 Thanks, Debbie. Thank you all for coming out tonight. I'm happy to share my research with you and I look forward to having a conversation with you afterwards. Debbie asked me to talk for about 45 minutes and I've divided the talk really into two separate parts. First, I'm going to talk about the book, which is on sale at Amazon.com. That's very cheap and I understand it's coming out in paperback next week, so it's even cheaper. You should buy it. It'll make your life better. I want to talk a little bit about the book first and then I want to talk about the data set that we built in order to write the book and what we're doing with that data set now. This book, as you... Did they read anything for this, Debbie? Okay. So you delivered that little article? Yeah. So this is the article that you read is based on this book. There's a short version of the book and the focus is on aid allocation, foreign aid allocation. What we were trying to understand in this book was where international environmental aid went and where dirty aid went and ultimately what is the likely impact of foreign aid on the natural environment? So one question to ask is who cares? Why do we... Why should we be doing this kind of research? Why is this important? The question we're asking is who gives what kind of aid to whom and what impact list have on the environment? That's the broader question. The first thing we notice, and I'll talk a little bit more about this momentarily, is that there's really bad data on foreign aid transfers. I don't know if any of you have ever done any research on development assistance. If you have, you've probably used the data from the OECD's Development Assistance Committee and that data is severely, is really problematic in ways that I'll talk about in a minute. And even within that, it's difficult to know what kind of aid is really good for the environment and what kind of aid is bad for the environment. So one of the reasons we wrote this book was to create a new data set and to put that data out there to let others in the academic world and the policy world use it. Second, I think this kind of research is important because if you paid attention during the Copenhagen meeting, a few weeks ago, or a month ago, you recall that all of the potential cooperative agreements that might be structured in a post-Kyoto world to address the issue of climate change, all of those agreements are going to require an enormous resource transfer from rich countries in the West to poor countries in the South. If there is not an enormous resource transfer, there will be no cooperation on dealing with international environmental issues in general, but especially on issues like climate change. So the numbers that you saw tossed around were $10 billion a year immediately, and this is what most Western governments are committed to informally, and then the real number they're talking about is $100 billion a year starting in about 10 years. So if you're going to get cooperation on international environmental issues, it's going to require an enormous amount of wealth transfer. One of the things we'll need to do in order to hold donors accountable and in order to make sure this cooperation occurs is to be able to find out who gave what to whom and what type of aid they gave. Third and finally, this book is about allocation. We're trying to explain why rich countries like the United States, Germany, and Japan, why do they give certain types of aid to developing countries. That's what we're trying to explain. We're going to describe the pattern of allocation and we're going to try to explain why they gave this much money to these countries. Another question that many of you might be interested in if you study development is not who gets what and why do they get it, you might be interested in once aid is given, does it actually achieve any of the objectives that it was designed to achieve? Does the aid reduce poverty? Does it improve education? Does it clean up the environment depending on what you're trying to do? And in order to figure out whether or not aid is effective, first we need good evidence on what aid has been given and who's it been given to. All previous research on this question and everyone who's ever looked at this from an academic point of view or from, you know, if you're an international organization like the UN, anyone in the policy world knows that the data that we have now on even defining what environmental aid is, much less where it goes, that data is bad. So empirically there's a good reason to do this project and to write this book so that we can simply describe what's going on in the world. Let me tell you a little bit, a little background on this book. It's actually a pretty interesting story. In 2001 to 2002, I was working with a friend of mine, another graduate school friend of mine named Dan Nielsen, and we were trying to understand environmental reform at the World Bank. The World Bank was talking a lot about the fact that it had greened its portfolio and changed its policies to be more environmentally friendly. There were plenty of environmentalists who were quite skeptical about those claims, but the bank kept saying that. So what we did was we collected all the, we created a database of all the projects that the World Bank had done and instead of using their own coding scheme, when they said, all right, we did 10 projects in Guatemala, you know, eight of them were for infrastructure and two were for the environment. Instead of taking their word for it, we went through and looked at the project documents and the descriptions and we coded them with our own set of criteria that would be consistent over time. And then what we did was we analyzed the degree to which the World Bank's loan portfolio had actually become more environmentally friendly or not. So we wrote this book, or excuse me, wrote this couple of articles, and then about the same time a student of mine named Brad Parks was interested in understanding not multilateral aid allocation from the World Bank, he was interested in bilateral aid allocation from the United States, Japan, Germany, the Netherlands. So he decided he was going to do an honor thesis analyzing it. To make a long story short, he wrote the best honor thesis I've ever seen. It was a piece of undergraduate research and at the end of the honor thesis defense, you always tell the person who wrote the paper to go out of the room while the professors decide to fake. And so he leaves the room and we all looked at each other and said, this is brilliant and we've got to give this kid highest honors. He came back in the room, we told him that, we said, you've got to turn this into a book. We will find some money. This is a kind of book that writes itself. So this was in the spring of 2003. We said, by the end of the summer, you'll be done with this book. And he said, well, as long as you guys write it with me, I'd be happy to stay here for the summer in Williamsburg and write the book. Well, whenever somebody says that book is going to write itself, you should be very skeptical. And this is no exception to the rule. About three weeks into the summer, Brad walked into my office and he said, Mike, we have a problem. The data that we're using to do our analysis with is fundamentally flawed. And I said, no, it's not. This is the same data that everybody uses. These are the official statistics from the OECD Development Assistance Committee. It's called the CRS Database. And every paper and every book that's ever been written on aid allocation or aid effectiveness uses this data. He said, sure, we can use this. And he said, well, we have a big problem with missing data. There are a whole bunch of donors that never reported in certain years how much they gave and what type of aid they gave. I said, well, that's not that big of a deal. We can use fancy statistical methods to impute what those numbers would be or we can just drop them and assume that it's just part of the randomness that they would have been similar whether they had reported or not. He said, well, there's another problem. We're missing donors. It's not just that we're missing years. There are lots of donors that give money that don't report to the OECD, especially multilateral donors. I said, well, I thought all the big multilateral donors are in there. The World Bank is in there. The Inter-American Development Bank, the UNDP. He says, that's true. But over the last couple of weeks, we've found that there are maybe 10 or 15 other multilateral donors that aren't in the database. I was like, okay, that's a problem, but we've got the big ones. And then he said, the biggest problem is that the projects are not accurately and consistently described. Now, this might not be a problem if all you cared about was aid flows, right? But if you cared about what is the impact of foreign aid on the environment, then what you need to know is what type of aid