Did the stimulus really destroy a million private-sector jobs?

Hey non-economists, want to see what's inside the guts of one of those econ papers you keep hearing about? Well that's why you have grad student bloggers like me. We read the papers so you don't have to.

This week, Greg Mankiw links us to a paper by Timothy Conley of Western Ontario and Bill Dupor of Ohio State University. The paper's eye-popping finding is that the American Recovery and Reinvestment Act (ARRA), also known as the Stimulus, was responsible for a net loss in jobs. No, really! From the paper's abstract:

Our benchmark results suggest that the ARRA created/saved approximately 450 thousand state and local government jobs and destroyed/forestalled roughly one million private sector jobs...The majority of destroyed/forestalled jobs were in growth industries including health, education, professional and business services.

Wow! Seriously? The stimulus directly resulted in a net loss of five hundred and fifty thousand jobs? That's it, I'm voting Republican from now on...

But wait. A still small voice is nagging me from the back of my mind, urging me to read beyond the abstract. And so my dissertation will have to wait 40 minutes while I wade through three dozen pages of PDF in search of an answer to my nagging doubts.

Because I do have some doubts about this result. Stimulus spending destroys jobs? How the heck is that supposed to work? I mean, maybe you believe in full Ricardian Equivalence, but that would just predict that stimulus is a wash. Perhaps people are cutting back spending in anticipation of the deadweight losses caused by the future taxes needed to pay back the stimulus-related borrowing*? Hmm, maybe, but that sounds so preposterous that I kind of expected something to be fishy about this paper from the get-go.

What Conley and Dupor do is to run a state-by-state regression. Different states received different amounts of ARRA spending, so looking at the differences in employment growth rate between those states after the passage of ARRA should tell us how many jobs ARRA created or destroyed. This should lead to a regression of the type:

Employment growth = A + B*Stimulus + C*Other Stuff + e

Now, you may say: "Wait, but states where employment goes down should be expected to get more stimulus money, since those are just the states that were hardest-hit by the recession!" And you'd be right: there is a big endogeneity problem here. After all, the fact that there's a bunch of sick people in the doctor's office doesn't mean that doctors make you sick. Messrs. Conley & Dupor deal with this problem by finding some "instruments" - natural sources of variation in the amount of stimulus money that a state gets, that have nothing to do with how bad the state's economy was doing. Usually, critics of an empirical paper like this will try to say that the instruments used are bad ones - that they actually can be affected by the business cycle, or that they don't give rise to enough variation in stimulus funding. 

I am not going to do that. I am going to give Conley & Dupor a free pass on their instrumental variables, because I already see one and possibly two gaping hole in their analysis that makes the instrument problem somewhat of a sideshow.

(Update: I had initially written about a second possible problem with this paper, but commenter Ivan found evidence that (thankfully) that problem didn't exist. So, in the interests of not making people read several pointless paragraphs, I've deleted the section that was previously here. Thanks, Ivan!!)

On page 20 of their paper (Table 4), Conley and Dupor have a table that shows their main result: the number of jobs that they estimate to have been created or destroyed by the stimulus. In all private sectors, the estimates are negative. BUT, check out the confidence intervals in Table 4. With one exception, the upper limits of all the confidence intervals are highly positive. This despite the fact that they use a less-rigorous 90% confidence interval (instead of the standard 95%).

This means that Conley and Dupor's results are statistically insignificant. Bluntly, what they have found is nothing. Formally, if we use their model to test the hypothesis that the stimulus caused a net increase in private-sector jobs, we will not be able to reject the hypothesis.

Conley and Dupor tweak their model with some alternative specifications. No change. As you can see in Table 7 and Table 9 (p.23-4), upper 90% confidence limits continue to be strongly positive. If the authors really did leave the intercept term in their regression equation, then that's probably why they got insignificant results; if not, then there's some other problem with their instruments or their specification, or maybe just the data itself.

