[This is Chapter
Five of Murphey’s book The Emerging Crisis of Economic Displacement.]
Chapter Five
WHY
TODAY'S ECONOMIC STATISTICS
There have been times, such as the
summer of 1997, when economic statistics for the United States have told an
almost-euphoric story of national well-being: inflation far down, unemployment
at the lowest levels in years, increasing productivity, a booming stock market,
and the federal budget deficit falling so rapidly that policy-makers have begun
to talk about what to do when the budget starts showing a surplus.
All the while, there have been
continuing news reports of corporate mergers, downsizings and restructurings,
with abundant layoffs. In their personal
lives many Americans have several acquaintances who are retiring early, or moving through a series of
temporary jobs, or working at employment far below their qualifications, or
waiting with bated breath to be ousted in a "reduction in force," or
retaining a job only by moving hundreds of miles to a new location, tearing up
roots with family and friends that the person had hoped would last a
lifetime. For many people, their own
income has hardly increased in years, certainly not to match inflation, and
they know that making any progress at all has depended upon a second income
brought in by a working spouse. Often at
the same time, however, if they are fortunate, they receive quarterly reports
from their retirement stockfund informing them of gains at a dizzying pace,
portending (if all remains well) an affluent old age.
How are these seemingly incongruous
elements to be reconciled?
More by theory, certainly, than by
statistics. It would seem that almost
anything can be "reconciled," or not, depending upon ones
wishes. Economic statistics today, in
the hands of differing interpreters, can and do give widely varying
pictures.
It is plausible to speak of "a
crisis of statistics." As we talk
in coming chapters about worker displacement and economic polarity, we will see
that every subject is lost in a welter of claims and counterclaims. All points of view cite statistics to confirm
their position, so that it is almost impossible to penetrate through to reality
on the basis of statistics alone. In
this vein, Business Week in March 1996 spoke of a resulting
"cognitive dissonance."[1] Kenneth Auchincloss in Newsweek,
talking about the figures for per capita income, says "the statistical
argument is an impenetrable thicket."[2] In an enlightening article entitled "The
Real Truth About the Economy: Are Government Statistics So Much Pulp
Fiction?," Business Week in November 1994 said that "the
economic statistics that the government issues every week should come with a
warning sticker," adding that "the government is pumping out a stream
of statistics that are nothing but myths and misinformation."[3]
Yet, it is striking that Alan
Greenspan, who as chairman of the Federal Reserve Board holds center stage in
making economic policy, is willing freely to use standard statistics on such
things as unemployment and the Gross Domestic Product as though they are fully
meaningful, which they probably are for his purposes, since he's certainly no
fool.[4] He sees the statistics, in their usual form,
as conveying significant information.
Just the same, he acknowledges the problem when he says that "the
list of shortcomings in U.S. economic data is depressingly long."[5]
Whatever continuing value the
statistics may have for some uses, for the purpose of understanding where the
United States' and the world's economies are going in response to global
high-tech, the criticisms I just referred to in Business Week have
considerable force. A current
statistical category such as "Gross National Product" was adopted after
World War II as a tool that was useful to economists at that time. With changing conditions, it and other
measures are becoming increasingly ill-suited.
The Economist suggests that a major reason "for the
inadequacy of official statistics may be that the economy is simply becoming
unmeasurable. Conventional methods of
measuring the economy are no longer up to the task, and economists cannot agree
on how to improve them."[6] The result is that we are forced to rely
more on theoretical insight than we would like to in knowing the current state
of things. We would prefer, whenever we
are dealing in the more purely empirical realm of trying simply to "know
what's going on," that the "facts do the talking." The facts should speak loudly, with theory's
role being to explain causation and relationships. But "the facts" are a welter of
confusion, and can be chosen to confirm almost anything.
My chapters on the displacement will
cite statistics at length, since that is naturally the form the discussion takes
in the literature, but we will need to keep in mind the weakness of the data
and eventually rely much more on two things: (a) the agreement that exists
among economists of all persuasions about the major trends that are occurring;
and (b) the fact that economic theory itself points to those trends and their
implications.
I offer these thoughts about
statistics not as a form of intellectual nihilism seeking to nullify empiricism
itself by denigrating its tools, but to inculcate a wholesome wariness about
the statistics one confronts at every hand today and a desire to use statistics
only with considerable sophistication about what, in any given case, the
statistic includes and does not include.
