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Political polls are dutifully reported with a margin of error,
which gives us a clue that they contain some uncertainty. Most of
the time when an economic prediction is presented, however,
only a single number is mentioned. The economy will create
150,000 jobs next month. GDP will grow by 3 percent next year.
Oil will rise to $120 per barrel.

This creates the perception that these forecasts are amazingly
accurate. Headlines expressing surprise at any minor deviation
from the prediction are common in coverage of the economy:

Unexpected Jump in Unemployment
Rate to 9.2% Stings Markets
—Denver Post, July 9, 20111

If you read the fine print of that article, you’d discover that the
“unexpected” result was that the unemployment rate had come in

at 9.2 percent—rather than 9.1 percent2 as economists had
forecasted. If a one-tenth of a percentage point error is enough to
make headlines, it seems like these forecasts must ordinarily be
very reliable.

Instead, economic forecasts are blunt instruments at best,
rarely being able to anticipate economic turning points more than
a few months in advance. Fairly often, in fact, these forecasts
have failed to “predict” recessions even once they were already
under way: a majority of economists did not think we were in
one when the three most recent recessions, in 1990, 2001, and
2007, were later determined to have begun.3

Forecasting something as large and complex as the American
economy is a very challenging task. The gap between how well
these forecasts actually do and how well they are perceived to
do is substantial.

Some economic forecasters wouldn’t want you to know that.
Like forecasters in most other disciplines, they see uncertainty as
the enemy—something that threatens their reputation. They don’t
estimate it accurately, making assumptions that lower the amount
of uncertainty in their forecast models but that don’t improve
their predictions in the real world. This tends to leave us less
prepared when a deluge hits.

The Importance of Communicating Uncertainty

In April 1997, the Red River of the North flooded Grand Forks,
North Dakota, overtopping the town’s levees and spilling more
than two miles into the city.*4 Although there was no loss of life,
nearly all of the city’s 50,000 residents had to be evacuated,
cleanup costs ran into the billions of dollars,5 and 75 percent of
the city’s homes were damaged or destroyed.6

Unlike a hurricane or an earthquake, the Grand Forks flood
may have been a preventable disaster. The city’s floodwalls
could have been reinforced using sandbags.7 It might also have
been possible to divert the overflow into depopulated areas—
into farmland instead of schools, churches, and homes.

Residents of Grand Forks had been aware of the flood threat
for months. Snowfall had been especially heavy in the Great
Plains that winter, and the National Weather Service,
anticipating runoff as the snow melted, had predicted the waters
of the Red River would crest to forty-nine feet, close to the
alltime record.

There was just one small problem. The levees in Grand Forks
had been built to handle a flood of fifty-one feet. Even a small
miss in the forty-nine-foot prediction could prove catastrophic.

In fact, the river crested to fifty-four feet. The Weather

Service’s forecast hadn’t been perfect by any means, but a five-
foot miss, two months in advance of a flood, is pretty
reasonable—about as well as these predictions had done on
average historically. The margin of error on the Weather
Service’s forecast—based on how well their flood forecasts had
done in the past—was about plus or minus nine feet. That
implied about a 35 percent chance of the levees being


The problem is that the Weather Service had explicitly
avoided communicating the uncertainty in their forecast to the
public, emphasizing only the forty-nine-foot prediction. The
forecasters later told researchers that they were afraid the public
might lose confidence in the forecast if they had conveyed any
uncertainty in the outlook.

Instead, of course, it would have made the public much better
prepared—and possibly able to prevent the flooding by

reinforcing the levees or diverting the river flow. Left to their
own devices, many residents became convinced they didn’t have
anything to worry about. (Very few of them bought flood
insurance.10) A prediction of a forty-nine-foot crest in the river,
expressed without any reservation, seemed to imply that the
flood would hit forty-nine feet exactly; the fifty-one-foot levees
would be just enough to keep them safe. Some residents even
interpreted the forecast of forty-nine feet as representing the
maximum possible extent of the flood.11

An oft-told joke: a statistician drowned crossing a river that
was only three feet deep on average. On average, the flood
might be forty-nine feet in the Weather Service’s forecast model,
but just a little bit higher and the town would be inundated.