is being funded, right? Because if I'm giving you money to build a bridge or a cement factory or chop down a bunch of trees, that's going to have a particularly negative effect on the environment. Whereas if I'm giving you money to save panda bears or rope off the rainforest or something, clean water, something like that, it's going to have a very different effect. So we have to have some way of knowing what kind of aid is being given. And what we wanted to do was use the CRS's sector codes that describe the type of aid that is being given. And what we found when we started digging was they were completely inconsistently applied. And so I'll show you what I mean by that in a minute. Make a long story short, we went out and got a National Science Foundation grant and we built a database, which I think is the most comprehensive database in the world on foreign aid transfers. The database that we built for this book had 428,000 projects in it. So that's 428,000 rows of data. Each project is a row and then there were 50 columns, which told you different things about the project, like how much money was, who gave it, what year was, what the terms of the loan were, etc. And what its environmental impact would be, I'll tell you about that in a minute. So the database runs from 78 to 2,000. We found lots of donors. There aren't just seven or eight big multilateral donors. There's over 40 multilateral donors. And I study international organization and I had never heard of most of these multilateral donors. We ended up finding it. Today, and by the way, I'll tell you about this in a minute, all the projects in the database, all 428,000 of these were systematically coded. Each line was coded by two undergraduate research assistants. So lots and lots of labor saying, what is the likely environmental impact of this project? Today, which is two years later, we have a database called the Project Level Aid Database, the Plaid Database, with about a million records. More about that later, Jeff. So let me tell you, let me show you what the problem is with using OECD sector codes. Before we had done this research, lots of people had studied for an aid and if they wanted to know what type of aid was being given, they used these OECD sector codes. And that's what we were going to do. We thought that was reasonable. That's what Brad had done in his honor thesis. But let me just show you a potential problem here. So there is an OECD sector code called Forestry. So if you were writing a book and somebody said, it's a forestry project. And then you said, okay, Mike, are you going to call this an environmentally dirty loan? It's kind of a damaging environment. Are you going to call it neutral? Are you going to call it environmentally friendly? It turns out when you go through and look at all the individual projects in the forestry sector in the OECD database, what you find is where you see brown here, all these brown projects are where the first few years have basically all you see are projects that are likely on average to do some damage to the natural environment. So these are probably funding clear cutting of rainforests, something like that. Where you see green within those sectors, those are projects that are designed to enhance the environment or do some kind of environmental remediation. So you're spending money to clean up the environment or prevent environmental damage. So you can now you can see what the problem is. If you're using that sector code to tell you across the board what is damaging or good for the environment, you've got a huge problem. Because in some years, all of the projects in that sector are bad for the environment. And in other years, the majority of the projects are good for the environment. So there are 200 and some odd sector codes. This is just one of them. And they're not consistently applied across donors or across time. So this shows you shows you across time the variation of green and brown. But if I showed you in the same year, I showed you a forestry project by the United States, it might be paying to rope off the rainforest. And if I showed you a forestry project from France, that might be a project that's designed to cut down the rainforest. So you can't use these sector codes if you want to know what the money is being spent for. Instead, you have to go through and code them yourself, which is what we did. Very briefly, we divided all 428,000 of these projects into five categories. I've collapsed them down to three here. But they went from dirty strictly defined, that's up there, to environmental strictly defined. And then there were intermediate categories, environmental broadly defined and dirty broadly defined. And then there were neutral projects. And in each of these categories, you probably had somewhere between 20 and 80 different keywords that would sort of indicate the type of projects that were being funded. These are just illustrative. So if you see projects like this, you know, you think that on average, they will be bad for the environment. We're not making any judgments about whether they're good for growth, whether they're good for human welfare, whether they, you know, have other positive benefits. We're just saying, are they good or bad for the natural environment? These are the ones that are supposed to be the best for the environment. And neutral projects on average, we argued, shouldn't have any particular impact positive or negative. In practice, of course, some are positive and some are negative, right? So we spent probably a year talking to lots of biologists and ecologists and people who study the impact of different types of projects to figure out what the coding scheme was, right? 35 page single space coding document that tells you how to categorize different projects. So in the book, we have four big research questions. Has a been green enough so by how much? Which donor government spend the most foreign assistance on the environment and why? Why does some donor government delegate responsibility for allocating their aid to international organizations while others just give it away themselves? And finally, which countries receive the most environmental aid and why? In the book, if you've looked at the book, you know that chapters three and four focus on one question, five and six on another and seven and eight on a third. And they're always designed so that one is a qualitative case study chapter. And it's sort of easier to read if you don't know econometrics. And then the very next chapter tries to answer the same question using econometric or statistical methods. Those aren't as fun to read. But arguably, with the data set this size, that's what you need to do. This didn't come out nearly as well on my slide as it does in the book. And in chapter one, there's a cooler picture, which basically the framework that we use to try to understand these four questions is a principal agent framework that says there are voters in donor countries. And those voters have the authority to make decisions about their lives. One decision is foreign policy. But in all all countries that I can think of, voters or citizens, if it's a democracy and all the donors that we looked at in our book were democratic countries, now we have donors in our database that are non-democratic. But all the donors for this book were democratic. The voters delegate authority to elected officials to make decisions. And one of the decisions that elected officials make is how much money should I give away to people in other countries? How much taxpayer dollars should I take and give away? And to whom should I give? So voters delegate to elected officials. Elected officials then either delegate to a bureaucrat, somebody working at the USAID, if you're in the US, or they delegate to DFID if you're a British elected official. So that's link number two. You could delegate to a domestic bureaucrat, or you can delegate to an international organization. You could say, I don't know who I want to give my money to, but I'll just give my money to the UNDP and we'll let the UNDP decide where the money goes. Or I'll give my money to the World Bank and let them decide. And it turns out most donor governments give some of the money away bilaterally, and then they delegate some of the money to be given away by a multilateral organization. All three of those that I talked about are principal-agent relationships where the principal, the voters, delegate authority to an agent, elected officials. And then there's a further delegation in this chain, this delegation chain of giving away money. The final link that we analyze in the book is not a delegation relationship. It's a bargaining relationship between an international organization in a recipient country, so the World Bank decides whether to give aid to Guatemala or Nicaragua, or USAID or some domestic bureaucracy decides to give aid to one country or another. So this is the theoretical