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Call for Papers
Federal Reserve Bank
of
2001 Conference on
Bank Structure and
Competition
Fourth Quarter 2000
perspec xves
2
A record current account deficit: Causes and implications
14
Effect of auto plant openings on net migration
in the auto corridor, 1980-97
32
Why do consumers pay bills electronically?
An empirical analysis
48
The effect of the run-up in the stock market on labor supply
66
Index for 2000
11
perspective s
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ISSN 0164-0682
Contents
Fourth Quarter 2000, Volume XXIV, Issue 4
A record current account deficit: Causes and implications
Jack L. Hervey and Loula S. Merkel
The U.S. current account deficit was at a record level in 1999 and is expected to
increase further in 2000. How large can this deficit get? Will an eventual adjustment
in the deficit place the U.S. economy at risk? This article examines three arguments often
put forth to explain the increase in the deficit—a consumption boom, the U.S. as a safe
haven for short-term foreign capital, and technological change affecting the U.S. economy.
The authors find the strongest evidence in support of technological change and suggest why,
under these conditions, an economic adjustment to the deficit need not have as adverse an
impact as some observers fear.
14
Effect of auto plant openings on net migration
in the auto corridor, 1980-97
Thomas H. Klier and Kenneth M. Johnson
In linking demographic trends of the last two decades to the geographic dispersion
of the auto industry, this article finds that the addition of a large plant significantly
influences the migration experience of the host county as well as counties adjacent to it.
Call for Papers
Why do consumers pay bills electronically? An empirical analysis
Brian Mantel
Why do consumers use electronic bill payment services? What do the differences between
nonusers, low users, and high users imply about the potential future market for these services?
How might public policy evolve in the future? Analyzing a unique consumer survey conducted
by the Federal Reserve’s Retail Product Office, the author finds important differences between
nonusers, low users, and high users of electronic bill payment. The analysis suggests that the
industry will need to address fundamental customer needs before a broader portion of
consumers will adopt these services.
The effect of the run-up in the stock market on labor supply
Ing-Haw Cheng and Eric French
This article presents estimates of the effect of the run-up in the stock market on labor supply.
The authors find that, in the absence of a run-up in the stock market, aggregate labor force
participation rates would have been about 1 percent higher than they are today.
Index for 2000
A record current account deficit: Causes and implications
Jack L. Hervey and Loula S. Merkel
Introduction and summary
The U.S. deficit in international trade soared to new
heights in 1998, again in 1999, and in all likelihood,
will increase even further this year. Mirroring these
deficits have been huge foreign capital inflows. In
1999, the U.S. current account deficitthat is, the
difference between exports and imports of goods,
services, receipts and payments of income from and
to foreigners, and unilateral transferstotaled $331
billion or 3.6 percent of nominal gross domestic
product (GDP). This record deficit compares with the
previous record of $217 billion (2.5 percent of GDP)
in 1998 and $141 billion (1.7 percent of GDP) in
1997. The magnitude of the recent year-to-year increases in this deficit, as well as its absolute dollar
size, has raised considerable concern among many
public and private observers of the U.S. economy.
Not since 1987, when the current account deficit
peaked at a then record $161 billion, has the condition of the U.S. international accounts so captured
the attention of economists, policymakers, and the
popular press.
Further compounding uneasiness about the current situation is the expectation by many economists
that the magnitude of the trade deficit will show a
further increase this year and that only a modest reduction, if any, is likely in 2001. Indeed, trade developments thus far in 2000 indicate that at least the first
half of that expectation (that is, an increase in the
year-to-year size of the deficit during 2000) will be
borne out. There are also fears surrounding an eventual economic adjustmentthe current account gap
is the single biggest threat to the current expansion
of the economy.1
There is nothing inherently bad (or good)
about a current account deficitor for that matter, a
current account surplus. However, the concern about
the deficit that has drawn the attention of reasonable
observers centers on a specific issue: Does the deficit
2
in the U.S. international accounts represent a
risk to our economic well-being in the near term
or in the longer term? To answer this question,
we need to identify the underlying cause of the
deficit. What developments during the past two
or three yearsin the domestic economy and
in the rest of the worldhave led the U.S. to
purchase dramatically more goods and services
from abroad than it sold abroad? Furthermore,
can the U.S. economy maintain a deficit of this
magnitude? And, if not, what are the likely implications of an adjustment for the U.S. economy?
Three rationales are commonly used to
explain the sudden and dramatic increase in the
U.S. current account deficit. The first rationale
contends that U.S. consumers have shifted their
preferences from saving for the futurewitness
the near zero personal savings ratetoward purchasing more consumption goods in the present.2
This surge in demand for domestic consumption
goods translates into a corresponding increase in
imported consumption goods. We call this the
consumption boom hypothesis. Certainly, trade
in consumer-type goods has increased in recent
years. Indeed, more than 60 percent ($52 billion)
of the year-to-year increase in the goods trade
deficit between 1998 and 1999 was accounted
for by the year-to-year increase in consumer
goods, foods and beverages, and automotive
Jack L. Hervey is a senior economist at the Federal
Reserve Bank of Chicago. Loula S. Merkel, formerly an
associate economist at the Federal Reserve Bank of
Chicago, is currently with McKinsey & Company, Inc.,
United States/Chicago. The authors especially wish to
express their appreciation to Michael Kouparitsas and
David Marshall for their helpful comments and suggestions
and to the seminar participants at the Federal Reserve
Bank of Chicago for their interest and suggestions.
Economic Perspectives
imports (most of which are broadly classed as consumer goods). If the consumption boom story is true,
it implies that there has been excessive borrowing
from abroad to finance a domestic consumption binge.
And according to this argument, since this borrowing
has not gone toward enhancing productivity, the economy will be forced to suffer a decline in consumption
in the future as resources are diverted away from
production for domestic use toward production to
service the foreign debt.
A second hypothesis suggests that the financial/
exchange rate crises in Asia, Russia, and Brazil from
mid-1997 through early 1999 contributed to a safe
haven inflow of short-term foreign capital into U.S.
markets.3 Briefly, the idea here is that the flight of
capital from the foreign economies takes away from
the productive and consuming capacity of those
economies; it not only detracts from the capacity of
their domestic economies to perform, but it also reduces their capacity to import from foreign markets,
namely, the U.S. From the U.S. perspective, this flight
of foreign capital into the economy does two things
it makes it more difficult for the U.S. to export goods
and services to these now poorer performing foreign
markets and it facilitates (makes cheaper, in terms of
dollars) the U.S. importation of goods and services
from these countries. Thus, other things remaining
the same, the U.S. current account deficit increases.
We call this the safe haven hypothesis. The concern
implicit within this explanation for the capital inflow
is that economic recovery and increased stability abroad
might result in an abrupt and substantial outflow of
short-term capital, with resulting disruption in U.S.
financial markets.
A third potential explanation for the recent rapid
increase in the current account deficit is associated
with the technological restructuring of the U.S. economy. This hypothesis implies that a technology shift
in the economy (largely related to the assimilation of
advances in computer and communication technology)
has increased the level of productivity, and returns on
investment, in the economy. Demand for investment
has increased in response to this technology shift,
which in turn has stimulated the inflow (supply) of
foreign capital in support of this new type of investment. We call this the technological change hypothesis. There is less concern about an eventual adverse
adjustment in the economy in this case, because this
hypothesis implies that productivity-enhancing investment will result in increased output in the economy,
thereby facilitating the servicing and eventual repayment of the increased level of borrowing from abroad
(the larger trade/current account deficit).4
Federal Reserve Bank of Chicago
Before we can examine the relationship between
the international accounts and the domestic economy,
we need to understand how these international transactions work. In the next section, we set out a simple
framework for understanding these relationships,
based on national income accounting identities. We
then review the three hypotheses outlined above,
which seek to explain the recent rapid increase in the
current account deficit/capital inflow, and analyze
how well they match the available evidence. Finally,
we consider whether the deficit is sustainable and, if
not, what the implications of each hypothesis might
be for an eventual adjustment in the U.S. economy.
We find little support for the consumption boom
explanation in the data. While consumption has increased, its share of total expenditures has declined.
We find some evidence to support the safe haven rationale for the increase in capital inflows. However,
because much of the capital inflow appears to represent
long-term investment rather than a short-term flight
to safety, we do not find the implications of this story
to be particularly worrisome for the health of the U.S.
economy. In other words, our view is that an unwinding of such capital inflows is unlikely to be overly
disruptive to domestic financial markets. Finally, we
find the technological change argument to have some
merit. Much of the recent increase in goods imports
has been in the investment goods categoriescapital
equipment, intermediate capital equipment components, and industrial supplies used in the production of
capital goods. Recent gains in productivity measures
and continuing structural changes across the spectrum
of U.S. industry suggest that the economy may be
shifting to a new and higher level of potential output.
An economy in the process of such a shift has an incentive to increase borrowing from abroad to fulfill
the increased demand for investment. We believe that
the available data on the current U.S. economic environment fit well with this scenario.
International trade—the current account
and the capital account
One can think of trade in the context of individual decision-making. Trade is a result of conscious
and voluntary decisions made by individuals, firms,
and public institutions. Any individual faces a budget
in which current expenditures are constrained by current income and the ability to borrow. More specifically, allowing subscript t to represent the current
period, the individuals flow budget constraint is:
1) yt + rt at = ct + it + (at+1 at),
3
where y is income, c is expenditure on the consumption of goods and services, i is investment expenditure,
r is the interest rate, and a is net assets, which could
be positive or negative. If at is greater than zero, then
the individual is a net creditor. If at is less than zero,
then the individual is a net debtor. The term rtat represents income or debt payments, depending on the
sign of at. What this accounting relationship says is
that ones current income is distributed over ones
current consumption and savings with any shortfall
resulting in an increased net liability in the next
period (at+1 at).
At the national level, the individual budget equation we presented above still holds, but some changes
in notation are useful to see how the collective individual decisionsthe sum of which are the national
private budget decisionare related to the international
economy. Allowing uppercase letters to represent the
sum of the individuals variables, the national budget
constraint, or equivalently, gross national product, is:
2) Yt + rtAt = Ct + It + (At+1 At).
In a closed economy, that is, a country not open to
foreign trade, the national debt must be zero, that is,
At = 0. This means that the sum of all borrowers debt
must exactly offset the sum of all lenders funds. In
other words, the current expenditures of a country as
a whole are constrained by its current incomeit
cannot borrow. In contrast, an economy that is open
to international trade has the option of financing its
aggregate demand for consumption and investment
by borrowing abroad, that is, At < 0. Similarly, an open
economy can lend or invest abroad, taking advantage
of a wider market and enhanced risk/return choices
for its assets.
The nations expenditures (Ct + It) can either be
spent on domestic goods consumed at home (Yt Xt)
or on imports (Mt):
3) Ct + It = (Yt Xt ) + Mt.
6) (Xt Mt) + rt At = (At+1 At).
This equation represents a countrys balance of international payments. The sum of the trade balance
(Xt Mt) and net income receipts on net foreign assets (or net payments on liabilities) (rt At) is the current
account balance. The change in the stock of foreign
assets held by domestic individuals or firms is a capital outflow, and the change in the stock of domestic
assets held by foreigners is a capital inflow, the net
of which (At+1 At) is the capital account balance.
Equation 6 shows that movements in the current
account are matched by identical movements in the
capital account. It also implies that if a country runs
a current account deficit, there will be an increase in
the stock of foreign liabilities in the next period to
finance the difference (that is, foreign borrowing or
a capital inflow). The interest rate is positive, so as
A becomes a larger negative, the income balance in
subsequent periods must be a larger negative, which
leads to a larger current account deficit, all else equal.
From here we can see that the increase in the stock
of foreign debt, also referred to as the net international investment position (NIIP), can lead to an
ever-increasing income deficit.
The recent behavior of these components of the
U.S. balance of payments is the cause of the current
concern. Data presented in figure 1 identify recent
trends that have rekindled the debate about the deterioration in the U.S. international trade position. From
1992 through 1997, U.S. trade grew impressively, with
the nominal value of exports of goods and services
up 8.7 percent per year, on average, and imports up
an even more robust 9.7 percent per year. The associated net export deficits for 1992 and 1997 rose from
$36 billion to $106 billion, respectively, or from 0.4
FIGURE 1
U.S. trade balance
percent of GDP
16
This is the familiar national accounting identity:
Imports, goods
and services
12
4) Yt = Ct + It + Xt Mt.
Rearranging terms in equation 4, we can express the
trade balance as:
5) Xt Mt = Yt Ct It.
By substituting the trade balance equation 5 into
the budget equation 2 and putting the rtAt term on the
left side, we get
4
8
Exports, goods
and services
4
0
Trade balance
-4
1960
’65
’70
’75
’80
’85
’90
’95
’00
Source: Bureau of Economic Analysis.
Economic Perspectives
percent to 1.1 percent of GDP. During the most recent
two and a half years, however, the differential in the
growth rates between exports and imports increased
markedly, resulting in a dramatic increase in the trade
deficit. Export growth slowed to an average of 1.3
percent per year, in the face of weak economic conditions in foreign markets, while average import growth
held at a relatively strong 8.6 percent ratestimulated
by the robust domestic expansion. The trade deficit for
1999 stood at $254 billion and its share of GDP rose
sharply, to 2.7 percentapproaching the record 3.0
percent reached in 1987. In the first half of 2000, the
deficit rose further to $348 billionat an annual rate,
a level equivalent to 3.5 percent of GDP.
Figure 2 shows the behavior of the current account balance. The trade balance is the largest component of the current account; however, it is not the
only item causing concern. Since 1998, the income
balance has been in deficit; in 1999 it showed a deficit of 0.2 percent of GDP. This figure means that the
income paid to foreigners on their holdings of U.S.
assets exceeded the income received on U.S. assets
held abroad. It is interesting to note that the nations
net international investment position (At) has been
negative since 1986; however, only since 1998 has
the income account been in deficit. This is because
the rate of return on U.S. assets held abroad has historically exceeded the rate of return paid to foreign
investors holding U.S. assets.5
The final category of international transactions
included in the current account, which we neglected
in our previous description, is unilateral transfers
government and private. These transactions are mostly
in the form of U.S. government grants and aid to foreign countries and international institutions. On net,
they have almost always constituted an outflow of
A countrys inflow of foreign capital (debt) can be
thought of as an import of foreign savings. A countrys
current income, less its current expenditures, equals
its savings (St). It is easy to show that national savings
less national investment is equivalent to the capital
FIGURE 2
FIGURE 3
Components of current account
Components of capital account
percent of GDP
2.5
funds from the U.S. The U.S. has recorded a net unilateral transfers inflow only in one year since World
War II; this was in 1991 as a result of foreign governments contributions to the U.S. for the Persian
Gulf War.
As shown above, the capital account reflects the
net acquisition or sale of assets by U.S. and foreign
parties. Asset changes include changes in official assets (international reserves), the net outflow of funds
from U.S. financial institutions, and direct investment
abroad from U.S. firms and individuals. They also
include the net inflow of funds from foreign financial
institutions and the inflow of direct investment funds
into the U.S. from foreign firms and individuals. Apart
from statistical discrepancy, the current and capital/
financial account balances are always equal. Figure 3
shows the components of the capital account. The sum
of all historical capital/financial account transactions
equals the nations NIIP. As mentioned earlier, the
U.S. is currently a net foreign debtor. In 1999, foreign
debt represented 15.9 percent of GDP.
Savings and investment imbalance
Looking again at the structural framework of the
national accounting identities, we can examine how
the equations presented above relate to the linkages
between a countrys domestic and international transactions. Equations 2 and 6 can be rearranged as:
7) [Yt + rt (At) Ct] It = (At+1 At) = (Xt Mt) + rt At.
percent of GDP
10
Change in U.S.
foreign liabilities
Income account
balance
Change in U.S.
foreign assets
5
0.0
Transfer
balance
Trade
balance
-2.5
-5.0
1960
’65
’70
’75
’80
Source: Bureau of Economic Analysis.
Federal Reserve Bank of Chicago
0
Current
account
balance
’85
’90
’95
Capital account
balance
’00
-5
1960
’65
’70
’75
’80
’85
’90
’95
’00
Source: Bureau of Economic Analysis.
5
account balance, which in turn equals the current
account balance:
8) St = [Yt + rt (At) Ct].
9) St It = (At+1 At) = (Xt Mt) + rt (At).
This means that if a countrys citizens in the aggregate
decide to invest more than their available savings,
then the country will run a current account deficit.
This is matched by an increase in the stock of foreign
debt. Thus, the current account deficit, representing
the shortfall of domestic savings, will be financed by
the net importation of foreign savings.6
To better understand this point, consider that when
a countrys aggregate demand for goods and services
exceeds the aggregate domestic supply, it runs a trade
deficit. Similarly, when aggregate supply outstrips
aggregate demand, the country posts a trade surplus.
This is what some economists term the safety valve
quality of international trade. In the absence of the
ability to trade with other countries, an excess aggregate demand situation would tend to bid up domestic
prices. The trade balance, therefore, whether in deficit
or surplus, is simply the residual of a countrys aggregate demand and supply.
If a country has a trade deficit, it must finance it
through foreign borrowing.7 This description of market transactions, however, may give the misleading
impression that debt is a sole consequence of trade
flows. In reality, a countrys net debt position is also
a function of the relative risk and return preferences
of investors. Foreign investors may want to invest in
U.S. assets because they expect a higher risk-adjusted
return than they might get at home or in a different
country. As risk/return profiles around the world
change, so do relative capital flows.
The relationship between a countrys investment
demand and the supply of domestic and foreign savings is shown in figure 4. In this graph of the market
for loanable funds, the downward sloping demand
curve for investment funds and the supply schedule
of funds available for investment, or savings, are equilibrated by the interest rate. We see that the excess of
domestic investment over savings is made up by foreign savings, or the current account deficit.
The size of an economy relative to the world economy is critical in determining the impact of changes in
its investment demand and supply of funds. For example, a small economy in a world characterized by
a high degree of capital mobility can have only a minuscule impact on the world interest rate. It essentially faces a perfectly elastic supply curve for funds;
6
that is, any change in its domestic investment demand
has little, if any, effect on the price it must pay for
funds. By contrast, a large economy like the U.S.,
whose liabilities account for around one-third of
the assets of the major OECD (Organization for Economic Cooperation and Development) countries,8 has
the power to influence the world interest rate. If, for
example, there was an increase in demand for investment in the U.S., all else equal, the world interest rate
would rise. Similarly, if for some exogenous reason,
U.S. residents chose to save more (for example, due
to a change in the demographic distribution over time),
the world supply of loanable funds would increase
and the world interest rate would fall.
Which hypothesis best fits the data?
Using the framework we developed in the previous section, we briefly review our three hypotheses
and examine to what extent they are supported by the
data. While these hypotheses are not mutually exclusive, they do imply very different effects on interest
rates and investment.
To many observers, the current boom in the U.S.
economy is clearly manifested in a surge in personal
consumption activity. U.S. consumers are purchasing
more goods, including imports. At the same time, the
personal saving rate has fallen to nearly zero (indeed,
in July 2000 it was estimated at 0.2 percent).9 These
indicators lead to the view that increased consumption
(and decreased saving) has led to the record trade
deficit and capital inflows. This view translates into
a shock to U.S. consumer tastes and preferences in
favor of current over future consumption. This is very
different from an increase in consumption associated
with an increase in wealth. In the present situation,
consumption is thought to be increasing at the expense
of savings, regardless of income. In the context of the
loanable funds market in a large economy, this would
be reflected in an inward shift in the supply curve for
funds, all else equal. From figure 4, we would expect
this scenario to lead to a decline in investment and a
rise in the interest rate.
The second potential explanation for the current
account deficit and record capital inflows contends
that the financial crises experienced by Asia, Russia,
and Brazil from mid-1997 led to capital, domestic
and foreign, fleeing these markets for the safer U.S
market. This equates to the U.S. experiencing a beneficial shock to the perceived risk of its assets. Foreign
capital (savings) flowed into the U.S. so more funds
were available at any given interest rate. Again, all
else equal, this would result in an outward shift in the
supply curve for loanable funds, which would cause
Economic Perspectives
FIGURE 4
Market for loanable funds
A. Large versus small country
r
D. Consumption boom
SD1 + S0F
r
(SD + SF )Large
SD
SD0 + S0F
r1
r = r*
r0
(SD + SF )Small
I
I
I
current
account deficit
I1
B. Safe haven
D
0
I
E. Technology shock
F
0
S +S
r
I0
r
SD + SF
D
0
S +S
F
1
r0
r1
r1
r0
I1
I
I0
I0
I
I1
I0
C. Capital outflow, small country
I1
I
F. Capital outflow, large country
r
r
SD0 + SF1
D
0
S +S
r1
risk
premium
F
1
SD0 + S0F
r0
SD0 + S0F
risk
premium
r1
r0
I
I1
I0
I
I1
I
I0
I
Notes: r* = world interest rate; SD = domestic savings; SF = foreign savings; I = investment.
interest rates to decline and investment to increase,
as shown in figure 4. Note that this explanation is not
inconsistent with an increase in the level of consumption, attributable to the increase in the quantity of
investment leading to higher income.
The final explanation we consider argues that
the U.S. is experiencing a positive technology shock,
which has increased the economys productivity and
long-run level of potential output. The productivity
Federal Reserve Bank of Chicago
of capital is higher and, hence, the incentives for
investment are higher. As shown in figure 4, all else
equal, this would correspond to an outward shift in
the demand curve for investment and an increase in
both interest rates and investment. Again, this explanation also justifies an increase in the level of consumption, since there is a wealth increase associated with
the productivity shock.
7
Which of these explanations fits best with the
recent behavior of the U.S. economy? Figure 5 shows
that both gross domestic saving and investment as
shares of GNP have been increasing since 1991.
Investment however, has been increasing at a faster
rate than national saving, resulting in the current
account deficit.10
Immediately, we see that the behavior of investment does not support the consumption boom argument. We also find other evidence that refutes the
consumption boom story. Imports of consumption
goods, broadly defined to include automotive products and food, did represent more than half of the increase in the goodstrade deficit between 1998 and
1999.11 However, looking at this figure alone may be
misleading. Consumer goods historically have been
the largest component of the goodstrade balance.
Looking at the behavior and composition of imports,
we see that capital goods, including non-oil industrial supplies and materials, actually comprise a larger
share of our total imports than consumer goods
(figure 6). Furthermore, there has been no apparent
increase in consumer goods share of imports. Note
that we still run a trade surplus overall in capital goods,
although it has been declining since 1970. Furthermore,
to the degree that our capital goods imports constitute
inputs into the production of intermediate and final
goods, these figures may be reflecting re-exports. Figure 7 plots the components of our trade balance in
capital goods, and indicates that since 1991 the U.S.
has been a net importer of computers and related
equipment. These capital goods are generally associated with productivity-enhancing investment.12
Perhaps a clearer view of whether the widening
of the current account deficit is a result of a consumption boom can be seen from consumption and
FIGURE 6
Share of total U.S. imports
percent
75
Capital goods and
intermediaries
50
Broad
consumer goods
25
Other
0
1979
’82
’85
’88
’91
’94
’98
investment shares of gross domestic purchases (which
include imports and exclude exports). This measure
is equivalent to the resource constraint described in
equation 3. Figure 8 plots these ratios (with government
spending removed). An increase in investment since
1990 (above its historical trend) is evident, while
total consumption expenditures have actually been
declining in relative terms. The ratios are in real (priceadjusted) values, so they capture the volume effects
of increased investment. Based on these statistics, one
could argue that while the U.S. continues to have a
growing trade deficit in consumption goods, these imports, as well as imports of capital goods, are allowing
the economy to reallocate highly employed, scarce
domestic resources toward productive investment.
While the investment behavior data refute the
consumption boom theory, they support both the safe
FIGURE 5
FIGURE 7
Savings and investment
Capital goods trade balance
percent of GDP
25
percent of GDP
2.0
Gross domestic
investment
20
1.5
15
’00
Note: Broad consumer goods comprises consumer goods,
foods, feeds and beverages, and automotive.
Source: Bureau of Economic Analysis.
Gross national
savings
Total capital
goods
Other
1.0
10
0.5
5
Current account
Airplanes,
etc.
0.0
0
Computers, etc.
-5
1960
-0.5
’65
’70
’75
’80
Source: Bureau of Economic Analysis.
8
’85
’90
’95
’00
1973 ’76
’79
’82
’85
’88
’91
’94
’97
’00
Source: Bureau of Economic Analysis.
Economic Perspectives
FIGURE 8
FIGURE 9
Share of gross domestic purchases
(less government spending)
percent
85
Ten-year Treasury bond rate
percent
30
percent
10
Consumption
(left axis)
8
80
25
75
20
6
Investment
(right axis)
70
1960
15
’65
’70
’75
’80
’85
’90
’95
’00
4
1990
’92
’94
’96
’98
’00
Source: Federal Reserve Board of Governors.
