonna get a lot of them, is worth a 1% increase in the annual mortality rate. So just one extra day.
	So right away you can see that the mortality impact in India are gonna be dramatic compared to United States. This is for all ages, ages one and up and my co-author and I made a similar figure for infant mortality 'cause they're separate data on that. Actually India's not, it doesn't get cold enough that you have the you flavor actually. So you're really just gonna get a bunch of days here and move them up to high mortality days.
	I'll add that up in a minute. But before I do that, I just want to show one thing when we dug deeper into the data. We can the data separately for urban and rural areas. When we do it in urban areas, it's not much of a relationship. We don't completely understand why that's the case, but we are investigating it. But when we look at rural areas, that's where all the action is. 
	Now unfortunately, a lot of India's population is in the rural areas. So we tried to understand what's going on in the rural areas and most everyone who lives in rural areas in India's engaged in agriculture of some kind. So we were able to find some data on the average wage and what you can see is that when those really hot days appear, the average wage goes down. 
	There's just not a lot of other things to do in these rural areas. They're not gonna show up at the factory gate and get a job doing some kind of manufacturing and in fact, we investigated whether that's true. What's remarkable is, their labor supply is completely unresponsive to the fact that the wage went down. So one extra day in this bin is about a half of percent reduction in the daily wage rate and they have no labor supply response. 
	So if our friends from Minnesota were here, this is not their view of the world. Their view of the world is there's no such thing- what a person should do is just take some unemployment, which is really vacation, and work harder the next year. But these guys are on a different margin. As you saw a moment ago, they're on the margin of life and death. So they have to keep working and they keep showing up for work, but they're making less money and as this graph showed, that shows up in their mortality rates.
	So here's just a way to summarize it. If we use the Hadley Three model, you get an increase in the annual mortality rate of about 36%. By the end of the century, a 36% increase in annual mortality rate. Then about 11% with the CCSM model. And here's just a summary of what happens to their agricultural wage, it goes down by about 30% and with one model about 12% by the other. 
	Now again, it's worth emphasizing that I am relying these year to year comparisons here. Presumably you think that people would migrate to some degree from the rural areas to the urban areas to avoid these large shocks to wages. But things move verily slowly in developing countries. I think it's reasonable to assume that they wouldn't migrate fast enough.
	So here's just a summary of the findings in that sum interpretation. So in the U.S., the mortality impacts are a two to three percent increase in the annual mortality rate by the end of the century. There's larger increases for some age categories, which I then show. And again, to put that in perspective, in the last 35 years, the annual mortality rate has declined by about one percent per year. So these look pretty small to me.
	There is predicted to be a pretty big increase in energy consumption. I want to emphasize that the full impact the United States is evident in both mortality and adaptation and that the present discounted value of the impact over the century is somewhere in the one and a half to 2.7 trillion dollar range.
	Before I make the next point, I just want to highlight, if you need a measure of why it's good to be rich, here's a pretty good measure. So instead of- I managed to convert the India data to fahrenheit, and basically I estimate the exact same models. So these are these response functions, this is how much the annual mortality rate increases in the United States on the same scale as the India one. 
	What you can see is this is that you relationship, which looks pretty flat because we had to widen the Y axis here for the United States. Here's the relationship in India. One interpretation of this, is that this is what being rich buys you. It buys you protection from all that extra mortality. This is a graphical way to summarize the differences in the results from two countries. 
	The mortality impacts in India are predicted to be substantially larger than the U.S. and concentrate on rural areas. There seems to be fewer opportunities for adaptation. I think one key thing, which you hear, and I never really knew where it came from, but I think this is some evidence that the cost of climate change are unlikely to be spread evenly across the globe.
	That has actually not so hot implications for there ever being an effective climate change policy. Because at the end of the day, India and China and other developing countries that are really gonna be producing a lot of the greenhouse gasses going forward, their view is that when you put all that stuff up there, we're not gonna- if you want us to stop, you're gonna have to pay us. That's more or less the line they take with the developed countries. 
	The tough part is gonna be that the United States and other developed countries are gonna have to pay for benefits that they'll never see. They're gonna accrue to other people. And that seems like a politically difficult sell to me. Since I think it's a tough enough sell to even do it for the U.S. to have rational policy if the damages were gonna be in the U.S. So I think that's one thing that comes out this. 
	Yeah, so again, this is part of a larger project and the hope is to ultimately identify what the efficient concentration of greenhouse gasses in the atmosphere. I think that's more or less what I had to say.