corruption

The credit rating agencies to be worried about aren’t the ones you think

PNRJ critique of neoclassical economics, macroeconomics, public policy , , , , , , , , , , , , , , , , , , , , , , ,

JDN 2457499

John Oliver is probably the best investigative journalist in America today, despite being neither American nor officially a journalist; last week he took on the subject of credit rating agencies, a classic example of his mantra “If you want to do something evil, put it inside something boring.” (note that it’s on HBO, so there is foul language):

As ever, his analysis of the subject is quite good—it’s absurd how much power these agencies have over our lives, and how little accountability they have for even assuring accuracy.

But I couldn’t help but feel that he was kind of missing the point. The credit rating agencies to really be worried about aren’t Equifax, Experian, and Transunion, the ones that assess credit ratings on individuals. They are Standard & Poor’s, Moody’s, and Fitch (which would have been even easier to skewer the way John Oliver did—perhaps we can get them confused with Standardly Poor, Moody, and Filch), the agencies which assess credit ratings on institutions.

These credit rating agencies have almost unimaginable power over our society. They are responsible for rating the risk of corporate bonds, certificates of deposit, stocks, derivatives such as mortgage-backed securities and collateralized debt obligations, and even municipal and government bonds.

S&P, Moody’s, and Fitch don’t just rate the creditworthiness of Goldman Sachs and J.P. Morgan Chase; they rate the creditworthiness of Detroit and Greece. (Indeed, they played an important role in the debt crisis of Greece, which I’ll talk about more in a later post.)

Moreover, they are proven corrupt. It’s a matter of public record.

Standard and Poor’s is the worst; they have been successfully sued for fraud by small banks in Pennsylvania and by the State of New Jersey; they have also settled fraud cases with the Securities and Exchange Commission and the Department of Justice.

Moody’s has also been sued for fraud by the Department of Justice, and all three have been prosecuted for fraud by the State of New York.

But in fact this underestimates the corruption, because the worst conflicts of interest aren’t even illegal, or weren’t until Dodd-Frank was passed in 2010. The basic structure of this credit rating system is fundamentally broken; the agencies are private, for-profit corporations, and they get their revenue entirely from the banks that pay them to assess their risk. If they rate a bank’s asset as too risky, the bank stops paying them, and instead goes to another agency that will offer a higher rating—and simply the threat of doing so keeps them in line. As a result their ratings are basically uncorrelated with real risk—they failed to predict the collapse of Lehman Brothers or the failure of mortgage-backed CDOs, and they didn’t “predict” the European debt crisis so much as cause it by their panic.

Then of course there’s the fact that they are obviously an oligopoly, and furthermore one that is explicitly protected under US law. But then it dawns upon you: Wait… US law? US law decides the structure of credit rating agencies that set the bond rates of entire nations? Yes, that’s right. You’d think that such ratings would be set by the World Bank or something, but they’re not; in fact here’s a paper published by the World Bank in 2004 about how rather than reform our credit rating system, we should instead tell poor countries to reform themselves so they can better impress the private credit rating agencies.

In fact the whole concept of “sovereign debt risk” is fundamentally defective; a country that borrows in its own currency should never have to default on debt under any circumstances. National debt is almost nothing like personal or corporate debt. Their fears should be inflation and unemployment—their monetary policy should be set to minimize the harm of these two basic macroeconomic problems, understanding that policies which mitigate one may enflame the other. There is such a thing as bad fiscal policy, but it has nothing to do with “running out of money to pay your debt” unless you are forced to borrow in a currency you can’t control (as Greece is, because they are on the Euro—their debt is less like the US national debt and more like the debt of Puerto Rico, which is suffering an ongoing debt crisis you may not have heard about). If you borrow in your own currency, you should be worried about excessive borrowing creating inflation and devaluing your currency—but not about suddenly being unable to repay your creditors. The whole concept of giving a sovereign nation a credit rating makes no sense. You will be repaid on time and in full, in nominal terms; if inflation or currency exchange has devalued the currency you are repaid in, that’s sort of like a partial default, but it’s a fundamentally different kind of “default” than simply not paying back the money—and credit ratings have no way of capturing that difference.