Source: Bureau of Economic Analysis.
haven and technology hypotheses. These two shocks
would have opposite effects on interest rates, however.
So how have U.S. interest rates behaved during the
past two years? Figure 9 plots interest rates on the
ten-year Treasury bond since 1990. From this figure
we see that long-term interest rates declined from the
middle of 1997 until late in 1998. From then until
recently, U.S. long rates trended upwards. Obviously,
these two effects did not operate in isolationinterest
rates are affected by numerous developments. However, the safe haven story is supported by the decline
in interest rates through 1998. During 1999, rates
began to rise and capital flows and investment continued strong. This would indicate that increased
domestic investment demand has been the dominating effect lately. So, while a combination of both the
safe haven and technological change stories may
have led to the record net capital inflows and current
account deficit, it appears that technological change
increased U.S. demand for investment associated
with the enhanced productivity of the economy
has dominated more recently.13
What would an adjustment mean?
With what weve learned about the likely sources
of the current account and trade deficits (along with
the corresponding capital flows), we now address
whether these deficits are sustainable in the long term
and, if not, what sort of adjustment the U.S. economy
might ultimately undergo. If we consider the intertemporal qualities of the current account and capital
account relationship, we can show that in the steady
state, a trade deficit can be sustained as long as the
growth rate of national income exceeds the rate of
return paid on the nations liabilities. Box 1 presents
Federal Reserve Bank of Chicago
the details. How does the U.S. NIIP measure up against
this sustainability requirement? Historically, the nominal growth rate of the U.S. economy has averaged
around 7.4 percent per year.14 The average nominal
rate of return paid on U.S. foreign liabilities over the
1976 to 1999 period was around 5.5 percent. The 2
percentage-point difference between these rates implies
that a trade deficit could be sustained.
The relationship also implies that for a given
sustainable trade deficit, the U.S. can accumulate foreign liabilities up to some maximum level. Has this
level been reached? If we consider that the average
net exports share of GDP over the 1976 to 1999 period
was 1.4 percent, and if we assume these averages to
be the long-run values of these variables, then the
corresponding long-run NIIP to GDP (or net foreign
debt) position would be 70 percent. This level of
indebtedness would likely not be desirable politically,
given that the current NIIP to GDP ratio of 15.9 percent for 1999 is considered by some to be too large.
Similarly, there is likely a foreign debt to GDP ratio
above which foreign creditors would consider additional lending to the U.S. imprudent and, therefore,
be less willing to purchase additional U.S. assets.
Conversely, if one believes that there is some appropriate or desirable long-run level of foreign debt to
income, say, for example, 20 percent, the corresponding long-run trade deficit to GDP ratio would be only
around 0.4 percent.15 If one were to subscribe to this
second idea, then the current size of the deficit is too
large and will adjust. What might an adjustment
entail under our three different scenarios?
If the consumption boom argument held merit,
then we would expect that, in the future, consumption would have to decline along with output as the
9
BOX 1
Sustainability of the current account deficit
The current account is the sum of the trade balance
(Xt Mt), income receipts on foreign assets held by
U.S. citizens, income payments on U.S. assets held
by foreigners, and unilateral transfers, which are
typically foreign aid. The income receipts and payments net out to simply the rate of return (r) paid
on the U.S.s net international investment position
(A), or stock of net foreign debt, multiplied by the
current stock of NIIP. The capital account is equal
to the change in the NIIP, and always equals the
current account. Ignoring the foreign aid component,
and allowing subscript t to represent the current
period, we have
(Xt Mt) + (1 + rt)At = At+1.
As discussed in the text, the current account is
equal to the capital account. Taking this relationship and dividing through by GDP (Y), we derive
Now, allowing an S prefix to indicate share of
GDP and g to be the growth rate of GDP, we can
rewrite the equation as follows:
(SXt SMt) + (1 + rt) SAt = (1 + gt) × SAt+1.
In the long-run steady state, this equation can
be represented as
(SX SM) = (g r) × SA.
From this equation it can be seen that in the
long-run steady state, the sustainability of a trade
deficit depends on the relationship between the
growth rate of the economy and the interest rate paid
on U.S. liabilities. Specifically, a negative trade balance could be sustained as long as the rate of growth
of income is greater than the interest rate (g > r).
((Xt Mt)/Yt) + (1 + rt) × (At/Yt) = (Yt+1/Yt) × (At+1/Yt+1).
U.S. begins running trade surpluses and repaying the
foreign debt. However, as we noted earlier, we find
this to be the least compelling hypothesis of the three
we consider. So, in our view, this type of adjustment
is unlikely.
Under the safe haven scenario, for which we do
find some support in the data, the adjustment would
entail a reversal of the beneficial shock to the U.S.
risk factor and a corresponding outflow of foreign
capital from the U.S. It is not surprising that there is
concern about the economic consequences of this
type of adjustment in the U.S. current account deficit,
given the financial crises we have seen in other parts
of the world in recent years. The Mexican peso crisis
in 199495 was followed by the Asian financial crisis
beginning in mid-1997, the Russian bond default
crisis in August 1998, and the Brazilian currency devaluation in early 1999. All of these developments displayed similar characteristics in that they involved a
discord between countries internal and external balances, capital flight, and sharp currency depreciations,
followed by much slower or negative GDP growth during the adjustment phase.
However, as we noted above, the link between a
countrys internal and external balance is the interest
rate, and a countrys ability to influence the world
interest rate is critical to the adjustment process. To
see why this is important, lets examine the sequence
10
of events if a country is confronted with an adverse
shock to its perceived risk factor. This is the opposite
of the shock described in the safe haven argument. In
this case, foreigners demand a risk premium in excess
of the world interest rate in order to supply the current
level of funds. This is equivalent to a reversal or
slowdown in capital inflows and would lead to a rise
in national interest rates and a depreciation of the national exchange rate. Higher interest rates would lead
to lower investment and consumption, while the exchange rate depreciation would lead to higher exports
and lower imports. The fall in consumption and investment should outweigh the rise in exports, which means
that output should fall. As shown in figure 4, however,
these effects are less severe in a large country than in
a small one.
Another important factor in determining the likely
severity of an adjustment is the extent to which the
capital inflows to the U.S. represent short-term, hot
money. Some observers argue that speculative foreign
capital has gone predominately into the U.S. stock
market. However, a review of the structure of the U.S.
capital account shows that this is not the case. Figure
10 shows that the largest shares of capital inflows
since 1997 have been in the form of direct investment,
defined as the purchase of a greater than 10 percent
equity stake in a U.S. firm, and in long-term, nonTreasury securities, which include stocks and corporate
Economic Perspectives
FIGURE 10
FIGURE 11
Share of capital inflows
Foreign purchases of non-Treasury securities
(199799 average)
percent
100
Other inflows
75
Stocks
and bonds
Other outstanding
bonds
22%
50
Stocks
28%
25
0
1960
Agency bonds
23%
Direct investment
’65
’70
’75
’80
’85
’90
’95
’99
Source: Bureau of Economic Analysis.
and municipal bonds with a maturity of at least one
year. Such debt has contractual characteristics that
make it harder to dissolve than short-term securities
and bank loans. This is not to say that these assets are
not liquid, but simply that they are not maturing and
revolving at very short intervals.16 Within the nonTreasury securities category, the share of equities was
higher on average in the 1997 to 1999 period (28.6
percent) than in the entire 1991 to 1999 period (16.8
percent). However, figure 11 shows that the bulk of
the inflows have been in the bonds, rather than the
stocks subcategory.
Another important aspect of the capital account
is that most of our existing debt, as well as the capital
inflow, is dollar denominated. This means that in a
capital outflow situation, the U.S. would not face
the difficulty that some countries have of exchanging
a depreciated domestic currency for the more expensive currencies in which payments need to be made.17
Finally, under the technological change scenario,
for which we also find support in the data, the investment in productivity-enhancing capital stock leads to
an increase in the output of the economy. As output
increases over consumption, the country exports the
excess and begins to run trade surpluses, thus beginning repayment of the foreign debt.18
An additional and related aspect of the technology
shift argument derives from the composition of U.S.
trade in goods versus services. Figure 12 shows that
while the U.S. has run a persistent deficit in goods, the
trade surplus in services is increasing. Trade in services
represented 30 percent of U.S. goods and services
exports in 1999. As the U.S. economy continues its
transition toward a service economy, and as foreign
countries continue to demand more services, we expect
this trend will increasingly offset the goods deficit.
Federal Reserve Bank of Chicago
New corporate
issues
27%
Source: Bureau of Economic Analysis.
The scope of international trade in services, and
the U.S.s relative position in this market, is seldom
given attention in policy discussions and the popular
press. In fact, economists have traditionally regarded
services as nontradable. Advances in technology,
however, have allowed many services to transcend
their historically local nature. While haircuts are still
difficult to export, technological advances in communications are increasingly allowing U.S. companies to
export business services, travel services, and financial
services around the world. Deregulation of service
industries within the U.S. and in other countries,
international trade liberalization in services, and
improvements in technology for service distribution
FIGURE 12
Trade balance and components
percent of GDP
2.5
Services
balance
0.0
Total
-2.5
Goods
balance
-5.0
1960
’65
’70
’75
’80
’85
’90
’95
’00
Source: Bureau of Economic Analysis.
11
channels will continue to expand the international market for services. With services already representing
over half of real GDP, this is an area in which the
U.S. will likely continue to be a competitive force in
the expanding world market.19
If the increase in investment demand is truly due
to an increase in productivity associated with new
technology, and if this technological transformation
continues to positively affect service industries, the
U.S. can expect to reap substantial income gains in
the future.20 This would mean that the current U.S.
international deficit position simply reflects the current
and expected future prosperity of the U.S. and, importantly, that the adjustment process should be automatic
and painless.
Conclusion
The U.S. deficit in international trade soared to
new heights in 1998, again in 1999, and in all likelihood, will increase even further this year. Mirroring
these deficits have been huge capital inflows from
foreign investors. Is the condition of the U.S. international accounts placing the domestic economy in jeopardy? Can the U.S. continue to run such large trade
deficitscontinue to borrow abroad to finance the
deficitswithout facing an adjustment that will
severely disrupt the domestic economy, along the
lines of what some other countries have experienced
in recent years?
In reviewing three commonly cited explanations
for the source of the current account deficitthat it
is a result of a boom in consumer spending at the expense of savings; that it is a result of short-term capital inflows fleeing disruptive economic conditions
abroad; and/or that it is a result of a transition of the
economy toward a higher level of productivitywe
find that the evidence supports the notion that the
current account deficit reflects a technological shift
that has led to an increase in the relative prosperity
of the U.S. economy. Furthermore, to the extent that
an adjustment in the international sector may take
place in the future, we would expect it to be relatively smooth and gradualan adjustment that can be
accomplished without serious adverse consequences
to the U.S. economy. We also find some support for
the safe haven story, but we do not believe that the
U.S. economy faces the kind of sudden reversal in
capital inflows that would be highly disruptive. This
view is based on the large relative size of the U.S.
economy, and its consequent ability to influence world
interest rates, and the fact that much of the capital
inflows of recent years have been in the form of
longer-term investments.
NOTES
1
Fred Bergsten, as quoted in Stevenson (2000).
2
Note that this reason implies that the increase in consumption
was a result of a shock to consumer tastes and preferences and
is very different from an increase associated with an increase
in wealth.
This explanation was put forth in Hervey and Kouparitsas (2000).
3
4
See, for example, Pakko (1999) and Hervey (1986).
5
The March 2000 issue of Survey of Current Business reviews the
behavior of returns to foreign direct investment in the U.S. The
author finds that the return on assets (ROA) of U.S. owned companies in the U.S. exceeded the ROA of foreign owned companies
in the U.S. by between 1 percent and 2 percent over the 198897
period.
In this description of a national budget constraint we ignore the
public sector. Government spending and saving would, of course,
also affect a countrys aggregate demand, and so its external balance. In particular, a government budget deficit lowers national
savings and so lowers the current account balance. This is the
basis of the twin deficits argument that was popular in explaining
current account behavior during the 1980s.
6
7
This is true if At is less than or equal to zero. If the country has
a net stock of foreign assets, it need not borrow but can simply
draw down its foreign asset stock.
12
8
Humpage (1998).
The personal savings rate may not be an appropriate measure,
however. See Velde (1999).
9
Another interesting aspect of figure 5 is that gross savings and
gross investment in the U.S. are highly positively correlated,
while investment and the current account deficit have a weaker,
negative correlation. A number of studies have shown that investment booms are highly negatively correlated with increases in
current account deficits, especially in smaller countries. See discussion in Baxter (1995). The difference between the excess
investment and the current account deficit presented in figure 5 is
termed the statistical discrepancy. Some argue that the increase in
this component of the national accounts since 1997 has led to the
current account deficit being overstated. See Koretz (2000).
10
Cars and trucks account for about 85 percent of the automotive
category. The food category here includes feed products.
11
The U.S. is running a surplus in advanced technology products,
although it declined from $32.3 billion in 1997 to $19.1 billion
in 1999.
12
What the dominant force has been on the way to the current
situation in the U.S. current account may not be as important
as whether these shocks are permanent or temporary. Here, we
assume that the shock is permanent or at least very persistent.
See Baxter (1995).
13
Economic Perspectives
14
Over the 195599 period.
15
Our choice of 20 percent in this example is arbitrary.
The maturity structure of countries foreign liabilities becomes
very important in a capital outflow situation. In comparison, of
Mexicos foreign liabilities in 1994, 55 percent were short term.
When foreign sentiment towards Mexican assets changed,
Mexico faced difficulty in rolling this debt over.
The technology shock would eventually be transmitted to the
rest of the world, making investment in foreign countries relatively more attractive and encouraging a current account adjustment
in the U.S.
18
16
17
Mann (1999), chapter 9.
In 1999, services represented 52 percent of real GDP. See Mann
(1999), chapter 6, for a further discussion of trade in services.
19
The jury is still out on whether the U.S. is truly experiencing a
new economy technology shock. See Kouparitsas (1999).
20
REFERENCES
Baxter, Marianne, 1995, International trade and
business cycles, in Handbook of International Economics, Vol. 3, G. Grossman and K. Rogoff (eds.),
New York: Elsevier, North Holland, chapter 35,
pp. 18011864.
Glick, Reuven, and Kenneth Rogoff, 1995, Global
versus country specific shocks and the current account,
Journal of Monetary Economics, Vol. 35, No. 1,
February, pp. 159192.
Hervey, Jack L., 1986, The internationalization of
Uncle Sam, Economic Perspectives, Federal Reserve
Bank of Chicago, May/June, pp. 314.
Hervey, Jack L., and Michael A. Kouparitsas,
2000, Should we be concerned about the current
account deficit?, Chicago Fed Letter, Federal Reserve
Bank of Chicago, April.
Humpage, Owen, 1998, Is the current account deficit sustainable?, Economic Commentary, Federal
Reserve Bank of Cleveland, October 15.
Koretz, Gene, 2000, The trade gap may be inflated,
Business Week, July 3, p. 32.
Kouparitsas, Michael A., 1999, Is there evidence of
the new economy in the data?, Federal Reserve Bank
of Chicago, working paper, No. 99-21, December.
Mann, Catherine L., 1999, Is the U.S. trade deficit
sustainable?, Washington, DC: Institute for International Economics, September.
Mataloni, Jr., Raymond J., 2000, An examination
of the low rates of return of foreign-owned U.S.
companies, Survey of Current Business, Bureau of
Economic Analysis, March.
McDonough, William, 2000, McDonough3:
Higher growth in Japan, Europe needed, quoted in
Dow Jones Newswire, March 30.
Obstfeld, Maurice, and Kenneth Rogoff, 1995,
The intertemporal approach to the current account,
in Handbook of International Economics, Vol. 3, G.
Grossman and K. Rogoff (eds.), New York: Elsevier,
North Holland, chapter 34, pp. 17311799.
Pakko, Michael R., 1999, The U.S. trade deficit
and the new economy, Review, Federal Reserve
Bank of St. Louis, September/October.
Stevenson, Richard W., 2000, Economy may have
a soft spot, New York Times, June 10, p. B-1.
Velde, François R., 1999, Americans are not saving:
Should we worry?, Chicago Fed Letter, Federal
Reserve Bank of Chicago, May.
Krugman, Paul R., and Maurice Obstfeld, 1994,
International EconomicsTheory and Policy, third
edition, New York: Harper Collins.
Federal Reserve Bank of Chicago
13
Effect of auto plant openings on net migration
in the auto corridor, 198097
Thomas H. Klier and Kenneth M. Johnson
Introduction and summary
How do newly opened auto plants influence the patterns of demographic change in an area? An answer
to this question has important policy implications.
Competition among communities to attract new manufacturing jobs is substantial. Local governments often
provide significant economic incentives to firms to
induce them to locate a new plant in a given community. Such financial commitments are often justified,
in part, by the argument that new jobs will aid the
community in retaining its population, particularly
its young adult population. The young population is
viewed as critical to the future of communities because it represents a significant amount of human
capital. In that context, we believe it is important to
document the impact that such industrial development
has on the local demographic structure. We would
expect such demographic change to be most evident
in the patterns of migration to and from the respective counties.
Our research links demographic trends of the last
two decades to the geographic dispersion of the auto
industry. The analysis focuses on the nonmetropolitan
areas of seven states that make up the core of the auto
industry. This auto corridor includes 66 percent of
the employees and 70 percent of the plants engaged
in the production of cars and light trucks in the U.S.
Our primary interest is in estimating the impact that
the presence of auto plants has on the pattern of migration in the immediate and proximate counties. We
accomplish this by combining county-level migration
data with data on the spatial and longitudinal distribution of auto industry plants. Our auto industry dataset
is unique, consisting of plant-level information for
auto assembly plants plus data on the notoriously
hard-to-track auto supplier plants. It encompasses
over 2,200 individual plants, representing just under
900,000 employees for the seven auto corridor states.
14
Such comprehensive coverage of this industry represents a significant contribution to the literature.
Our models estimate the impact of auto plants
on county-level net migration during the 1980s and
1990s. Explanatory variables include measures of
economic, locational, and demographic characteristics and several variables measuring the presence
and structure of auto plants within and proximate to
the county.
Consistent with previous empirical work, we find
that a set of background variables widely used in
demographic research accounts for the bulk of the
variation in county-level migration. However, including variables measuring the presence and addition
of auto plants does add to the explanatory power of
the model. The addition of a large plant to a county
appears to have a significant positive influence on
migration. This effect is evident not only in the county
where the plant locates, but also in the contiguous
counties. The effect of smaller plants is much more
limited, but it is in the expected direction.
Review of the literature
Our work draws on several strands of literature.
First, we examine demographic trends between 1980
and 1997. Review of such timely information is important, because metropolitan and nonmetropolitan
demographic trends have been extremely fluid during
the past 30 years in the nation as a whole (Long
and DeAre, 1988; and Frey and Johnson, 1998).
Thomas H. Klier is a senior economist at the Federal
Reserve Bank of Chicago. Kenneth M. Johnson is a
demographer and professor of sociology in the Department
of Sociology and Anthropology at Loyola University,
Chicago. The authors would like to thank Paul Huck and
David Marshall for helpful comments and Mike Rorke and
Tommy Scheiding for excellent research assistance.
Economic Perspectives
Historically, nonmetropolitan demographic change
both in the auto corridor and elsewhere in the U.S.
has been dominated by an excess of births over
deaths sufficient to offset the net outmigration of
population to metropolitan areas. This pattern of
slow nonmetropolitan population gain through an excess of natural increase over migration loss was so
consistent that it came to be taken for granted (Fuguitt et al., 1989). However, the pattern changed
abruptly in the 1970s with the onset of what came to
be called the nonmetropolitan population turnaround.
Nonmetropolitan areas experienced widespread and
substantial population gains and net inmigration during the turnaround (Beale, 1975; Johnson and Purdy,
1980; and Fuguitt, 1985). Nonmetropolitan population redistribution patterns shifted again in the 1980s.
Most nonmetropolitan counties lost population during the decade because they had a modest net outflow of population combined with low levels of natural
increase (Johnson, 1993). Many researchers regarded
the diminished nonmetropolitan growth of the 1980s
as evidence that U.S. population redistribution trends
had reverted to historical form, with the turnaround
of the 1970s being just a short-term fluctuation. Yet,
there is now evidence of another upturn in population
growth rates in nonmetropolitan areas during the late
1980s and 1990s (Johnson and Beale, 1994; and
Johnson, 1998). Our purpose here is to examine the
linkages between such demographic change and
trends in the spatial structure of the auto industry.
The U.S. auto industry has undergone major
changes during the last 30 years (see McAlinden and
Smith, 1993; Rubenstein, 1992; and Harbour, 1990).
Three developments have shaped the spatial pattern
of the industry during this periodthe reconcentration
of auto assembly facilities in the heart of the country,
the southward expansion of the traditional Midwest
Auto Belt, and the arrival of Japanese auto assembly
and parts plants.
The spatial changes affecting the auto industry
have been reported in Rubenstein (1992). Most notable
is the reconcentration of auto assembly facilities in
the heart of the country. In the early days of the industry, assembly plants were built close to population
centers, since it was cheaper to ship parts to assembly
plants than to ship finished vehicles across the country. This approach worked well as long as consumer
demand for specific models was sufficient to support
production at multiple assembly plant locations.
However, since the 1960s, the proliferation of different models of cars has far outstripped the growth in
overall production of vehicles. As a result, particular
models must now be produced and shipped from only
one or two assembly plants if they are to be profitable.
Federal Reserve Bank of Chicago
This development has led many companies to concentrate their assembly plants in the heart of the U.S. to
minimize the costs of distributing the final product to
a national market. It also allows the assembly plants
to be located near the plants that produce engines,
transmissions, drive trains, and a host of other components. The net result of this trend has been the closing
of many coastal assembly plants and the reconcentration of light vehicle assembly plants in the auto corridor (see table 1).
As the auto industry reconcentrated in the nations
heartland, much of the growth occurred in the historical Auto Belt around Detroit. However, the industry
simultaneously expanded southward, forming an
auto corridor that includes not only the traditional
auto states of Michigan, Illinois, Indiana, Wisconsin,
and Ohio, but also Kentucky and Tennessee. This
southward extension of the auto corridor started in
the 1970s with efforts by General Motors to lower
procurement costs by building component plants south
of the traditional auto region. Facilitating the southward expansion of the auto corridor was the arrival
of Japanese-owned assembly and supplier facilities
during the 1980s (see table 2) (see Kenney and Florida,
1992; McAlinden and Smith, 1993; Smith and Florida,
1994; and Head et al., 1995).1 Plant location decisions
by Japanese car companies reflect a preference for
greenfield locations on the southern periphery of the
traditional Auto Belt. Hence Kentucky and Tennessee
more than tripled their share of light vehicle assembly plants, from 4 percent to 13 percent, while the
other five states of the auto corridor increased their
overall share from 43 percent to 50 percent between
1970 and 1997 (table 1).
TABLE 1
Reconcentration of light vehicle assembly plants
Plants operational in
Auto corridor
1970
1997
Number (%)
Number (%)
Illinois
Indiana
Kentucky
Michigan
Ohio
Tennessee
Wisconsin
Total
2 (4)
0 (0)
2 (4)
15 (29)
3 (9)
0 (0)
2 (4)
24 (47)
3 (5)
2 (4)
5 (9)
15 (27)
7 (13)
2 (4)
1 (2)
35 (63)
U.S. total
51(100)
56(100)
Source: Ward’s Automotive Yearbook, various years.
15
TABLE 2
Newly opened light vehicle assembly
plants, 198097
Company
State
GM
GM
GM
Honda
Nissan
NUMMIb
GM
GM
GM
AutoAllianceb
DiamondStar
Toyota
Honda
Subaru-Isuzu
Saturn
Chrysler
Ford
BMW
Toyota
Mercedes-Benz
Kentucky
Louisianaa
Ohio
Ohio
Tennessee
Californiaa
Michigan
Missouria
Michigan
Michigan
Illinois
Kentucky
Ohio
Indiana
Tennessee
Michigan
Ohio
So. Carolinaa
Kentucky
Alabama a
Start-up year
1981
1981
1981
1982
1983
1984
1984
1984
1985
1987
1988
1988
1989
1989
1990
1991
1991
1994
1994
1997
a
Indicates state not in auto corridor.
Reopened previously closed facility.
Source: ELM Guide database, 1997.
b
Several papers examine the economic impact of
locating an auto assembly facility on the proximate
areas (see Haywood, 1998; Center for Business and
Economic Research, 1992; Fournier and Isserman,
1993; and Marvel and Shkurti, 1993). These studies
examine the balance between the incentives used to
attract a plant and the resulting development of the
region as measured by income and employment. Their
findings suggest that adding an assembly plant can
have spatially disparate effects on growth. For example, the host county for a Honda assembly plant that
opened in Ohio in 1982 experienced much stronger
employment and income growth than the contiguous
counties (Fournier and Issermann, 1993). At the state
level, the impact of attracting an assembly plant depends on the timing of a particular plant relative to
others in a region. Murray et al. (1999) suggest that
spinoff effects derived from the subsequent location
of supplier facilities near new assembly plants are
strongest in areas that were first to attract an automobile
assembly plant. The economic development literature,
however, provides very little evidence on possible
linkages between plant location and demographic
trends. Two studies of the impact of new assembly
16
plants suggest that approximately 80 percent of
those who migrated to obtain work in the newly
opened assembly plants came from within the same
state (Elhance and Chapman, 1992; and Marvel and
Shkurti, 1993).
