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Economic
Review
Federal Reserve Bank
of San. Francisco
Fall 1989
Ronald H. Schmidt
Number 4
Natural Resources and Regional Growth
John P. Judd and
Bharat Trehan
Unemployment-Rate Dynamics:
Aggregate-Demand and
-Supply Interactions
James A. Wilcox
Liquidity Constraints on Consumption:
The Real Effects of "Real" Lending Policies
Natural Resources and Regional G row th........................................................................... 3
Ronald H. Schmidt
Unemployment-Rate Dynamics:
Aggregate-Demand and
-Supply Interactions ...........................................................................................................20
John P. Judd and Bharat Trehan
Liquidity Constraints on Consumption:
The Real Effects o f 6‘Real” Lending P o licies.................................................................. 39
James A. Wilcox
Federal Reserve Bank of San Francisco
1
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2
Economic Review / Fall 1989
Natural Resources and Regional Growth
Ronald H. Schmidt
Senior Economist, Federal Reserve Bank of San Francisco. The author would like to thank Stephen O. Dean for
his assistance. Editorial committee members were Adrian
Throop, Carolyn Sherwood-Call, and Gary Zimmerman.
Evidence from the Gross State Product data suggests
that natural resource-dependent states out-performed
less resource-dependent states over the 1964-86 period.
Closer examination, however, suggests that the gains
largely were due to an increase in wealth associated
with positive resource-price shocks during the period.
Interestingly, unlike the Dutch disease problem of the
international literature, the non-resource industries of
resource-dependent states were the principal beneficiaries
offavorable natural resource price shocks.
Federal Reserve Bank of San Francisco
What is the relationship between a state's dependence on
natural resource production and its economic performance? Can differential growth rates of state economies be
traced in part to differences in their dependence on agriculture, forestry, mining, and energy?
Past studies have provided mixed evidence on the relationship between natural resources and relative economic
performance. The international literature has documented
cases where dependence on natural resources has had
detrimental side-effects on the economic structure of those
countries. Moreover, the boom-bust cycle of mining towns
offers evidence of the potentially ephemeral nature of
natural resource industries. In contrast, however, other
cases explicitly have linked spurts of economic growth to
natural resource industry stimulus.
The purpose of this article is to address the linkages
between a region's natural resource dependence and its
economic performance using information from the Gross
State Product (GSP) data prepared by the U.S. Bureau of
Economic Analysis. These data, which offer relatively
consistent information on the value added by each major
industry in each state, allow some cross-state comparisons
over the period from 1964 to 1986.
Using descriptive statistics from the data, the relative
economic growth of resource-dependent states is compared to that of less dependent states. In general, the
results show that resource-dependent states, particularly
energy and mining states, had economies that grew more
rapidly than average over the period, although they had
greater variance around their trend growth rates as well.
The evidence suggests, moreover, that the relative gains
were more likely a result of strong price movements that
generated significant wealth effects, rather than a result
of intrinsic advantages associated with natural resource
production.
The data also indicate that the "Dutch disease" problem
cited in the international literature may not apply to
regional economies. The "Dutch disease" refers to the
effects of a sharp increase in world natural resource prices
on a resource-dependent economy. The resource price
increase can cause the resource-dependent economy's currency to appreciate, making its other commodities less
competitive on world markets. As a result, a resource price
3
increase can work to the detriment of non-resource industries.
At the regional level, the exchange rate effect is not
present because ofa.common currency, but the increased
demand for factors from the resource sector can bid up
costs of those factors in the region and make its other
outputs less competitive with those of other regions.
Results from the state-level data, however, indicate that
contrary to the Dutch disease problem, non-resource
industries were the principal beneficiaries of resource
price shocks. The price shocks apparently stimulated nonresource production even more than resource production.
Differences in resource endowments across states are
described in the first sectionofthis article, followed in the
second section byanexamination oithe variety ofchannels
through which natural resource dependence might affect
the level, growth, and structure of a state's economy. The
third and fourth sections present empirical findings. Conclusions are then presented in the fifth section.
I. Differences in Natural Resource Dependence
In this section the degree to which natural resource
production varies across states is documented using GSP
data. These data, released in 1988for the first time, provide
a measure of the value added annually by each major
industrial grouping for the period from 1963 to 1986, and
generally provide a better measure of activity than do the
income or employment data.'
Each state's share of national output, both for the total
economy and by resource industry, are shown in Table 1for
1986. The first column presents each state's share of total
national output (the latter being the sum of state GSP
across states). The second column gives the share of
national natural resource output, comprising agriculture,
forestry and lumber, mining, and energy, that is contributed by each state.? By comparing these two columns, it is
clear that the states' shares of natural resource output
Percent
differ from their contributions to total output. Twenty
states can be categorized as relatively dependent on natural
resource production, in the sense that they contributed a
larger proportion of total national natural resource production than would be predicted by their share of national
output.