The National Weather Service has since come to recognize
the importance of communicating the uncertainty in their
forecasts accurately and honestly to the public, as we saw in
chapter 4. But this sort of attitude is rare among other kinds of
forecasters, especially when they predict the course of the

Are Economists Rational?

Now consider what happened in November 2007. It was just one
month before the Great Recession officially began. There were
already clear signs of trouble in the housing market: foreclosures
had doubled,12 and the mortgage lender Countrywide was on the
verge of bankruptcy.13 There were equally ominous signs in
credit markets.14

Economists in the Survey of Professional Forecasters, a
quarterly poll put out by the Federal Reserve Bank of
Philadelphia, nevertheless foresaw a recession as relatively
unlikely. Instead, they expected the economy to grow at a just
slightly below average rate of 2.4 percent in 2008. And they
thought there was almost no chance of a recession as severe as
the one that actually unfolded.

The Survey of Professional Forecasters is unique in that it
asks economists to explicitly indicate a range of outcomes for
where they see the economy headed. As I have emphasized
throughout this book, a probabilistic consideration of outcomes
is an essential part of a scientific forecast. If I asked you to
forecast the total that will be produced when you roll a pair of
six-sided dice, the correct answer is not any single number but
an enumeration of possible outcomes and their respective
probabilities, as in figure 6-2. Although you will roll 7 more
often than any other number, it is not intrinsically any more or
any less consistent with your forecast than a roll of 2 or 12,

provided that each number comes up in accordance with the
probability you assign it over the long run.

The economists in the Survey of Professional Forecasters are
asked to do something similar when they forecast GDP and other
variables—estimating, for instance, the probability that GDP
might come in at between 2 percent and 3 percent, or between 3
percent and 4 percent. This is what their forecast for GDP

looked like in November 2007 (figure 6-3):

As I mentioned, the economists in this survey thought that
GDP would end up at about 2.4 percent in 2008, slightly below
its long-term trend. This was a very bad forecast: GDP actually
shrank by 3.3 percent once the financial crisis hit. What may be
worse is that the economists were extremely confident in their
bad prediction. They assigned only a 3 percent chance to the
economy’s shrinking by any margin over the whole of 2008.15

And they gave it only about a 1-in-500 chance of shrinking by at

least 2 percent, as it did.16

Indeed, economists have for a long time been much too
confident in their ability to predict the direction of the economy.
In figure 6-4, I’ve plotted the forecasts of GDP growth from the
Survey of Professional Forecasters for the eighteen years
between 1993 and 2010.17 The bars in the chart represent the 90
percent prediction intervals as stated by the economists.

A prediction interval is a range of the most likely outcomes
that a forecast provides for, much like the margin of error in a
poll. A 90 percent prediction interval, for instance, is supposed
to cover 90 percent of the possible real-world outcomes, leaving
only the 10 percent of outlying cases at the tail ends of the
distribution. If the economists’ forecasts were as accurate as they
claimed, we’d expect the actual value for GDP to fall within
their prediction interval nine times out of ten, or all but about
twice in eighteen years.


In fact, the actual value for GDP fell outside the economists’
prediction interval six times in eighteen years, or fully one-third
of the time. Another study, 18 which ran these numbers back to the

beginnings of the Survey of Professional Forecasters in 1968,
found even worse results: the actual figure for GDP fell outside
the prediction interval almost half the time. There is almost no
chance19 that the economists have simply been unlucky; they
fundamentally overstate the reliability of their predictions.