But, given the lack of any statistically significant findings, this paper does not deliver the results that it advertised. Conley and Dupor's abstract should read "We find no evidence for a significant effect of the ARRA on job creation." That would be scientifically honest, but would not turn a lot of heads. Instead, the abstract makes the more politically incendiary claim that the ARRA destroyed jobs, which the authors actually did not find. They do leave themselves an escape rout by using the word "suggest," but I am not satisfied. In my opinion this is a paper that overstates its findings. (Note: Conley and Dupor have since revised their abstract significantly to more accurately reflect their results, for which I commend them!)

My guess is that papers like this get attention because of politics, not because of science. Dr. Mankiw linked to this paper without comment, evaluation, or qualification. But he could have just as easily linked to this paper by Daniel J. Wilson, which uses a methodology similar to that of Conley and Dupor, but finds strongly positive (and often strongly significant) effects of the stimulus.

Update: Arnold Kling is also not a fan of Conley-Dupor. 

* Actually, it's worse than that. You have to also assume that future deadweight losses from stimulus-payback taxation will be highly concentrated in the states that received the most stimulus funding; i.e., that taxes will be specifically targeted at those states! 

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Speculators and oil prices: what experiments tell us

Another gas price spike, another wave of articles blaming "speculators." Here's an editorial in USA Today by Representative Ed Markey, D-Mass:

[W]e must crack down on speculators in the oil market. Speculative money is seeking volatile investments. Since 2003, the size of the oil futures market has increased by a factor of 17...

When this massive speculative market meets manipulators such as Saudi Arabia and OPEC, consumers get gouged. Goldman Sachs has indicated as much as $20 per barrel is due to speculation, not supply and demand. Anyone doubting the volatility and added momentum speculation brings to the market need only look at Thursday's one-day drop of 9% in the price of oil. The oil market should be governed by the principles of supply and demand, not flittering on the whims of speculators. 

Most Americans seem to agree with this idea. But is it true? Do speculators cause oil and/or gas prices to rise above their "natural" or fundamental level?

First, an important distinction. When we talk about "speculation," we're typically talking about futures contracts. If a speculator buys an oil futures contract, (s)he is not buying a barrel of oil; (s)he is buying the right to buy a barrel of oil in the future, for a price that is determined (locked in) today. This is a very different thing than hoarding, which is purchasing the actual physical commodity and storing it, with the intent to sell if in the future at a profit when the price goes up. Everyone agrees that hoarding can cause today's prices to rise; the question of whether futures contract purchases can have the same effect is far trickier. In fact, most economists will tell you that futures speculation can only raise spot-market prices if it causes physical hoarding to increase.

The key question is: If we curbed activity on futures markets, would prices stabilize?

Theoretically, it's hard to see how that would work. If' I'm a speculator who believes that oil prices will rise, I have two options to make a profit: 1) I can buy an oil futures contract, or 2) I can buy an actual barrel of oil and store it. But what if there is no futures market? In that case, I only have one way to speculate: hoard physical oil. Since it is obvious that hoarding raises prices, but not obvious that futures contracting raises prices, it seems that curbing futures speculation - as Ed Markey would have us do - would push prices up rather than down.

But enough theory; what does the data say? Fortunately, this is one area of economics where good controlled experimental evidence exists. In 1995, Vernon Smith and David Porter conducted an experiment to examine the effect of futures markets on the formation of asset bubbles. They found that when people can buy and sell futures markets, asset bubbles tend to be much smaller and rarer than when futures trading is forbidden. In 2006, Charles Noussair and Steven Tucker did a more in-depth version of the experiment, and got exactly the same result. When futures markets aren't available, spot prices bubble and crash; when futures trading is allowed, futures prices oscillate wildly, but spot prices barely budge from the correct fundamental value.

This experimental evidence is important, because it is controlled. Looking at real data usually doesn't allow you to determine cause and effect; you can observe that futures prices and spot prices tend to move together, but (unless you find a good instrument) you can't pick apart which is causing which. Even if you find a historical case of futures markets being curbed, you don't know whether what happened after that was a result of the policy change, or any one of a bazillion other unrelated events. But in the laboratory, we know that only one thing has changed. So we know that it was the introduction of the futures market that killed the bubble in the lab.