framework we use in the book. I won't say anything more about that. I'll just start describing cool aid data trends. So the first question we ask, has aid been green? And if so, by how much? All right, just an empirical question. We never had the data really to answer this question before. If you look at just total flows, the total amount of what we call dirty aid, aid that is not likely to be beneficial to the environment, these are like infrastructure projects, some of the stuff I showed you before, has remained relatively flat if we look at constant US dollars. So that's billions. Range is somewhere between 25 and 40 billion dollars over this time series from 1980 to 2000. That's dirty aid. Look at neutral aid, this is the place where we see the biggest increase in foreign aid spending over the time series. Massive amounts of money spent on aid projects that aren't likely to necessarily have any positive or negative benefit or impact on the environment. And then if you look at green aid, or excuse me, environmental aid, it's gone up about three times, now about 10%. Range is from about two billion dollars a year into the more recent years up to around 10 billion dollars. So that's just the broadest possible trend. If you want to look at a ratio between dirty aid and green aid, you can really sort of see just how much foreign aid has been cleaned up during this 20-year period. The green line on top are multilateral donors, so if you take all multilateral aid in our database and look at the ratio between dirty to green, you go from 14 to 1 down to about 4 to 1 over the time series. So it's gotten a lot cleaner. Bilateral donors are even cleaner than multilateral donors in terms of the ratio between dirty and green and clean. In addition to the five categories I talked about, dirty to environmental strictly defined, all the environmental projects were then subdivided between green and brown. Green projects are projects that are designed to provide some global or regional public good. They're supposed to help all of us, like minimizing the size of the ozone hole, or minimizing global warming, or cleaning up the oceans. These are things that the world as a whole, theoretically, will benefit from if action is taken on those issues. Brown projects are also projects designed to clean up or do environmental remediation, but they're not likely to have global impact. Instead they're designed to help their local environmental goods. So the classic example of a local environmental good is a sewer treatment plant. There's lots of water projects that are done that are likely to have local but not necessarily global impacts. So all environmental projects are divided between brown and green. If you want to see the distributions of those you see that both are going up over time. The top one is bilateral environmental aid and the bottom one is multilateral environmental aid. We subdivided all environmental aid into four different sectors and did case studies on these sectors. So if you go back and look at the Rio, there was a big meeting of all these governments back in Rio de Janeiro, back in the early 1990s, where they sort of articulated what we needed to do to clean up in the environment and make development more sustainable. They articulated four different areas in which environmental aid should flow. And I'll show you what the doses were that they prescribed. What you'll see here is a huge amount of the aid over this time period is going to water projects and those are mostly urban and sewerage projects, water cleanup. Second, climate change aid, which is surged in the later part of the time series. And if this went on, by the way, from 2000, especially in the last five years, you would see a dramatic increase in both aid for climate change mitigation and climate change adaptation. And smaller amounts of money have been spent on desertification and biodiversity. If you go back and look at the agreement that was made, what they called the grand bargain in Rio de Janeiro, and you might look at the amounts of money that were prescribed to deal with these environmental projects, you see that there was enormous amount of money that was suggested. We will need to spend X billion dollars a year in order to successfully deal with these problems. What I have circled over here are the, so these are the, this is the dose prescribed in billions of dollars per year for each of these four sectors. This is the amount that we saw over the last 20 years, and these are the percentages. So we got very close to 100% of the aid that scientists thought we needed to deal with local water problems, but nothing close to the amount that we thought we needed in these other three sectors of environmental aid. Second research question, which donor government spend the most on foreign assistance for the environment? Why? You always want to know, how's my government doing relative to all the others? If you just look at this question on a per capita basis, dollars spent per citizen in the donor country, Denmark comes out on top by a large amount. Here you'll notice I'm only looking at the most recent five years in our time series, 1995 to 2000. United States I guess is somewhere just below the middle category. Sixteen dollars a year over that time period per American was spent on international aid to assist the environment. Another way you might look at this is not on a per capita basis, you might look at it in terms of the percentage of your total aid portfolio. To what degree has your aid portfolio been green and to what extent has it changed? So this is the 80 to 84 period, the first part of our time series, and this is the 95 to 99. You can see that most donors have gotten a lot greener over this time of series. That's the United States, the ratio of dirty aid to green aid. So the United States has gotten much cleaner over time based on the type of projects that have been implemented. I'm going to go through this really quickly because I don't really know what you're interested in so we'll get to Q&A and you can ask me whatever you want. In the statistical analysis we asked why are some donors greener than others? And instead of sort of saying we have a particular theory, we have a particular set of hypotheses that we want to test that are ours, what we did was we looked at the entire literature in sociology and economics and political science and found all the major hypotheses that were out there that were supposed to be able to explain environmental aid allocation. To make a very long story short there were lots of rich ideas about what would explain environmental aid allocation but there was very little testing done at a macro sort of global level like the kind of work that we were doing. Instead this literature is full of case studies so people will study an individual donor like there's lots of great books studying the Inter-American Development Bank or the European Investment Bank or the European Bank for Reconstruction and Development. But there are very few studies that look across donors and so obviously that's what we're doing. Here are just a bunch of specific variables that I'd be having to talk about in the Q&A if you want to that have come up in this big literature and that we tried to test in a systematic way using statistical evidence. What we found very briefly was that our models that we developed were much better at explaining the drop in dirty aid that you saw in that first graph. Our models did a good job of predicting which countries would reduce the dirty spending than they did increase environmental spending. Second, wealthier and more post-materialist countries invest less in dirty projects but they don't necessarily invest more in environmental projects. That was surprising to us. One of the best predictors comes out of this work by Robert Connolly. She argued that the countries that are going to give the most environmental aid are going to be countries that in their domestic politics you'll see coalitions between cutting-edge firms that sell cleanup you know smokestack scrubbers and things that benefit from cleaning up the environment like you've got business lobbies that sell that stuff with lots of environmentalists. So people that aren't normally unnecessarily business and environmentalists on the same side and you see those coalitions being particularly strong in a given country those countries are much more likely to give environmental aid. And finally countries with higher rates of environmental treaty ratification and compliance have much larger environmental aid budgets. So if your government is out there signing and ratifying international environmental treaties your government is also much more likely to give more environmental aid. I'm going to skip the research question number four which countries receive the most I'll go through this even faster. I just want to spend some time on this slide. So this is just from the last 10 