In sum, the literature suggests there have been
significant shifts in the demographic trends in the
auto corridor during the past three decades. During
the same period, the auto industry has experienced a
reconcentration of activity in the auto corridor and a
simultaneous southward expansion of this corridor.
The literature provides some evidence that the opening of new auto plants has an impact on the economic
and, perhaps, the demographic character of the proximate area. Our purpose here is to more clearly delineate the linkages between recent spatial shifts in the
location of the auto industry and demographic change
in the seven auto corridor states, using new data on
the distribution of auto industry plants.
Data and procedures
We use data on demographic change since 1990
from the FederalState Cooperative Population Estimates series, developed jointly by the U.S. Bureau
of the Census and the states. Additional data are from
the U.S. decennial censuses of population for 1970,
1980, and 1990. Births and deaths for 1980 to 1990
are from special tabulations of the FederalState
Cooperative series. The typology used to classify
counties by economic function was developed by the
Economic Research Service of the U.S. Department
of Agriculture (Cook and Mizer, 1994). The recreational specialty variable is from Beale and Johnson
(1998). We calculate net migration by subtracting
natural increase from the population change during
the appropriate period.
Counties are the unit of analysis and are appropriate for this purpose because they have historically
stable boundaries and are a basic unit for reporting
fertility, mortality, and census data. This article focuses
on the auto corridor, which is defined as the following seven states: Illinois, Indiana, Michigan, Ohio,
Kentucky, Tennessee, and Wisconsin. There are 652
counties in the auto corridor, with a total population
of 53.1 million people in 1997. This region encompasses about two-thirds of the total number of light
vehicle assembly and supplier plants in the U.S.
(see table 3).2
Metropolitan reclassification complicates our efforts
to compare the trends of various periods. We use the
latest (1993) metropolitan definition to classify counties as metropolitan or nonmetropolitan. According
to this definition, there were 455 nonmetropolitan
Economic Perspectives
Descriptive statistics
TABLE 3
Auto corridor share of auto industry, 1997
Plants
Employment
Number (%)
Number (%)
Major plants
156 (72)
353,392 (70)
Independent
suppliers
2,043 (68)
533,808 (60)
Total
2,199
887,200
Notes: Major plants are light vehicle assembly plants and
captive supplier plants. Numbers in parentheses indicate
percent of U.S. total.
Sources: ELM Guide database, 1997; various state
manufacturing directories, 1997
counties in the auto corridor and 197 metropolitan
counties in the auto corridor. Because counties are
reclassified from time to time as new metropolitan
areas are formed or territory is added to existing areas,
the demographic implications of using one definition
of metropolitan areas in preference to another are far
from trivial (Johnson, 1989). There is no simple resolution to the problem of metropolitan reclassification
nor is any one approach clearly superior to all others
(Fuguitt et al., 1988). Using the 1993 definition results
in greater nonmetropolitan losses during the 1980s
and slower nonmetropolitan gains during the early
1990s than would have been the case had we used
the earlier metropolitan definition.3
We use auto industry data from the ELM Guide
database, a set of plant-level data developed by a private company in Michigan. This database includes
information on auto assembly facilities, supplier plants
owned by assembly companies (so-called captive
suppliers), and independent supplier plants (the database focuses on suppliers that deal directly with assembly companies). The data represent the year 1997 and
identify, among other variables, for each plant the
address, a list of the plants products, the production
processes used, and employment. We obtained information on the plants start-up year from various state
manufacturing directories and the plants themselves.
The data represent over 2,200 individual plants and
approximately 900,000 employees.4 While the data are
very comprehensive for the year 1997, due to their
cross-sectional nature, they do not include information
on plant deaths during the period analyzed. In other
words, all information on plant opening years is conditional on the plant surviving through 1997, leading
to survivor bias in the data. (See box 1 for an explanation of the implications of this data problem.)
Federal Reserve Bank of Chicago
Demographics in the auto corridor
In a reversal of the trend of the 1980s, there was
widespread population growth in nonmetropolitan
areas of the auto corridor during the 1990s. More than
87 percent of the 455 counties in the auto corridor
classified as nonmetropolitan in 1993 gained population between 1990 and 1997 (table 4). In all, 192 more
nonmetropolitan counties gained population than in
the 1980s. The estimated nonmetropolitan population
gain in the auto corridor between April 1990 and July
1997 was 693,000. In contrast, nonmetropolitan areas
lost nearly 20,000 in population during the 1980s.
Although the nonmetropolitan population gain of 1.3
million in the auto corridor during the 1970s was
greater than the gain of the 1990s, this recent gain is
substantial compared with any other in recent decades.
The nonmetropolitan population gains are even
more surprising given that, historically, the metropolitan areas of the auto corridor have been the major
growth centers of the region. Yet, in two of the last
three decades, nonmetropolitan growth rates have
actually exceeded those in the regions metropolitan
areas. The nonmetropolitan population grew at a faster
pace (5.7 percent) than the metropolitan population
BOX 1
Survivor bias
The data represents information from 1997 and
includes the opening year for individual plants.
However, it does not represent time-series information as it only includes plants that were operational in 1997. In other words, the data include
survivor bias, because all information on the history of individual plants is conditional on their
surviving until 1997. Given that constraint, what
assumptions do we make in interpreting the empirical results?
In interpreting the data on spatial distribution
of plant location, we assume that plants located
in nonmetropolitan counties do not show higher
exit rates than plants located in metropolitan
counties. If they did, the dispersion of the industry into nonmetropolitan counties during the 1980s
and 1990s, as measured by plant openings, would
be overstated. To our knowledge, there is no empirical work that could back up this assumption.
However, it seems a reasonable assumption to
make. In fact, it might be rather conservative in
light of the fact that older manufacturing plants
tend to be concentrated in urban areas, which
might lead to higher exit rates for metropolitan
county plants.
17
TABLE 4
Auto corridor population by metro status
Population change
Net migration
Number
of cases
Initial
population
Absolute
change
Percent
change
Percent
growing
1970 to 1980
Nonmetropolitan
Metropolitan
Total
455
197
652
10,799,742
36,609,737
47,409,479
1,322,914
1,201,872
2,524,786
12.2
3.3
5.3
94.3
87.8
92.3
1980 to 1990
Nonmetropolitan
Metropolitan
Total
455
197
652
12,122,650
37,811,609
49,934,259
–19,966
657,130
637,164
–0.2
1.7
1.3
1990 to 1997
Nonmetropolitan
Metropolitan
Total
455
197
652
12,104,092
38,468,739
50,572,831
693,026
1,900,129
2,593,155
5.7
4.9
5.1
Absolute
change
Natural increase
Percent
change
Percent
growing
Absolute
change
Percent
change
Percent
growing
718,423
–1,465,839
–747,416
6.7
–4.0
–1.6
81.8
63.5
76.2
604,491
2,667,886
3,272,377
5.6
7.0
6.9
93.0
100.0
95.1
44.8
70.1
52.5
–544,481
–1,979,479
–2,523,960
–4.5
–5.2
–5.1
23.3
35.0
26.8
524,515
2,639,609
3,164,124
4.3
7.0
6.3
91.6
99.5
94.0
87.0
88.3
87.4
441,416
71
441,487
3.6
0.0
0.9
78.2
72.1
76.4
251,610
1,900,058
2,151,668
2.1
4.9
4.3
78.2
98.0
84.2
Note: 1993 metropolitan status used for all periods.
Sources: 1970–90 Census and Federal–State Cooperative Population Estimates.
(4.9 percent) between 1990 and 1997. Metropolitan
growth did exceed that in nonmetropolitan areas of
the auto corridor during the 1980s. However, during
the turnaround of the 1970s, nonmetropolitan gains
(12.2 percent) exceeded metropolitan gains (3.3 percent) by a substantial margin. Geographically, population gains were widespread in nonmetropolitan
areas of the corridor. Population losses were common in the core counties of the older industrial areas
of the region.
The renewed nonmetropolitan population growth
in the 1990s, as well as the earlier growth during the
1970s, is due, in large part, to migration gains. For
example, such migration gains accounted for 64 percent of the total estimated population increase between
April 1990 and July 1997. Nonmetropolitan areas
had an estimated net inflow of 441,000 people during
the period. In contrast, metropolitan areas of the auto
corridor experienced no migration gain during the
1990s. This is a sharp contrast to the pattern during
the 1980s when both metropolitan and nonmetropolitan areas had net outmigration. The auto corridors
metropolitan areas were particularly hard hit by outmigration during the 1970s and 1980s, losing nearly
3,445,000 people between 1970 and 1990. In comparison, nonmetropolitan areas enjoyed substantial
migration gains during the population turnaround of
the 1970s. The complex pattern of migration change
over the past three decades in the auto corridor is of
particular interest here because it coincides with a
period of change in the auto industry.
The differential impact of net migration on metropolitan and nonmetropolitan areas is clearly evident
when we look at spatial patterns (see figures 1 and 2
where migration patterns are shown). We see migration
18
from both metropolitan and nonmetropolitan areas during the 1980s. Nonmetropolitan counties with net inmigration are located primarily in recreational and
high amenity areas in the northern and southern periphery of the seven-state area. Migration from metropolitan counties was also evident, particularly in many
of the traditional Auto Belt cities of southern Michigan
and northern Ohio. The few metropolitan counties
that were growing were in suburban rings around
older cities.
There are dramatic changes in the spatial patterns
of migration in the 1990s. Nonmetropolitan migration
gains are extremely widespread except in agricultural
areas near the center of the corridor. We also see a migration recovery in the regions metropolitan counties,
though migration losses continued in many cities traditionally associated with car production.
Natural increase accounted for 36 percent of the
nonmetropolitan population increase in the auto corridor between April 1990 and July 1997. In all, births
exceeded deaths by 252,000 in nonmetropolitan areas.
The annualized gain through natural increase in nonmetropolitan areas was somewhat lower between
1990 and 1997 than it had been during the 1980s. In
contrast, the annualized rate of natural increase remained constant in the auto corridors metropolitan
regions, and natural increase accounted for all of the
metropolitan population increase in the 1990s.5
Nonmetropolitan population gains were more
likely in counties near metropolitan centers. Nearly
92 percent of these adjacent counties gained population in the 1990s, and 80 percent had net inmigration
(table 5). Even among more remote nonmetropolitan
counties, recent population gains have been significantly greater than during the 1980s. Growth occurred
Economic Perspectives
in 82 percent of counties not adjacent to metropolitan
areas in the 1990s. Such nonadjacent counties had net
inmigration (3.7 percent) during the 1990s.
Nonmetropolitan counties that were destinations
for retirees or centers of recreation were the fastest
growing counties during the early 1990s. All of the
24 nonmetropolitan retirement destination counties
in the auto corridor gained population and had net
inmigration between 1990 and 1997. These areas,
common in the Upper Great Lakes and Appalachians
(Cook and Mizer, 1994), are attracting retirees while
retaining their existing population (Fuguitt and
Heaton, 1993). Population gains also occurred in 95
percent of the 41 nonmetropolitan recreational counties during the 1990s, with a large majority (93 percent) receiving net inmigration. Such counties had
been prominent growth nodes during the 1970s and
1980s and the trend persisted in the 1990s. There is
FIGURE 1
1980s net migration for metro and nonmetro counties
Metro positive
Metro negative
Nonmetro positive
Nonmetro negative
Interstate highway
Note: Interstate highways are shown only for the seven auto corridor states.
Sources: 1970–90 Census and Federal–State Cooperative Population Estimates.
Federal Reserve Bank of Chicago
19
significant overlap between the recreational and retirement destination counties, because the amenities and
scenic advantages that attract vacationers and seasonal
residents also appeal to retirees.6
Nonmetropolitan population gains were also
widespread in manufacturing and commuting counties
in the auto corridor, though the gains were smaller
than those in recreational and retirement counties.
Growth in such counties was more evenly balanced
between natural increase and net migration. The
proximity of the lower Great Lakes manufacturing
belt and the emergence of new industrial areas in the
southern part of the region in recent years accounts
for the large number (178) of rural manufacturing
counties. The expansion of the auto industry during
the past several decades has certainly been a factor in
this. Both migration gains and natural increase were
common in manufacturing counties. A large number
of auto corridor nonmetropolitan counties have a
substantial share of their labor force commuting to
FIGURE 2
1990s net migration for metro and nonmetro counties
Metro positive
Metro negative
Nonmetro positive
Nonmetro negative
Interstate highway
Note: Interstate highways are shown only for the seven auto corridor states.
Sources: 1970–90 Census and Federal–State Cooperative Population Estimates .
20
Economic Perspectives
TABLE 5
Auto corridor population in nonmetropolitan counties, selected variables
Population change
Adjacent
Nonadjacent
Retirement
Recreational
Manufacturing
Commuting
Mining
Farming
Total nonmetropolitan
Net migration
Natural increase
Number
of cases
Percent
change
Percent
growing
Percent
change
Percent
growing
Percent
change
Percent
growing
241
214
24
41
178
117
33
28
455
6.1
5.2
14.7
9.8
6.5
7.7
0.9
5.0
5.7
92
82
100
95
93
92
52
93
87
3.6
3.7
14.0
8.5
3.9
5.7
–0.7
4.2
3.6
80
77
100
93
80
87
45
86
78
2.5
1.5
0.7
1.2
2.6
2.0
1.6
0.9
2.1
87
69
42
63
88
75
73
68
78
Notes: 1993 metropolitan definition. Percent change is aggregate change for all cases in category.
Recreational counties defined by Beale and Johnson (1998). All other types defined as in Cook and Mizer (1994).
Nonmetropolitan counties divided into those adjacent to a metropolitan county and those not adjacent.
Sources: 1970–90 Census and Federal–State Cooperative Population Estimates.
jobs in other counties, often in proximate metropolitan counties. This allows rural workers to access the
urban labor market, while retaining their rural place
of residence and lifestyle. The substantial migration
gains in such counties reflect their significant appeal.
The 33 counties dependent on mining in the auto
corridor were the least likely to gain population during
the 1990s. Only 52 percent of these counties gained
population and only 45 percent had net inmigration.
Population gains were considerably more widespread in
farming counties, but the magnitude of the gains was
relatively small. The smaller than average population
gains for mining and farming dependent counties in
the 1990s represents a continuation of the trends of
the 1980s. However, even among these counties the
population and migration trends moderated in the
1990s compared with the 1980s, when population decline and migration losses were much more prevalent.
Evolving spatial distribution in the auto corridor
Table 6 shows the distribution of auto plant
openings for plants surviving through 1997 across
time and by county type. It distinguishes independent
supplier plants from captive suppliers and light vehicle assembly plants (referred to as major plants). Because we do not know the
TABLE 6
employment history for the plants, we use
this distinction to approximate small and
Plant openings across time and county type
large plants. The average independent
Plants
Plants opened
open in
supplier plant employed 258 workers in
Prior to
1997, compared with 2,265 for an assem1970
1970s 1980s 1990s
1997
bly or captive parts plant.
Independent
Table 6 shows the industrys growth
supplier plants
during the last three decades as measured
Nonmetropolitan 265
110
215
75
665
by the growth in newly opened indepenMetropolitan
653
210
303
212
1,378
dent supplier plants. Their number more
Total
918
320
518
287
2,043
than doubled since 1970, with the largest
absolute increase occurring during the
Major plants
1980s. Since 1970, the industry has also
Nonmetropolitan
9
0
4
3
16
spread out within the auto corridor, indiMetropolitan
104
11
17
8
140
cated by the increase in the share of supTotal
113
11
21
11
156
plier plants located in nonmetropolitan
Note: Major plants are light vehicle assembly plant and captive
counties from 28 percent in 1970 to 33
supplier plants.
Sources: ELM Guide database, 1997; various state manufacturing
percent in 1997. Plant openings among
directories, 1997.
major plants largely reflect the opening
of new assembly plants.
Federal Reserve Bank of Chicago
21
As mentioned above, the location choices of auto
plants in the corridor states during the last 30 years
can be characterized by dispersion as well as southward expansion. Figure 3 shows this development
based on our database, using information on start-up
years for plants that were in business in 1997. The
counties are color-coded to indicate the decade during
which the first independent auto supplier plant
opened. Finally, figure 3 also shows interstate highways and the density of auto supplier plants in 1997.
Figure 3 shows the core of the industry to be located in southern Michigan, as well as northern Indiana,
northern Ohio, and the Chicago area.7 From there,
plants dispersed to the west and north, but mostly to the
south. Such dispersion peaked during the 1980s, when
64 counties that did not previously have auto supplier
plants gained at least one (versus 34 in the 1970s, and
14 in the 1990s). Most of the newly occupied auto
corridor counties were in Kentucky and Tennessee.
During the last three decades, the two southern states
FIGURE 3
Dispersion and plant density of auto suppliers
Supplier plant
1990s
1980s
1970s
Before 1970s
Interstate highway
Note: Interstate highways are shown only for the seven auto corridor states.
Sources: ELM Guide database, 1997; various state manufacturing directories, 1997.
22
Economic Perspectives
share of newly opened supplier plants has steadily increased from 41 percent in the 1970s, to 53 percent in
the 1980s and 57 percent in the 1990s. This trend also
holds for new assembly plants (see table 2).
The importance of highway transportation is also
evident, especially in the southern half of the auto
corridor.8 Nearly every county with an auto supplier
plant is on or near an interstate highway, and supplier
facilities cluster around transportation hubs such as
Indianapolis and Nashville.
Figure 4 adds a longitudinal perspective to the
analysis by showing the year during which the last
independent supplier plant was added to a county. It
complements figure 3 and demonstrates that the core
of the auto corridor continued to be the preferred
location choice for plant openings by auto supplier
companies in the 1990s. Correspondingly, the share
of counties last occupied within the two southern
states increased only slightly from 24 percent in the
1970s to 31 percent in the 1990s. Further, this figure
FIGURE 4
Auto corridor counties by last year of entry
Major plant
1990s
1980s
1970s
Before 1970s
Interstate highway
Note: Interstate highways are shown only for the seven auto corridor states.
Sources: ELM Guide database, 1997; various state manufacturing directories, 1997.
Federal Reserve Bank of Chicago
23
underscores the continuing importance of highway
transportation.
Model and results
The net migration evident in the auto corridor
during the 1980s and 1990s is the product of a myriad
of economic, demographic, locational, and historical
factors. To estimate the combined influence of these
factors, we need to perform a multivariate analysis.
Here, we examine the impact of these background
factors and the influence of the auto industry using
ordinary least squares regression. We estimate a separate cross-sectional model for each of the two decades. The dependent variable in each model is the
net migration during the decade (defined as population change net of natural increase) relative to the level of population at the beginning of the period. The
analysis covers the 455 nonmetropolitan counties in
the seven auto corridor states.
We group the independent variables into two major categories. The first represents economic, locational, and demographic variables recognized as important
in previous work (Johnson, 1998; and Goetz and
Rupasingha, 1999). We include measures of labor force
structure, commuting, metropolitan adjacency, and
whether the county is a retirement or recreational node
(Beale and Johnson, 1998; and Cook and Mizer, 1994).
Because there has been considerable regional variability in nonmetropolitan demographic trends recently, we
include a dummy variable to differentiate the two
southern states from the five midwestern states. Demographic change may also be influenced by the size of
the local population; therefore, we include a countys
population at the beginning of each period. Table 7
provides a detailed description of the variables included in the models.
We supplement these standard economic, locational, and demographic variables with a block of
TABLE 7
Variable key
Dependent variable
Net migration
Independent variables
Control variables
Metro adjacency
1 if nonmetropolitan county is adjacent to metropolitan county,
0 otherwise.
Recreational county
High proportion of spending and employment in recreational industries,
large concentration of second homes, high per capita spending on
hotels and motels, contextual data indicating presence of major
tourist activity.
Retirement county
Net migration gain for those over the age of 60 by 15 percent or
more between 1980 and 1990.
Percent employed in agriculture
Ratio of employment in agriculture to total employment at
beginning of decade.
Percent employed in manufacturing
Ratio of employment in manufacturing to total employment at
beginning of decade.
Percent work outside the county
Ratio of employees who had jobs outside the county of residence to total
employment at beginning of decade.
Population
Population at beginning of period.
South
1 if county is in Kentucky or Tennessee, 0 otherwise.
Unemployment rate
Annual average rate at beginning of decade.
Auto variables
Supplier base
24
Population change minus natural increase relative to population
at beginning of period.
Number of independent supplier plants at the beginning of decade.
Supplier addition
Number of independent supplier plants added during decade.
Major auto plant base
Number of assembly and captive supplier plants at beginning of decade.
Major auto plant addition
Number of assembly and captive supplier plants added during decade.
Contig. major auto plants base
Number of assembly and captive supplier plants in contiguous counties
at beginning of decade.
Addition of contiguous major plants
Number of assembly and captive supplier plants added in contiguous
counties during decade.
Economic Perspectives
variables measuring the presence of the auto industry
in a county. This characterization of the auto industry
distinguishes between the assembly and parts plants
owned by major foreign or domestic automakers
(labeled major) and independent supplier plants
(labeled supplier).9 The independent supplier plants
tend to be smaller, more numerous, and more widely
distributed throughout the nonmetropolitan areas of
the auto corridor. The company-owned plants are
considerably larger and tend to be located in metropolitan areas (see table 6). For each of these two major
plant types we measure the number of plants in operation at the beginning of the modeling period and the
number of plants added during the decade. Finally, it
is possible that the impact on migration of locating a
plant spills over into surrounding counties. We model
this so-called contiguity effect only for assembly and
captive parts plants, as these plants employ substantial
numbers of workers. We use variables measuring the
number of major plants in contiguous counties at the
beginning of the period as well as the number of new
plants added during the period.10
The explanatory power of the estimated model
for migration is similar in each period (see table 8 and
the appendix for the estimated coefficients). It accounts
for 37 percent of the variation in net migration between
1990 and 1997, compared with 43 percent between
1980 and 1990. There is also considerable consistency
in the contribution of specific variables during both
periods. Among the control variables, greater migration gains were likely in counties that were centers of
recreation and retirement, had a higher share of commuters to neighboring counties, and were located in
Kentucky or Tennessee. Other things being equal,
counties with employment concentrations in agriculture and those with a larger population tended to gain
less or lose more from migration than other counties.
For each of these variables, results for both periods
are statistically significant.
The block of six variables representing the auto
industry provides a statistically significant improvement in explanatory power during the 1990s.11 The
incremental improvement during the 1980s does not
quite reach statistical significance. The directional
impact of the individual variables is also quite consistent for the two decades. The addition of assembly
and captive supplier plants (major) in either the
county of interest or a contiguous county has a positive impact on migration. This effect is statistically
significant for the 1990s. In the immediate county it
increased net migration by 7.68 percent.
The effect on net migration spills into the
contiguous counties, albeit at a reduced
TABLE 8
level (3.29 percent in the 1990s). The
Summary of results for nonmetropolitan counties
size of this spillover effect is similar in
1990s
1980s
magnitude to the effect of being a retirement county.
Intercept
+
+
In contrast, counties containing asControl variables
sembly and captive supplier plants at the
Metropolitan adjacency
+
+
beginning of a given decade were likely to
Recreational county
+***
+**
be adversely affected with respect to miRetirement county
+***
+***
gration, though the impact was statistiPercent employed in agriculture
–**
–***
cally significant only for the 1980s. This
Percent employed in manufacturing
+*
–
Percent work outside the county
+***
+*
may reflect the cutbacks experienced in
Population
–***
**
the auto industry during the 1980s and
South
+***
+***
early 1990s. A county containing such
Unemployment rate
–***
+
plants at the beginning of the 1980s exAuto variables
perienced an additional net migration of
Supplier base
+
+
2.5 percent whereas the mean value for
Supplier addition
–
+*
nonmetropolitan counties during that deMajor auto plant base
–
–*
Major auto plant addition
+***
+*
cade was 3.9 percent. That result sugContiguous major plants base
–***
–***
gests that during the 1980s the presence
Contiguous major plants addition
+***
+
of auto plants worsened the negative miR-squared
0.37
0.43
gration experience of nonmetropolitan
counties. Once again, this effect spills
Number of observations
455
455
over into contiguous counties. For these
*Indicates significance level of 90 percent; **indicates 95 percent;
and ***indicates 99 percent.
counties we estimate that the presence
Note: See table 7 for variable definitions.
of assembly and captive supplier plants
Federal Reserve Bank of Chicago
25
2.348
(1.26)
–1.88
(–1.06)
0.854
(1.58)
0.615
(1.22)
3.321
(3.33)
2.06
(2.31)
Retirement county
10.970
(9.45)
13.590
(11.10)
Percent employed in agriculture
–0.145
(–2.07)
–0.315
(–6.48)
Percent employed in manufacturing
0.061
(1.83)
–0.040
(–1.06)
Percent work outside the county
0.107
(4.15)
0.058
(1.50)
–0.00005
(–2.76)
–0.00003
(–2.34)
2.865
(4.13)
2.310
(4.17)
to represent either large (1,000 employees
or more) or small plants. The estimates
we obtain are virtually identical to the
ones reported in table 9, which suggests
that locating a large plant in a nonmetropolitan county raises net inmigration into
that county by 7.68 percent and by 3.29
percent in the surrounding nonmetropolitan counties.13
In sum, we find that accounting for
the presence of auto plants adds to the
explanatory power of a model of countylevel net migration. Our results reproduce
earlier findings for a fairly standard set
of control variables. Furthermore, we find
that adding a large plant to a nonmetropolitan county triggers a sizeable positive
net migration response, both in the county
where the plant locates and in the surrounding counties.