The fact is that a proper use of economic statistics requires great
care. Many of the citations we see of
statistics don't inform the reader adequately of the precise methodology used,
especially with regard to what's included and what is not, and because of this
shouldn't be accepted at face value.
This will become especially clear as we review many of the main
statistical categories presently in use and see what those writing on economic
problems have had to say about them.
The commentary that follows is drawn
from that literature. In preparing to
write this book, I have read approximately 25,000 pages of recent economic
writing and collated notes on hundreds of separate points. This has made possible a broad overview of
the current economic dialogue, including its many disputes on the significance
or appropriateness of this or that statistic.
I am not a statistician myself and do not purport to bring expertise of
my own to the debate. My reading has,
however, gathered in one place the thoughts of several economists and economic
commentators. It will be informative to review what they have to say.
We will begin with some of the more
general observations about economic statistics and proceed to a review of such
specific statistical categories as, e.g., "Gross National Product,"
"unemployment" and the "Consumer Price Index." Readers interested in the main themes of this
book but not in technical detail may prefer to read the general comments, but
then just to scan the more specific review of the categories.
General
observations in the literature
.
That the changing economic reality is making many statistical
categories inadequate. The
observation is often made that the present and coming reality of goods and
services is shifting from underneath the customary statistical categories. It is with this in mind that Walter Wriston
speaks of "the increasing inaccuracy or irrelevance of our standards of
economic measurement," and says that "many of the terms we use today
to describe the economy no longer reflect reality."[7] According to Business Week, the problem
is largely that "most of the surging information economy -- including
software, telecommunications, and entertainment -- is poorly covered by the
data." Its discussion points out
that such categories as gross domestic product (
Of the authors I have read, Alvin
Toffler has looked furthest into the future so far as this increasing gap is
concerned. In Future Shock,
published as long ago as 1970, he foresaw a day when society will have changed
so much that "the very purposes of economic activity" will be
different than they now are, with the result that "even the most
sophisticated tools of today's economists [will be] helpless." He asked, as an example, whether Gross
National Product would remain a meaningful concept in a country where "no
growth" were to replace "growth" as the objective.[8]
. That in today's emerging high-tech economy,
statistics give little indication of the underlying direction of movement. Most of the applications and effects of
information-based, non-labor-intensive technology have barely begun to catch
hold. They show up in economic data more
as the proverbial "small cloud on the horizon" than as
already-established phenomena. A serious
discussion of society looking even to the near-term future requires an eye
toward emerging developments more than toward data that describe even the very
recent past. The difference in
perception that this occasions is part of the statistical debate between those
who describe the current market economy in glowing terms and those who see
vastly significant issues looming of worker displacement and income/wealth
polarity. For the latter, a look ahead
supercharges some of the data with more significance than they would seem to
merit at face value.
. That there are important reasons to be
suspicious of economic statistics. A century ago,
Henry George warned that:
As
for reliance upon statistics, that involves the additional difficulty of
knowing whether we have the right statistics.
Though ‘figures cannot lie,' there is in their collection and grouping
such liability to oversight and such temptation to bias that they are to be
distrusted in matters of controversy until they have been subjected to rigid
examination....Under their imposing appearance of exactness may lurk the
gravest errors and wildest assumptions.[9]
The innumerable ways that statistics
can convey an inaccurate impression comprise a subject of their own. Odds and ends from my notes help illustrate
this: economist Paul Krugman warns, for example, that "it is important to be
careful about starting and ending dates."[10] The varying phases of the trade cycle can
make a big difference. If data are taken
from the top of a boom and compared to those later from the bottom of a
recession, an unfavorable result will follow; if the peaks and valleys are
reversed in arriving at the "before and after," the results will be
much more upbeat; the most informative (and honest) finding will come from data
taken from comparable points in the trade cycle.
Some types of "data" are
squishy, even though social scientists may use them (with or without
caveats). Surveys, especially if they
ask for subjective responses, are especially "soft." The sample that is taken may in many ways
differ from the population the survey seeks to describe. The authors of one survey about
underemployment in Kansas acknowledged, as was proper, that there were
"biases" from too-great disparities relating to urban versus rural,
men versus women, the ages of the respondents, and levels of education. Often, many of those surveyed refuse to
answer questions, such as to income, and this introduces a significant
imponderable. And when somebody answers
a subjective question, such as about whether he believes himself to be
"underutilized," the answer itself depends upon personality and
subjective factors that obscure the meaning even though the numerical scoring
gives an impression of exactitude.[11]
In some subjects an element of
selectivity is introduced just because researchers have found reasons to study
some things more than others (sometimes, no doubt, in response to whatever
grants are made available for research).