years of the time series. These are the 10 countries and you'll see one of them is not even a country it's just called least developed countries but all these I think there are like 35 of the least developed countries in the world fit into this category. And what you see here all of the all of the aid in these bars is environmental aid but some of it is brown that deals with local environmental projects and some of it is green global environmental projects. And I think this graph is incredibly striking for a couple of reasons. Before I before I ask you I'm going to ask you a question about this so pay attention. So this bar graph is not accurate when the reporters were filling out the forms about where they sent their money. Some would list Burkina Faso as a recipient unless they like France would list Burkina Faso as a recipient and others would just if it went to Burkina Faso others would just list LDC as a abbreviation. So this actual number when you add up all the LDC is this actual number actually goes out to about right here. It's a huge it's a huge number because it's 35 different countries receiving aid. But I think the distinctive thing about it and the thing that sets it apart from the the ones on top of it is the proportion of green versus brown. These small very poor countries receive environmental aid that is disproportionately oriented toward the global commons. It's to prevent ozone depletion or it's to prevent global warming. It's to clean up the oceans. It's to prevent desertification. And these countries like Turkey and Argentina they receive overwhelmingly brown environment aid to help them with their local environmental needs. Why do you think that is? Why do you think that the least developed countries in the world that the Burkina Faso's of the world get lots of global green aid and Egypt and China get lots and lots of brown aid to build suits? I know. You can't talk. My guess is that the least developed countries like the infrastructure to actually make use of things such as sewers if they don't have you know very well developed bathroom systems they can't take advantage of sewers. Whereas other countries such as Turkey and Argentina already developed enough to be able to afford the type of advancements such as a nice sewer system or a better water plant. So that's an argument that says something about infrastructural capacity would shape the type of aid that would go there. And the implication of that hypothesis is that environmentally it is somehow efficient. It goes to places that can use it best. Burkina Faso can't really use a water treatment facility so we don't give it to them. Instead we'll give them global warming aid. That's a possibility. I don't know if that's true. In my statistical test we didn't control for that and we certainly could have. So that's possible. I don't know if maybe that's not driving some of this. That was not what I was thinking but that is I get the logic. Are there other hypotheses? Well one of the one of the hypotheses we tested in every chapter of this book has to do with power. I study international relations and so in international relations it's difficult to try to explain outcomes if you're not taking the power of states seriously taking them into account in your in your explanation. It's not to say that the relative power of states determines everything but if you think back to the to the chart that I showed you the principal agent chart and there were all these hierarchical relations of delegation until we got to stage number four where stage number four was a bargaining game between the recipient state Burkina Faso or Brazil and the donor the United States of the World Bank. Lots of people both people who study foreign aid and people who critique foreign aid agencies argue that donors determine the type of aid that all countries will receive. Countries you know sort of take what they can get and the IMF imposes a one-size-fits-all aid program or the World Bank goes through certain fads and when he goes through a particular fad that's the kind of aid that all World Bank recipients are going to get. We don't adopt that approach when we analyze foreign aid the way we analyze foreign aid is as a cooperation contract there are two sides trying to negotiate a contract usually it takes the form of I'll give you this money if you promise to use it in a certain way right and so both sides need to agree on that contract before the foreign aid is allocated. It's not the case that the United States gets to decide what aid Nicaragua will receive and Nicaragua will have to accept whatever the United States wants to give instead it's a bargain. Now the United States is much more powerful so maybe it didn't get a lot of what it wants but you'll notice that recipient countries vary in their power right and so countries like Mexico, Brazil, India, China can drive a much harder bargain than Burkina Faso can. So I think what we're seeing here and we have some indirect evidence in the econometric test that this is what's going on I think what's going on here is not a capacity story although it could be and I have to control for that. I think what's going on here is least developed countries take what they can get and more powerful countries larger developing countries drive a harder bargain and guess what larger countries want they don't care as much about the global environment they care more about delivering local goods to people that will support them politically. So if I give you a million dollars and you can either spend it on reducing CFC emissions in Brazil or I give you a million dollars and you can use it to build a sewer system for a slum in Sao Paulo which you may need support there to get elected probably you're going to be better off as a politician to get the latter rather than the former. So we argue that developing countries want brown aid more than they want green aid and the countries that have to take more green aid are those that can't get anything else. I'm going to skip through this. Make a long story short there were even more variables that we tested hypotheses on in this chapter and some of them came out. You have to read the book to know what drives that. There you go. Right, future directions. Let me say a couple of things about what we found in Burkina Faso. First we did see dramatic increases in bilateral and multilateral aid. My environmental aid does remain a small fraction of total aid and I should also say that there is enormous variation across both regions and donors in this study. What we did is take sort of a bird's eye view or really a satellite view of this question to sort of sketch out broad patterns rather than sort of going down and analyze each individual donor although we did do case studies of four bilateral and four multilateral donors. There are some real limitations of this book and in each case I'm going to talk about these seven limitations. In each case we're trying to deal with those limitations now but we're doing more research to try to figure them out. First this is an enormous data set over a long period of time and this really masks a lot of variation that's really interesting that varies by region and within countries it varies by district. So we have people doing research right now to look not just what country the aid is going to but look at where in the country the aid goes because again think back to the story I told you about the Sloan and Sao Paulo. If your political base of support is in district X and not in district Y where do you think the aid money is going to be spent? Second our models assume that allocation in one aid sector don't influence allocation in other aid sectors. That is not true but the models that we built basically assume that so we are developing fancier models to account for tradeoffs across sectors. Third not all of these projects are just gifts it's not just like here's a million dollars do what you will with the money I'll see you later many of them are loans and so what you might want to do is distinguish between grants that fund projects versus loans that fund projects. We have a database that allows us to do it but in this book we only looked at total flows. Fourth there's something that the World Bank pioneered and that lots of donors are talking about now which is mainstreaming environmental aid right. All the examples I gave you before remember we had categorized the project as either bad for the environment neutral or good well if you know anything about development you know that projects in real in the real world are much more complex than that. Sometimes they fund four different things here's a million dollars to do immunization you know child welfare education and building a road. How are you going to code that project? You need a much more sophisticated coding system that captures the different elements of the project. Second donor agencies have been much more attuned to the impact