http://nationalaffairs.com/publications/detail/crisis-economics To understand the challenge government economists have faced over the past year and a half, it is useful to imagine the case of a physician trying to treat an ill patient. The patient presents herself in terrible shape; the physician has never treated a condition with symptoms quite like hers before; and the causes of the ailments are... [more]
http://nationalaffairs.com/publications/detail/crisis-economics
To understand the challenge government economists have faced over the past year and a half, it is useful to imagine the case of a physician trying to treat an ill patient. The patient presents herself in terrible shape; the physician has never treated a condition with symptoms quite like hers before; and the causes of the ailments are unclear. The doctor remembers reading about a similar case in medical school — and, trying to recall as much of his training as possible, he endeavors to come up with a theory as to why the patient is sick and to determine what will make her better.
In an ideal world, the doctor would run a controlled experiment: He would assemble 100 patients with similar symptoms, give 50 of them the medicine that seems most likely to work and the other 50 a placebo, and then see whether the patients on the medicine in fact improved. But the doctor does not have 100 patients — he has only one. So, based on his assessment of what is causing the patient's troubles, and the most likely remedy, he takes a risk and administers the medicine.
The patient, however, returns a few weeks later; this time, her symptoms are worse. What, then, should the doctor conclude? He might decide that he gave the patient the wrong medicine. Or he might determine that the patient was even sicker than he originally thought, and thus that the medicine should be administered at an even higher dosage. Either conclusion is plausible, but there is no way the doctor can be sure. What he does know is that he must act before the situation gets even worse.
When the Obama administration came into office, the American economy was one very sick patient. To complicate matters, the financial crisis and the recession that ensued did not neatly follow the pattern of past downturns — instead combining a collapse of the housing market, a credit crisis, failures of large financial firms, and an assortment of other worrisome symptoms. Still, the new administration's economic advisors had no choice but to diagnose the problem and propose solutions. There was, however, little room for trial and error: Their only laboratory was the very economy they were seeking to heal, and time was of the essence, as markets continued to plunge, jobs swiftly evaporated, and bad news mounted.
In an economic assessment they released in January 2009, President Obama's advisors concluded that, if they did nothing, the unemployment rate would reach 9% — its highest level since 1983. So they developed their medicine: an ambitious plan to stimulate the economy by spending a great deal of taxpayer money. According to their estimates, that stimulus would help keep the unemployment rate from exceeding 8%. Today, however, the unemployment rate is nearly 10%. Clearly, things have not gone as the president's advisors expected.
For economists, this raises some obvious questions. Why were the administration's projections off the mark? What should their experience teach us regarding stimulus policies in future downturns? And what can the government do now, as unemployment remains very high, to help the patient heal faster? Trying to resolve these questions illustrates not only the difficulty confronting policymakers in a crisis, but also the inherent limitations of the economics profession — limitations that both economists and politicians would be wise to keep in mind.
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Response by Riversider
about 16 years ago
Posts: 13573
Member since: Apr 2009
To the question of why their patient — the U.S. economy — did not respond as expected, the Obama team's answer is that the patient was sicker at the beginning of 2009 than they had originally thought, not that they administered the wrong medicine. The spending-heavy fiscal stimulus, they argue, was the right approach and did some good; if the stimulus bill had not been enacted, unemployment today would be even higher. The reason the stimulus failed to cure the economy's woes is not that it was the wrong course of treatment: It simply wasn't a big enough dose. (Hence the repeated calls for a "second stimulus.")
There is no way to decisively prove or disprove the Obama administration's argument. All we can do is consider its premises. On that front, the reasoning emerging from the Obama White House has not been arbitrary, or even purely political. Rather, it represents the application of standard textbook theory — in this case, the theory that evolved from the work of John Maynard Keynes, the great British economist of the early 20th century.
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So, inspired by the view that fiscal policy can prop up aggregate demand, Obama's advisors (and their congressional allies) began to design a stimulus plan heavy on direct government spending. A few days before President Obama's inauguration, his economic advisors released a document titled "The Job Impact of the American Recovery and Reinvestment Plan," in which they detailed some of their economic assumptions. They determined that the "government-purchases multiplier" — that is, the multiplier for direct spending — would be 1.57, while the tax-cut multiplier would be 0.99. In other words, every dollar spent by the government would yield $1.57 in aggregate demand, while every dollar in reduced taxes would yield only 99 cents in increased demand. And because 1.57 is larger than 0.99, the Obama team concluded it was better to increase spending than to cut taxes.