In particular, it makes no sense for interest rates on government bonds to go up when a country is suffering some kind of macroeconomic problem.

The basic argument for why interest rates go up when risk is higher is that lenders expect to be paid more by those who do pay to compensate for what they lose from those who don’t pay. This is already much more problematic than most economists appreciate; I’ve been meaning to write a paper on how this system creates self-fulfilling prophecies of default and moral hazard from people who pay their debts being forced to subsidize those who don’t. But it at least makes some sense.

But if a country is a “high risk” in the sense of macroeconomic instability undermining the real value of their debt, we want to ensure that they can restore macroeconomic stability. But we know that when there is a surge in interest rates on government bonds, instability gets worse, not better. Fiscal policy is suddenly shifted away from real production into higher debt payments, and this creates unemployment and makes the economic crisis worse. As Paul Krugman writes about frequently, these policies of “austerity” cause enormous damage to national economies and ultimately benefit no one because they destroy the source of wealth that would have been used to repay the debt.

By letting credit rating agencies decide the rates at which governments must borrow, we are effectively treating national governments as a special case of corporations. But corporations, by design, act for profit and can go bankrupt. National governments are supposed to act for the public good and persist indefinitely. We can’t simply let Greece fail as we might let a bank fail (and of course we’ve seen that there are serious downsides even to that). We have to restructure the sovereign debt system so that it benefits the development of nations rather than detracting from it. The first step is removing the power of private for-profit corporations in the US to decide the “creditworthiness” of entire countries. If we need to assess such risks at all, they should be done by international institutions like the UN or the World Bank.

But right now people are so stuck in the idea that national debt is basically the same as personal or corporate debt that they can’t even understand the problem. For after all, one must repay one’s debts.

We all know lobbying is corrupt. What can we do about it?

PNRJ public policy, Uncategorized , , , , , , , , , , , , , , , , ,

JDN 2457439

It’s so well-known as to almost seem cliche: Our political lobbying system is clearly corrupt.

Juan Cole, a historian and public intellectual from the University of Michigan, even went so far as to say that the United States is the most corrupt country in the world. He clearly went too far, or else left out a word; the US may well be the most corrupt county in the First World, though most rankings say Italy. In any case, the US is definitely not the most corrupt country in the whole world; no, that title goes to Somalia and/or North Korea.

Still, lobbying in the US is clearly a major source of corruption. Indeed, economists who study corruption often have trouble coming up with a sound definition of “corruption” that doesn’t end up including lobbying, despite the fact that lobbying is quite legal. Bribery means giving politicians money to get them to do things for you. Lobbying means giving politicians money and asking them to do things. In the letter of the law, that makes all the difference.

One thing that does make a difference is that lobbyists are required to register who they are and record their campaign contributions (unless of course they launder—I mean reallocate—them through a Super PAC of course). Many corporate lobbyists claim that it’s not that they go around trying to find politicians to influence, but rather politicians who call them up demanding money.

One of the biggest problems with lobbying is what’s called the revolving door—politicians are often re-hired as lobbyists, or lobbyists as politicians, based on the personal connections formed in the lobbying process—or possibly actual deals between lobbying companies over legislation, though if done explicitly that would be illegal. Almost 400 lobbyists working right now used to be legislators; almost 3,000 more worked as Congressional staff. Many lobbyists will do a tour as a Congressional staffer as a resume-builder, like an internship.

Studies have shown that lobbying does have an impact on policy—in terms of carving out tax loopholes it offers a huge return on investment.

Our current systems to disinventize the revolving door are not working. While there is reason to think that establishing a “cooling-off period” of a few years could make a difference, under current policy we already have some cooling-off periods and it’s clearly not enough.

So, now that we know the problem, let’s start talking about solutions.