Now, you can argue that lab experiments don't have external validity; that real-world markets are so different that exactly the opposite thing happens when you allow futures markets in the real world. And maybe you'd be right. But as things stand, the weight of evidence is firmly against the idea that futures speculators raise oil prices.

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Bob Shiller and Greg Mankiw stick up for Science

"Measure what is measurable, and make measurable what is not so." - Galileo Galilei

"I appear to be wiser than he, because I do not fancy I know what I do not know." - Socrates

Writing some of my recent posts has gotten me thinking a lot about economics as a science. It seems to me that all too few economists view their field the way natural scientists do their own - as a potential tool for understanding and mastering the Universe. Plenty of economists value models that are "interesting" or "thought-provoking," that tell "good stories," or that have a priori plausible assumptions. That is how journalism or philosophy works, but it is not how Science works. 

So it is extremely gratifying and refreshing to hear leading economists stick up for two of the key elements of science: observation and doubt.

Here's Robert Shiller in the New York Times:

[T]he theory of outlier events doesn’t actually say that they cannot eventually be predicted. Many of them can be, if the right questions are asked and we use new and better data. Hurricanes, for example, were once black-swan events. Now we can forecast their likely formation and path pretty well, enough to significantly reduce the loss of life. Such predictions are a crucial challenge in economics, too, and they are why data collection need not be a dull or a routine field...

Armchair scientists will never get far; observation makes all the difference. Think of the advances that came with the microscope and telescope. So it is with measurements in economics, too.

I produced a century-long series of home prices, which revealed how unusual the housing-price boom was [in the mid-00s]. General talk about the nature of bubbles didn’t convince many people that a bubble was forming, but the data I collected did convince at least some that we were in a very risky and historically unparalleled situation...

We need another measurement revolution like that of G.D.P. or flow-of-funds accounting. For example, Markus Brunnermeier of Princeton, Gary Gorton of Yale and Arvind Krishnamurthy of Northwestern are developing what they call “risk topography.”...We should respond just as we did to the Depression, by starting the long process of redefining our measurements so we can better understand the risk of another financial shock. (emphasis mine)

This is absolutely right. To figure out how the world works, you have to actually look out the window. The revolution in astronomy in the 1600s - which led to and motivated the invention of physics itself - depended crucially on improvement in telescopes, like the ones invented by Galileo and Newton. Similarly, we didn't correct classical physics (with relativity and quantum mechanics) until we mastered electricity and observed electric phenomena that didn't square with existing theories.

As Shiller notes, the big data revolution in econ came after the Depression, when we invented things like the National Income and Product Accounts. All the macro we have today, from RBC to New Keynesian models to more outlandish stuff, is an attempt to explain what we see in the NIPA. Those theories are extremely limited; if we're going to improve upon them, we need better data, not just to pick from the cornucopia of models we have now, but to develop new and more useful ones. Shiller talks about better financial data (also see Hernando de Soto on that subject), but another source of good data is coming from experimental economics, which is rapidly becoming more central to the field.

But to find theories that work, we also need another pillar of the scientific approach: doubt. That is why I was pretty happy to see Greg Mankiw write this in the Times:

After more than a quarter-century as a professional economist, I have a confession to make: There is a lot I don’t know about the economy. Indeed, the area of economics where I have devoted most of my energy and attention — the ups and downs of the business cycle — is where I find myself most often confronting important questions without obvious answers...

The inflation rate that the economy gets is, in large measure, based on the inflation rate that people expect...Even if expectations are as important as the conventional canon presumes, it isn’t obvious what determines those expectations. Are people merely backward-looking, extrapolating recent experience into the future? Or are the expectations based on the credibility of policy makers? And if credibility matters, how is it established? Are people making rational judgments, or are they easily overcome by fear and influenced by extraneous events?...

I just cannot express how refreshing it is to see this kind of scientific humility being expressed by one of macroeconomics' most respected practitioners. Yes, Mankiw is using doubt to score political points over his opponents; the ideas about which he waxes skeptical are things like "We should worry about unemployment more than inflation" and "The U.S. government can safely borrow more money." But that's absolutely fine! There will be plenty of people on the other side of the political spectrum to cast doubt on the idea that we should worry about inflation and deficits. Don't worry.