As shown in the table, the distribution of resource
production is highly skewed across states and across resources. Rhode Island contributes only 0.05 percent of
total natural resource production, while Texas provides
20.78 percent. The top 10 states in each natural resource
subcategory, respectively, account for 50.4 percent of U.S.
agricultural production, 54.8 percent of the nation's forestry and lumber, 56.9 percent of mineral mining, and
84.7 percent of national energy extraction. 3
Chart 1
Resource Share of Total Gross State Product
50
40
30
20
10
o
4
EconomicReview / Fall 1989
Federal Reserve Bank of San Francisco
5
The information in Table 1 does not account for differences in the sizes of states' economies. Such a comparison is presented in Chart 1. Resource industry shares of
total state asp are calculated using averages from the
1964-86 period. As can be seen by comparing the chart,
which has shares of state output, with Table 1, which
reports shares of national output, differences in national
natural resource production shares have been translated
into differences in concentration in resource production.
The uneven dispersion of resources across states has resulted in differences in reliance on natural resource industries. Resource dependence, as measured by the share of
real asp accounted for by the resource industries, varies
widely across states and across resources. As shown in
Chart 1, the average share of real asp contributed by
resource industries (agriculture, forestry and fisheries,
mining, and fuel mining) over the period 1964-86 ranged
from virtually zero in Rhode Island to nearly 50 percent in
Wyoming.
The composition of resource endowments varies significantly across states as well. As shown in charts 2 through
5, the states with the largest shares of asp in each resource
are different across resources, with little overlap between
agricultural states and mining states.
The magnitude of dependence on resources varies
across resources as well as across states. Among the
resource-dependent states, energy stands out as the most
dominant single source of resource output. The top six
Percent
energy-dependent states have between 20 and 40 percent of
their gross state product originating in the energy sector.
Agriculture also plays a major role in. the agriculturedependent states, with five states reporting an average of
10 to 20 percent of their asp from agricultural production.
Incontrast, mining and forestry play less dominant roles
even in the states with the highest concentrations of those
activities. Lumber and wood products account for less than
five percent of output in all states except Oregon, while
mining accounts for less than four percent of total output
even in the states with the highest concentrations of mining
output. (Mining in this article refers to non-energy mining;
coal, oil, and natural gas outputs are combined to form the
energy category.)
These figures, however, may understate the importance
of the natural resource industries to state economies,
particularly in the short run. For example, according to the
1977 California input-output model, agriculture has a
multiplier of 3.2 in the economy, suggesting that a 10
percent increase in agricultural output generates a 32
percent increase in aggregate output through the associated increase in demand for inputs, processing, marketing,
transporting, and retailing (State of California, 1980.)
Because such measures tend to assume that these factors
could not be shifted to other uses, however, the multiplier
effects tend to overstate the importance of resource industries."
Chart 2
Agriculture Share of Total Gross State Product
20
15
10
5
o
6
Economic Review / Fall 1989
II. Natural Resources and Production
These differences in resource endowments can be expected to result in different economic orientations, and
thereby to affect the level of activity and the rate of growth
of regional economies. In this section, several strands of
the literature are summarized to shed some light on the
effects that a natural resource orientation can have on
different regional economies.
Comparative Advantage
The most obvious effect of differential endowments of
natural resources is that states with large shares of a
particular resource specialize in the production of that
resource. This result displays the basic concept of comparative advantage laid out in the Heckser-Ohlin theorem
in the international trade literature. Regions (or countries)
with different factor proportions can generate higher total
output and consumption by specializing in the production
of commodities in which they have a relative abundance of
the needed inputs. Total output is maximized when regions
specialize in the production of commodities that best
reflect the area's relative resource mix (its comparative
advantage) and then trade with areas that have a comparative advantage in producing other goods. Consequently,
regions with abundant natural resources would be expected to specialize in natural resource-intensive production, using resource outputs to trade for non-resource
commodities from other regions.
In the regional economics literature, differences in
natural resource endowments are important in explaining
different regional production processes. If all regions had
identical resource endowments, there would be little reason to expect specialization across regions. In fact, differences in resource production and therefore, in industry
mix across states, have led to different responses to technology or price shocks. A recent study by Cox and Hill
(1988), for example, predicts the differential incidence of
exchange-rate shocks at the state level by taking into
account industry-mix characteristics and the relative tradesensitivity of those sectors. Similar research has examined
the differential regional effect of oil price shocks. These
studies explicitly assume that differences in factor endowments promote different productive processes and outputs,
and hence, expose regional economies to different external
forces.
Levels vs. Growth Rates
While the principle of comparative advantage predicts
that differences in resource endowments would affect the
composition of output across regions, natural resources
also may be key factors determining both the level and rate
of growth of regional economies.
In the case of natural resources, the answer to the
question "is more better?" seems obvious at first glance.
From the standpoint of an economy, being resource-rich
Chart 3
Lumber Share of Total Gross State Product
Percent
10
8
6
4
2
o
Federal Reserve Bank of San Francisco
7
increases productivity. Abundant natural resources lower
the cost of certain inputs, enhancing the competitiveness
of resource-intensive production.