Option 1: Ban campaign contributions

One possibility would be to eliminate campaign contributions entirely, which we could do by establishing a law that nobody can ever give money or in-kind favors to politicians ever under any circumstances. It would still be legal to meet with politicians and talk to them about issues, but if you take a Senator out for dinner we’d have to require that the Senator pay for their own food and transportation, lest wining-and-dining still be an effective means of manipulation. Then all elections would have to be completely publicly financed. This is a radical solution, but it would almost certainly work. MoveOn has a petition you can sign if you like this solution, and there’s a site called public-campaign-financing.org that will tell you how it could realistically be implemented (beware, their webmaster appears to be a time traveler from the 1990s who thinks that automatic music and tiled backgrounds constitute good web design).

There are a couple of problems with this solution, however:

First, it would be declared Unconstitutional by the Supreme Court. Under the (literally Orwellian) dicta that “corporations are people” and “money is speech” established in Citizens United vs. FEC, any restrictions on donating money to politicians constitute restrictions on free speech, and are therefore subject to strict scrutiny.

Second, there is actually a real restriction on freedom here, not because money is speech, but because money facilitates speech. Since eliminating all campaign donations would require total public financing of elections, we would need some way of deciding which candidates to finance publicly, because obviously you can’t give the same amount of money to everyone in the country or even everyone who decides to run. It simply doesn’t make sense to provide the same campaign financing for Hillary Clinton that you would for Vermin Supreme. But then, however this mechanism works, it could readily be manipulated to give even more advantages to the two major parties (not that they appear to need any more). If you’re fine with having exactly two parties to choose from, then providing funding for their, say, top 5 candidates in each primary, and then for their nominee in the general election, would work. But I for one would like to have more options than that, and that means devising some mechanism for funding third parties that have a realistic shot (like Ralph Nader or Ross Perot) but not those who don’t (like the aforementioned Vermin Supreme)—but at the same time we need to make sure that it’s not biased or self-fulfilling.

So let’s suppose we don’t eliminate campaign contributions completely. What else could we do that would curb corruption?

Option 2: Donation caps and “Democracy Credits”

I particularly like this proposal, self-titled the American Anti-Corruption Act (beware self-titled laws: USA PATRIOT ACT, anyone?), which would require full transparency—yes, even you, Super PACs—and place reasonable caps on donations so that large amounts of funds must be raised from large numbers of people rather than from a handful of people with a huge amount of money. It also includes an interesting proposal called “Democracy Credits” (again, the titles are a bit heavy-handed), which are basically an independent monetary system, used only to finance elections, and doled out exactly equally to all US citizens to spend on the candidates they like. The credits would then be exchangeable for real money, but only by the candidates themselves. This is a great idea, but sadly I doubt anyone in our political system is likely to go for it.

Actually, I would like to see these “Democracy Credits” used as votes—whoever gets the most credits wins the election, automatically. This is not quite as good as range voting, because it is not cloneproof or independent of irrelevant alternatives (briefly put, if you run two candidates that are exactly alike, their votes get split and they both lose, even if everyone likes them; and similarly, if you add a new candidate that doesn’t win you can still affect who does end up winning. Range voting is basically the only system that doesn’t have these problems, aside from a few really weird “voting” systems like “random ballot”). But still, it would be much better than our current plurality “first past the post” system, and would give third-party candidates a much fairer shot at winning elections. Indeed, it is very similar to CTT monetary voting, which is provably optimal in certain (idealized) circumstances. Of course, that’s even more of a pipe dream.

The donation caps are realistic, however; we used to have them, in fact, before Citizens United vs. FEC. Perhaps future Supreme Court decisions can overturn it and restore some semblance of balance in our campaign finance system.

Option 3: Treat campaign contributions as a conflict of interest

Jack Abramoff, a former lobbyist who was actually so corrupt he got convicted for it, has somewhat ironically made another proposal for how to reduce corrupting in the lobbying system. I suppose he would know, though I must wonder what incentives he has to actually do this properly (and corrupt people are precisely the sort of people with whom you should definitely be looking at the monetary incentives).

Abramoff would essentially use Option 1, but applied only to individuals and corporations with direct interests in the laws being made. As Gawker put it, “If you get money or perks from elected officials, […] you shouldn’t be permitted to give them so much as one dollar.” The way it avoids requiring total public financing is by saying that if you don’t get perks, you can still donate.