Obama and his advisors arrived at these numbers through a standard macroeconometric model of the sort economists have been using for years. Such models take various past relationships among economic variables (inflation and unemployment, for instance) and extrapolate them into the future. In essence, the economy is modeled as a system of equations, each describing how one variable responds to many others. University of Chicago economist (and Nobel laureate) Robert Lucas famously criticized these models for lacking an appreciation of people's changing expectations; many economists, however, still find such models valuable, and have continued to employ them for forecasting and policy analysis.
The question for economists now is whether the administration's assumptions, and the model based on them, were correct. After all, if we could be sure their model was right, we would know what to conclude when their stimulus plan was followed by 10% unemployment: The patient was sicker than they thought, and unemployment would surely have been higher still if not for the stimulus. (Indeed, since Obama's advisors do believe their model was right, this is the conclusion they have reached.)
The trouble is, we have no way of knowing for sure if the model was in fact correct. To react to a model's failure to predict events accurately by insisting that the model was nonetheless right — as Obama's economic advisors have done — is hardly the most obvious course. Careful economists should instead respond with humility. When their predictions fail — as they often do — they should not dig in their heels, but should instead be willing to go back to their starting assumptions and question their validity.
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At first glance, the Obama administration would seem to be taking such an empirical approach. In an attempt to "know" as much as possible about the consequences of the stimulus bill, the administration has been compiling data to measure its effects. Indeed, the vaunted stimulus web site (recovery.gov) claims to provide state-level job-creation "data," reported to two decimals of accuracy.
In reality, however, this ostensible effort at transparency is actually the least credible part of the whole case for the 2009 stimulus bill. For one thing, the reporting errors involved in the data collection are enormous, as hardly anyone accurately fills out the government's questionnaires about the jobs "saved or created" with stimulus money. Some employers, for instance, have counted money used to provide pay raises to existing employees as "creating" jobs. Thus the Wall Street Journal reported last November that the Mid-Willamette Valley Community Action Agency in Oregon had claimed to create 205 jobs with its $397,761 in stimulus money — spending less than $2,000 per "new" job.
The results of gathering economic data this way can be downright comical. A shoe-store owner in Kentucky who sold boots to the U.S. Army Corps of Engineers (for work on a project made possible by stimulus funds) claimed to have created nine jobs with $889 — a feat that would certainly make him the most efficient job creator in the country. The store owner apparently reasoned that he was creating one job for every pair of boots he sold the Army; after all, a soldier could not go to work on the project without a pair of boots. The episode received attention only because a reporter discovered the ridiculous claim, and the owner then asserted that he had been confused by the government form.
The administration has nevertheless accepted such reports, using them as the basis of their stimulus evaluations. But even if the reporting were perfectly correct, the exercise would still make little sense as a way of assessing the broader macroeconomic effects of the stimulus money. When we talk about the impact of government purchases on aggregate demand, and therefore on job creation, we must take into account an enormous number of "general equilibrium effects" — that is, the indirect effects that occur as one economic variable influences another, which in turn influences yet another, and so on. Such effects can be modeled and analyzed to some extent, but they cannot possibly be captured by crude job-creation surveys, or easily conveyed through administration web sites and talking points.
To the question of why their patient — the U.S. economy — did not respond as expected, the Obama team's answer is that the patient was sicker at the beginning of 2009 than they had originally thought, not that they administered the wrong medicine. The spending-heavy fiscal stimulus, they argue, was the right approach and did some good; if the stimulus bill had not been enacted, unemployment today would be even higher. The reason the stimulus failed to cure the economy's woes is not that it was the wrong course of treatment: It simply wasn't a big enough dose. (Hence the repeated calls for a "second stimulus.")