Because something bigger is at stake here. By invoking doubt, and by admitting his ignorance and the limitations of his models, Greg Mankiw is doing the economics field a great service. Mankiw is probably the ultimate virtuoso practitioner of macro's dominant DSGE paradigm. By admitting that that paradigm has failed to answer some of its own central questions, he is reminding us that - in a field filled with chest-thumping and argument-from-authority - the crucial idea of scientific doubt is not quite dead.

Observation and doubt. Human thinkers have not always valued these things. Economics is far behind the natural sciences - and marginally behind the field of psychology- in recognizing their importance. Kudos to Shiller and Mankiw for nagging us to abandon "armchair science" and do some real science.

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What I learned in econ grad school, Part 2

When I wrote my earlier post recounting what I learned in econ grad school, I realized shortly after I finished it that I might have sounded like I was being a little too harsh on my own econ department, which is really quite a good one. That's why I added the following:

In my second year I took a macro field sequence, which taught me all about demand-based models, frictions, heterogeneity, and other interesting stuff. I don't want to make it sound like graduate school taught me nothing about how to understand the recession...it taught me plenty. It just all came in the field course...

I realize now that this update deserved its own post. After all, the course I described in my last post was one single semester, out of four that I've spent learning macro. If we're trying to assess how well grad school trains macroeconomists, we should talk about the field classes that they're required to take.

My second-year field class was divided into four half-semester portions. Each had its own theme. Broadly, these were: 1) Heterogeneous-agent models, 2) Sticky-price models, 3) Neo-monetarism, and 4) Labor search. Some highlights:

* We spent quite a lot of time on heterogeneous-agent models, e.g. Krusell-Smith model. These models turn out to be very tricky to solve numerically. So far, they have also been mostly wrong in their predictions. But they are very interesting nonetheless.

* We learned about sticky-price models and their cousins, Greg Mankiw's sticky-information models (Mankiw is pictured above). I really liked Mankiw's model; although it (like most macro models) is a "storytelling" model with some implausible assumptions and no real predictive power, the story it tells points in some very interesting research directions, since it involves much more interesting microfoundations than the standard "tastes and technology."

* We briefly covered structural vector autoregressions, or SVARs (I also learned these in a stats class). I liked these because the focus was on making forecasts...finally, someone calculating something! Also, they were honest about their limitations; their standard error bars were so big that they had very little predictive power more than one quarter into the future, but they admitted and prominently displayed this fact, instead of using something like "moment matching" to try to exaggerate their empirical success.

* We studied this very interesting paper by Basu, Fernald and Kimball. Basically, the paper constructs a very general form of the RBC model, and finds that it can't explain economic fluctuations. The reason is that improvements in technology, which are what cause booms in Prescott's original RBC setup, actually cause recessions once you allow for things like imperfect competition. This reinforces similar results by Jordi Gali, who used SVARs but arrived at the exact same conclusion.

* We learned some neo-monetarist models (by the way, what I learned was called "neo-monetarism" seems very different from what Stephen Williamson thinks it is). The neo-monetarist policy response to recessions, I learned, is quantitative easing. Or, as my advisor Miles Kimball put it: "Print money and buy stuff!" (He actually repeated this line four times in a row. When I asked him later what he thought of Bernanke's response to the recession, he grinned hugely and said "He printed money and bought stuff!") I also learned that some neo-monetarist models have a role for fiscal policy, but only for a short time after a particularly severe drop in investment.

* We studied labor search models, e.g. the Mortensen-Pissarides model (which recently won its creators the pseudo-Nobel). Although these models, like the heterogeneity models, make some incorrect predictions, they are commendable for admitting this fact. I liked these models because they relied on interesting and observable microfoundations (e.g. the job matching function).

The field course addressed some, but not all, of the complaints I had had about my first-year course. There was more focus on calculating observable quantities, and on making predictions about phenomena other than the ones that inspired a model's creation. That was very good.