His plan would also extend the “cooling off” idea to its logical limit—once you work for Congress, you can never work for a lobbying organization for the rest of your life, and vice versa. That seems like a lot of commitment to ask of twentysomething Congressional interns (“If you take this job, unemployed graduate, you can never ever take that other job!”), but I suppose if it works it might be worth it.

He also wants to establish term limits for Congress, which seems pretty reasonable to me. If we’re going to have term limits for the Executive branch, why not the other branches as well? They could be longer, but if term limits are necessary at all we should use them consistently.

Abramoff also says we should repeal the 17th Amendment, because apparently making our Senators less representative of the population will somehow advance democracy. Best I can figure, he’s coming from an aristocratic attitude here, this notion that we should let “better people” make the important decisions if we want better decisions. And this sounds seductive, given how many really bad decisions people make in this world. But of course which people were the better people was precisely the question representative democracy was intended to answer. At least if Senators are chosen by state legislatures there’s a sort of meta-representation going on, which is obviously better than no representation at all; but still, adding layers of non-democracy by definition cannot make a system more democratic.

But Abramoff really goes off the rails when he proposes making it a conflict of interest to legislate about your own state. “Pork-barrel spending”, as it is known, or earmarks as they are formally called, are actually a tiny portion of our budget (about 0.1% of our GDP) and really not worth worrying about. Sure, sometimes a Senator gets a bridge built that only three people will ever use, but it’s not that much money in the scheme of things, and there’s no harm in keeping our construction workers at work. The much bigger problem would be if legislators could no longer represent their own constituents in any way, thus defeating the basic purpose of having a representative legislature. (There is a thorny question about how much a Senator is responsible for their own state versus the country as a whole; but clearly their responsibility to their own state is not zero.)

Even aside from that ridiculous last part, there’s a serious problem with this idea of “no contributions from anyone who gets perks”: What constitutes a “perk”? Is a subsidy for solar power a perk for solar companies, or a smart environmental policy (can it be both?)? Does paying for road construction “affect” auto manufacturers in the relevant sense? What about policies that harm particular corporations? Since a carbon tax would hurt oil company profits, are oil companies allowed to lobby against it on the ground that it is the opposite of a “perk”?

Voting for representatives who will do things you want is kind of the point of representative democracy. (No, New York Post, it is not “pandering” to support women’s rights and interests—women are the majority of our population. If there is one group of people that our government should represent, it is women.) Taken to its logical extreme, this policy would mean that once the government ever truly acts in the public interest, all campaign contributions are henceforth forever banned. I presume that’s not actually what Abramoff intends, but he offers no clear guidelines on how we would distinguish a special interest to be excluded from donations as opposed to a legitimate public interest that creates no such exclusion. Could we flesh this out in the actual legislative process? Is this something courts would decide?

In all, I think the best reform right now is to put the cap back on campaign contributions. It’s simple to do, and we had it before and it seemed to work (mostly). We could also combine that with longer cooling-off periods, perhaps three or five years instead of only one, and potentially even term limits for Congress. These reforms would certainly not eliminate corruption in the lobbying system, but they would most likely reduce it substantially, without stepping on fundamental freedoms.

Of course I’d really like to see those “Democracy Credits”; but that’s clearly not going to happen.

What does correlation have to do with causation?

PNRJ core principles, statistics , , , , , , , , , , , ,

JDN 2457345

I’ve been thinking of expanding the topics of this blog into some basic statistics and econometrics. It has been said that there are “Lies, damn lies, and statistics”; but in fact it’s almost the opposite—there are truths, whole truths, and statistics. Almost everything in the world that we know—not merely guess, or suppose, or intuit, or believe, but actually know, with a quantifiable level of certainty—is done by means of statistics. All sciences are based on them, from physics (when they say the Higgs discovery is a “5-sigma event”, that’s a statistic) to psychology, ecology to economics. Far from being something we cannot trust, they are in a sense the only thing we can trust.