of their projects on the environment so if you looked at a World Bank project from 1980 to build a bridge across a river it would have a much more negative impact on the environment than the same World Bank project built in 2009. There would be much more money in the budget to prevent environmental damage as a result of that building. Fifth it may be that all the dirty projects that went away in that time series it just sort of you know dirty aid stayed like this it didn't go up with the amount of aid going up it may be that those are just now being financed by other public agencies that aren't captured in this in this database. Sixth the coding scheme that we have remember the title of the book is understanding the environmental impact of development assistance and if you think our coding scheme's decent and if you think projects basically go for what they say they're going to go for you can probably guess yeah those dollars spent are likely to have those impacts but we didn't actually track the amount to the project level and look what happened when they built this bridge what happened when they did this education project and you got that and so what that one might do that and take a much more worm's eye view rather than the satellite view that we took. And seventh I don't want to give you the impression that enormous amounts of money are not flowing into the developing world now and doing damage to the environment taxpayer dollars are much cleaner than they used to be but that doesn't mean that City Bank has greened its portfolio or the foreign direct investment to build factories is cleaner it may be that all the infrastructure projects that were being done by the World Bank and the UNDP are now just being done by Bank of America so I'm not trying to give you the this arose the impression that development is much greener now I'm giving the impression that the dollars we spend from taxpayers are not funding environmental destruction to the same degree that they were before last two slides so we just wrote a book on environment on foreign aid allocation who gets what how much is it what type of aid is it another big question for people who are studying foreign aid is they're trying to understand whether or not foreign aid is effective and the standard approach in the literature to understand foreign aid effective effectiveness is to take all aid flows sum them up by year to country x and then lag your statistical analysis by year or two and see whether or not all that money had a positive or a negative effect on economic growth or a positive or negative effect on poverty levels right that's reasonable development assistance is supposed to reduce poverty and increase economic growth so let's do control for all the other factors we know impact those things and see whether aid has a positive impact lots of research now showing that you know some people are aid is not only irrelevant to growth it doesn't help but it actually hurts so this is the standard approach take all different types of aid and see whether it impacts economic growth that's a plausible way to start but if i'm giving you aid to give immunizations to kids or i'm giving you aid to rope off the rainforest and you're trying to figure out whether that aid is effective you shouldn't necessarily be studying economic growth as your dependent variable right if i'm giving you aid for immunizations why not study a rate of communicable disease or if i'm giving you aid for education why study literacy rates or if i'm giving you aid for the environment why not study you know environmental regulations or amount of forest roped off etc the answer is you can't do that if your data is crappy right if you don't know what the dollars are being spent for because no one's gone through and categorized and coded it then you can't do more fine-grained aid effectiveness studies but if you have good data then you can do this right you want to study water aid your dependent variable is not economic growth it's water quality right that's you can see sort of where this is going so right now we're writing a book and look at a bunch of different sectors development sectors and ask does aid have the effects that it was intended to have and we think this sort of parsing out of the different types of aid will actually give more fine-grained answers to whether or not it is effective and under what conditions it is okay okay that's what i wanted to tell you about greening aid i just want to take five more minutes and talk about where this project is going now to be very fast on this let's see how do i start this thing down here right i told you we had this thing called the project level aid database plaid turns out in the last three or four months we've switched from plaid to aid data we joined forces with a non-governmental organization called development gateway and i'm going to tell you a very brief story uh when we started this as you could tell by my description of the book we were trying to do social science we gathered better data because we wanted to test hypotheses from political science and economics that's why we got the data but right about the time we were finishing the book people from a well-named challenge corporation and the world bank and usa id started calling up to us and saying we heard you guys have a database that tracks all these different projects do you really have that can we see that i'm like oh sure here it is you know uh they didn't want it to test hypotheses the first people approach is one of it so that they could do better coordination the first guy i talked to was somebody who worked in guatemala and he worked on water projects and he said what would be really useful for me is if i had a database to allow me to go in and look over the last three or four years what other donors had done water projects in guatemala so i could call the guy and say hey who should i talk to in the ministry of planning who's not corrupt what kind of pipes should i use in guatemala because when i was over in tanzania we use these kind of pipes and they were fine but i don't know if they're finding guatemala right that makes sense so to better coordinate it we hear all these horror stories about somebody showing up to do a project in a village uh you know like an immunization project and they find out that two weeks before that the french had been there to do a immunization project that doesn't make sense that's wasting a lot of money right wouldn't it be better if we could coordinate our aid better so that our aid could be more effective okay so that's what they were interested in uh a different group of a different group of people that i've met over the last couple years wanted something different right if you are so so far i just described the donor governments and the folks that work at the world bank what do they want why would why would they think this resource is useful another group that really wants better data on aid are recipient governments wouldn't you think they're recipient governments and the government of guatemala would you think they would know what kind of aid projects were being done in their country well that would be true if all the donors gave aid to the government and then put it on the government budget and then the government could look at its total aid revenues and they'd say okay this year we have four billion dollars in aid revenue let's see we're going to send it some for hospitals over there some for immunization over there but that's not how aid works some donors give aid on budget and some donors left the united states do not give aid to governments they tend to give foreign aid to non-state actors and NGOs or to american contractors who then go into the country and do immunizations right that's what they do so the guatemala government doesn't necessarily know where all the projects are where the money's being spent and they may use their own domestic tax resources to do the same thing very inefficient for planning so it turns out recipient governments want better data on aid the third group of people who want better data on aid are people who are advocacy groups so if you watch bono and sting you know therefore sort of famous for yelling governments were not following through on their commitments to give aid but there are lots of other advocacy groups that try to track what donors are promised versus what they give and then to try to hold them accountable right and so they want better they want better data on aid and the final group and this is sort of well i'll wrap up with this uh the gates foundation private foundation it's not taxpayer dollars turns out the gates foundation is now by far the largest provider of private aid flows in the world right and so about two years ago they came to us and they said hey we understand you guys have this database yeah so you guys are studying aid effectiveness yeah who else studies aid effectiveness and so we