–0.257
(–2.55)
0.023
(0.28)
Conclusion
TABLE 9
Results for nonmetropolitan counties
1990s
Intercept
Control variables
Metropolitan adjacency
Recreational county
Population
South
Unemployment rate
1980s
This article addresses possible linkages between the recent spatial shifts in
the auto industry and demographic change
Supplier addition
–0.002
0.347
at the county level, a question that had
(–0.006)
(1.85)
Major auto plant base
–0.971
–2.470
previously received very little attention.
(–0.70)
(–1.74)
We perform the analysis for the seven
Major auto plant addition
7.683
2.560
states that represent the core of the U.S.
(6.41)
(1.80)
auto industry. We use a standard set of
Contiguous major plants base
–0.429
–0.300
control variables measuring economic,
(–4.04)
(–3.86)
Contiguous major plants addition
3.293
0.531
locational, and demographic characteris(4.34)
(1.00)
tics together with a comprehensive set
R-squared
0.37
0.43
of data on the distribution of auto plants
Number of observations
455
455
across space and time.
Consistent with previous empirical
Notes: See table 7 for variable definitions. Numbers in parentheses
are t-stats. The error terms are White-corrected for heteroskedasticity.
work, we find that the background variables widely used in demographic research
account for a substantial proportion of
the variation in county-level migration.
at the beginning of the decade lowered net migration
However, adding variables measuring both presence
by 0.4 percent in the 1990s and 0.3 percent in the
and addition of two types of auto plants adds to the
1980s. The estimated effects of the presence and adexplanatory power of the model. As a group, the auto
dition of independent supplier plants, which generally
industry variables provide a modest incremental imare much smaller plants, tend not to be statistically
provement in explanatory power for net migration.
significant. However, for the 1980s, the decade that
Most prominent among the industry variables is the
saw the largest number of independent supplier plants
addition of a large plant (that is, 1,000 employees
start up during the time period analyzed (see table 6),
or more) to a county. This has a significant positive
adding a supplier plant increases net migration by
influence on migration. This effect is evident both
0.3 percent.12
in the county that receives the plant and in those
In order to address the effect of plant size more
contiguous to it.
directly, we reestimate the model for the 1990s, distinOur finding regarding the importance of auto inguishing auto plants by their employment level. Condustry variables has significant policy implications. It
sequently, we redefine all the auto industry variables
Auto variables
Supplier base
26
0.051
(0.41)
0.191
(1.55)
Economic Perspectives
suggests that development efforts aimed at retaining or
attracting population will have greater immediate success if they focus on attracting larger plants. Furthermore, this result underscores the importance of
cooperative efforts to obtain such plants, given that they
positively affect population in a multicounty area. Future research will look more specifically at the effect on
migration of the young adult population.14
APPENDIX
APPENDIX
Means and standard deviations (455 nonmetropolitan counties)
Mean
Standard deviation
Net migration 1990s
Net migration 1980s
5.135
3.872
6.809
6.615
Control variables
Metro adjacency
Recreational county
Retirement county
Percent employed in agriculture, 1990s
Percent employed in agriculture, 1980s
Percent employed in manufacturing, 1990s
Percent employed in manufacturing, 1980s
Percent work outside the county, 1990s
Percent work outside the county, 1980s
Population, 1990
Population, 1980
South
Unemployment rate, 1991
Unemployment rate, 1981
0.530
0.090
0.053
6.457
11.260
25.570
27.420
31.112
25.880
26,602
26,643
0.367
9.040
11.010
0.500
0.287
0.224
4.214
7.170
9.729
10.120
13.808
12.860
19,014
19,068
0.483
2.960
3.630
1.295
0.165
0.822
0.473
0.029
0.007
0.020
0.009
0.532
0.059
0.413
0.119
2.169
0.515
1.526
1.108
0.191
0.105
0.168
0.093
1.923
0.279
1.792
0.419
Auto variables
Supplier base, 1990
Supplier addition, 1990s
Supplier base, 1980
Supplier addition, 1980s
Major auto plant base, 1990
Major auto plant addition, 1990s
Major auto plant base, 1980
Major auto plant addition, 1980s
Contiguous major plants base, 1990
Contiguous major plants addition, 1990s
Contiguous major plants base, 1980
Contiguous major plants addition, 1980s
Note: See table 7 for variable definitions.
NOTES
The number of plant openings by Japanese auto parts suppliers
in the U.S. peaked in the late 1980s (Klier, 1994).
1
Except for Illinois, all the states in the auto corridor have a
higher than average motor vehicle and equipment (Standard Industrial Classification 371) share of gross state product (GSP). The
data are averaged over 1995, 1996, and 1997. The specific industry shares of GSP are: Illinois, 0.79 percent; Indiana, 4.91 percent;
2
Federal Reserve Bank of Chicago
Kentucky, 5.27 percent; Michigan, 8.52 percent; Ohio, 3.66 percent;
Tennessee, 2.74 percent; and Wisconsin, 1.46 percent. The U.S.
average for that period is 1.09 percent (data from U.S. Bureau
of Economic Analysis).
Between 1970 and 1993, 43 formerly nonmetropolitan counties
were redefined as metropolitan, and 13 formerly metropolitan
counties were reclassified as nonmetropolitan.
3
27
4
In all, 8.4 percent of the original database entries could not be
confirmed by review of state directories nor could they be
reached by phone. However, we are confident that the coverage
afforded by the database is high. The employment estimates for
Michigan assembly and supplier plants are only slightly below
those reported by McAliden and Smith (1999) using ES 202 data.
facilities is delivered by truck, whereas the final product is distributed across the country by a combination of truck and rail.
5
The demographic trends in the auto corridor during the past 30
years have been generally consistent with those in the nation. The
only exception to this general consistency between population
growth patterns in the auto corridor and the nation is that metropolitan areas of the auto corridor lost a significant amount of
population during the 1970s and 1980s, whereas metropolitan
areas in the U.S. as a whole generally gained population.
10
6
Fourteen counties of the nonmetropolitan counties in the auto
corridor are both recreational and retirement counties.
We chose this categorization, which indirectly distinguishes
plant size, because plant-level employment data are available
only for 1997.
9
In defining this variable, we take account of contiguous plants
in metropolitan and nonmetropolitan counties.
11
An F-test shows it to be significant at the 95 percent level.
The difference in the estimated effect of adding a major plant
and adding a supplier plant for the 1980s is approximately commensurate to the factor by which an average major plant is larger,
in terms of employment, than an average supplier plant.
12
That corresponds to an estimated net inmigration of 12.7 percent for the immediate county. The actual net migration rates for
the two nonmetropolitan counties in which large plants opened
during the 1990s, two Saturn facilities in Spring Hill, Tennessee,
and one large independent supplier plant in central Michigan, are
22.1 percent and 6.1 percent, respectively.
13
It slightly overstates the concentration of independent supplier
plants in Michigan by showing all plants, regardless of their age.
If we were to present information on plants opened since 1980
only, Michigans share would fall from 39 percent to 36 percent.
7
8
Within a just-in-time production environment, inventories at
assembly and supplier plants are being minimized, which puts a
premium on being able to deliver parts on time to the assembly
line. Consequently, the majority of parts shipments to assembly
Data will not become available until after the release of the
2000 Census.
14
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Fournier, Stephen F., and Andrew M. Isserman,
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Fuguitt, G. V., and T. B. Heaton, 1993, The impact
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Johnson, Kenneth M., 1998, Renewed population
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, 1989, Recent population redistribution
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Kenney, Martin, and Richard Florida, 1992, The
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Klier, Thomas H., 2000, Does just-in-time mean
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, 1994, The impact of lean manufacturing
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McAlinden, Sean P., and Brett C. Smith, 1999,
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Study of Automotive Transportation, February.
Johnson, K. M., and C. L. Beale, 1994, The recent
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Murray, N. Matthew, Paula Dowell, and David T.
Mayes, 1999, The location decision of automotive
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Urban Economics, Vol. 36, No. 1, pp. 2341.
29
('
Ti
The 37th Annual Conference on Bank Structure and Competition
MAY 9-11 * 2001
The
The Federal Reserve Bank of Chicago
invites the submission of research and policyoriented papers for the 37th annual Conference
on Bank Structure and Competition to be held
May 9-11, 2001, at the Fairmont Hotel in
Chicago. Since its inception, the conference
has aimed to encourage an ongoing dialogue
on current public policy issues affecting the
financial services industry. Although we are
interested in papers related to the conference
theme, we are most interested in high quality
research addressing public policy and the
financial services industry.
The theme of the 2001 conference will be the purpose, role, and
implications of the current structure of the financial industry safety net.
The most obvious component of the safety net, and the one that receives
most public attention, is deposit insurance. The reach of the safety
net, however, is more extensive than the stated coverage extended
by the deposit insurance funds. It includes both implicit and explicit
guarantees, the means by which these guarantees are delivered, and
the resulting behavioral changes induced by their presence.
Public discussion of these safety net issues has grown louder in recent
years. There has been significant criticism of the current structure of
the federal deposit insurance system and numerous recommendations
for its reform. Recently the FDIC initiated what was intended to be a
comprehensive review of the deposit insurance system in three major
areas: pricing, fund maintenance, and extent of coverage. Input from
banks, consumer groups, and trade associations has been requested and
will be used in determining what adjustments, if any, should be made.
There has recently been discussion in the U.S. about reforming the
lender of last resort function: the Federal Reserve's discount window.
Numerous concerns have also been expressed about the appropriate
role of government-sponsored enterprises (GSEs). For example, Fannie
Mae and Freddie Mac have established lines of credit with the U.S.
Treasury, and it is widely perceived that their debt is guaranteed by the
U.S. government. This gives them a funding advantage in debt markets
and in their capital requirements compared with other purely private
mortgage market participants. The competitive advantages and
potential taxpayer liability implied by this arrangement have brought
these institutions under close scrutiny by both Congress and the industry.
Similar concerns have been raised about the Federal Home Loan Banks
(FHLB) as more and more banks join the FHLB System. Another area
of concern is the liability guarantees available to nondepository institu
tions (for example, insurance companies have access to explicit state
guarantee programs). These programs offer a different laboratory to
examine the consequences of liability guarantee systems. Perhaps the
The 2001 conference will focus on these and related questions. There
will also be a number of additional sessions on industry structure and
regulation concerning topics such as:
/
X
■ Financial modernization or the implications of the
Gramm-Leach-Bliley Act;
■ Bank capital standards;
■ Fair lending issues and predatory pricing issues;
■ Measuring and managing risk, particularly for
transnational /global financial services companies;
■ Alternative approaches to dealing with financial crises;
■ Reforming the international financial institutions; and
J
■ The implications of technology on bank delivery systems
(for example, Internet banking) and payment innovations.
Financial Safety Net:
Its Role, Benefits, and Costs
most widely discussed feature of the safety net is the expectation that
large banks are "too-big-to-fail," a perception that endures despite
legislative and regulatory efforts to change it. Such implicit guarantees
may encourage risk-taking by large banks, and may increase the relative
funding cost of small banks.
These safety net issues have raised a number of public policy questions.
Has the financial safety net expanded in recent years? Does the GrammLeach-Bliley Act, by allowing for broader product expansion of financial
holding companies, open the door for "safety net leakages" to a host
of nonbanks? If so, what are the implications of this expansion and
how can it best be contained? After years of criticism, are regulators
now ready to introduce true reform through an incentive-compatible
deposit insurance scheme? What are the implications of such reform for
small banks, which apparently have been having problems attracting
the deposits that are fundamental to their success? For large banks?
Has the modern-day role of deposit insurance changed? Concerning
the discount window, is there a subsidy associated with the current
means by which the Federal Reserve operates its credit facility? Would
movement toward use of an above-market or "penalty" rate be bene
ficial? Do these directed liquidity injections by the central bank have
advantages over general liquidity injections which can then be allocated
in private markets?
Concerning implicit guarantees, how can GSE oversight best be handled?
Is bank-like oversight appropriate? What is the extent of the subsidy
associated with the current government guarantees? Should the activities
of GSEs be constrained? And finally, do the markets believe a too-bigto-fail policy is still in effect? What are the resulting market distortions?
Continuing the format of recent years, the final session of the conference
will feature a panel of industry experts who will discuss the purpose,
structure, problems, and proposed changes associated with an impor
tant and topical banking regulation. A record of the panel presentations
will be included in the Proceedings of the conference. Past topics
discussed at this session include bank antitrust analysis, bank capital
regulation, optimal regulatory structures, and the appropriate role of
the lender-of-last-resort. Proposals for this session are also welcome.
If you would like to present a paper at the
conference, please submit four copies of the completed paper or a
detailed abstract (the more complete the paper the better) with your
name, address, affiliation, telephone number, and e-mail address, and
those of any co-authors, by December 18, 2000. Correspondence
should be addressed to:
Conference on Bank Structure and Competition
Research Department
Federal Reserve Bank of Chicago
230 South LaSalle Street
Chicago, Illinois 60604-1413
For additional information contact:
Portia Jackson
Douglas Evanoff
(312) 322-5814
devanoff@frbchi. org
or
(312) 322-5775
portia.jackson@chi. frb. org
Why do consumers pay bills electronically?
An empirical analysis
Brian Mantel
Introduction and summary
Although the checkless society has been predicted
for decades, checks remain the most frequently used
noncash payment method in the U.S., contrary to trends
in a number of other countries. Despite the debate
over why consumers do or do not adopt new payments
technology, little is known about the subject. Given
unsuccessful efforts to induce a shift away from checks,
some industry observers have even suggested that
consumers are irrationally wedded to their checks.
As a result, the financial services industry faces significant uncertainty regarding potential investments
in electronic bill payment technologies as well as in
debit cards, smart cards, stored value, e-cash, check
imaging, and check conversion technologies. The goal
of this article is to provide some insight into the consumers decision to use electronic payments technologyWhat factors influence this decision and what
might financial industry leaders do to encourage
greater numbers of consumers to make the transition
to electronic payments?
The study of payment methods is of interest for
several reasons. First, technology is enabling new
payment methods to be introduced more easily and
frequently. As a result, the very characteristics of what
constitutes a payment instrument are changing over
time. Second, recent research highlights the importance
of payment-related revenues to financial institutions.1
Consequently, payment providers will continue to
look for ways to increase the value of payment products to customers, thereby enhancing potential revenue
streams. Likewise, companies will continue to look
for ways to reduce the costs of payments (for example,
by reducing the fees they pay to payment providers).
For instance, checks are being converted from paper
into electronic items and cleared via the automated
clearinghouse (ACH) at the point of sale.2 Firms are
32
also considering new ways to leverage current electronic payment networks to make payments electronically,
for instance, experimenting with the ACH network to
make debit transactions at the point of sale3 or using
automated teller machine (ATM) networks to make
debit transactions for Internet payments.4
Ultimately, some combination of consumers,
corporations, and financial service providers will determine the success of various payment instruments.
These innovations will put increasing pressure on the
structure of the rights, warranties, and incentives associated with different payment instruments. Therefore,
in order to make better forecasts for business planning
and enhance public policy decision-making, we need
to better understand the factors influencing consumer
choice among alternative payment options.
This article analyzes the extent to which various
factors influence consumers willingness to use electronic bill payment. I review the economic, marketing,
consumer decision-making, and payments literatures.
Then, I analyze a unique 1,300-person survey to evaluate the factors associated with usage of electronic
bill payment. I find that several broad factors influence
the consumers preference for electronic bill payment:
Brian Mantel is the Emerging Payment Studies Department
program manager at the Federal Reserve Bank of
Chicago. The author would like to thank Dan Aaronson,
David Allardice, Eric Berggren, Ed Green, Rick Kolsky,
David Marshall, Kathy Paese, Ann Spiotto, Joanna
Stavins, and Dan Sullivan for their comments and
suggestions. The article benefited from comments from
participants at the Financial Services Technology
Consortiums Annual Spring Meeting, the University
of Michigans Electronic Payments Symposium, and a
Federal Reserve Bank of Chicago research seminar.
The author would also like to acknowledge the excellent
research assistance of Patricia Rozaklis, Sonalee Shah,
and Alpa Shah.
Economic Perspectives
1) wealth; 2) personal preferences for control, record
keeping, convenience, incentives, personal involvement, and/or privacy; and 3) transaction-specific factors
associated with different types of payments. I also
find that certain demographic factors are significantly
associated with the use of electronic bill payment
services. My findings are consistent with new product
adoption theories, supporting the idea that some consumer segments are natural first adopters of electronic bill payment services. However, while new
product diffusion theories assume that consumers
will begin to experiment and adopt innovations as
they learn more about the products features, my
analysis suggests that fundamental consumer needs
still must be addressed before a broader portion of
consumers will adopt electronic bill payment services.
As a result, I find that an important portion of
consumers do not perceive checks and some electronic
bill payment services as substitutes at this time. Some
analysts suggest that many consumers are likely to
remain reluctant to adopt new payment technologies.5
However, my analysis suggests that a larger fraction
of consumers would adopt these new technologies if
important product features such as error resolution,
service level guarantees, customer service, the ability
to make partial payments, and more convenient signup were bundled with electronic bill payment services.
My results suggest that the next stage of migration
towards electronic bill payment may depend more on
firms willingness to fund the development of these
new product features than on overcoming consumer
resistance to change. This article also highlights the
need for policymakers to better understand the diversity of consumer preferences when considering public policy questions relating to consumer protection.
Overview of the payments marketplace
The payments mechanism, like the electricity
power grid, is an important piece of the foundation
that supports our economy. Todays payment instruments have evolved from barter to commodity-based,
to currency and coin, to card-based and, more recently,
to electronic network-based systems. The introduction
of commodity money reduced the costs and risks associated with trade. Coins and paper currency brought
greater standardization, broader acceptance, and lower
transaction costs than previous commodity-money or
barter-based economies. Card-based systems have
extended the reach of ones wealth and creditworthiness, lowered costs, and improved access to customer information. Recent advances in technology now
make further improvements possible when consumers
value them and when providers have a clear business
case to offer the improvement.
According to McKinsey and Company research
(Stevenson, 1997), consumers initiate approximately
90 percent of all transactions. Table 1 provides an overview of the mix of different payment instruments across
the U.S. economy. See MacKie-Mason and White
(1996) for a detailed comparison of the different attributes associated with different payment instruments.
Two theories of how new products are adopted
There are two general, complementary theories
of how new products are adopted. The first theory,
the new product diffusion model, assumes that the
primary determinant of new product adoption is the
time it takes consumers to learn about a product, to
experiment with it, and then ultimately to use it.6
This theory assumes that consumers view a new
product or service as a clear and valuable substitute
TABLE 1
Estimates of historical U.S. payment volumes
(Items in billions)
Payment
1995
1996
1997
1998
1999
CAGR
Currency
Postal money order
Check
Credit card
Electronic funds transfer
ATM
Debit at point of sale
ACH
500
0.2
63.0
14.9
10.5
9.7
0.7
3.4
—
0.2
64.7
16.1
11.8
10.7
1.1
3.9
—
0.2
66.0
16.9
12.6
11.0
1.6
4.5
—
0.2
67.5
17.5
13.2
11.2
2.0
5.3
—
.2
68.8
NR
13.3
10.9
2.4
6.2
—
1.5%
2.3%
11.9%
6.3%
3.1%
14.8%
16.0%
Notes: Volumes include payments initiated by business and government, in addition to those by consumers.
NR indicates not reported. Columns may not total due to rounding.
Sources: Hancock and Humphrey (1997); Federal Reserve Bank of St. Louis, Annual Report, Green Sheet,
various years; Faulkner & Gray; National Automated Clearinghouse Association; and Bank for International Settlements.
Federal Reserve Bank of Chicago
33
for past products or services and that risks associated
with trial can be managed by some combination of
consumers, distributors, and producers. According to
the new product diffusion model, if consumers perceive the new product to be a substitute for a product
they currently use and understand, providers can
more easily leverage existing distribution and communications channels to generate awareness and demand for the innovation.
The second theory, the new market development
model, suggests that a new product by itself will
have a limited market potential. In order to reach
mass consumer markets, firms need to offer additional product features, services, and/or infrastructure
over time, tailoring the product to new customer segments and/or to new uses, as well as making products interoperable.7 Under this theory, new products
are introduced and evolve, new features are added,
and over time the product reaches a mass and mature
stage of acceptance.
The first model suggests a heavy focus on building awareness and trial while the second theory suggests staged introduction of new product features to
new and different customer segments. Thus, it is critical to assess whether consumers perceive new payment innovations to be substitutes for past products
or whether new innovations are viewed as fundamentally new products, requiring significantly more resources to promote adoption.
Literature review
Staten (1999) investigate consumer preferences
among debit cards, credit cards, and cash for gasoline purchases. Higher levels of education and income and having more than one credit card are
associated with greater use of credit cards than cash.
However, convenience, rather than borrowing capacity, was the greatest determinant of a credit card user.
Lastly, Carow and Staten find that a consumers
ownership of credit cards and use of credit cards are
related, because having a certain type of account reveals payment preferences. The American Bankers
Association and Dove Associates (1999) analyze a
survey of 1,400 consumers to investigate the factors
motivating consumer payment instrument choice between online and offline debit. They find that consumers exhibit strong and distinct payment preferences,
with different segments of consumers valuing different
debit attributes.
Wells (1996) finds that check float does not explain the persistence of consumer check use; alternative explanations include the consumer perception of
checks and ACH as dissimilar payment instruments,
market failure, and measurement errors. Using 1997
data to investigate consumer responsiveness to
changes in checking account costs, Stavins (1999)
finds that the supply of bank deposits to checking accounts is sensitive to banks per item fees and check
return, teller, and foreign ATM restrictions. Using the
Federal Reserve Boards Terms of Credit Card Plans
Survey to investigate consumers willingness to pay
for credit card service, Stavins (1996) finds that consumers respond to product offerings that bundle other services. Research suggests that despite the fact
that banks could earn higher revenues by lowering
Consumer payments decision-making
In an extensive survey of the payments literature, Hancock and Humphrey (1997) provide an
overview of the factors associated with
electronic banking adoption, including inTABLE 2
centives, the nature of a countrys financial
Factors in use of payment technology
infrastructure, and the role of network economics in electronic banking adoption.
Financial
Income
assets
Age
Using a longitudinal Norwegian survey
(198995), Humphrey, Kim, and Vale
In person
–
+
0
(1998) conclude that efficient payment inMail
+
+
–
Telephone
+
+
0
strument pricing would induce greater
Electronic transfer
0
+
–
electronic payment use because of its lowATM
0
+
–
er cost relative to paper-based payments.
Debit card
0
+
–
Using the Federal Reserves 1995 SurAutomatic deposit/withdrawal
–
+
+
vey of Consumer Finances, Kennickell and
Direct deposit
–
+
+
Pre-authorized debit
0
+
–
Kwast (1997) analyze the influence of deComputer
0
+
–
mographic characteristics on the likelihood
Smart card
0
0
0
of electronic payment instrument usage. As
Note: Statistically significant positive/negative factor (+/–);
shown in table 2, higher levels of education
not statistically significant factor (0).
and financial assets increase the likelihood
Source: Kennickell and Kwast (1997).
of electronic payment usage. Carow and
34
Education
0
+
+
+
+
+
+
+
+
+
+
Economic Perspectives
the price of services, they would not necessarily maximize the profit from each account.
MacKie-Mason and White (1996) provide a detailed review of the characteristics that are important
to consider when designing new payment innovations.
Mantel (2000) surveys the literature on consumer
payment decision-making and proposes a framework
in which three factors explain consumer electronic
banking usage: 1) wealth; 2) personal preferences,
such as incentives, convenience, control, budgeting,
privacy, security, and personal involvement; and 3)
transaction-specific factors. Including this broad list
of factors helps explain sometimes hard-to-explain or
inconsistent behaviors. For instance, Mantels (2000)
framework helps explain why consumers are increasingly choosing to use debit cards, based on the changes
in the attributes financial institutions have begun
bundling with debit cards, although credit cards are
well known for providing convenience and short-term,
interest-free loans. Similarly, this framework helps
explain why consumers in different countries have
adopted smart cards at significantly different rates,
again based on the nature and/or importance of the
attributes bundled with these payment products.