The reason it sometimes feels like we cannot trust statistics is that most people do not understand statistics very well; this creates opportunities for both accidental confusion and willful distortion. My hope is therefore to provide you with some of the basic statistical knowledge you need to combat the worst distortions and correct the worst confusions.

I wasn’t quite sure where to start on this quest, but I suppose I have to start somewhere. I figured I may as well start with an adage about statistics that I hear commonly abused: “Correlation does not imply causation.”

Taken at its original meaning, this is definitely true. Unfortunately, it can be easily abused or misunderstood.

In its original meaning, the formal sense of the word “imply” meaning logical implication, to “imply” something is an extremely strong statement. It means that you logically entail that result, that if the antecedent is true, the consequent must be true, on pain of logical contradiction. Logical implication is for most practical purposes synonymous with mathematical proof. (Unfortunately, it’s not quite synonymous, because of things like Gödel’s incompleteness theorems and Löb’s theorem.)

And indeed, correlation does not logically entail causation; it’s quite possible to have correlations without any causal connection whatsoever, simply by chance. One of my former professors liked to brag that from 1990 to 2010 whether or not she ate breakfast had a statistically significant positive correlation with that day’s closing price for the Dow Jones Industrial Average.

How is this possible? Did my professor actually somehow influence the stock market by eating breakfast? Of course not; if she could do that, she’d be a billionaire by now. And obviously the Dow’s price at 17:00 couldn’t influence whether she ate breakfast at 09:00. Could there be some common cause driving both of them, like the weather? I guess it’s possible; maybe in good weather she gets up earlier and people are in better moods so they buy more stocks. But the most likely reason for this correlation is much simpler than that: She tried a whole bunch of different combinations until she found two things that correlated. At the usual significance level of 0.05, on average you need to try about 20 combinations of totally unrelated things before two of them will show up as correlated. (My guess is she used a number of different stock indexes and varied the starting and ending year. That’s a way to generate a surprisingly large number of degrees of freedom without it seeming like you’re doing anything particularly nefarious.)

But how do we know they aren’t actually causally related? Well, I suppose we don’t. Especially if the universe is ultimately deterministic and nonlocal (as I’ve become increasingly convinced by the results of recent quantum experiments), any two data sets could be causally related somehow. But the point is they don’t have to be; you can pick any randomly-generated datasets, pair them up in 20 different ways, and odds are, one of those ways will show a statistically significant correlation.

All of that is true, and important to understand. Finding a correlation between eating grapefruit and getting breast cancer, or between liking bitter foods and being a psychopath, does not necessarily mean that there is any real causal link between the two. If we can replicate these results in a bunch of other studies, that would suggest that the link is real; but typically, such findings cannot be replicated. There is something deeply wrong with the way science journalists operate; they like to publish the new and exciting findings, which 9 times out of 10 turn out to be completely wrong. They never want to talk about the really important and fascinating things that we know are true because we’ve been confirming them over hundreds of different experiments, because that’s “old news”. The journalistic desire to be new and first fundamentally contradicts the scientific requirement of being replicated and confirmed.

So, yes, it’s quite possible to have a correlation that tells you absolutely nothing about causation.

But this is exceptional. In most cases, correlation actually tells you quite a bit about causation.

And this is why I don’t like the adage; “imply” has a very different meaning in common speech, meaning merely to suggest or evoke. Almost everything you say implies all sorts of things in this broader sense, some more strongly than others, even though it may logically entail none of them.

Correlation does in fact suggest causation. Like any suggestion, it can be overridden. If we know that 20 different combinations were tried until one finally yielded a correlation, we have reason to distrust that correlation. If we find a correlation between A and B but there is no logical way they can be connected, we infer that it is simply an odd coincidence.

But when we encounter any given correlation, there are three other scenarios which are far more likely than mere coincidence: A causes B, B causes A, or some other factor C causes A and B. These are also not mutually exclusive; they can all be true to some extent, and in many cases are.

A great deal of work in science, and particularly in economics, is based upon using correlation to infer causation, and has to be—because there is simply no alternative means of approaching the problem.