But it was telling that even when the models made wrong predictions, this was not presented as a reason to reject the models (as it would be in, say, biology). This was how I realized that macroeconomics is a science in its extreme infancy. Basically, we don't have any macro models that really work, in the sense that models "work" in biology or meteorology. Often, therefore the measure of a good theory is whether it seems to point us in the direction of models that might work someday.

Anyway, Brad DeLong would still probably have some issues with my field course. We did learn a lot of demand-side models, and a bit of history as well (I learned about Wicksell, and about the Great Depression, both for the first time). But never once was finance mentioned. I learned about the existence of financial accelerator models in an email from a friend at Berkeley...

There were two other big conclusions I drew from that course.

The first was that the DSGE framework is a straitjacket that is strangling the field. It's very costly in terms of time and computing resources to solve a model with more than one or two "frictions" (i.e. realistic elements), with more than a few structural parameters, with hysteresis, or with heterogeneity, etc. This means that what ends up getting published are the very simplest models - the basic RBC model, for example. (Incidentally, that also biases the field toward models in which markets are close to efficient, and in which government policy thus plays only a small role.) 

Worse, all of the mathematical formalism and kludgy numerical solutions of DSGE give you basically zero forecasting ability (and, in almost all cases, no better than an SVAR). All you get from using DSGE, it seems, is the opportunity to puff up your chest and say "Well, MY model is fully microfounded, and contains only 'deep structural' parameters like tastes and technology!"...Well, that, and a shot at publication in a top journal.

Finally, my field course taught me what a bad deal the whole neoclassical paradigm was. When people like Jordi Gali found that RBC models didn't square with the evidence, it did not give any discernible pause to the multitudes of researchers who assume that technology shocks cause recessions. The aforementioned paper by Basu, Fernald and Kimball uses RBC's own framework to show its internal contradictions - it jumps through all the hoops set up by Lucas and Prescott - but I don't exactly expect it to derail the neoclassical program any more than did Gali.

It was only after taking the macro field course that I began to suspect that there might be a political motive behind the neoclassical research program (I catch on quick, eh?). "Why does anyone still use RBC?" I asked one of the profs (not an RBC supporter himself). "Well," he said, stroking his chin, "it's very politically appealing to a lot of people. There's no role for government." 

That made me mad! "Politically appealing"?! What about Science? What about the creation of technologies that give humankind mastery over our universe? Maybe macro models aren't very useful right now, but might they not be in the future? The fact is, there are plenty of smart, serious macroeconomists out there trying to find something that works. But they are swimming against not one, but three onrushing tides - the limited nature of the data, the difficulty of replicating a macroeconomy, and the political pressure for economists to come up with models that tell the government to sit on its hands.

Macro is a noble undertaking, but it's 0.01 steps forward, N(0,1) steps back...

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A halfhearted semi-defense of Casey Mulligan

Wow, I almost can't believe I just wrote those words. Anyway...

Paul Krugman and Brad DeLong are quick to jump all over Casey Mulligan for his recent blog post attacking New Keynesian macro theories. Mulligan writes:

Our labor market has long-term problems that are not addressed by Keynesian economic theory. New Keynesian economics is built on the assumption that employers charge too much for the products that their employees make and are too slow to cut their prices when demand falls. With prices too high, customers are discouraged from buying, especially during recessions, and there is not enough demand to maintain employment.

When the financial crisis hit in 2008...New Keynesian fears seem to have been realized: consumer prices had to fall to maintain employment, but too few employers were willing or able to make the price cuts quickly enough. The result was going to be a severe recession that could be partly cured, in the short term, by fiscal stimulus or, in the longer term, as more companies had the time needed to cut their prices...[but] the low employment rates we have today are too persistent to be blamed on price adjustment lags[.]

Krugman doesn't like this one bit:

It looks as if he’s assuming that nominal demand is constant, so that a fall in prices would lead one for one to a rise in real output. But where’s that coming from?