We are told, for example, that "most of the systematic study of the
diffusion of information technology is biased in significant ways. The majority of studies deal with
manufacturing process technologies, and with nothing equivalent for services or
office applications...."[12] The little-studied service area is, of
course, the one that looms ever larger as we enter the Information Age. Similar gaps in the data appear when it comes
to comparing something like employment around the world: statistics just aren't
available for many countries and certainly for much of the past; and where they
are available, the quality differs widely.[13]
.
That economic statistics are often manipulated for political or
ideological purposes. The point
hardly need be made, since it is so obvious, that statistics can easily become
the handmaidens of charlatans, ideologues and special pleaders. Unfortunately, no point along the social-political
spectrum can claim that all its partisans are free of this, even though we
would like to think our own point of view, being correct, presents its case
with an integrity unknown to the others.
The debates of our own time offer many examples of the misuse of
statistics.
Labor Secretary Robert Reich's
pessimistic data about American jobs and income is challenged by free-market
economist Richard Vedder when he points out "that Reich's data do not
include fringe benefits, such as medical insurance, paid vacations, and pension
plans." This criticism makes sense,
since data stripped of those elements tell just part of the story. In the column that speaks of Vedder's
criticism, economist Mark Skousen says that it is often better to look at the
"quantity, quality, and variety of goods and services" people are
consuming than to look at average real wages.[14]
On the other hand, free-market
economists John H. Hinderaker and Scott W. Johnson, in an article much welcomed
by free-market supporters defending today's economy from alleged extremes of
inequality, use selective data to their own advantage when they note, for
purposes of making a comparison with today to show that there is less
inequality than there used to be, that "in 1913, the richest five percent
of the population earned about thirty percent of aggregate income (excluding
capital gains)."[15] But why exclude capital gains? A true picture of the "earnings" of
well-to-do people can hardly be given while eliminating one of their principal
sources of wealth-enhancement. (We will
see in the later section here on income statistics that the capital gains issue
is a complicated one, indeed.) At least
Hinderaker and Johnson tell their readers of the exclusion. But the notification hardly seems enough in a
context where the insufficient "earnings" data are used to make a
seemingly telling point. Without the
more complete comparison of 1913 with today, we don't know what to think of
their argument. They would like us to
believe, though, that they have made their case. (None of this is to say that their argument
is less than honest. It is almost
impossible to make any contention persuasively, especially in writing for a
popular audience, while encumbering the material with all of the qualifications
that a complex subject requires.)
Few will be surprised today that the
government engages in statistical sleight-of-hand by leaving out important
information. Hank Brown and Richard
Lamm, former U. S. Senator and governor respectively of Colorado, make the point
that "the government's accounting system does not honestly account for the
full yearly deficit or the total debt...The real cost of government services
has to include the ‘unfunded liabilities' from services we consumed and
promises we have made. The official debt
figures do not reflect the unfunded costs of military pensions, federal Civil
Service pensions, unfunded liabilities of Social Security and
Medicare...."[16] Can we imagine someone's filling out a
balance sheet as part of an application for a personal loan and not including
most of his legally binding liabilities?
He would run considerable risk of being charged with fraud.
.
That economic statistics are available to give diametrically opposite
impressions about the economy. In
the passage that spoke of "cognitive dissonance," Business Week
in 1996 told how some data report "8 million jobs created in four years,
the unemployment rate at 5.8%, inflation down to 2.7%, corporate profits on a
four-year roll," all very favorable, while at the same time other data say
"real wages have been stagnant for most the past two decades" and
that "the distribution of income among Americans has become more unequal
over the same period."[17] A good example is provided by David Awbrey's
May 1996 column in the Wichita Eagle telling how Kansas had benefited
from U.S. trade policies; he cited the thousands of new jobs created by
international trade, and how aircraft manufacture and wheat sales were up. A responding column by Alan Tonelson and
Vanessa Fuhrmans of the U.S. Business and Industrial Council Educational
Foundation argued that "real median wages in Kansas -- the best measure of
living standards -- have dropped by 8 percent for men and 2 percent for women
since 1989... [T]he changing composition of jobs in Kansas recently explains
why... Kansas' biggest job-creating industries from 1983 to 1993 were in
low-paying sectors...." The debate
proceeds from there, with Tonelson-Fuhrmans pointing out how Boeing had recently
shifted significant production to China.[18]
That particular debate is just a
microcosm of a similar difference of opinion -- and of statistics -- at all
levels and on virtually every point.