explained to them who else is studying they said yeah those are the people we've been talking to and they all tell us that they're not really sure of their conclusions because the data that they're using is not complete and it's difficult to use it's not accessible they don't know what it means uh is there any change what why do you think the gates foundation cares about this because they want to know whether the aid they're giving is effective they're really nervous that they're giving this money for bed nets and they don't know whether or not it's preventing the spread of malaria so they suddenly got interested themselves in learning how to track aid and then to do aid effectiveness studies so fortunately for us we were there at the right time they decided they were going to give us money to help us do this first round of of the plaid database and with any luck they will see the value of their investment and they will re up and give us a lot more money take home message in when is that going to occur here it is in march of 2010 you can go to aiddata.org and you'll be able to access this database that I can describe in going term for the last 45 minutes and see whether it's really useful to you in your classes or in your research or in deciding how you're going to help people in developing countries we have about a million records in this database when it when it goes public in march and we're going to then use the next few years to try to surround the data with a series of tools that will make it easier for people who are not statisticians to use the data it use the data so what you want is an ability to mash up aid data with data you care about like you might be interested in the impact of aid on civil wars or you may be interested in the impact of aid on levels of democracy or levels of democracy on aid etc so we want to be able to give you tools and other data sources so you can mash up those things on the fly and do analysis we also want to be able to give you an opportunity to map the map the aid to see where it's distributed graphically across the globe or even within countries and finally and this I think is more than a year or two off we won't what we would like to do is have a lockdown version of the database that has been cleaned and vetted and coded by us where we saying this is the right answer this is how much money France gave to Nicaragua this year but we also want people to be able to interact with each row of the database so it's sort of imagine it's wikipedia without the ability to affect all parts of wikipedia imagine if there was a wikipedia page with something you could not change but then you could interact with each thing so imagine a row that says you know Germany promises two million dollars to build a school in this village in tanzania by 2012 what we would like is for either the the Germans who were working on the project to be able to write things into a field and say hey we've expanded the project to now include this or a villager to write in or to use a cell phone and take a picture and say hey there was supposed to be a school here and look here's the foundation i'm taking a picture it's not here right to make the whole process from the top donor level all the way down to the recipient much more transparent so that's the vision going forward i talked a little bit about what we've already done with it in the past thanks very much so what the norms are here i'm happy to take questions on either the book or a data or development whatever you whatever you want to talk about you're able to show that the data analysis showed that you could explain better the decline in brown aid than you could the increase in green aid and why would that be what why would you be able to explain the decline in dirty aid better than you can explain the increase in in environmentally sensitive i think there's two possible reasons one is well one is things just to say when we take as the dependent variable environmental aid this is just a description we take that as dependent variable and we plug in the usual independent variables to test these hypotheses we don't get anything that is statistically significant that's what i meant now why is that it could be that the the number of environmental projects are just smaller there are there are fewer observations so that could be one thing is driving it but the second thing could i think could easily be that we just don't our theories aren't as good at uh explaining uh sort of you know environmental projects as they are reduction in brown if i had to guess i would say this when environmental NGOs and environmentalists who care a lot green party activists who care a lot when they go campaigning for things qualitatively the things in the past they tended to campaign for are you fall into the principle of do no harm right there are all these big infrastructure projects that did tremendous damage to the environment they were very obvious and they were they allowed people to politically to rally around them and say stop doing that so those things became much more visible and if you couldn't stop doing that you were going to pay a political price on the other hand if i just gave a few more scrubbers to some developing country or if i roped off rain forest or invested in research on biodiversity maybe there wasn't as much of a political payoff for doing that and therefore our variables that should predict that increase that we did predict an increase in green aid they just don't matter maybe because it's yeah it's harder to see and harder to appreciate but i don't know that i'm just and qualitatively that that's certainly what was going on i think that was what's going on in the world bank the world bank was much less has been hammered much less hard by these NGO groups once they stopped doing these very damaging infrastructure projects they certainly never got much credit you know you don't see people you don't see environmentalists marching in the street saying yay for the world bank they just gave more by relay you just don't see it so anyway those that's my those are my two guesses one sort of methodological and the other one is yeah political question about i know there's there's graduate students in the audience that are interested what are the you know we all love to have data and the key to going from this wanting to have data to actually having it is having some sort of systematic ways you could just if you could describe exactly like what did you do to get this data wouldn't you look at how could you systematically look at these and how can you be sure that you have been systematic and what you looked at so yeah that's very good so there were two questions there as i understood is one how do you get the data how do you get the actual numbers in a row and then the second then what do you do with it how do you categorize it as dirty or environmental etc how do you know you've categorized it properly so on how do you get the data we thought this was going to be relatively easy and this is going to sound really weird the existing donors the oecd donors germany france the united states italy all the rich countries in the world that have been giving aid for 40 years 30 years those when we approach those governments and say we would like to talk to you about your aid flows and getting better descriptive information about your project we've had almost no success with any of those governments they already have a system in place of filling out a survey every year reporting it to the oecd in paris and they don't want to talk to us they're like who are you guys from william and mary what do you know why we want to talk to you so they just basically shut us down all the additional data we've got on those countries from those governments has come because we went out and went to libraries and gathered it or we went to websites their own websites they wouldn't wouldn't give us the underlying database and we web scraped and we augmented their data but the two types of donors that we've done a much better job on because they're much more receptive are multilateral donors there are about 40 of them in the world that we've identified and they don't report to the oecd right and they're much they were traditionally starting in 2003 and up to now are much more receptive if we want to talk to them like oh yeah i can download the database and send it to you and then we just have to map the fields and stuff uh so the mdbs have been much easier and they have better websites with and they do the great uh end of year annual reports with lots of information in them the third category which is by far the most interesting category is non-back bilateral donors so non-oecd countries Czechoslovakia, Poland, Mexico, Iran, Russia, China, Venezuela, Israel, right you're like yeah these guys give aid but no one's ever tracked their data before we're trying to track their data so we've got now 20 of the 40 