In reality, when a group of economists give you their GDP
forecast, the true 90 percent prediction interval—based on how
these forecasts have actually performed20 and not on how
accurate the economists claim them to be—spans about 6.4
points of GDP (equivalent to a margin of error of plus or minus
3.2 percent).*

When you hear on the news that GDP will grow by 2.5
percent next year, that means it could quite easily grow at a
spectacular rate of 5.7 percent instead. Or it could fall by 0.7
percent—a fairly serious recession. Economists haven’t been
able to do any better than that, and there isn’t much evidence that
their forecasts are improving. The old joke about economists’
having called nine out of the last six recessions correctly has
some truth to it; one actual statistic is that in the 1990s,
economists predicted only 2 of the 60 recessions around the
world a year ahead of time.21

Economists aren’t unique in this regard. Results like these are
the rule; experts either aren’t very good at providing an honest
description of the uncertainty in their forecasts, or they aren’t

very interested in doing so. This property of overconfident
predictions has been identified in many other fields, including
medical research, political science, finance, and psychology. It
seems to apply both when we use our judgment to make a
forecast (as Phil Tetlock’s political scientists did) and when we
use a statistical model to do so (as in the case of the failed
earthquake forecasts that I described in chapter 5).

But economists, perhaps, have fewer excuses than those in
other professions for making these mistakes. For one thing, their
predictions have not just been overconfident but also quite poor
in a real-world sense, often missing the actual GDP figure by a
very large and economically meaningful margin. For another,
organized efforts to predict variables like GDP have been
around for many years, dating back to the Livingston Survey in
1946, and these results are well-documented and freely
available. Getting feedback about how well our predictions have
done is one way—perhaps the essential way—to improve them.
Economic forecasters get more feedback than people in most
other professions, but they haven’t chosen to correct for their
bias toward overconfidence.

Isn’t economics supposed to be the field that studies the
rationality of human behavior? Sure, you might expect someone
in another field—an anthropologist, say—to show bias when he
makes a forecast. But not an economist.

Actually, however, that may be part of the problem.
Economists understand a lot about rationality—which means
they also understand a lot about how our incentives work. If
they’re making biased forecasts, perhaps this is a sign that they
don’t have much incentive to make good ones.

“Nobody Has a Clue”

Given the track record of their forecasts, there was one type of
economist I was most inclined to seek out—an economist who
would be honest about how difficult his job is and how easily
his forecast might turn out to be wrong. I was able to find one:
Jan Hatzius, the chief economist at Goldman Sachs.

Hatzius can at least claim to have been more reliable than his
competitors in recent years. In November 2007, a time when
most economists still thought a recession of any kind to be
unlikely, Hatzius penned a provocative memo entitled
“Leveraged Losses: Why Mortgage Defaults Matter.” It warned
of a scenario in which millions of homeowners could default on
their mortgages and trigger a domino effect on credit and

financial markets, producing trillions of dollars in losses and a
potentially very severe recession—pretty much exactly the
scenario that unfolded. Hatzius and his team were also quick to
discount the possibility of a miraculous postcrisis recovery. In
February 2009, a month after the stimulus package had been
passed and the White House had claimed it would reduce
unemployment to 7.8 percent by the end of 2009, Hatzius
projected unemployment to rise to 9.5 percent22 (quite close to
the actual figure of 9.9 percent).

Hatzius, a mellow to the point of melancholy German who
became Goldman Sachs’s chief economist in 2005,23 eight years
after starting at the firm, draws respect even from those who take
a skeptical view of the big banks. “[Jan is] very good,” Paul
Krugman told me. “I hope that Lloyd Blankfein’s malevolence
won’t spill over to Jan and his people.” Hatzius also has a
refreshingly humble attitude about his ability to forecast the
direction of the U.S. economy.

“Nobody has a clue,” he told me when I met him at Goldman’s
glassy office on West Street in New York. “It’s hugely difficult
to forecast the business cycle. Understanding an organism as
complex as the economy is very hard.”

As Hatzius sees it, economic forecasters face three
fundamental challenges. First, it is very hard to determine cause
and effect from economic statistics alone. Second, the economy

is always changing, so explanations of economic behavior that
hold in one business cycle may not apply to future ones. And
third, as bad as their forecasts have been, the data that
economists have to work with isn’t much good either.