Specific Statistical Categories
Income Data
Each statistical category can be
useful -- even if a number of things are not counted in arriving at it that
seem to pertain -- if the statistic is used by professionals who bring to it a
sharp awareness of precisely what they want it to measure. To the extent, however, that something like
"income data" is put forward in the general literature as telling
non-specialists about changes in peoples' well-being, the debate over what
should and should not be counted is very pertinent. Recent income data have been criticized as
leaving quite a lot out of account.
.
Failure to consider the effect of taxes on comparative incomes. In Upward Dreams, Downward Mobility,
Frederick Strobel is critical of an income study made by Michael Horrigan and
Steven Haugen that found (as summarized by Strobel) that "the middle-class
decline in the 1969-1986 period was largely due to increased mobility to the
upper class." The study was based
only on before-tax income data, as Horrigan and Haugen themselves pointed out
when they wrote that "the ideal data, after-tax income[,] are not
available." Strobel argues that the
owners of capital have managed to avoid taxes on income and property, with the
incidence of taxation accordingly falling onto others.[19] This means that the unavailability of
after-tax data could reverse the outcome of the study. Note, however, that from an opposing point of
view Edwin Rubenstein argues that the exclusion of taxes from household-income
data makes the rich look better off than they are.[20] Despite their differences, Strobel, Horrigan,
Haugen and Rubenstein all apparently agree that before-tax income data give a
misleading picture.
. Failure adequately to consider capital gains
and losses. Rubenstein says it is "a
common complaint" against the Bureau of the Census figures on average
household income that "they exclude capital gains and therefore understate
income at the top."
He counters this in part when he
says that "to add the capital gains and not subtract the taxes, as some
CBO [Congressional Budget Office] figures do, is indefensible." To this he adds that it is useless to
estimate capital gains from a sample of tax returns, since asset-owners'
willingness to sell assets and report capital gains will vary from year to year
based on such a thing as a change in the capital gain tax rate. Further, he argues that it makes no sense to
omit capital losses that are above the tax-deductible $3,000, as the CBO
has. Still further, a lot of capital
gain isn't real earnings, but the mere increase in nominal value of an asset
caused by inflation.[21] He gives the example of a farmer who buys a
farm for $50,000 and forty years later sells it for $260,000. It looks as though the farmer has made a lot
of money, but when inflation is considered he actually isn't any wealthier (and
even has taxes to pay on the supposed increase!)[22] Rubenstein points out, too, that the CBO is
inconsistent when it counts some capital gain but doesn't take into
consideration "capital gains and other income accruing to the
middle-income brackets in the form of pension funds (which amount to some $3
trillion) or home values."[23] He argues that, in fact, capital gains should
be excluded from the income data, since many realizations of capital gains are
"one-time or sporadic events" that skew any picture of annual
earnings.
All this points to the fact that an
inclusion of capital gains introduces serious problems. Just the same, how is it possible to say that
income data give an accurate picture without them? All of Rubenstein's criticisms are well
taken, but they leave intact the essential observation that the appreciation of
the value of property can be, and is, an important source of earnings for many
people (especially the rich, professional people with stockfund holdings for
retirement, and homeowners). Earnings in
a modern economy are far from just "payroll checks received." If what we want to measure are changes in and
comparisons of well-being, it is untidy in a variety of ways to include capital
gains, but also very deceptive not to. A
point to keep in mind is that most presentations of "income" figures
one sees tell the reader nothing, or almost nothing, about how all these issues
of inclusion, exclusion and adjustment have been resolved in arriving at the
figures. Accordingly, the true
information-content of the data for the average reader should be considered
next to nil.