If he had read anything — anything at all — that Keynesians have written about policy at the zero lower bound, he would have learned that there is no reason to expect falling wages and prices to raise employment — in fact, quite the contrary in the face of a debt overhang.

Nor does DeLong:

For firms and workers to cut their prices in a downturn has, New Keynesians (and Old Keynesians, and monetarists, and Fisherians, and Wicksellians, and a host of others) think, two effects:

1. With lower prices the same flow of nominal demand purchases more commodities and employs more people. 
2. With lower prices the collateral and cash flow cover of nominal debt erodes, and so nominal debt becomes even riskier. If the problem is indeed an excess demand at full employment for safe assets, allowing deflation to reduce the supply of safe assets really does not help.

Back in 1933, I think, Irving Fisher argued most strenuously that deflation was destabilizing: that downward moves in nominal wages and prices were not the cure but the cause of Great Depressions.

It is much better to use other policies--open-market operations, quantitative easing, commitments to further expansion in the future, loan guarantees, government spending, tax cuts--to boost nominal demand in both the short and long run than to sit back and wait for deflation to someday, somehow restore the proper functioning of the market system and return the economy to full employment.

Both DeLong and Krugman are right, in the sense that the existence of debt deflation and liquidity traps mean that you can't just sit there and wait for falling prices to cure a depressed economy.

But they are defending New Keynesians, or the New Keynesian movement. Many economists who count themselves as New Keynesians (or Old Keynesians, or monetarists, or Fisherians, or Wicksellians) understand and believe in the existence of debt deflation and liquidity traps. But that is a very different thing from defending New Keynesian models Mulligan is actually right about the particular New Keynesian model that he is criticizing. 

The classic New Keynesian model is a sticky-price model. In that model, recessions happen when firms are unable to lower their prices in response to falling demand. The solution is for the Fed to cut interest rates, thus raising demand. In these models, if the Fed does not cut interest rates, deflation eventually brings the economy back to full employment at a lower price level, just as Mulligan says.

That's it. No debt deflation, no liquidity trap. The economy can be perfectly stabilized by the implementation of a Taylor-type rule governing nominal interest rates. This is what you will find if you read Michael Woodford's book.

Now, it's true that New Keynesian models have developed far beyond this baseline. And, of course, people who call themselves "Keynesian" (New or otherwise) in no way believe that this model fully describes the economy. But that doesn't erase the grain of truth at the core of Mulligan's crude caricature. The basic "New Keynesian" sticky-price model is not very useful in describing the situation in which we now find ourselves.

Nor was it intended to be. The original sticky-price models were not intended to be a "theory of everything," they were intended to tell the simplest possible story of why demand might matter for the macroeconomy. At the time that Mankiw and Calvo and others were laying the foundations of New Keynesian theory, the "neoclassical" paradigm and the RBC model were in the ascendant; many macroeconomists had been convinced that all recessions were caused by supply shocks, and that demand basically didn't matter at all. Sticky-price models were a way of saying "No, wait, demand shocks could matter as well," in a way that fit into the DSGE framework that neoclassicals insisted everyone use.

The sad truth of the matter is that when macro models are created to tell stories instead of make predictions, it becomes pretty easy for anyone to poke holes in their political opponents' baseline models. And it's also true that stories have power; many smart New Keynesian economists were convinced, before the 2008 crisis shattered their faith, that the Fed really could manage the economy with things like interest-rate targeting.

That turned out not to be true. And to their credit, New Keynesian (and Old Keynesian, and monetarist) economists rapidly realized that their framework had been too narrow, and turned to an older and more diverse set of models to help them understand what they were seeing...while neoclassical economists like Casey Mulligan mainly buried their heads in the sand and blamed the recession on Obama or other chimeras. So it is a little rich for Mulligan to be taking potshots at twenty-year-old New Keynesian formalism, at a time when the people who made or endorsed that formalism have basically moved on.

Final note: I should point out that after he points out the weakness of the classic sticky-price model, Mulligan goes on to say a whole bunch of nonsense things about labor costs, minimum wages, etc. I do not want this blog post to be read as an endorsement of any of that silly stuff.