Correlations Without Causation

The government produces data on literally 45,000 economic
indicators each year.24 Private data providers track as many as
four million statistics.25 The temptation that some economists
succumb to is to put all this data into a blender and claim that the
resulting gruel is haute cuisine. There have been only eleven
recessions since the end of World War II. 26 If you have a
statistical model that seeks to explain eleven outputs but has to
choose from among four million inputs to do so, many of the
relationships it identifies are going to be spurious. (This is
another classic case of overfitting—mistaking noise for a signal-
–the problem that befell earthquake forecasters in chapter 5.)

Consider how creative you might be when you have a stack of
economic variables as thick as a phone book. A once-famous
“leading indicator” of economic performance, for instance, was

the winner of the Super Bowl. From Super Bowl I in 1967
through Super Bowl XXXI in 1997, the stock market27 gained an
average of 14 percent for the rest of the year when a team from
the original National Football League (NFL) won the game.28 But
it fell by almost 10 percent when a team from the original
American Football League (AFL) won instead.

Through 1997, this indicator had correctly “predicted” the
direction of the stock market in twenty-eight of thirty-one years.
A standard test of statistical significance,29 if taken literally,
would have implied that there was only about a 1-in-4,700,000
possibility that the relationship had emerged from chance alone.

I t was just a coincidence, of course. And eventually, the
indicator began to perform badly. In 1998, the Denver Broncos,
an original AFL team, won the Super Bowl—supposedly a bad
omen. But rather than falling, the stock market gained 28 percent
amid the dot-com boom. In 2008, the NFL’s New York Giants
came from behind to upset the AFL’s New England Patriots on
David Tyree’s spectacular catch—but Tyree couldn’t prevent the
collapse of the housing bubble, which caused the market to crash
by 35 percent. Since 1998, in fact, the stock market has done
about 10 percent better when the AFL team won the Super
Bowl, exactly the opposite of what the indicator was fabled to

How does an indicator that supposedly had just a 1-in-

4,700,000 chance of failing flop so badly? For the same reason
that, even though the odds of winning the Powerball lottery are
only 1 chance in 195 million,30 somebody wins it every few
weeks. The odds are hugely against any one person winning the
lottery—but millions of tickets are bought, so somebody is going
to get lucky. Likewise, of the millions of statistical indicators in
the world, a few will have happened to correlate especially
well with stock prices or GDP or the unemployment rate. If not
the winner of the Super Bowl, it might be chicken production in
Uganda. But the relationship is merely coincidental.

Although economists might not take the Super Bowl indicator
seriously, they can talk themselves into believing that other types
of variables—anything that has any semblance of economic
meaning—are critical “leading indicators” foretelling a
recession or recovery months in advance. One forecasting firm
brags about how it looks at four hundred such variables,31 far
more than the two or three dozen major ones that Hatzius says
contain most of the economic substance.* Other forecasters have
touted the predictive power of such relatively obscure indicators
as the ratio of bookings-to-billings at semiconductor
companies.32 With so many economic variables to pick from,
you’re sure to find something that fits the noise in the past data

It’s much harder to find something that identifies the signal;

variables that are leading indicators in one economic cycle often
turn out to be lagging ones in the next. Of the seven so-called
leading indicators in a 2003 Inc. magazine article,33 all of which
had been good predictors of the 1990 and 2001 recessions, only
two—housing prices and temporary hiring—led the recession
that began in 2007 to any appreciable degree. Others, like
commercial lending, did not begin to turn downward until a year
after the recession began.

Even the well-regarded Leading Economic Index, a composite
of ten economic indicators published by the Conference Board,
has had its share of problems. The Leading Economic Index has
generally declined a couple of months in advance of recessions.
But it has given roughly as many false alarms—including most
infamously in 1984, when it sharply declined for three straight
months,34 signaling a recession, but the economy continued to
zoom upward at a 6 percent rate of growth. Some studies have
even claimed that the Leading Economic Index has no predictive
power at all when applied in real time.35

“There’s very little that’s really predictive,” Hatzius told me.
“Figuring out what’s truly causal and what’s correlation is very
difficult to do.”