Consumer awareness
New product diffusion theories point to the important role of consumer awareness in promoting
adoption. There is relatively little public data on consumer awareness and perceptions of electronic bill
payment. A 1998 Federal Reserve Bank of St. Louis
study finds that 99 percent of consumers say they
understand direct deposit and 97 percent of current
users report satisfaction with the system. However,
only 55 percent of consumers feel they understand
electronic bill payment and ACH well, while 84 percent of electronic bill payment users report satisfaction
with this type of payment instrument. A study for
the New York Clearing House (1997) conducted by
Wirthlin Worldwide measures direct deposit usage
before and after a marketing campaign was employed
from September 1996 to February 1997. Roughly
half of all nonusers surveyed remembered the principal messages of the campaign, including the ideas
that direct deposit is convenient (18 percent), easy to
use (17 percent), and available (16 percent). However,
the study does not find evidence that communication
efforts increase usage.
While the fact that a significant fraction of consumers may not fully understand electronic bill payment services might indicate a problem to some, the
new market development model might suggest that
this is not a problem per se. After all, if the evidence
continues to suggest that an important fraction of
Federal Reserve Bank of Chicago
consumers do not yet perceive electronic bill payment
and checks as clear substitutes, it may be that the
electronic bill payment market is still developing.
In this case, a significant fraction of consumers will
likely continue to report a lack of familiarity, even as
significant improvements are made over time, until
the products functionality is fully developed. When
future studies find evidence that a larger portion of
consumers see electronic bill payment and checks as
clear substitutes, then firms then may be better able
to target their communications campaigns to consumers unique needs. Clearly, communications efforts in
the early stages of a product life cycle will continue
to be important; nonetheless, they will likely serve
different purposes than communications efforts used
for more mature products.
Analysis
Description of data and variables
I use a dataset collected by Vantis International
on behalf of the Federal Reserve Bank of St. Louis
and the Federal Reserve Bank of Atlanta that consists
of responses to a national 1,300-person survey on
consumer decision-making among competing bill
payment instruments. The households primary bill
payer served as the survey respondent. The survey
collected data pertaining to consumer demographic
characteristics, payment behaviors, and self-reported
payment preferences and evaluations of different
payment options. The survey focused primarily on
consumers experiences with checks and electronic
bill payment, but also considered other payment instruments such as debit cards, credit cards, and money orders. The appendix provides descriptions and
summary statistics of the variables included in the
analysis.8 I expect these factors to influence the likelihood of electronic bill payment use generally and/
or the likelihood of high usage. For ease of interpretation, I group them into the following broad categories: demographic, new product adoption, control
and budgeting, convenience, incentives, privacy and
security, and personal involvement.
Model
In this article, I analyze why consumers choose
among alternative payment instruments for bill payment. This analysis uses a series of binomial logistic
regressions, a statistical technique that allows one to
examine the extent to which various factors influence
the likelihood of direct bill payment usage. First, I explore the factors that affect the initial choice of whether
to use electronic bill payment. Second, I investigate
the factors that influence the extent or frequency of
electronic bill payment use among users of these
35
types of services. For this part of the analysis, I classify
consumers as low users if they pay fewer than 20
percent of their bills electronically and high users
if they pay more than 30 percent of bills with direct
bill payment technology.9 Third, I examine the factors
that influence the use of electronic bill payment for
specific types of billsmortgage, loan, lease, telephone, cable, credit card, and insurance.
The regression model analyzes consumer Cis
choice of payment instrument for bill payment Pi (for
example, mortgage payment, credit card payment,
or telephone bill).10 The consumer has two payment
options1) paper-based payment instruments such
as cash, checks, or money orders and 2) electronicbased payment instruments such as electronic bill
payment and ACH.11 I impose several simplifying
assumptions on the analysis. First, I assume that all
payments initiated electronically by the consumer are
paid electronically by the payment intermediary. This
assumption allows the analysis to focus only on consumers willingness to choose electronic payments.
Next, I assume that consumers have access to all
payment options. Therefore, findings are applicable
only to the 90 percent of consumers who have checking accounts. This article does not address the important issue of identifying the needs and preferences of
unbanked consumers.
Limitations of the analysis
First, my analysis focuses solely on consumer
decision-making pertaining to electronic bill payment
and does not address the perspective of the payment
provider or the entity receiving the payment. I am
primarily interested here in whether consumer preferences are a significant limiting factor to the migration
towards electronic payments. Second, because this
model evaluates the attributes of desirable payment
instruments using self-reported data, my results depend
on the accuracy with which consumers recall and report actual behavior. Third, the survey focuses on the
primary bill payer rather than households in general.
As a result, one must exercise care in extending these
results to the general population. Finally, this research
does not consider how consumer behavior has changed
over time nor does it provide insight into how and
when a specific factor (such as price, product attributes,
or promotions) induces changes in electronic bill
payment use.
Empirical results
Table 3 provides the results of binomial logistic
regressions comparing nonusers of electronic bill
payment with users and low users with high users.
36
Table 4 provides the results of binomial logistic regressions comparing nonusers and users by type of
bill (that is, cable, credit card, insurance, loan, mortgage, telephone, and utility). Overall, four general
findings emerge. First, there are important and significant differences between nonusers, low users,
and high users of electronic bill payment. Second,
consumer demographic and financial characteristics
are important in influencing whether consumers use
electronic bill payment. However, these factors do
not strongly distinguish high users from low users,
lending support for the idea that there are certain natural first adopters of electronic bill payment technology.
Third, several consumer preferences, including the
desire for control, convenience, incentives, privacy,
and personal involvement, are also significant in
whether consumers use electronic payments. Furthermore, these factors distinguish low users from high
users. At a more detailed level, this analysis suggests
that incentives may be a valuable tool to induce introductory usage of electronic bill payment, although
they may not be needed to increase usage. Similarly,
services that promote greater consumer recourse and
control may be important in inducing some consumers to adopt electronic bill payment and others to
expand the number of bills they pay electronically.
Finally, payment-specific factors, such as the dollar
size of a payment and whether a payment amount
varies, are also important in explaining why certain
consumers choose electronic bill payment or paper
checks to pay for certain bills.
Comparison of users and nonusers
Referring to table 3, I find that several demographic factors influence the initial decision of whether
to use direct payment methods. Holding other factors
constant, older individuals are more likely to use
direct electronic payments than younger individuals.
For instance, a 40-year-old is 2.3 percent more likely
to use electronic bill payment than a 39-year-old.
Women are 49.2 percent more likely to use electronic
bill payment. Neither college education nor market
size variables are statistically significant factors in
influencing the use of direct electronic payment. The
absence of a statistically significant education variable
differs from Kennickell and Kwasts (1997) findings,
but is attributable to the inclusion of other nondemographic factors, such as personal preferences, income,
and lifestage (for example, single, married with children, or retired), that are related to education level.12
New product adoption factors do play a role in
influencing the likelihood of electronic bill payment
usage across the proposed factors. Consumers who
Economic Perspectives
TABLE 3
Binomial regression results, odds ratio
Nonusers
vs. users
Low vs.
high users
1.023***
(0.007)
1.031***
(0.008)
Female
1.492**
(0.196)
0.623**
(0.235)
Race
1.013
(0.259)
College
Demographics
Age
Market size:
under 100,000a
Market size:
100,000–499,999
Nonusers
vs. users
Low vs.
high users
0.898*
(0.061)
0.873**
(0.067)
Option to stop payment
1.093**
(0.041)
1.001
(0.050)
1.570
(0.331)
Receipt for payment
0.891***
(0.036)
0.989
(0.041)
0.910
(0.193)
1.031
(0.219)
Balance checkbook
once/month
1.050
(0.033)
0.929*
(0.042)
1.441
(0.233)
0.741
(0.278)
Disciplined about finances
0.988
(0.034)
1.047
(0.043)
1.241
(0.231)
0.914
(0.271)
0.951*
(0.027)
1.031
(0.032)
0.989
(0.046)
0.879**
(0.056)
1.065*
(0.037)
1.099*
(0.051)
1.061
(0.047)
0.980
(0.066)
1.007
(0.031)
1.106***
(0.038)
1.025
(0.046)
1.065
(0.064)
1.071**
(0.033)
1.042
(0.040)
0.977
(0.055)
1.098
(0.074)
1.882***
(0.241)
1.060
(0.379)
0.984
(0.027)
1.039
(0.224)
0.806***
(0.031)
0.914**
(0.013)
0.965
(0.037)
0.900**
(0.044)
1.082**
(0.031)
1.069*
(0.036)
Control
Control when bill is paid
Use toll-free number to
check balances
Person available
Market size:
500,000–1,999,999
1.121
(0.251)
0.864
(0.303)
1.768***
(0.135)
0.767
(0.199)
1.122***
(0.033)
1.029
(0.036)
PC owner
2.106***
(0.195)
1.085
(0.217)
Cellular phone owner
1.159
(0.204)
0.762
(0.234)
Internet purchase
0.902
(0.285)
0.997
(0.340)
New product adoption
Understand direct payment
Understand set-up of
direct payment
Consumer financial
Household income:
$20,000–$39,999b
Convenience
Bill paid when out of town
Saving time
Banks not open
convenient hours
Incentives
Least expensive
payment method
Use shopping coupons
Avoid penalties for
late payments
2.144***
(0.239)
1.257
(0.312)
1.836**
(0.257)
1.088
(0.324)
2.038**
(0.352)
2.382**
(0.432)
Homeowner
1.439*
(0.208)
1.268
(0.276)
Savings account
1.264
(0.214)
1.492
(0.266)
Credit card
1.485**
(0.179)
0.671*
(0.222)
1.154
(0.186)
0.878
(0.216)
Personal involvement
Enjoy talking with
bank teller
Credit union account
1.630***
(0.183)
0.708
(0.215)
Number in sample
Brokerage account
0.984
(0.269)
0.755
(0.220)
0.794
(0.302)
0.913
(0.262)
Household income:
$40,000–$74,999
Household income:
over $75,000
Regional/national
bank accountc
Savings and loan account
5% discount
Privacy/security
Not comfortable giving
account number
to salesperson
Dislike automatic
withdrawal
Credit card on Internet
is secure
Likelihood ratio
956
387.6***
556
120.4***
a
Market size reference variable baseline characteristic: under 100,000.
Income reference variable baseline characteristic: $20,000 and under.
Financial institution reference variable baseline characteristics: local bank.
*Indicates 0.10 statistical significance level; ** indicates 0.05 significance level; and *** indicates 0.01 significance level.
Notes: Standard errors are in parentheses. Of the 956 cases, 400 are nonusers. Of the 556 users of electronic payments,
236 are low users and 320 are high users. See appendix for description of variables.
b
c
Federal Reserve Bank of Chicago
37
TABLE 4
Statistically significant results by bill type
Cable
Demographic factors
Age
Credit
card
Insurancec
+**
+***
Race
Mortgagee
Telephone
+***
+**
Market size
–*
–*
+**
+*
–**
Understand set-up of direct payment
+*
+***
+**
PC owner
+*
+**
+**
+***
Consumer financial
Household income: $20,000–$39,999a
+*
Household income: $40,000–$74,999
+**
Household income: over $75,000
+**
+*
+**
Savings account
+**
+**
+***
Regional/national bank accountb
Credit union account
+***
+***
Purchase on Internet
Homeowner
Utilitiesf
+*
+***
Lifestage
New product adoption
Understand direct payment
Loand
–**
+*
–*
Control
Control when bill is paid
–**
–***
–**
Disciplined about finances
–*
+***
Use toll-free number to check balances
–*
Option to stop payment
+*
Receipt for payment
–***
Balance checkbook once/month
+*
Person available
–**
Convenience
Bill paid when out of town
+*
–***
+*
+**
+***
Saving time
Banks not open convenient hours
+*
+**
+**
Incentives
Least expensive payment method
Use shopping coupons
5% discount
–**
Privacy/security
Not comfortable giving account
number to salesperson
–***
Dislike automatic withdrawal
–**
+*
–***
–***
–***
–***
+*
+**
+**
–***
Credit card on Internet is secure
Personal involvement
Enjoy talking to bank teller
+**
–**
–**
+*
+***
+*
a
Income reference variable baseline characteristic: $20,000 and under.
Account reference baseline characteristic: local/community bank.
Market size is under 100,000 people.
d
Lifestage is young couple, retired couple, or middle parent.
e
Lifestage is middle-aged single.
f
Lifestage is middle-aged single, older single, or retired couple. Market size is 100,000–499,999.
*Indicates 0.01 significance level; ** indicates 0.05 significance level; and *** indicates 0.10 significance level.
Notes: “+” Indicates variable increased odds of electronic payment use; “–” indicates variable decreased odds.
See appendix for description of variables. Lifestage denotes period in life. For example, young single, middle-aged married
with children, or elderly with a roommate are lifestage categories.
b
c
38
Economic Perspectives
own personal computers are twice as likely to use
electronic bill payment. Consumers who report understanding direct payment technologies are 79 percent
more likely to use electronic bill payment. Likewise,
understanding how to set up direct payment is
associated with a 12 percent greater likelihood of using
electronic bill payment. However, these findings regarding consumer awareness are contrary to the results
of the New York Clearing House (1997) study that
finds no correlation between increased direct electronic payment use and consumers understanding of
key communication messages. Furthermore, I find
that cellular phone ownership and a self-reported
perception that the Internet is secure for purchases
are not associated with greater usage of electronic
bill payment. Clearly, we need to know more about
how and why consumers choose to adopt new electronic payment technologies.
Consistent with Kennickell and Kwast (1997)
and others, I find a significant relationship between
consumer financial characteristicsincome and home
ownership as imperfect proxies for wealthand increased electronic bill payment usage. Relative to
households with incomes under $20,000, consumers
at all other income levels are approximately twice as
likely to use direct payment technologies. I also find
that homeownership is positively related to the use
of direct electronic payment methods, with homeowners being 44 percent more likely to use electronic
bill payment. Consumers with credit cards are 49
percent more likely to be electronic bill payment users.
Members of credit unions are 63 percent more likely
to use direct electronic bill payment than other consumers. It may be worth exploring why this occurs.
For instance, are there synergies between credit unions
and their members workplaces that promote better
communication, make sign-up easier, or make customer service and error resolution more effective or
credible? Are users of credit unions fundamentally
different from other consumers?
I find that preferences for greater control over
payments and household finances have a significant
influence on the likelihood of direct electronic payment usage. As the importance placed on having
control over when a bill is paid increases, the likelihood of electronic bill payment use decreases by 11
percent. Increased importance of receiving a receipt
of payment is associated with an 11 percent lower
likelihood of being a user of electronic bill payment
methods. Consumers who report using toll-free telephone numbers to check account balances are 5 percent less likely to use electronic bill payment. These
factors prevail over measures of financial behaviors
such as self-reported financial discipline and the
Federal Reserve Bank of Chicago
frequency of checkbook balancing. I find one result
to the contrary. The greater the importance consumers
place on the ability to stop a payment, the higher
the likelihood of electronic payment use by 9 percent.
While this is a surprising result, the survey instrument
does not allow one to distinguish between consumers
who pay bills electronically through automatic, preauthorized electronic debits and those who pay bills
through electronic banking packages. The latter form
of electronic bill payment gives consumers some
improved ability to stop payments, which would be
consistent with the above finding.
Contrary to a priori expectations, I do not find
strong support for the importance of increased convenience in distinguishing users of electronic bill payment from nonusers. Only one hypothesis for this
category is slightly significant (at the 10 percent level).
Those who report a higher importance of having
bills paid when out of town are 6.5 percent more
likely to use electronic bill payment. These findings
are not surprising, given that many bills offer grace
periods of several weeks and convenience may be
relevant only for intense electronic bill payment users.
Indeed, preferences for convenience do influence the
level of use of electronic bill payment, a topic I explore
in the next section.
Some incentive factors influence the likelihood
of electronic bill payment use. Consumer preference
for using the least expensive method of payment
and importance placed on avoiding penalties due to
late payments do not influence the probability of
electronic bill payment use. Consumers who report
they would use direct payment if offered a 5 percent
discount on the monthly bill are 88 percent more
likely to use electronic bill payment. The heavier
use of coupons when shopping is associated with
a 7 percent greater likelihood of using electronic bill
payment.
Self-reported preferences for privacy and security are generally not significant in determining the
likelihood of choosing direct payments. Self-reported
dislike of the idea of someone automatically withdrawing money from ones bank account is associated
with a 20 percent decreased likelihood of electronic bill
payment use. Consumer perceptions of Internet
transaction security and self-reported discomfort
with giving account information to sales representatives do not influence the decision to use electronic
bill payment. Contrary to popular belief, other factors
that reflect consumers potential distrust of technology
and security, such as distrust of ATMs and selfreported preferences for account privacy, are not
statistically significant determinants of direct payment
39
use. Nonetheless, I do find that privacy concerns influence the intensity of direct payment use, as discussed
in the next section.
Preferences for personal involvement are associated with an increased probability of being a direct
payment user. Contrary to prior expectations, consumers who report they enjoy talking with bank tellers
are 8 percent more likely to be electronic bill payment
users. However, this finding may be the result of segmentation across consumer choice of banking institutions. For instance, consumers who choose to use
direct payment may have stronger relationships with
their banks, making them more likely to report enjoying interactions with bank personnel. This finding
warrants further investigation, given the importance
of the related electronic banking product strategy, customer service, and branch infrastructure issues that
financial services industry leaders face.
Comparison of low users and high users
Table 3 also highlights important differences between low and high users of electronic bill payment.
As noted earlier, low users pay less than 20 percent
of all bills using electronic bill payment and high users pay 30 percent or more of their bills electronically.
Of the demographic variables included in the analysis,
only age is statistically significant. A 38-year-old
consumer is 3 percent more likely to be a high electronic bill payment user than a 37-year-old. While
women are more likely to be direct payment users,
they are 40 percent less likely to be high users of electronic bill payment. Race, educational attainment, and
market size are not statistically significant factors.
The new product adoption factors proposed, a
priori, to influence the likelihood of high electronic
bill payment use are not statistically significant. This
might suggest that new product adoption factors influence the likelihood one will adopt the technology in
the first place, but do not affect how much one uses
it upon adoption.
In terms of wealth and financial variables, households with incomes over $75,000 are three times more
likely to be high users of electronic bill payment than
households with incomes below $20,000. Households
with incomes between $20,000 and $75,000 are not
more likely than low-income consumers to be high
users of electronic bill payment. This emphasizes the
critical role that wealth and budgeting play in enabling
electronic bill payment. Home ownership is not statistically significant in explaining the differences between
low and high users. Account ownership at different
types of financial institutions also does not influence
the intensity of electronic bill payment use. While
owning a credit card increases the likelihood that one
40
uses electronic bill payment, perhaps as a proxy for
financial activity, individuals who own at least one
credit card are 33 percent less likely to be high users
of electronic bill payment than low users. One explanation for this is that the analysis needs to account for
the outstanding dollar value on credit cards as well. I
tested the potential role of credit card debt levels, but
found it to be insignificant. It is possible that consumers systematically misreport their level of credit
card balances and/or that the survey instrument does
not allow for adequate variation in responses. Clearly,
we need to know more about this.
In terms of control-related factors, several variables are statistically significant in distinguishing low
users from high users. The greater the importance
placed on control over when a bill is paid, the lower
the likelihood that a consumer is a high user of electronic bill payment by 13 percent. A self-reported
importance rating of a person being available if a
problem arises is associated with a 13 percent lower
likelihood of being a high user of electronic bill payment. Individuals who agree with the statement that
they balance their checkbooks at least once per
month are 9 percent less likely to be high users of
electronic bill payment. While this finding points to
the potential convenience, it may be important to better understand consumer budgeting practices in the
context of using electronic banking services. To what
extent do consumers rely on PC-based balancing of
accounts? How much emphasis do consumers now
place on managing traditional checking/transaction
accounts?
Of the convenience factors in the survey, consumers who place higher ratings on the statement
that bank hours are inconvenient are 11 percent
more likely to be high users of electronic bill payment.
Consumers who place importance on having a bill
paid when out of town are 10 percent more likely to
be high electronic bill payment users. Preferences for
saving time do not influence the likelihood of high
electronic bill payment use.
I find that incentive factors do not influence the
likelihood of high electronic bill payment use. As with
new product adoption factors, this might suggest that
incentives influence the initial decision between traditional payment instruments and electronic bill payment, but do not affect the degree to which a consumer
later uses electronic bill payment.
My results show that privacy concerns and preferences for personal involvement influence the likelihood of high electronic bill payment use. Self-reported
dislike of the idea of someone automatically withdrawing money from ones account decreases the
likelihood that a consumer is a high user of electronic
Economic Perspectives
bill payment by 9 percent. The belief that the Internet
is secure for purchases is associated with a 10 percent lower likelihood of high electronic bill payment
use. This counterintuitive result is consistent with the
findings that younger consumers, who are more likely
to view the Internet as secure, are less likely to pay
bills electronically because of lower incomes and
other factors correlated with lifestage (for example,
moving more often than older consumers and having
to change payment relationships). Lastly, similar to
the findings for the initial electronic bill payment
choice, consumers who report that they enjoy talking with bank tellers are 7 percent more likely to be
high electronic bill payment users.
Comparison of users and nonusers by bill type
Table 4 provides the statistically significant results of my binomial logistic regressions of nonusers
versus users of electronic bill payment by bill type.
The bill types I consider are cable, credit card, insurance, general consumer installment loan, mortgage,
telephone, and utilities.13 In terms of demographic
and new product adoption factors, older consumers
are more likely to use electronic bill payment than
younger consumers for credit card, insurance, mortgage, and utility bills, though not for cable, loans,
and telephone bills. Nonwhite consumers are significantly less likely to pay telephone bills electronically,
but more likely to pay loan bills via electronic bill
payment than white consumers. Lifestage factors
prove to be significant in explaining the use of electronic bill payment for certain types of bills. Young
and retired couples and middle-aged parents are more
likely to pay loans electronically; middle-aged singles
are less likely to pay mortgages electronically; and
middle-aged and older singles and retired couples
are more likely to pay utility bills electronically.
Consumers living in markets with fewer than
100,000 people are more likely to pay insurance bills
electronically. Consumers in markets with population
between 100,000 and 500,000 are more likely to pay
utility bills electronically. Contrary to popular belief,
in no case does living in a very large urban area increase the likelihood of electronic bill payment use.
Future research will likely want to investigate such
questions as whether institutions in smaller markets
tend to promote electronic bill payment more frequently
or whether consumers perceive that billing authorities
in these markets provide better service. The extent
to which a consumer understands how to set up direct payment positively influences the likelihood
of paying credit card, insurance, mortgage, telephone,
and utility bills electronically. Consumers who have
made purchases over the Internet are also more likely
to use electronic bill payment for credit card bills.
Federal Reserve Bank of Chicago
Relative to consumers with incomes below
$20,000, higher income levels are associated with a
greater likelihood of using electronic bill payment
for mortgages and telephone bills. I find that homeowner status increases the probability of using direct
payment for cable, loan, telephone, and utility bills.
Savings account ownership increases the likelihood
of making loan payments electronically.
Consumers self-reported preference for controlling when a bill is paid decreases the likelihood
of paying mortgage, telephone, and utility bills electronically. Control over when a bill is paid may be
related to consumer budgeting concerns and a desire
to minimize the risk of insufficient funds. This concern could also stem from a consumers preference to
review a variable bill and minimize the risk of errors.
Having the option to stop payment increases the
likelihood of electronic bill payment for insurance bills,
while the importance placed on receiving a receipt of
payment decreases the likelihood of electronic payment
for insurance. This finding underscores the notion that
consumers want receipts and control over bills they
see as critical, so as to avoid potentially larger bills
in the future or the potential loss of insurance coverage. Higher self-reported scores for financial discipline and use of toll-free telephone numbers to check
account balances decrease the likelihood of paying
telephone bills electronically. This finding may be
explained by the variable-dollar nature of some bills.
Consumers who check account balances may be more
likely to be financially constrained and less likely to
use electronic bill payment if they worry about having
sufficient funds to cover variable bills.
The greater the importance placed on having a
bill paid when out of town, the greater the likelihood
of using electronic bill payment for insurance, mortgage, telephone, and utility bills. The extent to which
a consumer believes that bank hours are not convenient increases the probability of paying credit card,
loan, and utility bills electronically. Incentives such
as a 5 percent bill discount if electronic payment is
used positively influence the likelihood of paying
insurance and loan bills electronically. Higher levels
of discomfort associated with giving ones account
number to a salesperson and dislike of someone
automatically withdrawing funds from ones account
decrease the likelihood of paying cable, credit card,
insurance, loan, telephone, and utility bills electronically. Contrary to prior expectations, the more a consumer reports he or she enjoys talking with bank
tellers, the greater the likelihood of paying cable,
credit card, insurance, loan, telephone, and utility
bills electronically. The results for cable bills across
41
the board indicate that consumers view them differently from all other bill types.