. Failure to consider unreported income and
non-cash benefits to the poor, the elderly, and others. The polarization of income will be vastly
overstated if major sources of support going to groups with lower or medium
earnings aren't counted. For example, Business
Week reports a study that, based on tax rather than survey data, reveals
that "Census Bureau surveys of income far understate the amount of pension
income going to retired Americans."[24]
In their 1988 book The Great
U-Turn, Bennett Harrison and Barry Bluestone described the polarization of
income and wealth in the United States, but in the course of it they rightly
pointed to a vast offsetting factor: that even with the Reagan cutbacks in the
number of federal transfer programs, "the total level of federal transfer
payments to persons has doubled since 1979, while state and local government
assistance has expanded by nearly 90 percent," all for an annual total of
more than half a trillion dollars in 1987.[25] Rubenstein cites a study in 1988 showing that
the poor manage to spend twice as much as the total of their apparent
income. How is this possible? Because "many in-kind government
transfers (such as Food Stamps) are not counted as ‘money income.'"[26] Data that don't include transfer payments of
all types -- and this should include payments to the middle class and rich as
well as to the poor -- just don't convey the whole story.
The absence of unreported income
also skews the results. Even though his
book on the urban poor is sympathetic, William Wilson cites a study of how
welfare mothers have supplemented "their monthly AFDC checks" by
money from piecework and other jobs, and from boyfriends. The study says little of this is reported.[27] Rubenstein tells us that Census surveys show
that "a fourth of the cash income from welfare and pensions is
unreported."[28] And of course such income does not show up in
income statistics. Whether it should
(even if it could) depends upon the purpose for which we seek the statistics:
we will want even criminally-acquired income included if our purpose is to
evaluate the actual means that people have; we won't want it included if we
desire to assess, say, what comparative incomes would amount to from legitimate
sources.
. Incomes don't convey the entire picture if a
reason for them is that people are working either more or less. Frederick Strobel says family income
statistics shouldn't be taken at face value when "longer hours worked by
more members of the family" have been required to earn the money.[29]
. Effect of family size and type on income
statistics. Hinderaker and Johnson point
out that the number of adults in the average American family has been declining
since the 1970s, due to several factors that include the high divorce
rate. They say this causes an
understatement of the increase in real (i.e., adjusted-for-inflation) income,
since a household with fewer adults simply isn't comparable in earnings comparisons
to one with more adults. If individual
income were used rather than household income, a more favorable picture would
emerge. In addition, they criticize the
CBO's lumping of "unrelated individuals" [i.e., single people] into
the category of "families."[30]
Rubenstein observes that household
income comparisons are skewed by the fact that there are now many more
households headed by women, often not working but without a husband, than there
used to be.[31] Thus, comparisons are an
"apples-and-oranges" proposition.
Given the nature of all this, it
can't be surprising that Harrison and Bluestone make an observation that points
the other direction. Since World War II
in the United States, things have changed drastically about how many adults are
employed per household. The norm at one
time was that only the husband earned an income; now, many wives bring in
earnings. The result is that "the
wages of every single worker in the economy could decline and family income
could still rise," which is what Harrison and Bluestone say has happened
in many families.[32]
. Effect of weakness of income-survey data. Karl Zinsmeister in American Enterprise
speaks of the unreliability of statistics based on surveys: they "are only
as accurate as the answers given to surveyers [sic] who knock on people's doors
or call on the phone."[33] A weak reed, indeed.
. Problems with both averages and medians. Kevin Phillips points out that a serious
problem with citing an average for incomes, so far as showing the
well-being of most people is concerned, is that the average is skewed upward by
large income increases at the top.[34] Wallace Peterson says this criticism applies
especially to averages about the 1980s, "when the income of families in
the top tenth of the population grew many times faster than the income of all
other families."[35] A median [the figure at which half
the population is above the figure and half below it] is more descriptive than
an average in cases where the distribution isn't bell-shaped. Nevertheless, median-income figures are only
as good as the issues of inclusion, exclusion and adjustment we have mentioned
(and others that have escaped mention here) allow them to be.[36]
Much of the difficulty we have noted
thus far has had to do with household income comparisons. If the focus is shifted, though, to per
capita comparisons, some of the same problems remain and new problems
enter. Among the former, the problem
about averages, if they are used, applies.[37] Ravi Batra points to a new problem when he
says that a woman's work while a full-time homemaker hasn't been counted in the
data at all, whereas when she takes an outside job the earnings show up as
income. The result is that per capita
income is shown to leap upward, when in fact "the rise does not necessarily
reflect rising prosperity."[38] Wallace Peterson adds that, to know what is
happening, it is necessary to look at the rate of increase in a figure
like per capita income, and not just at whether there has been an
increase.[39] Kevin Phillips observes that a worker's
increased expenses (such as of a woman who is working and incurs child care
costs) aren't deducted as offsets to the income.[40]
. Figures about "the top 1 percent." Those who stress the growing economic polarity
within the United States often talk about the wealth and earnings of the top
one percent. Rubenstein makes us aware,
however, that "the Census Bureau keeps track only of the top 5
percent," so that "nobody knows exactly how much income is needed to
be counted among the top 1 percent."[41] The fact that there are indeterminancies
doesn't necessarily invalidate discussions of the top 1%, but a reader needs to
know they are there.