mdbs that we've identified have been very cooperative the other 20 have not uh Iran is a great story okay i mean on one hand i'm rooting for the green revolution on tv i'm seeing these guys marching in the street i'm like yeah look at those reformers they're great but because that happened when it did i lost all the iranian data like i was going to get all the data on iran and now i don't have it because akhmedinajad won't let the finance ministry talk to us because we're evil americans um okay so basically the answer to debbie's question is how do you give today is you go directly to the donors that ask for it and if they won't give it to you you try to get it through other means libraries but the real question is okay that's great mike but now you've got all these rows how do you know what's environmentally damaging or not so you got to do it's basically a three step process step number one you have to do research and consult scientists who will know have an understanding of this substantive area who will know on average what type of environmental effects certain behaviors and activities have right and then you have to write that up as a rule for a coder a coder is someone like an undergraduate student or a graduate student whose job it is is to look at a row of data and then pick one of five categories environmental strictly defined all the way to dirty strictly defined and there i have a 32 page document that tells you what to do when you see that row of data right the third step is you know making sure that you have the results are reliable and that anyone who looks at that same row will be able to get the exact we'll code it in the exact same way they'll put it in the same one of one of five bins and so what we do is we have two coders for every project and the coders don't talk to each other they don't even know who each other are so you would code project number one and then a few days later you would code project number one and you would say it's environmental strictly defined and you read it and you like apply the exact same criteria look at the code you said yep it's environmental strictly defined a click and then the computer would see ah both coders agree it's probably environmental strictly defined it goes into the database as a project that is environmental strictly defined based on our rules it doesn't mean it really is environmental strictly defined it means that if you follow my rules then we'll categorize the same way if you don't agree if you say environmental strictly defined and you say environmental broadly defined then the project goes to another human who's a senior coder who knows the database better someone like me and I arbitrate between and I decide what it is and I click a button and then it goes into the database right so those are the formal steps briefly described on sort of creating this data set so so probably what you would be if you have the resources what you would be expected to do in research methods but if you don't have the resources if you have to do all the work yourself then maybe you're not doing double blind coding and so the reason I pointed that out about I mean this is my last point the reason I pointed out about whether it may be right or not we don't you never know in science whether you're right or not all you know is you follow the rules and your rules were transparent that way if someone's smarter than me and they realize that cement factories aren't actually very strictly defined they're actually neutral in their impact on the environment because they know more about environmental science then they can go back through the same database and they can recode all the cement projects and say oh these are all neutral right so I'm not saying that I've got the right answers for all of the different categories what I'm saying is my answers are transparent and you know how I created the data right that's science but fair enough right yeah I guess part of when I when I was suggested wondering how you know what you have is is is this the the systematicness of your sort of collection of differentiated donor governments and that sounds like potentially less systematic than your coding rules and that that so so in a sense you know when you say you know things about sort of brains of countries and what they did that that actually made you more reliable or reliant on things that you can't control and you can't be transparent about than your actual like the fact that I don't have Iranian data and if you want to make claims about environmental aid allocation and you're missing I think this becomes important actually in the next 10 years if you don't have data on China's foreign aid and you're making broad sweeping claims about environmental aid allocation that's increasingly going to become a problem because the proportion of the total population of aid projects the China's funding is going up and we don't have a good data source on on that data we have two separate data sources and we think that neither one of them is very good so when this goes public in March you'll be able to go to the website and you'll be able to download the entire data set there'll be no China data in there you go to another part of the website and there would be two separate tables of China data that other people have collected your inferior processes I think and you can look at it if you want and you know buyer beware basically so yeah I think that's a great point do we have the largest donors by far yes we do in dollar amounts and in terms of number of projects we definitely have the largest donors in the world but and we have the biggest most comprehensive database in the world is our sample consistent with the underlying population of all aid projects probably not because the ones we're missing are donors that don't want to share their data right Iran does not want to now does not want to share its data it's probably not distributed like german aid yes sir I think you should have a flu category for clean water because I especially I've taken several water courses and people there are there's a billion people in the world that for more they don't have access to clean water and they suffer from a lot of diseases because of that they and I would argue that giving giving them access to clean water and tensor systems is is more important to them and that should be accomplished first before you worry so much about their effect on the carbon dioxide so I mean I would like to go through the database and make a flu category happy to see it's so much of a plus for you yeah and to add to that there's complexity if you build a dam you have it as somehow for some reason I don't understand why I'm only disrupting but the dam could provide idle power in which case it helps lower CO2 emissions and we reclaim water in Orange County and water or campus with it because otherwise we have to pay to pipe it up over the Datchby mountains which provides which is actually a major fraction of the household energy a lot of these water projects can save you energy right so three quick responses one the cool hopefully the cool thing about this database will be that you can become you can log on say you want to create a username what's your name so Dennis could sign on and say he wants to create a Dennis column wrote yeah column a Dennis column so let's right now I think are 78 columns in the database so you can have a new one it's a Dennis column and then you can recode any project you want you know using your own categorization scheme so for any example you get you might want to take all the dam projects that we were coding as dirty and say actually I know more about you know the environmental impacts here I'm recoding it and rerunning rerunning the data right well that was the same the same thing I was going to say is that was probably that example when I showed you the dirty so in the book the way the way dams we had big fights about this over dams and nuclear the way dams are categorized in our scheme is if the dam is over a certain size if it's a massive hydroelectric project like Aswan dam or something like that then those were the classic examples of dams that sort of destroyed ecosystems they buried them and that three gorgeous dam in China etc so in our coding scheme it's dirty when it's over a certain size and these things called micro hydrodams were not categorized as environmentally damaging and again what you should say is okay that's plausible or I know more about dams than you do and that's not plausible but you know exactly how I categorized it so you can correct for it if I'm wrong if I just told you if I didn't show you my coding scheme then you wouldn't know on your first point for this I think I've got an answer you'll love the first point about you need a blue category so in the current plaid coding scheme there I mentioned earlier there are about 200 sector codes that the OECD has in the plaid's code coding scheme we