Summary of model results
The results of the model suggest that there are important differences between nonusers, low users, and
high users of electronic bill payment services. Heavy
users of electronic bill payment tend to be wealthier
and to place a higher premium on convenience. Moderate users of electronic bill payment have at least
modest levels of wealth and tend to value convenience.
Nonetheless, these individuals do not use electronic
bill payment for a broad segment of bills because
of the potential risk of errors, which could result in
overdrawn checking accounts or require significant
time and energy following up with financial institutions and merchants. More importantly, these consumers are also subject to periodic swings in incomes or
expenses that create budgeting challenges. For this
broad group of consumers, the choice not to use electronic bill payment is akin to the purchase of a lowcost insurance contract that limits the potential risk of
payments-related problems.14
Why do other consumers not use electronic bill
payment? Clearly, some consumers expect some sort
of incentive to change. These consumers shop for the
best deal and may not change until they receive a
benefit, particularly if they believe their institution is
benefiting by moving to a more efficient form of
payment. There are also consumers who do not pay
electronically because it is not convenient enough or
because some bills cannot be paid electronically. But
more importantly, there is a significant fraction of
consumers who do not use electronic bill payment
because they lack the financial resources to even consider paying electronically. Some low-income consumers may use the ability to avoid paying a bill on
time as a short-term funding vehicle, sometimes at
low cost if there are limited penalties associated with
late payment. Other consumers prefer a personal involvement in bill payment or seek to limit potential
risks to their privacy by avoiding the use of electronic
bill payment. These groups, some of which are of potentially significant size, do not yet perceive checks and
electronic bill payment services as clear substitutes.
Conclusion
This analysis highlights the importance for
policymakers to understand consumers varying preferences and needs.15 After all, consumers desires for
control vary significantly and encompass concerns
about the ability to review bills, initiate payments,
and have errors resolved. Public sector involvement
in the rights, warranties, consumer protections, and
42
incentives associated with different payment instruments may have significant implications for the adoption of electronic payments. To some extent, legally
mandated business practices and consumer protection
may motivate increased adoption of electronic payments.16 An argument can be made that there is a
positive externality in setting standards or in developing common rules for consumer protection, particularly in an industry with significant fragmentation
and perhaps uneven bargaining power between consumers and financial institutions.17
Yet, we must recognize that setting these types
of rules may in some cases bring costs as well as
benefits. For instance, rules on what firms must do to
resolve errors may have the effect of implementing a
price floor, which may lead to the unintended result
that it is uneconomical or unprofitable to serve some
consumer segments.18 One potential alternative is to
have public entities work to coordinate the development of a reasonably small number of standards rather
than one standard. The net effect would be a greater
emphasis on transparency and disclosure and less on
public determination of the final outcome.19 Clearly,
more needs to be known about the costs and benefits
of potential public policy decisions. At a minimum,
frameworks like the one presented in this article help
identify where public policy decisions may be expected
to have an effect, as well as where unintended consequences may arise.
This analysis suggests that, despite speculation
to the contrary, consumers may not be as resistant to
new payment innovations as has been proposed in the
past. My results show that consumers choices are
consistent with their preferences. These preferences
vary across bills and depend on the consumers level
of wealth, but include elements of preferences for
control, convenience, incentives, privacy, and personal involvement. Consumers financial positions
and transaction-specific characteristics clearly have a
significant impact on their decisions. The importance
of these factors may help explain why consumers sometimes appear to exhibit irrational behavior, that is,
behavior that is not consistent with self-reported
preferences. This behavior may be driven by situational factors. My work suggests that the next stage
of migration towards electronic bill payment may be
more dependent on establishing the business cases
to justify investment in new product features that
address consumer preferences than on overcoming
consumer resistance to change.
There is an important need to perform this type
of research on data representing actual consumer behavior rather than on self-reported data. There are many
Economic Perspectives
unanswered questions for future research to address,
some of which I raised earlier in this article. Researchers may also want to assess the links between consumer income, expenditures, savings, borrowing,
and payment methods. How are consumers payment
preferences changing over time and, more specifically,
how are they responding to market stimuli, such as
pricing, advertising, and changes in product attributes?
Which payment instruments are substitutes and for
which consumer segments? How do consumers perceive the relative merits of different payment instruments and different account structures? Finally, how
do public policy decisions influence the migration to
alternative payment methods?
APPENDIX: VARIABLE DESCRIPTIONS
Variable
Demographics
Age of respondent
Scale
Summary statistics
continuous
Mean: 50.4
Female
0: male, 1: female
34% male
66% female
Race
0: white, 1: non-white
86% white
14% non-white
College
0: no, 1: yes
64% no
36% yes
Market size: under 100,000 people
0: no, 1: yesa
23% under 100,000
Market size: 100,000499,999
0: no, 1: yes
a
16% 100,000499,999
Market size: 500,0001.9 million
0: no, 1: yes
a
21% 500,0001 million
New product adoption
Understand direct payment
1: no4: yes
Mean: 3.5
Understand how to set up direct payment
1: disagree completely
10: agree completely
Mean: 4.2
PC owner
0: no, 1: yes
71% no
29% yes
Cellular phone owner
0: no, 1: yes
75% no
25% yes
Purchase over the Internet
0: no, 1: yes
88.9% no
11.1% yes
Consumer financial
Household income: $20,000$39,999
0: no, 1: yes b
30% under $20,000
29% $20,000$39,999
Household income: $40,000$74,999
0: no, 1: yes b
30% $40,000$74,999
Household income: over $75,000
0: no, 1: yes
11% over $75,000
Homeowner
0: no, 1: yes
24% no
76% yes
Savings account
0: no, 1: yes
25% no
75% yes
Credit card
0: no, 1: yes
38% no
62% yes
Regional/national bank account
0: no, 1: yesc
36% regional/national bank
54% local bank
Credit union account
0: no, 1: yesc
41% credit union
Brokerage account
0: no, 1: yes
c
12% brokerage
Savings and loan account
0: no, 1: yes
c
20% savings and loan
Federal Reserve Bank of Chicago
b
43
Variable
Scale
Summary statistics
1: not important
10: extremely important
Mean: 8.9
Option to stop payment
1: not important
10: extremely important
Mean: 8.1
Receipt for payment
1: not important
10: extremely important
Mean: 7.8
Balance checkbook at least once/month
1: disagree completely
10: agree completely
Mean: 8.3
Disciplined about finances
1: disagree completely
10: agree completely
Mean: 7.4
Frequently use toll-free number
to check account balances
1: disagree completely
10: agree completely
Mean: 4.4
Person available to talk to if theres a problem
1: not important
10: extremely important
Mean: 8.4
1: not important
10: extremely important
Mean: 7.2
Saving time
1: not important
10: extremely important
Mean: 7.7
Banks not open convenient hours
1: disagree completely
10: agree completely
Mean: 4.6
1: not important
10: extremely important
Mean: 7.6
Frequently use shopping coupons
1: disagree completely
10: agree completely
Mean: 7.8
Avoid penalties for late payments
1: not important
10: extremely important
Mean: 8.9
Would use electronic payment if
offered a 5% discount on monthly bill
0: no
1: yes
21% no
79% yes
1: disagree completely
10: agree completely
Mean: 7.0
Dislike someone automatically
withdrawing from my account
1: disagree completely
10: agree completely
Mean: 6.6
Paying with credit card or giving checking
account number on Internet is secure
1: disagree completely
10: agree completely
Mean: 2.7
1: disagree completely
10: agree completely
Mean: 5.4
Control
Control when bill is paid
Convenience
Bill paid even when Im out of town
Incentives
Least expensive payment method
Privacy/security
Not comfortable giving my account
number to salesperson
Personal involvement
Enjoy talking with bank teller
The baseline for this variable is market size over 2 million.
The baseline for this variable is income under $20,000.
c
The baseline for this variable is account at local bank.
a
b
44
Economic Perspectives
NOTES
1
See Radecki (1999) and Ernst and Young (1999).
12
2
See Janssen (1999).
13
3
See Hood (1999).
4
See Bank Systems Technology, Inc. (2000).
5
For instance, see Snell (1999a).
6
See Kotler (1994).
See Jolly (1997) for an introduction to this subject and Ferguson
(1998) for background on the role of infrastructure in new payment instrument adoption. One important issue that is beyond the
scope of this article is to consider products with a network nature
to their use, such as the cell phone, where the first products sold
are of little value since customers value having the ability to reach
many others. See Good (1997) for a discussion of network externalities relating to payments.
7
The dataset contains variables pertaining to electronic bill payment usage that are in some cases highly correlated. I used a correlation matrix to discern the relationship between responses
across questions. The variables included in the final model are
the responses to the questions emphasized by theory.
8
Note, I analyzed various cutoff points for low and high users and
the choice of a given cutoff point did not significantly affect the
outcome from the regression.
9
Recall that the binomial logistic model takes the functional form
where the probability that a consumer uses electronic bill payment = eβx/1+eβx, where X is a vector of variables proposed to be
related to electronic bill payment usage. See Greene (1993).
10
Subsequent analysis confirmed this.
While the survey also asked consumers about other bills, such
as membership bills and tuition payments, I exclude them from
my analysis because of the small sample size of individuals with
these specific bills.
14
See Mantel (2000) for an overview of the types of initiatives
which might be undertaken to address consumers concerns with
the use of electronic bill payment. In general, they tend to promote
services that are similar to the Giro systems common in some
European countries (for example, consumer initiated, ability to
easily build in partial payments, or access to customer service).
For one perspective advocating the need to look actively at this,
see Mann (1999). For a second perspective advocating monitoring
these types of developments but being careful to act only when
there is a clear and compelling reason to do so, see Perritt (1999).
15
16
See Mann (1999).
For instance, individual consumers may not have an incentive to
negotiate for small dollar adjustments to their accounts, even if
they are completely justified, if they expect this process to require
significant cost and/or time.
17
This is a particularly important question given the work underway to promote financial relationships for unbanked consumers
as part of the EFT99 legislation.
18
The approach of allowing multiple standards to flourish is
clearly what many public entities do by abstaining from getting
involved in these discussions. But, when public entities do get
involved, it may still be worth considering advocating multiple
standards rather than just one.
19
Electronic bill payments are defined as pre-authorized ACH
debits or debits by personal computer banking. Nonelectronic
payments include cash, checks, and money orders.
11
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47
The effect of the run-up in the stock market on labor supply
Ing-Haw Cheng and Eric French
Introduction and summary
There are many anecdotes of people who quit their
job after having their stock market wealth increase
dramatically. This article assesses whether these anecdotes represent isolated incidents or whether the stock
market has significantly affected U.S. labor supply.
There are two main reasons why this is an important
question. First, quantifying the effects of stock market
fluctuations may help forecast future variation in labor
force growth, employment, and unemployment. If the
stock market suddenly dropped, it is possible that many
people would rapidly reenter the labor market in order
to rebuild enough wealth to finance their retirement.
This would cause the number of potential workers in
the economy to increase. If the number of new jobs
grew more slowly than the number of new workers,
short-term unemployment problems would result.
This would exacerbate the potential unemployment
problems caused by more conservative hiring practices
of employers after a market downturn.
Second, we are interested in evaluating the extent
to which the consumption response to variations in
stock prices is consistent with economic theory. Current estimates of the marginal propensity to consume
out of stock wealth, that is, the wealth effect often
described in the popular press, range from .01 to .05.
This means that each additional dollar in stock wealth
increases consumption one to five cents annually.
The estimate more consistent with simple economic
models that posit that people eventually consume
their wealth (see Poterba, 2000) is .05. If a dollar increase in stock wealth results in only a one cent increase in consumption, then 99 cents would be saved
until next year. Assuming the 99 cents earns a 3 percent
post-tax rate of interest, it would grow to approximately $1.02 next year. Therefore, people would not
eventually consume all of their wealth, contrary to the
simple economic models. If the post-tax interest rate
48
is 3 percent, people must have a marginal propensity
to consume of at least .03. Poterba (2000) suggests
.04 as a reasonable lower bound.
However, these simple economic models assume
that labor supply does not respond to variations in
wealth. If much of the stock market wealth goes toward affording people increased leisure in addition to
increased consumption of market goods, then the .01
estimate for the marginal propensity to consume market goods may be consistent with economic models that
account for the effect of wealth on labor supply. People would eventually consume all of their wealth, but
mostly in the form of increased leisure. If individuals
consume three cents worth of leisure in the form of
reduced earnings (that is, their earnings drop by three
cents each year) in addition to a one cent increase in
consumption of market goods in response to a $1
increase in wealth, then total consumption would
be four cents in response to a $1 increase in wealth.
This story is perfectly consistent with the theory that
individuals eventually consume all their wealth.
In this article, we present estimates of the size
of the increase in wealth in the U.S. economy from
1994 to 1999. Recent stock returns are high by historical standards. We also show that growth rates in
stock prices are difficult to predict. Therefore, most
of the recent increase in wealth caused by rising stock
prices represents an unanticipated increase to national
wealth. We estimate that every dollar held in stocks on
December 31, 1994, resulted in $1.12 in unanticipated
wealth shocks if those stocks were held until December
31, 1999. We estimate that the unanticipated component
Ing-Haw Cheng is a student at the University of Chicago
and Eric French is an economist at the Federal Reserve
Bank of Chicago. The authors thank Dan Sullivan and
David Marshall for helpful comments and David Marshall
for data.
Economic Perspectives
of the increase in national wealth from 1994 to 1999
was $5.8 trillion in 1999 dollars.
In order to understand how many people may
have been affected by the run-up in the stock market,
we examine the distribution of stock market wealth
in the economy. The more concentrated the distribution,
the fewer people whose labor supply will be directly
affected by stock market variations. Using data on
the distribution of stock market wealth and on stock
returns, we estimate the distribution of unanticipated
increases in wealth for different groups of the population. We show that about 15 percent of all individuals
aged 55 and over had an unanticipated wealth increase
of $50,000 in 1999 dollars or more between December
31, 1994, and December 31, 1999.
Next, we show changes in labor force participation rates for different age groups in different years.
Holding all else equal, we would expect groups with
large unanticipated increases in wealth to reduce their
labor force participation rates. As it turns out, this is
not the case. Individuals aged 55 and above have the
highest levels of stock wealth (both directly and
through pensions) and, thus, have had the greatest unanticipated increases in wealth. However, labor force
participation rates for individuals aged 55 and older
have increased over the last five years.
In our view, one should not take these counterintuitive results as evidence against the theory that the
run-up in stock market wealth has decreased labor
force participation rates. Instead, we believe these
results imply that the run-up in the stock market has
not been the primary determinant of recent changes
in labor force participation rates. There are many other
reasons that labor force participation rates should be
rising for older workers. For example, the strong economy has resulted in increased wages and improved
employment opportunities for older workers. Importantly, too, the Social Security System has reduced
the work disincentives for those eligible for Social
Security benefits.
In order to understand how increases in stock
market wealth affect aggregate labor supply, we use
two basic approaches. First, we use estimates from
two previous studies to predict the change in labor
supply for a given unexpected change in wealth.
Imbens et al. (1999) estimate the effect on labor supply
of winning a lottery, which presumably represents an
unanticipated change in wealth. Again assuming that
the wealth increase is unanticipated, Holtz-Eakin et al.
(1993) estimate the labor supply effect of receiving an
inheritance. Both papers suggest that unanticipated
increases in wealth reduce work hours and labor force
participation rates. Using these estimates and the
distribution of wealth, we predict the likely decline
Federal Reserve Bank of Chicago
in work hours caused by the run-up in the stock market. Our estimates suggest that in the absence of the
run-up in the stock market (but holding all else
equal), labor force participation rates today would be
.78 percentage points higher for men aged 5564,
1.94 percentage points higher for women aged 55
64, and 1.16 percentage points higher on aggregate.
Our second approach to predicting the effect of
the run-up in the stock market on labor supply is to
simulate the effect using a dynamic structural model
described in French (2000). French estimates the
model using data on life cycle profiles for assets,
hours worked, and labor force participation rates.
Simulations from the model closely mimic the life
cycle profiles in the data. Therefore, the model is also
potentially able to closely mimic the behavioral effects
of the run-up in the stock market. Our simulations
imply that in the absence of a run-up in the stock
market, labor force participation rates would have
been 1.3 percentage points higher for men aged 65
and above and 3.2 percentage points higher for men
aged 5564. In other words, the simulation model
predicts much larger behavioral responses than the
estimates from other studies. We discuss why the
simulation model may overestimate the behavioral
responses and the estimates from other studies may
underestimate the behavioral responses later in the
article. Overall, our view is that the predictions from
the lottery and inheritance studies form a lower bound
on the effect of the stock market on labor supply and
the simulation model forms an upper bound.
Lastly, we present estimates of the marginal propensity to consume leisure (also known as the marginal propensity to earn out of wealth). Recall that
an estimate of the marginal propensity to consume
market goods of .01 is consistent with the life cycle
model only if the marginal propensity to consume
leisure is at least .03. The estimates from the direct
lottery and inheritance studies are in the range of .01.
In other words, for every $1 increase in wealth, aggregate earnings decline one cent. The simulation model
predicts a larger marginal propensity to consume leisureabout .02. In either case, the marginal propensity to consume leisure is too small to reconcile a
marginal propensity to consume consumption goods of
.01 with a life cycle model. Therefore, either the life
cycle models are wrong or the .01 estimate of the marginal propensity to consume market goods is wrong.
Increase in national wealth
in the 1990s
We provide evidence that the increase in wealth
caused by the run-up in stocks was largely unanticipated and estimate the unanticipated wealth shock.
49
The issue of whether the increase in the
FIGURE 1
level of wealth was anticipated is important.
Five-year average growth rate of household wealth
If people knew in December 1994 that five
(1999 billions of dollars)
years in the future they would have higher
percent
levels of wealth, then it is possible that
30
they would have reduced the number of
hours worked in 199599, knowing that
20
Net
they would be able to finance their low
worth
levels of work because of the anticipated
10
run-up in the stock market.1 Therefore, we
Nonequity
wealth
would not expect to see any correlation be0
tween stock market gains and labor supply.2
Figure 1 shows the growth in houseEquities
hold net worth in the economy from 1945
-10
to 1999. While household wealth not in
equities rose at a moderate rate of 27 percent
-20
over the decade, the market value of equities
1950
’57
’64
’71
’78
’85
’92
’99
Source: Data from Board of Governors of the Federal Reserve System,
increased 260 percent between December
Flow of Funds Accounts, 1946–99.
31, 1989, and December 31, 1999. The
value of equities rose by almost $9.5 trillion during the 1990s, comprising about 64
Figure 3 shows there have been very large differpercent of the growth in wealth. Figure 1 also shows
ences between the predicted return and the five-year
that changes in household wealth in this period are
realized return. Over the past five years, the average
largely explained by changes in the value of equities.
annual rate of return in excess of the Treasury bond
Figure 2 shows growth rates in the value of equities,
rate has been 16.4 percent. The five-year excess rebased on Federal Reserve Board data, and growth
turn was 93 percent. This is well above the historical
rates in the stock market, as measured by the Center
average of a 45 percent excess return over five years.
for Research in Security Prices (CRSP). The appenIt has not been since the 1950s that there has been
dix describes the CRSP measure in greater detail.
such a large, sustained increase in the stock market.
The two measures are almost perfectly correlated
Moreover, because stocks represented about twice as
(with differences relating to treatment of dividends
large of a share of national wealth in 1994 than in
and American stockholdings overseas.)
1950, the growth in national wealth was greater in
Figure 3 shows rates of return in the stock market over five-year horizons, with the latest
being the rate of return between December
FIGURE 2
31, 1994 and December 31, 1999. Figure 3
One-year return in the stock market
also shows the results from a simple forecasting model (described in box 1), which
percent
60
uses information on five-year Treasury
bond yields and stock returns from 1950 to
40
1999 to predict stock returns. We compute
CRSP
the difference between five-year total
20
stock returns and total returns on five-year
Treasury bonds, that is, the excess return
0
on the stock market. Between 1950 and
1999, the excess return was 45 percent
-20
over five years. The forecast of the fiveyear return in the stock market is the sum
-40
Flow of funds
of the excess return (which is assumed
constant) plus the five-year Treasury bond
-60
1950 ’55
’60
’65
’70
’75
’80
’85
’90
’95
’00
return. The predicted five-year return has
Sources: Board of Governors of the Federal Reserve System, Flow of Funds
increased over time because interest rates
Accounts, 1950–99, and Center for Research in Security Prices, 1950–2000.
have increased.
50
Economic Perspectives
and should perform poorly in the near future. We do see this pattern in stock market data from 1950 onwards. However, in
1994 price/dividend ratios were already
high. Therefore, the statistical evidence
indicated that stocks would perform poorly
in 199599. If people were making forecasts according to this simple statistical
model, every dollar in the stock market
on December 31, 1994, would have led
to a $1.46 unexpected gain in wealth by
December 31, 1999. Therefore, assuming
a constant excess return of stocks over
bonds leads to a conservative estimate of
the unexpected shock to the stock market.
FIGURE 3
Five-year total return in the stock market
percent
200
100
Expected
0
Realized
-100
1950
’55
’60
’65
’70
’75
’80
’85
’90
Note: In 1999 dollars.
Sources: Center for Research in Security Prices, 1946–2000, and
Treasury bond data, 1946–2000.
the late 1990s than in the 1950s. Figure 3 shows that
$1 invested in December 31, 1994, would have reached
$2.82 in December 31, 1999, compared with a predicted level, based on the historical average, of $1.70.
This means that every $1 invested in December 31,
1994, resulted in a windfall gain of $1.12 by December
31, 1999. Because stock market wealth constituted
just over 15 percent of aggregate wealth in 1994, the
run-up in stock market wealth resulted in national
wealth being 17 percent greater in 1999 than it would
have been if returns had been as expected. The run-up
in the stock market represents a $5.8 trillion shock to
national wealth.
It seems unlikely that people anticipated the high
rates of return during the late 1990s. For one thing,
why did many people not invest in stocks at all? Rates
of return on risk-free assets declined in the late 1990s.
If stocks were a sure bet, nobody would ever prefer
bonds to stocks.
Another way of looking at the problem is to ask
whether any historical relationships would predict
high stock returns in the late 1990s. We investigate
two relationships, described in detail in box 1. First,
people might believe that if returns were high in the
recent past they would continue to be high in the future.
We find that since 1950 high returns over the previous
four years and the previous ten years have indicated
high returns in the near future. However, as figure 3
shows, the 1980s and early 1990s were not remarkably good years for the stock market. The second
historical relationship we investigate is that between
price/dividend ratios and stock returns. When price/
dividend ratios are high, stocks are possibly overpriced
Federal Reserve Bank of Chicago
’95
’00
Unexpected wealth changes
in the population
Given a U.S. population of just under
300 million and an aggregate wealth
shock of about $5.8 trillion, the run-up
in the stock market from 1994 to 1999 represents an
unanticipated increase in wealth of $20,000 per person. This is roughly enough to finance one year out of
the labor market with no change in consumption of
market goods for every individual in the U.S. However, because wealth in stocks is highly concentrated
among the wealthy, we would expect the effect on
labor supply to be smaller than it would be if stock
market wealth were evenly distributed. Our information on the distribution of stock market wealth comes
from two sourcesdata on non-pension stock market
wealth from the Panel Study of Income Dynamics
(PSID) and pension wealth data from other studies
that used data from the Health and Retirement
Survey (HRS).
The PSID is a nationally representative dataset
that includes demographic information, the value of
non-pension wealth held by individuals, and breakdowns of wealth into various components, including
stock wealth. The stock market wealth measure includes the value of stocks in mutual funds, IRAs, and
Keogh plans, in addition to directly held stocks. Juster
et al. (1999) show that respondents in the PSID report
over 85 percent of their stock market wealth and over
75 percent of total wealth.3 They also find that the
distribution of wealth in the PSID is extremely accurate for everyone but the wealthiest 2 percent of the
population. To adjust for the slight underreporting of
stock market wealth in the PSID, we multiply stock
market wealth in the PSID by 1/.85 = 1.18. The PSID
does not provide information on who controls the
wealth within households. We assume that non-pension
51
BOX 1
Predicting stock returns
This section shows the method we use to compute the difference between the k-year realized and
expected returns. Denote the return over the past
year as ri (for example, r1995 is the one-year rate of
return from the end of December 31, 1994, through
the end of December 31, 1995) and the gross return
over a k-year horizon as Rt0 → tk , where tk = t0 + k (for
example, if k = 5 and t0 = 1994, the five-year return
is R1994→1999, the return from the end of December 31,
1994, through December 31, 1999). We measure all
returns and growth rates in real terms. The k period
rate of return is
k
1) Rt0 → tk = ∏ (1+ rt0 + i ).
i =1
We can compare this to the gross k-year expected
return, which we forecast as:
2) Rˆt0 → tk = Rt0f →tk +excess t 0 →tk ,
where
3) excesst0 →tk = Rt 0 → tk – Rt0f →tk =α + εt ,
α is the constant excess rate of return, εt is a whitenoise random variable, and Rt0f →tk represents the
continuously compounded return on a k-year riskfree asset. The rationale for forecasting the excess
stock return (that is, stock returns net the risk-free
return) is that the k-year total return of a risk-free
asset is known in advance at time t0; for example,
we can easily find information regarding yields on
five-year Treasury bonds today, and thus compute
the associated five-year holding period return (assuming the bond is held until maturity). Thus, the
only variables in the forecast at time t0 of future expected returns is the average excess return over the
sample period and the five-year Treasury bond return.