Yes, sometimes you can do randomized controlled experiments, and some really important new findings in behavioral economics and development economics have been made this way. Indeed, much of the work that I hope to do over the course of my career is based on randomized controlled experiments, because they truly are the foundation of scientific knowledge. But sometimes, that’s just not an option.

Let’s consider an example: In my master’s thesis I found a strong correlation between the level of corruption in a country (as estimated by the World Bank) and the proportion of that country’s income which goes to the top 0.01% of the population. Countries that have higher levels of corruption also tend to have a larger proportion of income that accrues to the top 0.01%. That correlation is a fact; it’s there. There’s no denying it. But where does it come from? That’s the real question.

Could it be pure coincidence? Well, maybe; but when it keeps showing up in several different models with different variables included, that becomes unlikely. A single p < 0.05 will happen about 1 in 20 times by chance; but five in a row should happen less than 1 in 1 million times (assuming they’re independent, which, to be fair, they usually aren’t).

Could it be some artifact of the measurement methods? It’s possible. In particular, I was concerned about the possibility of Halo Effect, in which people tend to assume that something which is better (or worse) in one way is automatically better (or worse) in other ways as well. People might think of their country as more corrupt simply because it has higher inequality, even if there is no real connection. But it would have taken a very large halo bias to explain this effect.

So, does corruption cause income inequality? It’s not hard to see how that might happen: More corrupt individuals could bribe leaders or exploit loopholes to make themselves extremely rich, and thereby increase inequality.

Does inequality cause corruption? This also makes some sense, since it’s a lot easier to bribe leaders and manipulate regulations when you have a lot of money to work with in the first place.

Does something else cause both corruption and inequality? Also quite plausible. Maybe some general cultural factors are involved, or certain economic policies lead to both corruption and inequality. I did try to control for such things, but I obviously couldn’t include all possible variables.

So, which way does the causation run? Unfortunately, I don’t know. I tried some clever statistical techniques to try to figure this out; in particular, I looked at which tends to come first—the corruption or the inequality—and whether they could be used to predict each other, a method called Granger causality. Those results were inconclusive, however. I could neither verify nor exclude a causal connection in either direction. But is there a causal connection? I think so. It’s too robust to just be coincidence. I simply don’t know whether A causes B, B causes A, or C causes A and B.

Imagine trying to do this same study as a randomized controlled experiment. Are we supposed to create two societies and flip a coin to decide which one we make more corrupt? Or which one we give more income inequality? Perhaps you could do some sort of experiment with a proxy for corruption (cheating on a test or something like that), and then have unequal payoffs in the experiment—but that is very far removed from how corruption actually works in the real world, and worse, it’s prohibitively expensive to make really life-altering income inequality within an experimental context. Sure, we can give one participant $1 and the other $1,000; but we can’t give one participant $10,000 and the other $10 million, and it’s the latter that we’re really talking about when we deal with real-world income inequality. I’m not opposed to doing such an experiment, but it can only tell us so much. At some point you need to actually test the validity of your theory in the real world, and for that we need to use statistical correlations.

Or think about macroeconomics; how exactly are you supposed to test a theory of the business cycle experimentally? I guess theoretically you could subject an entire country to a new monetary policy selected at random, but the consequences of being put into the wrong experimental group would be disastrous. Moreover, nobody is going to accept a random monetary policy democratically, so you’d have to introduce it against the will of the population, by some sort of tyranny or at least technocracy. Even if this is theoretically possible, it’s mind-bogglingly unethical.

Now, you might be thinking: But we do change real-world policies, right? Couldn’t we use those changes as a sort of “experiment”? Yes, absolutely; that’s called a quasi-experiment or a natural experiment. They are tremendously useful. But since they are not truly randomized, they aren’t quite experiments. Ultimately, everything you get out of a quasi-experiment is based on statistical correlations.

Thus, abuse of the adage “Correlation does not imply causation” can lead to ignoring whole subfields of science, because there is no realistic way of running experiments in those subfields. Sometimes, statistics are all we have to work with.

This is why I like to say it a little differently:

Correlation does not prove causation. But correlation definitely can suggest causation.