have 800 different activity codes so you can get very very specific information very granular information about projects and I don't know the number I'm just going to make up this number but maybe of those 800 probably 30 20 or 30 have something to do with water and so what you could do if you did a search if you could just select all 30 of the purpose codes that have anything to do with water and then go return all records and you would get I don't know 150,000 records there were all water so you said all I want to do is sort by water and you could do it and the only reason you can do it is because we have this very very granular coding system donor the data from the United States that's a great question I would assume I don't know I would assume we took the midpoint of the time series so that the number I showed you was between 90 95 I would assume we took the midpoint of the time series and then looked at like the World Bank data on population by country in 1995 and said that's the number we're using there's something behind your question I I would think it was the I don't know my answer is I don't know if I had a guess I would say it was either the World Bank or maybe it wouldn't have been the CIA World Factbook maybe the World Bank or the UN who then they track you know every year they put out new statistics on population would be my guess I don't know I have it in the book we could look it up was there something behind your question like do you think that the standard numbers that people use on the population in the US are inflated or deflated it yes yes yeah you mentioned that the total aid between 1970 and 2000 was 2.3 trillion dollars on what basis was it 2.3 trillion dollars calculated was that a constant two thousand dollars two thousand and ten dollars two thousand okay two thousand yeah it is two thousand okay and well I've got your attention and let me just let me just answer real quickly many people wouldn't call all that money aid so we counted all flows for development purposes so some people this is stuff called official development assistance which means the transfer has to be at least 25 percent concessionary but basically 25 percent if I give you a hundred dollars then at least 25 of those dollars you never have to pay me back for and if it doesn't do that then it's not a so we counted all that plus we counted stuff given by the World Bank and the IVRD that did have to be paid back even if the money just because the money was given for development purposes so our number is going to be larger than any other number you see but just for definitional reasons not because you know and because we have more donors yeah sorry I had one more question it was it was different you showed that India is receiving a lot of aid but it was split between brown and green I was wondering why is India which is getting all this aid not have more brown aid which they need rather than trying to be humanitarian I guess and devoting half of it to re-projects I don't know I don't know that much about India in particular I just know that if you look at the look at the data that's what you see um India does I would think that the donors would would like to give a lot of green aid to India because India does have huge they have they have both lots of environmental damage that needs to be fixed just because of population and they also have lots of environmental resources so one of the things we looked at was like if you look at Chad the the importance of Chad to the global environment is minimal because there's nothing there it's just there's not a lot of water resources there's not a lot of big rainforests there there's not a lot of biodiversity so it just doesn't matter uh not because of its size because it's in the middle of the desert but India is really important it has lots of stuff uh lots of biodiversity lots of water resources and so donors should want to give uh green aid there but I really don't know that much about India yes I appreciate what you're saying from the beginning to this um actually I missed something here I don't know uh I know you've been doing a lot of research work over the years and you know ties have changed but but you probably we just talked about India um how about Haiti this is a short question mark uh how does its present economic situation enter into the picture in spite of all the day donors who have an awful lot of money how do you figure that into into formula um in this neither in this book we haven't looked at the impact of the environmental conditions uh on aid allocation but as as it turns out I'm writing a paper right now on the impact of economic recessions on donor allocations of money and here's our early known I've never told anyone this before here's what we know here's what we think we know these are the early results what we find is that when a donor country goes into a session when its GDP growth per year goes below 1% what we find is that there is a uh a statistically significant decrease in aid flows for the subsequent three years that's the way we set it up so as we go into recession in the if you take the last three recessions as your benchmark we predict that you're going to see a reduction in aid flows coming from countries that are in recession we also found that when you come out of recession and that when you have very high growth rates you do not see a similar rebound so you don't see uh like we had 8% growth rate you wouldn't see as large of an increase in aid donations as if you had a dramatic 8% decrease in our growth rate so it's a sort of a ratchet effect so that's what we found by looking at from 1980 to the present but I have a sneaking suspicion because well just because I'm reading the newspaper I don't know what's going to happen in this global recession but a lot of donors are making very different noises this time uh lots and lots of donors are saying we need to dramatically increase for an aid even though there's a recession which would be I think politically weird I think voters don't like it uh in hard times when their governments give more money away but there are lots of big donors today including the United States and Great Britain sort of leading a charge advocating for a counter cyclical surge of of lending in fact uh in December the the G was a G20 not just the G8 anymore the G20 approved a dramatic increase in World Bank and IMF uh lending to try to get more money out there as a counter cyclical lending it's what some people have called the largest Keynesian uh stimulus plan right it's not just stimulus at home you're trying to give money away to prime the demand side now maybe that's just cheap talk you know maybe at the end of the day politics will uh you know be what it is and don't and voters will say no you're not sending my taxpayer dollars away not in these hard times but they're making a lot of noises right now like they're going to increase aid over the next video so we'll see in some ways I mean this ties into sort of a bigger theoretical question about the way globalization is working because to the degree that the temperature employment unemployment rate in the United States is tied to the shutdown of industries in other parts of the world or the incapacity of consumers in other parts of the world then you know Keynesianism maybe can't be simply domestic it has to be global and and that it strikes me that a lot of the rhetoric is based on the logic coming from those kinds of models um and so then it's an issue of whether politicians can in fact educate voters right or not yeah and that's I completely agree so I'm sure Barack Obama believes that Debbie and and I do too uh and I just I think it'd be a real challenge to convince voters that they ought to spend an extra couple billion dollars sending their month their taxpayer dollars of raw when unemployment is 10 or less percent but I I think the economic logic is right yeah it's important for voters to pay attention yeah two yeah Keynes we have time we have time for like one more question okay the answer is if the money originated from taxpayer dollars then yes so if the United States government says they want to do micro lending in Bangladesh which they do by the way then you will be able to find that that's it micro lending has an actual sector code so you'll be able to look at that and look it up World Bank does it you can look it up but if a private organization does it like a church or some charity we do not track that so the only thing you'll find in our database is the official aid flows dollars that flow from taxpayers either directly from their government or through an international organization an international governmental organization so even though we are funded by the Gates Foundation we have no projects in the database that were funded by Gates I'm hoping that will change by the way but this only tracks taxpayer dollars okay hey thank you very much you've been a great audience