Most of you will have heard the maxim “correlation does not
imply causation.” Just because two variables have a statistical
relationship with each other does not mean that one is

responsible for the other. For instance, ice cream sales and
forest fires are correlated because both occur more often in the
summer heat. But there is no causation; you don’t light a patch of
the Montana brush on fire when you buy a pint of Hagen-Dazs.

If this concept is easily expressed, however, it can be hard to
apply in practice, particularly when it comes to understanding
the causal relationships in the economy. Hatzius noted, for
instance, that the unemployment rate is usually taken to be a
lagging indicator. And sometimes it is. After a recession,
businesses may not hire new employees until they are confident
about the prospects for recovery, and it can take a long time to
get all the unemployed back to work again. But the
unemployment rate can also be a leading indicator for consumer
demand, since unemployed people don’t have much ability to
purchase new goods and services. During recessions, the
economy can fall into a vicious cycle: businesses won’t hire until
they see more consumer demand, but consumer demand is low
because businesses aren’t hiring and consumers can’t afford their

Consumer confidence is another notoriously tricky variable.
Sometimes consumers are among the first to pick up warning
signs in the economy. But they can also be among the last to
detect recoveries, with the public often perceiving the economy
to be in recession long after a recession is technically over.

Thus, economists debate whether consumer confidence is a
leading or lagging indicator,36 and the answer may be contingent
on the point in the business cycle the economy finds itself at.
Moreover, since consumer confidence affects consumer
behavior, there may be all kinds of feedback loops between
expectations about the economy and the reality of it.

An Economic Uncertainty Principle

Perhaps an even more problematic set of feedback loops are
those between economic forecasts and economic policy. If, for
instance, the economy is forecasted to go into recession, the
government and the Federal Reserve will presumably take steps
to ameliorate the risk or at least soften the blow. Part of the
problem, then, is that forecasters like Hatzius have to predict
political decisions as well as economic ones, which can be a
challenge in a country where the Congress has a 10 percent
approval rating.

But this issue also runs a little deeper. As pointed out by the
Nobel Prize–winning economist Robert Lucas37 in 1976, the past
data that an economic model is premised on resulted in part from
policy decisions in place at the time. Thus, it may not be enough
to know what current policy makers will do; you also need to

know what fiscal and monetary policy looked like during the
Nixon administration. A related doctrine known as Goodhart’s
law, after the London School of Economics professor who
proposed it,38 holds that once policy makers begin to target a
particular variable, it may begin to lose its value as an economic
indicator. For instance, if the government artificially takes steps
to inflate housing prices, they might well increase, but they will
no longer be good measures of overall economic health.

At its logical extreme, this is a bit like the observer effect
(often mistaken for a related concept, the Heisenberg uncertainty
principle): once we begin to measure something, its behavior
starts to change. Most statistical models are built on the notion
that there are independent variables and dependent variables,
inputs and outputs, and they can be kept pretty much separate
from one another.39 When it comes to the economy, they are all
lumped together in one hot mess.

An Ever-Changing Economy

Even if they could resolve all these problems, economists would
still have to contend with a moving target. The American and

global economies are always evolving, and the relationships
between different economic variables can change over the
course of time.

Historically, for instance, there has been a reasonably strong
correlation between GDP growth and job growth. Economists
refer to this as Okun’s law. During the Long Boom of 1947
through 1999, the rate of job growth40 had normally been about
half the rate of GDP growth, so if GDP increased by 4 percent
during a year, the number of jobs would increase by about 2

The relationship still exists—more growth is certainly better
for job seekers. But its dynamics seem to have changed. After
each of the last couple of recessions, considerably fewer jobs
were created than would have been expected during the Long
Boom years. In the year after the stimulus package was passed in
2009, for instance, GDP was growing fast enough to create about
two million jobs according to Okun’s law.41 Instead, an
additional 3.5 million jobs were lost during the period.