The unexpected windfall for an investor at
time t1, who had M dollars in the stock market at
time t0 is thus simply
∆Aˆt ≡ M(Rt0 → tk – Rˆt0 → tk ),
where Rt f →t is the predicted stock market return
0
k
given by equations 2 and 3.
52
Equation 3 is an extremely simple model of
forecasting excess stock returns; in fact, the predicted
· t0 → t1 is simply the mean of the risk prevalue excess
mium over the entire time span. Other models have
been suggested. Cochrane (1997) recommends several possible indicators that track long-horizon market movements relatively well. In particular, he
suggests that the price/dividend (P/D) ratio is a good
indicator of long-horizon market movements. When
P/D ratios are high, stocks are overpriced and, thus,
stock prices should grow slowly.
The regression
4) excesst0 → tk = Rt0 → tk − Rt0f →tk =α +β
Pt0
Dt0
+ εt
using excess returns over k = five-year horizons of
the CRSP NYSE value-weighted portfolio from the
end of 1954 to the end of 1996 (that is, using stock
market information from the start of 1950 to the end
of 1996) and five-year Treasury yields (see the appendix for more information) gives a point estimate of
β = 5.30 with an R2 of 0.54. Using these estimates,
the predicted excess return for December 31, 1994,
to December 31, 1999, is 27 percent. This is an
implausible prediction given that if people expected
stock returns to be lower than bond returns, nobody
would invest in stocks. However, extending the regression to include 199799 as in-sample years cuts
the point estimate to 2.79. Using these estimates,
the predicted excess return for December 31, 1994,
to December 31, 1999, was 12 percent. If anything,
these estimates indicate that our model would actually underestimate the unexpected wealth shock
during 199499, since our model simply predicts
the market to return the mean historical excess return plus the going return on bonds, whereas more
complex models predicted the market would perform
poorly. In fact, our estimate for unexpected windfalls
199499 using equation 3 is $1.12 on every dollar,
whereas the same estimate using equation 4 and our
own data is $1.46 on every dollar (in 1999 dollars).
Interestingly, forecasting the following unrestricted version of equation 3 using 195499 data,
Rt0 → tk =α +βRt0f → tk + ε t ,
yields a point estimate of β = 0.95 with a standard error of 0.40 (not including adjustments for heteroskedasticity and serial correlation), so one cannot
statistically reject our model where we assume β = 1.
Economic Perspectives
FIGURE 4
FIGURE 5
Fraction of women in stock value groups
Fraction of men in stock value groups
fraction
1.0
fraction
1.0
0.8
<$50,000
0.8
0.6
0.6
0.4
0.4
$50,000–
250,000
0.2
>$250,000
<$50,000
>$250,000
$50,000–
250,000
0.2
0.0
0.0
< 25
25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 65+
age group
< 25
Federal Reserve Bank of Chicago
65+
Sources: PSID data, 1994, and authors’ calculations.
Sources: PSID data, 1994, and authors’ calculations.
household wealth is split evenly among spouses, with
each receiving 50 percent. Browning et al. (1994)
show that consumption between husbands and wives
is close to an even split, regardless of the resources
brought into a household.4 We attribute pension wealth
to the individual receiving the pension.
Figures 4 and 5 show the distribution of stock
market wealth by age of the individual for women
and men. The data show two things. First, older individuals have more stock market wealth than younger
individuals. The fraction of the population with over
$50,000 in stock market wealth is less than 4 percent
for households where the head is younger than 54. Second, most individuals have little stock market wealth.
Even for households aged 55 and older, less than 13
percent had over $50,000 in the stock market (including
stocks, mutual funds, IRAs, and Keogh plans).
Gustman and Steinmeier (1999) show that pension wealth is very broadly held in the population
and constitutes a large portion of overall wealth. Not
surprisingly, the run-up in the stock market has led to
an increase in pension wealth for many people. Figure
6 shows total national wealth held in defined contribution pension plans. In this type of plan, individuals
contribute a portion of their income and the accounts
value grows by that amount plus the rate of return on
the plans portfolio of assets. Data from the Federal
Reserve Boards Flow of Funds shows that by 1999
about 12 percent of all U.S. wealth in equities was held
by defined contribution pension plans and that the
amount of stock wealth in defined contribution pension plans rose 182 percent from 1994 to 1999.
The other major type of pension plan, the defined benefit pension plan, provides benefits that
are specified by the employer. These benefits do not
depend on the rate of return for assets in the pension
25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64
age group
fund. If there is a run-up in the stock market, the employer gets the windfall. Likewise, in the event of a
market crash, the employer must make up the shortfall
if pension fund reserves are low. Therefore, changes
in the stock market affect the stock price of the firm
holding the pension reserves but do not affect the
wealth level of employees at the firm.
Gustman and Steinmeier (1999) show that in their
HRS sample of older workers, 66 percent of all households are covered by a pension plan. Of the households
covered, 48 percent are covered by a defined benefit
plan, 21 percent are covered by a defined contribution
plan, and 31 percent are covered by some combination
of defined benefit and defined contribution plans.
Defined contribution pension plans tend to be less
generous than defined benefit pension plans and joint
FIGURE 6
Defined contribution pension wealth
and equity composition
gross amount, billions of dollars, 1999
3,000
percent
75
Fraction of DC
wealth in equities
(right scale)
50
2,000
Total DC
plan wealth
1,000
25
DC plan wealth
in equities
0
0
1985
’87
’89
’91
’93
’95
’97
’99
Source: Board of Governors of the Federal Reserve
System, Flow of Funds Accounts, 1985–99.
53
plans. In 1992, average wealth held in defined contribution plans at age 65 was $57,000 in 1999 dollars.
In contrast, the amount held in defined benefit plans
was $135,000 and the amount held in combined plans
was $153,000. We assume that half the wealth in combined plans is in the form of defined benefit wealth
and the other half is in defined contribution wealth.
This means that 35 percent of all households in the
HRS held an average level of wealth in defined contribution plans equal to $69,000 at age 65 in 1994.
The other 65 percent held no defined contribution
wealth. Given that 40 percent of defined contribution
plan pension wealth is in the form of equities, 35
percent of all elderly households would have an average of $28,000 in the stock market by age 65. Since
$28,000 invested in the stock market on December
31, 1994, would have resulted in an unanticipated
windfall of about $31,000 by December 31, 1999,
a large number of elderly households would have received a large unexpected increase in wealth because
of their pensions.
Because the PSID only has data on whether respondents were covered by a pension plan and whether
they contributed to that plan in 1989, we assume that
those who were contributing to a defined contribution
plan in 1989 were also contributing in 1994. If the
individual was not contributing in 1989, we assume
that person never contributed to a defined contribution
pension plan. The fraction of the population covered
by a pension does not vary much by age, except for
those under 35 who have lower coverage rates. We
assume that individuals over 35 who are contributing
to a defined contribution plan contribute a fixed amount
after age 35. We assume individuals younger than 35
contribute for only one year.
Younger households would also have had windfalls from increases in stock market wealth, although
the windfalls would be smaller. To calculate the amount
of pension plan wealth at each age, we assume a 2.3
percent real rate of return on pension investments, the
same amount of pension contributions each year, that
the worker starts working at a firm that provides a
defined contribution plan at age 35, and that the level
of wealth in the defined contribution plan would be
$69,000 at age 65, on average. For example, an individual who contributes $1,550 annually would have
an imputed defined contribution wealth of $10,000 at
age 40, $28,000 at age 50, and $53,000 at age 60.5
Given the distribution of stock market wealth in
the economy and the rates of return on stocks computed in the previous section, we compute a measure
of unexpected wealth increases for different segments
of the population. Recall that $1 invested in December
31, 1994, would have resulted in $1.12 in unanticipated wealth gains by December 31, 1999. Figures 7
and 8 show the distribution of wealth shocks in the
economy for women and men. The differences between these figures and figures 4 and 5 are twofold.
First, figures 7 and 8 include information on pensions
for 1994. Second, figures 7 and 8 do not describe total
stock wealth but how stock wealth in 1994 became
wealth shocks in 1999. These figures make two
points clear. First, there is a sizable minority of individuals who received wealth shocks in excess of
$50,000. Second, individuals aged 55 and older received most of the wealth shocks; 21 percent of all
individuals aged 55 and older received unanticipated
wealth gains in excess of $50,000. Given that most
individuals had earnings below $50,000, an unanticipated wealth gain of $50,000 could replace at least
one year of earnings for most individuals.
FIGURE 7
FIGURE 8
Unexpected wealth shocks for women by age
Unexpected wealth shocks for men by age
fraction
1.0
fraction
1.0
<$50,000
<$50,000
0.8
0.8
0.6
0.6
0.4
0.4
0.2
$50,000–
250,000
>$250,000
>$250,000
0.0
0.0
< 25
25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64
age group
65+
Sources: PSID data, 1989 and 1994, and authors’ calculations.
54
$50,000–
250,000
0.2
< 25
25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64
age group
65+
Sources: PSID data, 1989 and 1994, and authors’ calculations.
Economic Perspectives
Changes in labor force participation rates
Estimates of the level of unexpected wealth increases show that a sizable minority of the population
had large unexpected increases in wealth. Most of
these increases in wealth are concentrated among
individuals aged 55 and above in 1999 (or 50 and
above in 1994). Therefore, we would expect this group
to have the largest declines in labor force participation rates between 1994 and 1999.
Figures 9 and 10 show labor force participation
rates between 1980 and 2000. For men aged 55 and
above, labor force participation rates have been rising
recently, following a steady decline from 1980 to 1993.
For women aged 65 and above, labor force participation rates have remained steady since 1994. For women
aged 5564, labor force participation rates have been
rising since 1994. These data show that the rise in the
stock market has not been the dominant source of
changes in labor force participation rates for individuals aged 55 and over. The trends do not support the
wealth effect hypothesis.
However, we argue that this should not be taken
as evidence that the unanticipated increase in wealth
has resulted in no change or an increase in labor force
participation rates. Instead, in our view, the data provide evidence that other factors have offset the effects
of the increase in the stock market. Among these factors are recent increases in wages in the economy.
Moreover, the Social Security System has reduced
the work disincentives for individuals 65 and older.
Social Security benefit accrual is now closer to actuarially fair for individuals aged 6570 than it was in
1994.6 It is not clear what effect the stock market
may have had on labor participation rates for individuals aged 55 and above in the absence of other factors.
Next, we look at the likely behavioral responses to
the run-up in the stock market, holding all else equal.
Estimates of the effect of unanticipated
wealth increases on labor supply
Here, we present estimates of the effect of unanticipated wealth increases on labor supply holding all
else equal. Estimating the effect of an unanticipated
wealth increase on labor supply is difficult because,
usually, changes in wealth are anticipated or are accompanied by wage changes. The labor supply response
to an anticipated wealth increase is different from the
labor supply response to an unanticipated wealth
increase. If the wealth change is anticipated, we expect a small labor supply response after receipt of the
wealth. If people know they will receive a large sum
of money tomorrow, their labor supply may not change
much between today and tomorrow. They may already
have reduced their labor supply in anticipation of
having the wealth in the near future.
Inheritance and lottery studies
Table 1 presents estimates of the effects of inheritances on labor supply. Inheritances cause plausibly
unanticipated changes in wealth. Holtz-Eakin et al.
(1993) estimate the effect of receiving an inheritance
on labor force participation rates. Using tax records,
they observe whether reported earnings are positive
(our measure of labor force participation) both before
(in 1982) and after (in 1986) the receipt of an inheritance. They find fairly large effects on labor force participation and earnings. Among single households who
receive a small inheritance (average of $13,000) in
their sample, labor force participation rates increase
from 89.9 percent to 91.1 percent (column 4), or an
increase of 1.2 percent (column 5). Single households
FIGURE 9
FIGURE 10
Labor force participation rates, women
Labor force participation rates, men
percent
100
percent
100
25–34
35–44
75
35–44
45–54
25–34
75
55–64
45–54
50
50
55–64
25
25
65+
65+
0
0
1980
’82
’84
’86
’88
’90
Source: CPS data, 1980–2000.
Federal Reserve Bank of Chicago
’92
’94
’96
’98
’00
1980
’82
’84
’86
’88
’90
’92
’94
’96
’98
’00
Source: CPS data, 1980–2000.
55
who receive a medium-sized inheritance (average of
$120,000) show a labor force participation rate decline
from 82.7 percent to 80.5 percent, or a decline of 2.2
percent. We interpret these changes in labor force
participation rates to mean that in the absence of a
medium-sized inheritance, labor force participation
rates would have increased 1.2 percent instead of
declining 2.2 percent for those who received the
medium-sized inheritance. Therefore, labor force
participation rates would have been 3.4 percent higher
(column 6) had those individuals not received a medium-sized inheritance. Those who receive a large
inheritance (average of $609,000) show a labor force
participation rate decline of 9.8 percent from 75.4
percent to 65.6 percent. If these people had not received an inheritance, their labor force participation
rate would have increased 1.2 percent. Therefore, receiving the inheritance potentially reduces their labor
force participation by 11.0 percent. Holtz-Eakin et al.
find similar results for married couples. Receiving a
medium inheritance reduces average labor force participation rates within the household by 3.8 percent,
and a large inheritance reduces labor participation by
4.2 percent.
Most of the individuals who received inheritances
were young. Singles who received small inheritances
(the youngest group) were aged 33.4, on average,
and the mean age of couples who received large inheritances (the oldest group) was 44.7 years. Therefore,
the sample in the Holtz-Eakin et al. study is significantly younger than the segment of the general population that has received most of the stock wealth gains.
Since it is likely that large wealth gains have larger
labor supply effects for those who are nearing retirement than for younger individuals, our view is that
the inheritance study most likely understates the labor
supply effects from the run-up in the stock market.
Joulfaian and Wilhelm (1994) find slightly
smaller but similar effects using data from the PSID.
Their results show that the results in Holtz-Eakin et
al. are not specific to a particular dataset. Joulfaian
and Wilhelm also estimate the effect of inheritances
on consumption using PSID data.7 They find that the
marginal propensity to consume all goods out of inheritance wealth is about .0012. This is an order of magnitude smaller than the .01 to .05 marginal propensity
to consume out of stock wealth estimated in most
studies. This evidence suggests that people may anticipate inheritances and that the inheritance estimates,
therefore, may underestimate the effect of unanticipated
wealth changes on labor supply.
TABLE 1
Effect of inheritance on labor force participation
Mean
inheritance
level
Inheritance
difference
Mean
pre-inheritance
income
(--------------------------Dollars--------------------------)
Participation
rate
Participation
change
Inheritance
effect
Observations
(--------------------------Percent--------------------------)
Single
Small
13,359
20,863
1982
1985
89.9
91.1
1.2
730
Medium
119,610
106,251
23,027
1982
1985
82.7
80.5
–2.2
–3.4
544
Large
608,858
595,499
19,586
1982
1985
75.4
65.6
–9.8
–11.0
358
Married
couples
Small
13,323
60,867
1982
1985
77.0
76.9
–0.1
1,078
Medium
125,554
112,231
59,340
1982
1985
73.1
69.2
–3.9
–3.8
994
Large
597,037
583,714
66,804
1982
1985
68.8
64.4
–4.4
–4.2
628
Notes: Participation rate is the sum of people working divided by two multiplied by the number of households. In 1999 dollars.
“Inheritance difference” is the difference between the inheritance level received and a small inheritance level. “Inheritance effect” is
the difference between the participation change for a given inheritance level and the participation change for a small inheritance
level. Small inheritance level is $0–43,000; medium is $43,000–255,000; and large is $255,000 and above.
56
Economic Perspectives
Imbens et al. (1999) use data from the state lottery
of Massachusetts to estimate the effect of winning the
lottery on changes in hours worked and earnings. They
use data on individuals who received a prize, ranging in
present value from $100 to over $1,000,000.8 A subsample of winners received a questionnaire about purchases
made, labor supply, and earnings several years after
they won the prize. Many of the players released their
Social Security earnings records. Therefore, one can see
the earnings of an individual both before and after winning the prize as measured by earnings reported to the
Social Security Administration.
Unfortunately, those who won medium and large
prizes included both season ticket holders and those
who purchased tickets one at a time, whereas those
who won a small prize included only season ticket
holders. As a result, individuals who won the small
prizes were much older (average age of 53.2) than individuals who won medium-sized (average age of 44.6)
or large (average age of 48.5) prizes. This makes the
lottery study less than perfect, although Imbens et al.
attempt to overcome this problem. Moreover, sample
sizes in the study are relatively small. There were a total
of 496 respondents in the entire study.
Given these caveats, Imbens et al. (1999) estimate
the effect of annual lottery winnings on annual labor
income. Lottery winners who won a medium-sized
or large prize (that is, more than several thousand
dollars) received labor income for 20 years. We compute the present value of the lottery winnings and use
their estimate of the effect of annual winnings on labor
income to compute the effect of lottery winnings on
labor income, a measure of labor force participation.
Results from these computations suggest that $1 in
lottery winnings reduces labor income by one cent
annually. In other words, the marginal propensity to
consume leisure out of wealth shocks is about .01.
Imbens et al. also find that the marginal propensity to consume leisure out of wealth shocks is greatest for individuals ages 5565. For example, they
find that for individuals younger than 55, the marginal
propensity to consume leisure is .0082, whereas for
individuals aged 5565, the marginal propensity to
consume leisure is .0132. This is an important point
given that much of the stock market wealth is held
by individuals aged 5565. Imbens et al. also find
that the marginal propensity to consume leisure is the
same for both men and women. Lastly, they find that
the marginal propensity to consume leisure is greater
for individuals who won small amounts than for individuals who won large amounts. For example, it is
.0091 for individual with almost no winnings and
Federal Reserve Bank of Chicago
.0076 for individuals with close to $500,000 in winnings. It is these final two numbers that we will use to
predict the labor supply response to changes in
wealth.
Also somewhat interestingly from this study, the
Social Security earnings records show that the labor
supply response to winning a lottery is not immediate. It is several years before labor supply fully declines in response to the wealth effect. Therefore, the
labor supply response to the run-up in the stock market may not be immediate either. Other studies have
also found that the consumption response to changes
in the stock market is not immediate (see Dynan and
Maki, 2000, for example).
The lottery and inheritance studies are not the
only studies of the effect of financial resources on
labor supply. Blundell and MaCurdy (1999) survey a
wide range of approaches to estimating the effect of
income on labor supply. The majority of these studies
find that increased non-labor income reduces labor
supply. Assuming that income is constant over the
life cycle, one can compute the annuity value of a
lifetime increase in income. Given the estimates surveyed in Blundell and MaCurdy and the computed
annuity value of increases in income, we estimate the
change in labor supply given a change in wealth.
Measured against the results from most other studies,
the estimates in the lottery and inheritance papers are
relatively small, although there are enormous differences in estimates from study to study. An average
estimate of the effect of the annuity value of income
on labor supply from Blundell and MaCurdy is about
twice as large as the inheritance and lottery estimates.
Therefore, our view is that the results from the inheritance and lottery surveys represent conservative estimates of the true effects of unanticipated wealth gains
on labor supply.
Our interpretation of the studies
We expect unanticipated changes in wealth to
lead to larger changes in labor supply for low-income
workers than for high-income workers. An unanticipated $50,000 wealth change replaces two years of
labor income if a worker earns only $25,000 dollars
per year. In other words, this worker could retire two
years earlier and still consume that same amount at
each age as a result of the unanticipated $50,000
wealth change. On the other hand, if the same worker
earns $50,000 per year, the $50,000 unanticipated
wealth change replaces only one year of earnings.
High-wage workers have been receiving most of
the wealth gains from the stock market. Mean annual
57
income for all households in the 1994 PSID is $36,500,
but mean income is $52,900 in 1994 for households
with unanticipated stock wealth gains of $50,000
$250,000 and $94,300 for households with unanticipated stock wealth gains in excess of $250,000.
Therefore, $1 in unanticipated wealth gains probably
has a smaller effect for individuals with large stock
wealth gains than for people who receive an inheritance. We overcome this problem by measuring the
labor supply response to wealth divided by income,
as described below. This procedure assumes someone
with twice the income of another person would need
twice the unanticipated wealth gain of the other person
to cause the same labor supply response.
Figure 11 uses the information in table 1 and results from the lottery study to plot changes in labor
force participation rates against the amount of unanticipated wealth change divided by the mean earnings
of people with that unanticipated wealth change.
Labor force participation rates and unanticipated
wealth shocks are relative to the reference group of
small inheritance receivers in the inheritance study.
Thus, the points for labor force participation rates are
shown in column 6 of table 1 and the points for inheritances are shown in column 2 of table 1. The average
pre-inheritance earnings for the different groups are
shown in column 3. Therefore, four points in figure
11 are the four values in column 6 plotted against the
values in column 2 divided by the values in column
3. We divide inheritances by two for married couples
(the husband and wife each get one half), just as we
divide household unanticipated wealth shocks by two
for married couples in the PSID. The other two points
on figure 11 are the two points previously described
from the lottery study.
For example, single individuals who receive an
average $120,000 inheritance ($106,000 above the
reference group of those who receive a small $12,000
inheritance) have $23,000 in income before receipt of
the inheritance. Therefore, the value of their unanticipated wealth gain divided by earnings is 4.6. They
show a 3.4 percent drop in labor force participation.
Couples who receive an average inheritance of
$125,000 (or $112,000 above the reference group of
couples) have an average of $59,000 in annual earnings. This results in both the husband and wife having
$56,000 in inheritance wealth gain and $29,500 each
in annual earnings. Therefore, the wealth shock divided
by average earnings is (56,000/29,500) = 1.9. Both
husbands and wives show an average decline in labor
force participation of 3.8 percent.
58
FIGURE 11
Predicted participation rate response
to wealth shocks
Decline in labor force participation rate
(percentage points)
25
20
Large, lottery
15
Large, singles
10
Medium,
couples
Large, couples
5
Medium, singles
Medium, lottery
0
0
5
10
15
20
25
30
(wealth shock)/(mean earnings)
35
Note: “Medium, singles” refers to singles who received a
medium inheritance; “large, singles” refers to singles who
received a large inheritance; “medium, couples” refers to
couples who received a medium inheritance; “large, couples”
refers to couples who received a large inheritance; “medium,
lottery” refers to winners of a medium-sized lottery prize;
“large, lottery” refers to winners of a large lottery prize.
Source: Holtz-Eakin et al. (1993) and Imbens et al. (1999).
To use the stock market wealth gain information
to predict the effect of the stock market run-up on
labor force participation rates, we need a functional
form for the effect of unanticipated wealth gains on
labor force participation. Because we have only six
data points to fit and some of the data points seem
more reliable than others, we use no formal criteria
to measure the functional form for how stock market
gains affect labor force participation. Instead, we fit
the data free-hand to an assumed functional form. We
follow three guidelines. First, the functional form is
close to the individual points in figure 11. Second,
we believe that the incremental (or marginal) effect
of increasing stock wealth gains on labor force participation is smaller for very high levels of stock wealth
gains than for low stock wealth gains. The millionth
dollar increase in stock market gains will most likely
have a smaller effect on the probability that one drops
out of the labor force than ones first dollar of gains.
Finally, an unanticipated wealth shock that is close to
zero should have a labor supply response that is close
to zero. Our assumed functional form for the effect
of an unanticipated wealth shock on the labor force
participation rate is
∆Aˆ ∆Aˆ
5) E ∆LFPRit it = β it ,
Ej Ej
Economic Perspectives
where
∆Aˆ ∆Aˆ
.010 × it if it < 8
Ej Ej
∆Aˆ
∆Aˆ
∆Aˆ
6) β it = .032 + .006 × it if 8 ≤ it < 30
Ej
Ej
Ej
ˆ
ˆ
.152 + .002 × ∆Ait if ∆Ait ≥ 30,
E E
j j
∆ Aˆ it
E ] is the expected change
j
in labor force participation rates given a change in
Aˆ it
ˆ
E and ∆ Ait is the unexpected wealth shock of
j
individual i at time t. Also, E j is mean earnings for individuals in an unexpected wealth shock cell (for example, men with over $250,000 of unexpected wealth
gains) in 1994. The functional form for equation 5 is
plotted on figure 11.