. Suggestions to improve measures of income. We have already seen the suggestion that it
would be better to take objective measures of well-being -- such as the average
number of cars Americans drive, television sets they own, life expectancy, etc.
-- than to tip-toe through the minefield of income statistics. I am reminded, though, of my son, who in his
late twenties quite deliberately lives a life with few amenities and low
earnings, while enjoying backpacking and playing a musical instrument. A counting of "objective measures"
won't give the picture unless a value is placed on leisure. How does one do that? Probably the answer is that no single measure
should be relied upon by itself, but rather an array of measures of different
kinds. To this it will be worthwhile to
add a recommendation for a fair modicum of intellectual humility, acknowledging
that in many ways reality is too complex and transitory to be measured more than approximately.
Some authors urge that income
averages and medians should be understood largely through the prism of
"income mobility." Thus,
Rubenstein about the common practice of describing the population by
"income quintiles": "Income mobility is a far better
indicator," because non-recurring tragedies or windfalls often put people
or families in earning quintiles that are not typical of them.[42] An individual tends to move from low-income
to high-income and then back to low-income again as he moves through the phases
of life, with the result that a placement of him in a certain quintile at any
given time almost totally misrepresents his economic well-being.
This suggests that a use of
"comparables" might often help, just as it does in real estate
appraisal. How, say, do 25-year-old male
college graduates (but with no graduate education) in a given year compare with
others just like themselves from twenty years before? Karl Zinsmeister, accordingly, has argued
that it is necessary to "match equivalent households" rather than
talk in terms of medians or averages; "you must be sure you are looking at
the same kind of family."[43] This is not to say that
"comparables" don't pose problems, especially when values and
conditions change over time.
National Income
Data: Gross National Product (GNP) and Gross Domestic Product (
National-income data are of
relatively recent origin. The U.S.
Department of Commerce began publishing them in 1942, and Daniel Bell tells us
that "the concept of GNP was first broached by Franklin D. Roosevelt in
his budget message in 1944."[44]
These statistical categories involve
an adding-together of all monetary transactions. The advantage is that those are the
transactions most easily counted, but Fred Block says anyone is wide of the
mark who puts GNP/
A good place to start is with the
oddity that the statistics count only monetary transactions, although there is
also some "imputing of income," such as to homeowners for the benefit
they receive from their residences.[47] In the first place, the aggregation of all
monetary transactions is inclusive of too much.
Daniel Bell says "it does not discriminate between a genuine
addition to welfare and what, in effect, may be a subtraction." He gives the example of a steel mill. Its output of steel is counted, but so also
is the money that is spent cleaning up a lake if it pollutes one. Both go into the total because they involve
monetary expenditure.[48] Sir James Goldsmith illustrates the same
point by pointing to a hurricane, earthquake or epidemic.[49] All cause the spending of money, but society
will usually be less well off than it was before. And, Goldsmith says, the value of a product
may be counted several times as it passes through middlemen to the ultimate
consumer.[50]
It is at the same time commonplace
to point out that there are many value-producing activities that monetary
transactions don't include. Two that are
often cited are house-work and home-making.[51]
Nobel laureate economist Gary Becker says that "when a family hires
someone to care for the children, clean the house, and cook, that work is
counted... When a parent does it, it is not."[52] This is why John Maynard Keynes once observed
that "a man who marries his housekeeper diminishes total GNP."[53] Fred Block points to several other things
that are uncounted but that are important to well-being: environmental quality,
life expectancy, the value of leisure, nonpecuniary rewards individuals derive
from work (or, we might add, from anything else), economic security, and the
value of services as distinct from their dollar cost.[54] There is also an "underground
economy" of unreported expenditures, such as in the drug trade and even
some activities that are more legitimate in themselves but as to which the
parties don't want the transactions known for tax or other purposes.