There is no way to decisively prove or disprove the Obama administration's argument. All we can do is consider its premises. On that front, the reasoning emerging from the Obama White House has not been arbitrary, or even purely political. Rather, it represents the application of standard textbook theory — in this case, the theory that evolved from the work of John Maynard Keynes, the great British economist of the early 20th century.
--------------------------------------------------------------------------------------------------------------
So, inspired by the view that fiscal policy can prop up aggregate demand, Obama's advisors (and their congressional allies) began to design a stimulus plan heavy on direct government spending. A few days before President Obama's inauguration, his economic advisors released a document titled "The Job Impact of the American Recovery and Reinvestment Plan," in which they detailed some of their economic assumptions. They determined that the "government-purchases multiplier" — that is, the multiplier for direct spending — would be 1.57, while the tax-cut multiplier would be 0.99. In other words, every dollar spent by the government would yield $1.57 in aggregate demand, while every dollar in reduced taxes would yield only 99 cents in increased demand. And because 1.57 is larger than 0.99, the Obama team concluded it was better to increase spending than to cut taxes.
Obama and his advisors arrived at these numbers through a standard macroeconometric model of the sort economists have been using for years. Such models take various past relationships among economic variables (inflation and unemployment, for instance) and extrapolate them into the future. In essence, the economy is modeled as a system of equations, each describing how one variable responds to many others. University of Chicago economist (and Nobel laureate) Robert Lucas famously criticized these models for lacking an appreciation of people's changing expectations; many economists, however, still find such models valuable, and have continued to employ them for forecasting and policy analysis.
The question for economists now is whether the administration's assumptions, and the model based on them, were correct. After all, if we could be sure their model was right, we would know what to conclude when their stimulus plan was followed by 10% unemployment: The patient was sicker than they thought, and unemployment would surely have been higher still if not for the stimulus. (Indeed, since Obama's advisors do believe their model was right, this is the conclusion they have reached.)
The trouble is, we have no way of knowing for sure if the model was in fact correct. To react to a model's failure to predict events accurately by insisting that the model was nonetheless right — as Obama's economic advisors have done — is hardly the most obvious course. Careful economists should instead respond with humility. When their predictions fail — as they often do — they should not dig in their heels, but should instead be willing to go back to their starting assumptions and question their validity.
--------------------------------------------------------------------------------------------------------------
At first glance, the Obama administration would seem to be taking such an empirical approach. In an attempt to "know" as much as possible about the consequences of the stimulus bill, the administration has been compiling data to measure its effects. Indeed, the vaunted stimulus web site (recovery.gov) claims to provide state-level job-creation "data," reported to two decimals of accuracy.
In reality, however, this ostensible effort at transparency is actually the least credible part of the whole case for the 2009 stimulus bill. For one thing, the reporting errors involved in the data collection are enormous, as hardly anyone accurately fills out the government's questionnaires about the jobs "saved or created" with stimulus money. Some employers, for instance, have counted money used to provide pay raises to existing employees as "creating" jobs. Thus the Wall Street Journal reported last November that the Mid-Willamette Valley Community Action Agency in Oregon had claimed to create 205 jobs with its $397,761 in stimulus money — spending less than $2,000 per "new" job.
The results of gathering economic data this way can be downright comical. A shoe-store owner in Kentucky who sold boots to the U.S. Army Corps of Engineers (for work on a project made possible by stimulus funds) claimed to have created nine jobs with $889 — a feat that would certainly make him the most efficient job creator in the country. The store owner apparently reasoned that he was creating one job for every pair of boots he sold the Army; after all, a soldier could not go to work on the project without a pair of boots. The episode received attention only because a reporter discovered the ridiculous claim, and the owner then asserted that he had been confused by the government form.
The administration has nevertheless accepted such reports, using them as the basis of their stimulus evaluations. But even if the reporting were perfectly correct, the exercise would still make little sense as a way of assessing the broader macroeconomic effects of the stimulus money. When we talk about the impact of government purchases on aggregate demand, and therefore on job creation, we must take into account an enormous number of "general equilibrium effects" — that is, the indirect effects that occur as one economic variable influences another, which in turn influences yet another, and so on. Such effects can be modeled and analyzed to some extent, but they cannot possibly be captured by crude job-creation surveys, or easily conveyed through administration web sites and talking points.