Economists often debate about what the change means. The
most pessimistic interpretation, advanced by economists
including Jeffrey Sachs of Columbia University, is that the
pattern reflects profound structural problems in the American
economy: among them, increasing competition from other
countries, an imbalance between the service and manufacturing

sectors, an aging population, a declining middle class, and a
rising national debt. Under this theory, we have entered a new
and unhealthy normal, and the problems may get worse unless
fundamental changes are made. “We were underestimating the
role of global change in causing U.S. change,” Sachs told me.
“The loss of jobs internationally to China and emerging markets
have really jolted the American economy.”

The bigger question is whether the volatility of the 2000s is
more representative of the long-run condition of the economy—
perhaps the long boom years had been the outlier. During the
Long Boom, the economy was in recession only 15 percent of the
time. But the rate was more than twice that—36 percent—from
1900 through 1945.42

Although most economists believe that some progress has
been made in stabilizing the business cycle, we may have been
lucky to avoid more problems. This particularly holds in the
period between 1983 and 2006—a subset of the Long Boom that
is sometimes called the Great Moderation—when the economy
was in recession just 3 percent of the time. But much of the
growth was fueled by large increases in government and
consumer debt, as well as by various asset-price bubbles.
Advanced economies have no divine right to grow at Great
Moderation rates: Japan’s, which grew at 5 percent annually
during the 1980s, has grown by barely one percent per year

since then.43

This may be one reason why forecasters and policy makers
were taken so much by surprise by the depth of the 2007
recession. Not only were they failing to account for events like
the Great Depression*—they were sometimes calibrating their
forecasts according to the Great Moderation years, which were
an outlier, historically speaking.

Don’t Throw Out Data

The Federal Open Market Committee, which is charged with
setting interest rates, is required by law to release
macroeconomic forecasts to Congress at least twice per year.
The Fed was in some ways ahead of the curve by late 2007:
their forecasts of GDP growth were slightly more bearish than
those issued by private-sector forecasters, prompting them to
lower interest rates four times toward the end of the year.

Still, in the Fed’s extensive minutes from a late October 2007
meeting, the term “recession” was not used even once in its
discussion of the economy.44 The Fed is careful with its language,
and the possibility of a recession may nevertheless have been
implied through the use of phrases like downside risks. But they
were not betting on a recession (their forecast still projected

growth), and there was little indication that they were
entertaining the possibility of as severe a recession as actually

Part of the reason may have been that the Fed was looking at
data from the Great Moderation years to set their expectations
for the accuracy of their forecasts. In particular, they relied
heavily upon a paper that looked at how economic forecasts had
performed from 1986 through 2006.45 The problem with looking
at only these years is that they contained very little economic
volatility: just two relatively mild recessions in 1990–1991 and
in 2001. “By gauging current uncertainty with data from the mid-
1980s on,” the authors warned, “we are implicitly assuming that
the calm conditions since the Great Moderation will persist into
the future.” This was an awfully big assumption to make. The
Fed may have concluded that a severe recession was unlikely in
2007 in part because they had chosen to ignore years in which
there were severe recessions.

A forecaster should almost never ignore data, especially when
she is studying rare events like recessions or presidential
elections, about which there isn’t very much data to begin with.
Ignoring data is often a tip-off that the forecaster is
overconfident, or is overfitting her model—that she is interested
in showing off rather than trying to be accurate.

In this particular case, it was not obvious that economists had

improved much at forecasting the business cycle. In figure 6-5a,
I’ve compared predicted levels of GDP growth from the Survey
of Professional Forecasters against the actual figures for the
years 1968 through 1985—these are the years the Fed could
have looked at but chose to throw out. You’ll see there’s quite a
lot of economic volatility in this period, such as during the
inflation-driven recessions of the mid-1970s and early 1980s.
Still, the results are not completely discouraging for forecasters,
in that the forecasted and actual outcomes have a reasonably
strong correlation with one another.