Then there are conceptual
insufficiencies. Economist William Allen
observes that "from 1979 to 1980, GNP went up almost 9 percent. That sounds great. But prices went up close to 10 percent."
He warns that "in dealing with GNP, watch out for inflation." Also, it is well to keep changes in
population in mind: "If real output goes up 15 percent over a period while
the number of people increases by 20 percent, output per person
falls." Income distribution is also
something to consider: "Several oil-producing countries experienced huge
increases in GNP during the 1970s, but 95 percent of the residents did not
gain."[55]
Allen further discusses the problem
of qualitative change in goods. This is
the phenomenon of "capital savings."
Two computer-purchase transactions involving the expenditure of $2,500
in 1982 and 1997, respectively, would have the same dollar amount so far as
being counted in GNP/
Ravi Batra mentions the need for
reductions. Tangible durable goods
deteriorate constantly (such as when a building or other property depreciates),
but this isn't reflected as a reduction in GNP/
Wriston raises another conceptual
difficulty when he criticizes the selectivity of "imputing" income to
homeowners but not doing so to owners of other durable assets such as cars and
dishwashers.[59]
With all these things in mind (and
they are just part of what could be mentioned), it is necessary to ask how much
real information one receives the next time he reads a report giving
national-income statistics.
Unemployment
Statistics
Data for employment and unemployment
are frequently reported in the press and are heavily relied upon in economic
discussions as indicators of national well-being. At the annual Federal Reserve symposium at
Jackson Hole in 1994, economist John P. Martin said, however, that "there
are long-running debates in many countries as to the adequacy of [the]
conventional measures of unemployment."[60]
It is commonly said that the United
States opts for flexible wages, and hence for low unemployment, while European
countries insist on inflexibly high wages and underwrite them with the welfare
state, the result of which is high unemployment and
"Eurosclerosis." So variable
is the perception of facts about unemployment, however, that MIT economist
Lester Thurow "puts the real unemployment rate [in the United States] at
over 14%, or higher than European levels."[61] Here is the detail Thurow gives in an
interview with Technology Review: "The United States nominally has
one of the lowest rates of unemployment in the industrialized world because we
don't count a lot of people as unemployed." There are five or six million people, he
says, "who say they want to work but who don't meet any of the technical
criteria for being unemployed, such as that they visited a potential employer
in the previous week. We've also got
about 4 and a half million people who work part time but want to work full
time, and we've got 8 million people who are temporary workers. We have another million people who are
self-employed ‘consultants,' many of them professionals affected by
downsizing... who would be glad to take a regular job... And then we have,
between the ages of 20 and 60, 6 million missing men... they're dropouts."[62]
Kevin Phillips agrees, pointing to
differences in what is included and not included in Europe and the United
States. "In contrast to many
European countries, the United States in compiling jobless data excluded
persons without employment who had stopped looking for work, while part-time
workers who wanted full-time jobs were nevertheless counted as entirely
employed."[63] Aaron Bernstein in Business Week adds
that "our current low unemployment rate [in the United States] masks an
economy full of workers hungry for better jobs."[64] It should be noted at the same time that Sir
James Goldsmith argues that French statistics have similar problems; they omit,
he says, 1.8 million people who should be counted as unemployed.[65]
Wallace Peterson makes the point
that "unemployed" should have added to it the number of people who do
have jobs but who are below the "poverty line." In 1990 this was 14.4 million people. If they were added to create a truer picture
of where the United States is in terms of human well-being, the rate would be
16 percent.[66] Of course, even though this seems a valid
enough point, a political and non-scientific element would be introduced that
could greatly affect this statistic: the decision about where the "poverty
line" is to be drawn.