Recall from the introduction that understanding
the marginal propensity to consume leisure is of central
importance to understanding the marginal propensity to
consume goods. Also recall that the life cycle model
predicts that the marginal propensity to consume leisure plus the marginal propensity to consume market
goods should add up to at least .04. Our interest is in
whether the marginal propensity to consume leisure
is a large fraction of that .04 number. An attractive
feature of the specification in equation 5 is that the
marginal propensity to consume leisure through the
labor force participation decision is parameterized.
For example, equation 6 shows that if
∆Aˆit
∆Aˆit
∆Aˆit
E < 8, E[∆LFPRit ( E )] = .010 × E .
j
j
j
Note that this means that the average change in
labor income for individuals in wealth cell j is
where E [ ∆ LFPRit
E[∆LFPRit (
∆Aˆit
)] × Ej = .010 × (∆Aˆit ), or a marginal
Ej
propensity to consume leisure of .01. In other words,
for every unexpected $1 gain in wealth, earnings decline by one cent. This number is below the estimates
of a one- to five-cent increase in consumption that
most studies have found for the effect of stock market values on consumption. Therefore, the dominant
behavioral response to increases in stock market
wealth is in the form of increased consumption of
goods, not in reduced labor force participation that
leads to reduced earnings. Because the earnings response to changes in stock market wealth is small, life
cycle models that ignore the effect of wealth on labor
supply are not severely biased.
Predicted changes in labor participation
due to stock market
Given the assumed labor supply function and
the distribution of wealth shocks in the economy, we
predict the aggregate labor supply response to the
increase in stock wealth. Figure 12 shows that the
predicted decline in labor force participation rates is
about 1 percent for most groups, but that these estimates vary by group. For men aged 2534, the predicted decline in labor force participation rates is .05
percent. Because older men have greater stock wealth
than younger men, the predicted decline in labor force
participation rates is greater for older men.9 percent
for men 55 and older.
We find larger predicted labor supply effects from
the stock market for women. Women have lower earnings than men, but we assume women in married
households have 50 percent of household wealth.
Therefore, a $1 increase in wealth for a woman replaces
a larger share of her lifetime resources than a $1 increase in wealth for a man. While this result depends
critically upon the assumption of a 50/50 split of wealth
for married households, most studies of income effects
show larger income effects for women than for men,
so we believe the results presented here are reasonable.
The predicted labor supply response of women aged
2534 to the increase in stock market wealth is a .17
percent decline in labor participation. The predicted
FIGURE 12
Change in labor force participation induced
by wealth effect
percentage points
2.5
Men
0.0
Women
-2.5
-5.0
1960
’65
’70
’75
’80
’85
age group
’90
’95
’00
Sources: Authors’ calculations and PSID data.
Federal Reserve Bank of Chicago
59
decline becomes greater with age. For women aged
55 and over, the predicted response is a 2 percent
decline in labor force participation rates.
Simulations of effect of unexpected
wealth changes
Next, we describe an alternative approach to predicting the labor supply response to unanticipated
changes in wealth. These results are from a dynamic
model described in French (2000), which aims to accurately model the incentives individuals face over
their life times. In this model, we characterize the preferences of people in the economy for consumption
versus leisure, and we model how consumption and labor supply decisions by people of various ages are affected by changes in wages, wealth, taxes, and the
structure of Social Security benefits. Individuals within the model choose consumption, work hours (including the labor force participation decision), and
whether to apply for Social Security benefits. They
are allowed to save, although assets must be non-negative. Therefore, they trade off the value of consumption in the present against the value of consumption
in the future. Their annual income depends on asset income, labor income, and Social Security benefits. Individuals face federal and state income taxes as well
as payroll taxes. When making these decisions, they
are faced with several forms of uncertainty: survival
uncertainty, health uncertainty, and wage uncertainty.
The most interesting aspect of the model is the detailed modeling of the Social Security incentives to
exit the labor market. Individuals who are younger
than age 62 are ineligible for Social Security benefits.
Once eligible for Social Security benefits, the individual faces a tradeoff of the value of receiving benefits in the present versus deferring them and
receiving greater annual benefits in the future. Once
the individual is drawing Social Security benefits, he
or she faces the Social Security earnings test, which
is a large tax on labor income above a certain threshold level.
There are seven preference parameters within
the model. One parameter describes an individuals
willingness to trade consumption in the present for
consumption in the future. Another parameter describes
an individuals willingness to trade goods for leisure.
These preference parameters are estimated using data
from the PSID. Given that individuals in the model
face the same incentives as individuals in the data,
they should behave just like individuals in the data
at the true preference parameters. At the estimated
parameters, the decisions of individuals in the model
are very similar to those of individuals in the data.
60
Therefore, we believe that the estimated parameters
are close to the true preference parameters and that
the model accurately describes how people behave.
Consequently, we believe we can usefully apply the
model to understand how the run-up in the stock
market affects labor supply. We discuss the estimation
of preference parameters in box 2.
Our simulated life cycle profiles for hours, labor
force participation rates, and assets match the data
very well. Simulated labor force participation rates
begin to decline around age 55 and decline very rapidly
at the exact ages of 62 and 65, when there are the
strongest Social Security incentives to exit the labor
market. Given that the model fits the data very well
within sample, it potentially predicts well. Further
details are in French (2000).
The model is useful in that it overcomes the previous problems in the lottery and inheritance studies.
Most importantly, predictions from the lottery and
inheritance studies assume that two individuals of
different ages should have the same labor supply
response to a $50,000 wealth shock given the same
income. However, our expectation is that $50,000 in
wealth at age 60 would generate a larger labor supply
response than $50,000 at age 30. The 30-year-old
will most likely save the money toward an early retirement, whereas the 60-year-old may use the money
BOX 2
Method of simulated moments
The method of estimating preference parameters in the simulation model is called the Method of Simulated Moments. It can be described as
follows. First, we estimate life cycle profiles for
assets, hours worked, and labor force participation rates using Panel Study of Income Dynamics
(PSID) data. Second, we estimate individual histories of health and wage shocks using PSID
data. Third, we solve the model backwards, obtaining optimal decisions for consumption, work
hours, and whether to apply for Social Security
benefits for each possible level of assets, wages,
health status, and potential Social Security benefits. Fourth, we simulate individual life cycle
profiles for assets, hours worked, and labor force
participation rates using the individual histories
of health and wage shocks and the decision rules
from the structural model. Finally, we aggregate
the simulated profiles and the data profiles by age
and compare them. Preference parameters that
create simulated profiles that look like the profiles
from the data are considered the true preference
parameters. Details are in French (2000).
Economic Perspectives
immediately to retire early. The lottery and inheritance
studies do not address this problem.
This model also overcomes the question of
whether the wealth changes in the inheritance studies
are anticipated. If inheritances are anticipated, estimates from the inheritance study will be biased towards zero effect on labor supply. Therefore, the
predicted decline in labor force participation rates
caused by the run-up in the stock market is biased toward a zero effect. Using simulations, one can generate wealth shocks that are completely unanticipated.
Earlier, we estimated that the run-up in the stock
market resulted in aggregate wealth levels being 17
percent higher than they would have been had the late
1990s been average years for the stock market. We
assume that these increases in wealth are taxed at 33
percent. Therefore, we assume that every $1 in wealth
results in $0.17 in pretax unanticipated wealth gains
and $0.11 in post-tax unanticipated wealth gains.
Because we do not formally model rates of return
as a function of wealth and age, the simulation model
potentially overstates the labor supply response to
the run-up in the stock market. Using PSID data, we
find that older individuals and individuals with high
wealth have more of their portfolios invested in stocks
than younger and lower wealth individuals. This tends
to overstate the effect of the run-up in the stock market on labor supply. In our model, low-income individuals receive too much unanticipated wealth; and
it is low-income people who are the most prone to
dropping out of the labor market. However, wealthier
and older individuals usually pay higher taxes. This
attenuates the problem of high wealth people having
higher rates of return since more of the return is taxed
away. To the extent that the model does not completely overcome this problem, we are overstating wealth
shocks for low wealth (and, thus, low-income) people.
Figure 13 presents the simulated changes in labor
force participation rates for men of different age groups.
There are two striking differences when comparing
the simulated changes against the predicted changes
using the lottery and inheritance studies. The first is
that the simulation study predicts much larger effects
than the inheritance and lottery studies. For men aged
5564, the simulations predict a 3.2 percentage point
decline in labor force participation, whereas the inheritance and lottery studies predict only a .78 percentage
point decline. As we described earlier, the inheritance
and lottery studies might understate the effect of unanticipated wealth changes on labor supply. Inheritances are potentially anticipated and younger individuals
usually receive inheritances. In the lottery study, the
small prize group is much older than the medium and
Federal Reserve Bank of Chicago
large prize groups, so the small prize winners are more
likely to retire.
The second striking difference between the two
sets of predictions is that our simulation only gives
such large predictions for men aged 5564. For men
aged 65 and older, the simulation study predicts a 1.3
percentage point decline in labor force participation
rates. This result is much closer to the results of the
inheritance and lottery studies, which show a .90 percentage point increase for men aged 65 and above.
The simulation study therefore provides a useful insight. Men younger than 55 are unlikely to drop out
of the labor force regardless of the positive wealth
shock. Most men older than 65 have already dropped
out of the labor force. Men aged 5564 are near the
time when they exit the labor market. Therefore, the
estimates from the other studies probably understate
the effect of stock wealth on labor supply between
the ages of 5564 vis-à-vis other ages. Recall that the
lottery study came to the same conclusion.
This last point is particularly important for assessing the estimates from the inheritance study. Recall
that the inheritance study mostly uses information on
individuals aged 3544. Note that the predicted decline
in labor force participation from the simulation study
is only .27 percent. This is only twice as large as the
prediction for men aged 3544 when using data from
the inheritance study. This reaffirms our earlier point
that by focusing on individuals aged 3544, the inheritance study probably underestimates the labor supply response to changes in wealth for individuals
aged 5564. Figure 13 shows that this underestimate
is likely to be significant.
FIGURE 13
Simulated change in labor force participation
for men induced by wealth effect
percentage points
0.0
-2.0
-4.0
25-34
35-44
45-54
age group
55-64
65+
61
Conclusion
In this article, we quantitatively assess the effect
of the run-up in the stock market on aggregate labor
supply. We arrive at our conclusions using three steps.
First, we estimate the total size of the aggregate wealth
shock. We find that every dollar invested in the stock
market on December 31, 1994, produced on average
$1.12 in stock wealth gains by December 31, 1999.
Given the aggregate level of wealth in stocks in 1994,
the aggregate unanticipated increase in wealth between
1994 and 1999 was $5.8 trillion, which represents an
unanticipated increase in wealth of almost $20,000
per person in the U.S.
Second, we estimate the magnitude of the unanticipated wealth shock for different age groups. Using
PSID data, we find that very few people younger
than age 55 today benefited greatly from the run-up
in the stock market. About 15 percent of all individuals
aged 55 and above had unanticipated wealth increases
of greater than $50,000. For most individuals, $50,000
would be more than enough to afford an additional
year of retirement without any change in the consumption of market goods.
Third, we predict the effect of the run-up in the
stock market on labor supply. We find that labor force
participation rates for individuals aged 55 and older
have increased since 1995. Increases in stock market
wealth should cause reductions in labor force participation rates, all else equal. This implies that the stock
market has not been the dominant factor influencing
labor force participation rates from 1995 to the present.
Other factors, such as rapidly rising wages, seem to
be more important.
We use two approaches to predict the effect of
rising stock prices on labor supply. In the first approach,
we take estimates of the size of the wealth effect from
other studies. Although nobody has used variation in
stock prices to estimate the wealth effect on labor
supply, researchers have used data on inheritances
and lotteries to estimate the effect of wealth on labor
supply. Using estimates from these studies and the
estimated distribution of wealth shocks to different
groups of people in the economy, we estimate that in
the absence of a run-up in the stock market, aggregate
labor force participation rates would be 1.16 percent
higher today. We believe that these are conservative
predictions of the stock market effect.
Our second approach is to use simulations from
a model described in French (2000). We find that
simulations from this model give much larger predictions of the effect of the run-up in the stock market.
The predicted decline in labor force participation rates
for men is over 1 percent, on average. (The model
does not address the labor supply response of women.)
The simulations also predict that the largest effects
should be at age 5564, when men are considering
exiting the labor force. For this age group, the predicted
decline in labor force participation rate is 3.2 percent.
These results might overstate the effect. Therefore,
we interpret the predictions based on estimates from
the lottery and inheritance studies as a lower bound
on the effect and the simulations as an upper bound.
Lastly, we note that the lottery, inheritance, and
simulation studies imply that for every $1 in increased
wealth, earnings decline by one to two cents. As we
noted at the outset, total consumption of goods plus
leisure must rise by at least four cents to be consistent
with the life cycle model. This means that consumption of goods must rise by at least two or three cents
in order to be consistent with the life cycle model.
Most empirical estimates are in the range of one to
five cents; as such, results at the lower end of this
range are at odds with the life cycle model. Therefore,
our work provides additional evidence that either the
marginal propensity to consume market goods is at
least 23 percent or that the life cycle model is not a
reasonable model of consumer behavior.
APPENDIX: DESCRIPTION OF DATA
Risk-free asset data
We calculate the risk-free five-year return as the
continuously compounded return on holding five-year
Treasury instruments to maturity. To obtain the zero
coupon rate, we use data that have been adjusted using
a Fisher/Zervos technique. We obtained Fisher/Zervos
estimates for 1961 to the present from the Federal
Reserve Board of Governors, Division of Research
and Statistics (courtesy of Mark Carey). For returns
prior to 1961, we use five-year Treasury bonds. There
62
is only a small difference in returns between the two
data series (during periods of overlap).
Stock market data
Stock market annual returns (including dividends)
for 1926 to the present are from the Center for Research
in Security Prices (CRSP) Index Series, No. 100080,
a value-weighted portfolio of all NYSE, AMEX, and
NASDAQ stocks. We impute missing entries for 1999
2000 using 19702000 S&P 500 total return (including
Economic Perspectives
dividend reinvestment) data. Values represent end of
December 31 to end of December 31 returns.
Flow of funds data
Our data on the market value of equities owned
by households (and related data) are from the Federal
Reserve Board of Governors, Flow of Funds Accounts
of the United States. All values are in 1999 billions
of dollars and represent year-end levels. Equities in
pensions include defined contributions pensions only.
Equities are defined as shares of ownership in financial
and nonfinancial corporate businesses, both common
and preferred shares of domestic corporations, and
U.S. purchases of shares of foreign corporations (including ADRs).
Price level data
We use December levels from the Consumer Price
Index for all urban consumers (1999 = 100) to make
our price adjustments.
PSID data
We use the 1989 and 1994 waves of PSID data
in our analysis of stock and pension wealth. Our 1989
sample excludes those who do not provide an answer
regarding pension status or do not respond to whether
they contributed to a pension (for either husband or
wife in the household). The 1994 sample includes
only those families in the 1989 sample, less those
who changed marital status or whose head of household had changed since 1989. We use the 1989 weights
wherever applicable.
Juster et al. (1999) show that the 1989 PSID
accounts for approximately 85 percent of household
stock wealth in the Survey of Consumer Finances
(SCF). Limiting the sample to only those who match
between 1989 and 1994 (less those who experienced
a change in marital status or change in household
head during those years) results in a 30 percent higher
mean stock wealth than the full 1994 sample (using
1994 weights, when both are scaled by 1/0.85 = 1.18),
so we adjust by scaling down 1994 stock wealth by
a total factor of 1.18/1.30 = 0.91. In order to analyze
men and women separately, we assume that allocation
of non-pension stock wealth in married households is
50 percent to each spouse.
To analyze 1989 pension wealth, we use a simplified model assuming constant lifetime accrual amounts
(in 1999 dollars) with a real return of 2.3 percent per
year. Using figures from Gustman and Steinmeier
(1999) and assuming that half the wealth in combined
pension plans is in the form of defined benefit wealth,
the average level of wealth in defined contribution
Federal Reserve Bank of Chicago
plans per household is $69,000 (1999 dollars). Assuming that it is at age 65 when the amount is $69,000,
and that it is at age 35 when the worker starts contributing, we compute a schedule of pension wealth at
each age. We give workers less than 35 years old one
years worth of pension accrual and workers older
than 65 the maximum amount ($69,000). Assuming
a 5 percent contribution rate, we use our imputed annual accrual level to impute the associated level of
annual earnings. We then assign each worker (who
has a defined contribution plan) a level of pension
wealth equal to the previously calculated pension
wealth level (associated with their age), scaled by the
ratio of their 1988 earnings (from the PSID) to the
imputed mean level of earnings from the HRS. To
analyze the amount of pension wealth in stocks, we
assume that stocks comprise 50 percent of pension
wealth. To find pension wealth in 1994, we use the
previously calculated schedule of pension wealth to
assign a new level of pension wealth based on the
individuals new age, again using 1988 earnings to
scale the amount.
We make a number of other imputations to account for shortcomings in the PSID data. First, since
the 1989 PSID data contains only 1988 earnings (not
the pre-retirement earnings level, which is the earnings level in question), we impute the level of earnings
of pre-retirement work as follows. For individuals
who are covered by a pension and work more than
1,000 hours, we do not modify their level of earnings;
if they work less than 1,000 hours, we take their preretirement earnings as the earnings in the data plus
the mean earnings of those who work more than 1,000
hours, less the mean earnings of those who work less
than 1,000 hours. To obtain a persons 1994 earnings
(required for the computation of labor supply elasticities), we look to the persons earnings in the 1995 PSID
and proceed in a similar fashion.
The 1989 PSID pension question does not allow
retired persons to indicate whether they were covered
by a pension while they were working; therefore, we
take positive pension income as an indicator for a preretirement pension. To find whether that pension is a
defined contribution pension, we perform the following procedure. If a person indicates pension coverage
in response to the direct PSID question regarding
pension coverage, we take the response to whether
they contribute toward that pension as given. If a
person indicates no pension coverage, but is receiving positive pension income, we assign the person a
random number (according to a uniform [0,1] distribution); if that number is less than the probability of
63
having a defined contribution plan (given age and
pension coverage), we assume the pre-retirement pension is a defined contribution plan (otherwise not).
We calculate the probability of having a defined contribution plan as follows:
Pr(DC | age, pension = yes) = Pr(DC | age) /
Pr(Pension | age),
where
Pr(DC | age) = Pr(PSID DC = yes | age)
+ [Pr(PSID DC = yes | PSID Pen = yes) ×
Pr(receiving pension income | age)].
Lastly, to find the people who are covered by defined
contribution plans in 1994, we simply carry over those
who were covered in 1989, since the 1994 PSID data
release at this time does not include any questions regarding defined contribution pensions.
NOTES
1
This effect would be ambiguous, however, as people may have
wished to work more hours in 199599 in order to generate
more wealth. Increased wealth could in turn be invested in the
stock market.
2
One caveat to this article is that it is not clear why the stock market rose in the first place. Our analysis assumes that the stock
market rose for reasons unrelated to future productivity growth
perhaps financial markets have become more efficient. If the
stock market rose because of beliefs about increasing productivity in the future, then there are three effects that we do not consider here. First, people should believe that wages will rise
rapidly in the future because of increased productivity. Not only
would stockholders feel wealthier, but so would all individuals
who believe that they will be working in the future. If this is true,
our analysis underestimates the true wealth shock to the economy
and, thus, underestimates the true effect of the run-up in the stock
market on labor supply. Second, any change in future beliefs
about productivity is likely to be accompanied by wage changes
in the present and near future. This potentially increases hours
worked as incentives for work are greater. This offsets the wealth
effect. Third, interest rates should rise if people believe productivity will rise, because higher productivity leads to higher demand for capital. If interest rates are relatively high (as they are
today), people should work more hours today so that they can develop greater wealth that will earn a high rate of return. Again,
this offsets the wealth effect. Any rapid change in stock prices
will likely be accompanied by these three additional effects, if the
change in prices reflects changing beliefs about future productivity. Therefore, it is not clear how labor supply would respond to a
large stock market change in the future. However, we believe that
we have increased understanding of the effect of the stock market
on labor supply by focusing on the direct effect.
This is done by comparing the PSID to another dataset, the Survey
of Consumer Finances, and assuming that respondents in the Survey of Consumer Finances report 100 percent of their assets. The
Survey of Consumer Finances is considered to have extremely
high quality data on wealth, although the respondents probably
report slightly less than 100 percent of their assets.
3
They do note that when the husbands income accounts for 75
percent of total household income instead of 50 percent, the
husbands share of consumption rises by about 2 percentage points.
This shows that assuming an even split is not perfect but is roughly
correct. They also note that consumption of womens clothing is
slightly higher than mens, but again assuming an even split of
resources is roughly correct.
4
Using the previously described procedure to estimate defined
contribution wealth, we aggregate defined contribution wealth in
our PSID sample up to the national level. In other words, we take
aggregate wealth in the PSID and multiply it by the ratio of U.S.
households to PSID households. We compare this estimate to
defined contribution wealth in the Flow of Funds. We find that
our PSID defined contribution measure is 15 percent greater than
the Flow of Funds measure.
5
See Social Security Bulletin Annual Statistical Supplement,
1997, p. 60.
6
Unfortunately, the PSID measures only food consumption not total consumption. The consumption results show that the marginal
propensity to consume food out of inheritances is about .1 percent,
far lower than the 1 percent to 5 percent marginal propensity to
consume out of changes in stock wealth that most studies find
(Parker, 1999; Ludvigson and Steindel, 1999; and Dynan and
Maki, 2000). Because food is a necessity, the marginal propensity
to consume food is lower than the marginal propensity to consume
all consumption goods. For example, Attanasio and Weber (1995)
show that for every 1 percent increase in food consumption, total
consumption rises about 1.2 percent.
7
All calculations assume that the after-tax real interest rate is 2.3
percent and the inflation rate is 3.3 percent.
8
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Attanasio, Orazio, and Guglielmo Weber, 1995,
Is consumption growth consistent with intertemporal
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Blundell, R., and T. MaCurdy, 1999, Labor supply,
Handbook of Labor Economics, Orley Ashenfelter
and David Card (eds.), Amsterdam: Elsevier, North
Holland.
Economic Perspectives
Board of Governors of the Federal Reserve System,
2000, Flow of Funds Accounts of the United States:
Flows and Outstandings, Fourth Quarter 1999,
Washington: Board of Governors of the Federal
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Uncertain facts and novel theories, Economic
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Sacerdote, 1999, Estimating the effect of unearned
income on labor supply, earnings, savings, and consumption: Evidence from a survey of lottery players,
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Vol. 29, No. 4, pp. 12051234.
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Stafford, 1999, The measurement and structure
of household wealth, University of Michigan,
manuscript.
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Governors of the Federal Reserve System, mimeo.
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65
Index for 2000
Title & author
BANKING, CREDIT, AND FINANCE
The price of bank mergers in the 1990s
Elijah Brewer III, William E. Jackson III, Julapa A. Jagtiani,
and Thong Nguyen
Subordinated debt as bank capital: A proposal for regulatory reform
Douglas D. Evanoff and Larry D. Wall
Banking and currency crises and systemic risk:
Lessons from recent events
George G. Kaufman
Understanding intraday credit in large-value payment systems
Ruilin Zhou
Why do consumers pay bills electronically? An empirical analysis
Brian Mantel
ECONOMIC CONDITIONS
Black/white differences in wealth
Joseph G. Altonji, Ulrich Doraszelski, and Lewis Segal
Unemployment and wage growth: Recent cross-state evidence
Daniel Aaronson and Daniel Sullivan
The effect of the run-up in the stock market on labor supply
Ing-Haw Cheng and Eric French
Pages
Issue
2-23
First Quarter
Second Quarter
40-53
Third Quarter
9-28
Third Quarter
29-44
Fourth Quarter
32-47
First Quarter
38-50
Second Quarter
54-71
Fourth Quarter
48-65
INTERNATIONAL ISSUES
Dollarization in Argentina
Francois R. Velde and Marcelo Veracierto
Understanding the Korean and Thai currency crises
Craig Burnside, Martin Eichenbaum, and Sergio Rebelo
First Quarter
24-37
Third Quarter
45-60
REGIONAL ISSUES
Effect of auto plant openings on net migration in the
auto corridor, 1980-97
Thomas H. Klier and Kenneth M. Johnson
Fourth Quarter
14-29
Second Quarter
2-20
Second Quarter
21-39
MONEY AND MONETARY POLICY
Income inequality and redistribution in five countries
Mariacristina De Nardi, Liqian Ren, and Chao Wei
The expectations trap hypothesis
Lawrence J. Christiano and Christopher Gust
Disruptions in global financial markets: The role of public policy
Michael H. Moskow
A record current account deficit: Causes and implications
Jack L. Hervey and Loula S. Merkel
Third Quarter
2-8
Fourth Quarter
2-13
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Federal Reserve Bank of Chicago
Public Information Center, P.O. Box 834
Chicago, IL 60690-0834
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66
Economic Perspectives
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