If you make the same plot for the years 1986 through 2006 (as
in figure 6-5b), you’ll find just the reverse. Most of the data
points—both the forecasted values for GDP and the actual ones-
–are bunched closely together in a narrow range between about
2 percent and 5 percent annual growth. Because there was so

little volatility during this time, the average error in the forecast
was less than in the previous period.* However, to the extent
there was any variability in the economy, like the mild
recessions of 1990–91 or in 2001, the forecasts weren’t doing a
very good job of capturing it—in fact, there was almost no
correlation between the predicted and actual results. There was
little indication that economists had become more skilled at
forecasting the course of the economy. Instead, their jobs had
become temporarily easier because of the calm economic winds,
as a weather forecaster in Honolulu faces an easier task than one
in Buffalo.

The other rationale you’ll sometimes hear for throwing out
data is that there has been some sort of fundamental shift in the
problem you are trying to solve. Sometimes these arguments are
valid to a certain extent: the American economy is a constantly
evolving thing and periodically undergoes structural shifts

(recently, for instance, from an economy dominated by
manufacturing to one dominated by its service sector). This isn’t
baseball, where the game is always played by the same rules.

The problem with this is that you never know when the next
paradigm shift will occur, and whether it will tend to make the
economy more volatile or less so, stronger or weaker. An
economic model conditioned on the notion that nothing major
will change is a useless one. But anticipating these turning points
is not easy.

Economic Data Is Very Noisy

The third major challenge for economic forecasters is that their
raw data isn’t much good. I mentioned earlier that economic
forecasters rarely provide their prediction intervals when they
produce their forecasts—probably because doing so would
undermine the public’s confidence in their expertise. “Why do
people not give intervals? Because they’re embarrassed,”
Hatzius says. “I think that’s the reason. People are embarrassed.”

The uncertainty, however, applies not just to economic
forecasts but also to the economic variables themselves. Most

economic data series are subject to revision, a process that can
go on for months and even years after the statistics are first
published. The revisions are sometimes enormous.46 One
somewhat infamous example was the government’s estimate of
GDP growth in the last quarter of 2008. Initially reported as
“only” a 3.8 percent rate of decline, the economy is now
believed to have been declining at almost 9 percent. Had they
known the real size of the economic hole, the White House’s
economists might have pushed for a larger stimulus package in
January 2009, or they might have realized how deep the
problems were and promoted a longer-term solution rather than
attempting a quick fix.

Large errors like these have been fairly common. Between
1965 and 2009,47 the government’s initial estimates of quarterly
GDP were eventually revised, on average, by 1.7 points. That is
the average change; the range of possi


Defining Collaborative Leadership in Human Service Organizations

For this assignment, select a human service organization from a public, nonprofit, or government sector that you are familiar with, or one that you find interesting. You will use this organization to complete all of the course assignments. You must be able to access information about the organizations governance, financial sources and practices, mission, population served, and its political and social landscape. Review all the assignments now to verify the types of information you will need about the organization in order to complete them.
The following list provides examples of acceptable types of organizations. You can select an organization of the types included on this list or propose another type of organization to your instructor. The organization must provide human service program services. The selected organization will be included in all your assignments, so you will look at leadership and collaboration practices for that organization through several areas of focus.
Possible Organization Types
City, county, or state human services or mental health programs.
State hospitals (Western State Hospital, Milwaukee County Hospital, or another state or county hospital in your area).
School-based human services or case management programs.
Private mental health organizations.
Employee assistance programs.
For-profit hospital or health care organizations (Humana, Kaiser-Permanente, Aurora, etcetera).
Catholic community services.
Lutheran Social Services.
In your assignment submission, include the following:
In a paragraph format, describe the organization. Provide a brief overview of the mission, target population, and programs provided by the organization.
Discuss the form of governance used by the organization and why the governance practices used are effective for the organization.
Address the funding practices and sources for the organization.
Include at least three current empirical research articles (no more than five years old) that address governance and funding practices for the type of organization you selected.
Assignment Requirements
Written communication:Written communication should be free of errors that detract from the overall message.
APA formatting: Headings, resources, and citations should be formatted according to current APA style and formatting.
Length of paper:35 typed, double-spaced pages, excluding the cover page and references.
Font and font size:Times New Roman, 12 point.


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