On the other hand, some things are
omitted that, if added, would point the other direction, making conditions
appear more favorable by boosting employment and lowering unemployment. John P. Martin mentions "the proportion
of the unemployed who are engaged in concealed employment."[67] William Allen refers to "the bloating of
both the labor force and the number of unemployed by those who pretend to want
a job but actually will not accept work."[68]
Some conceptual matters
pertain. For example, in any given
recovery there may be an apparently favorable unemployment rate, but a look at
the rate of improvement might show it to be higher or lower than in
other recoveries.[69] Pointing to another factor, Frederick Strobel
says that "the economics profession has... by and large accepted
uncritically a low unemployment rate as an indicator of economic
well-being," to which he adds that "the unemployment rate says very
little about the distribution of income between capital and labor, or about the
quality of jobs."[70]
The National Debt
and the Annual Fiscal Deficit
We have already seen the statement
by former Senator Hank Brown and former governor Richard Lamm to the effect
that the national debt figures leave out vast legally-committed-to
liabilities. Similarly, Ross Perot said
in 1993 that "the official figure has us $4 trillion in debt, but... the
total obligations of the United States government are actually around $15
trillion... We have a total of $15 trillion guaranteed by the United States
taxpayers. These guarantees cover banks,
savings and loans, pension funds, home mortgages, and more."[71]
A related point applies to the
annual federal budget deficit (or surplus, if one really emerges). Michael Boskin, the chairman of the Council
of Economic Advisors under President Bush, wrote in 1989 that "a very
large amount of government spending occurs off-budget."[72] He called for revamping the accounting system
used by the federal government.
"The fact is that congressmen today are receiving irrelevant and
inaccurate information about everything, from the level and growth of federal
spending to the nature of costs in the various programs."[73]
It reinforces the main theme of this
discussion to observe that other factors can make the picture look entirely
different. Wallace Peterson tells of the
1985 book on the deficit by Robert Eisner of Northwestern University. It warned of improper measurement that leads
to overstatement. What is needed is
"correcting for inflation, eliminating deposit-insurance transactions from
the calculations of the deficit because they don't reflect current income and
production, deducting estimated federal outlays for nonmilitary capital, and
offsetting the federal deficit by the combined budget surplus of state and
local governments."[74]
Walter Wriston in 1992 criticized
what he sees as the aberrational accounting system used in federal government
budgeting. "Since 1969, the U.S.
government has used a unified cash-based budget that does not produce results
congruent with generally accepted accounting principles." He points to the failure to include any
capital accounts. Even such a thing as a
multi-billion dollar road system is "expensed." If it were a bookkeeping system used in the
private sector, he said, its user might well be taken to court for fraud.[75]
It is well to remember, as we review
these statistical categories, a point we made earlier: that some of the problem
comes from the data's being generated and used in a hotly disputed political
and ideological context. Paul Krugman
says, for example, that the deficit figure of $123 billion for 1990 "is
partly a fraud, thanks to the desperate accounting expedients used to postpone
the Gramm-Rudman [deficit reduction] targets; the true deficit was more like
$150 billion."[76] Speaking of the long-term budget projections
that feature so prominently in the "budget deals" between the
Congress and the President, Rob Norton in Fortune says that they
"are almost entirely worthless. For
one thing, they depend crucially on economic forecasts, and economists can't
always predict what the economy will do next week, let alone next decade."[77]
Balance of Trade
Statistics
Robert Reich describes the almost
total bankruptcy of trade statistics as they now stand. He says they are "notoriously imprecise,
subject to wide swings and seemingly inexplicable corrections. The truth is that these days no one knows
exactly, at any given time, whether America's (or any other nation's)
international trade is in or out of balance, by how much...."[78] Wriston agrees: "The current trade
accounting system is totally inadequate."
He gives an example in which an American author sends a manuscript to
Taiwan, where the book is manufactured and sent to the United States for sale,
resulting in royalties to the author.
"So far as the balance-of-trade figures are concerned, Taiwan runs
a trade surplus... [even though] the lion's share of the return on this capital
is generated in the United States."[79]
Much international trade occurs
among companies and their foreign subsidiaries, contrary to the traditional
pattern. Alfred Balk points to the
massive artificialities this introduces: he says that when U.S. corporations
sell abroad through their subsidiaries and affiliates there, which is today
more and more frequently the way sales are made, "their sales do not count
as U.S. exports." If they were
counted, Balk argues, a 1985 trade deficit of $148.5 billion would have been
transformed into a trade surplus of $151.4 billion -- a swing of $300
billion! Cutting the other way, when
foreign-owned factories in the United States send goods abroad, those shipments
do count as "American exports."
On the import side, if U.S. firms' foreign subsidiaries sell goods in
the U.S. market, such sales count as "imports," making up as much as
60 percent of the trade shortfall.[80]
A 1994 article in Business Week says government economists believe American exports are undervalued by 10 percent or more. A major reason is that "computer software, an industry dominated by U.S. producers, barely shows up... The government calculates only the value of instruction manuals and the blank disks... The value of the software itself simply disappears."[81]