Update: Brad DeLong emails to point out that the financial accelerator models of Bernanke and Gertler are considered "New Keynesian" models. To be honest, I didn't know that. Actually, sad to say, I never even learned those models from any of the macro classes I took (time for "What I learned in econ grad school, Part 2"?), and only found out about their existence on a tip from a friend at Berkeley! So my defense of Mulligan's terminology can be chalked up to my own Dark Age ignorance. Doh.

But, I reiterate the points I was trying to make: Mulligan discusses one single "New Keynesian" model, the sticky-price model of Calvo, Woodford, etc. He is right insofar as he is saying that that model is sorely inadequate. He would be right if he had pointed out that its inadequacy is mainly a result of the use of macro models to "tell stories" (but he did not point this out).  He is wrong insofar as he is claiming that the "New Keynesian" paradigm or movement is thus discredited. And he is kind of bizarre in his claim that we should be focusing on long-run supply-side policies.

Update 2:  My halfhearted semi-defense is smacked down by Paul Krugman.

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Church, state, and holy wars

 

In honor of the death of bin Laden, I think I'll take a break from economics and do some random, off-the-cuff historical musing.

I've always been struck by the parallels between the Christian Crusades and the modern-day "jihads" roiling the Islamic world. Back in 1000 AD, "Christendom" was a backwater. It was a poverty-stricken civilization, nostalgic for its vanished glories. It was also a violent place, ruled by local strongmen, suffering from explosive fertility rates, with no real separation of church and state. In other words, it was experiencing more severe versions of the problems facing many Islamic countries in recent decades.

Into this volatile mix emerged a religious leader (Pope Urban II) who declared a holy war on a richer, more cosmopolitan infidel civilization. Ancient European glory would be recaptured with a wave of pure religious zeal. Generations of starry-eyed French, German, British, and Italian youth flung themselves against the lands of Islam in a ravening horde; sheer ferocity and the element of surprise gave them a few initial victories, which they celebrated with savage brutality. But eventually the superior technology and organization of the Islamic nations slapped the Crusaders down like the annoyance they were. In the end, the Crusaders ended up doing the most harm, not to Islam, but to a rival sect of Christianity, when they sacked Orthodox Constantinople in 1204.

A very similar thing is happening with today's jihads - although al-Qaeda (the modern-day Knights Templar?) had a few spectacular initial successes, and have slaughtered a great number of their co-religionists, they've never really done any serious damage to the West. Yes, that could change if al-Qaeda gets nukes. But right now, in the wake of the ignominious death of Osama bin Laden, it's looking like the jihads have failed.

So why is this important? Well, according to my personal reading of history, the Crusades were a big turning point in European civilization. They demonstrated that religious zeal and high birth rates were not sufficient to win wars. And, more crucially, they showed that theocracy is a poor foundation upon which to build civilizational greatness. After the Crusades, Europe started moving toward the separation of church and state, and never stopped. The results, in retrospect, seem pretty darn positive.

I hope, therefore, that the failure of Osama bin Laden's program for restoration of Muslim glory will provoke a similar rethink. Al-Qaeda's jihad, like the Crusades, has turned out to be both nihilistic and weak. This will hopefully convince Muslim countries from Saudi Arabia to Pakistan to Iran that "rendering unto Caesar" represents best practice. In fact, the Muslim world seems much better positioned today to make that transition than Christendom was a thousand years ago - countries like Indonesia, Turkey, and (to some degree) Egypt have already separated church and state.

In other words, I'm optimistic regarding the Islamic world. It took Europe five centuries to throw off the yoke of theocracy after the Crusades crashed and burned, but don't be surprised if it takes the Middle East and other Muslim lands only a few decades. In fact, I predict that well before the end of this century, the term "Islamic world" will be as obsolete as "Christendom." And make no mistake, that will be a very good thing for those countries. If you want your nation to be rich, stable, and powerful, separation of church and state is clearly the only way to go.

Update: Fadi Hakura agrees.

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Not much to say but...

 

OWNED.

Now stop touching my junk, America.

That is all.

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