Simpleproduction-function models of an economy suggest that the level of output is a direct function of the
resource base. As long as some factor substitution is
possible, an abundance of a given resource boosts total
output.
Theeffect of the resource stock on the. rate of growth is
lessdear. Natural resources could.increase .the rate.of
growth of an economy if theresource stock grew overtime,
or if the "effective stock" (that is, the actual stock adjustedforchanges in thatstock'sproductivity) were torise
overtime because of technological efficiency gains. Conversely, natural resources couldcausetherateofgrowth to
slow, if increasing scarcity of the natural resources constrained the growth of related sectors.
Consequently, theory suggests that a higher stock of a
natural resource should raise the level of activity, but the
effecton therateofgrowth depends inpartonthedegree to
which natural resource production expands andspurs other
industries to develop. As shown in the remainder of this
section, theoretical evidence on the extent to which resources spur growth is not conclusive.
Agglomeration Effects vs. Boom Towns
The classic model highlighting the role of natural resources as a stimulus for regional economic growth was
developed by North (1955). In North's export-base model,
Percent
natural resources are viewed as a driving force for economic growth, modeled along thelinesof economic development in theUnited States. Earlysettlers to a regionoften
are attractedby the economic potential of thatarea, which
typically includes its natural resources. Trappers were
attracted to the western areas by available wildlife populations ..Farmers were attracted to the Midwest and West by
available low-cost land. Miners were attracted to the
western states by discoveries of gold and silver. Loggers
were attracted to thePacific North",estandupperMid\Vest
by available supplies of timber. Discoveries of massive oil
reserves brought an influx of people to Texas.
In many parts of .the country,therefore, economic
growth was either initiated or boosted by the influx of
people seeking to use natural resources. Accordingly,
exports of natural resource products to other regions and
countries became important in the early development of
regional economies.
In the export-base literature, the creation of exportable
commodities shapes a region's economy and spurs further
growth. As production of the resource increases, rising
wages and returns to capital encourage the migration of
productive factors from otherregions (Borts [1960], Borts
and Stein [1964], and Schmidt [1985]). Population rises
because of immigration, and export earnings and investment allow the capital stockto grow.
In the early stages, this investment is closely tiedto the
resource sector. Processing and transportation facilities
develop, along with services to support theresource industry's employees and production.
Chart 4
Mining Share of Total Gross State Product
4
3
8
Economic Review / Fall 1989
Over time, however, other industries not directly tied to
resource production develop to take advantage of the
growing economic and social infrastructures. Moreover,
firms that initially support resource industries diversify
into other products. For example, Texas Instruments began
as a company manufacturing seismic equipment for oil
drilling. As the company grew, it expanded into other
electronic instruments. Today, equipment for the oil industry is only a small part of the company's sales.
Natural resources, therefore, can be a primary source of
early development, followed by diversification of the economy into other fields not tied to an area's natural resources.
But as evidenced by boom towns, natural resources are not
always a source of lasting growth. In the case of extractive
or nonrenewable natural resources, the extent to which a
region diversifies into non-resource production may determine the sustainability of its economic growth. In many
cases, natural resource booms have led to temporary
growth, followed by decline. This idiosyncratic relationship between growth and natural resources has been described by economic historian Jonathan Hughes in the
following way:
Apart from agriculture, no doubt the best known cause of
increased economic growth in the past came from the
discovery and exploitation of natural resources. The ghost
towns of the Rockies and the capped oil and gas wells of
[the oil states] are witness to the fragile tenure of economic
growth from such sources. Exploitation growth via nonreproducible natural resources usually involves only the
relatively short-lived creation of fungible wealth that is
Percent
carried off, leaving a hole in the ground, a stumped-over
woodland, or an ocean stripped of one of its main species.
In the past, there have been many examples of this purely
ephemeral kind of growth. It was caricatured by historian
Christopher Lasch once with these lines: 'American capitalism's idea of economic development was to leave the
continent a smoking ruin.' Sometimes one must agree with
Lasch on this point. Fortunately, the smoking ruin is the
exception and not the rule. [Hughes 1985, p.5]
Natural resources, therefore, can be the impetus for
sustained growth, but that is not always the case.
Information Effects
Dependence on natural resources also may affect regional economic growth because of the way natural
resource price movements and/or discoveries of new
deposits tend to change perceptions of the economic opportunities in the affected region. As shown in Plaut and Pluta
(1983), Miernyck (1985), Gruben, Martens, and Schmidt
(1988), and Gruben and Schmidt (1989), energy price
shocks were instrumental in explaining shifts of labor and
capital among regions. Those regions that were energy
exporters benefited markedly from rising oil prices relative
to non-energy producing states. The factor flows were not
directed solely to energy industries, but rather to the
broader economy. Although part of the expansion and
contraction in the energy states can be directly linked to
energy-supporting industries, the factor movements appeared to reflect investors' and migrants' expectations of
rapid growth in other sectors as well.
Chart 5
Energy Share of Total Gross State Product
40
30
20
10
Federal Reserve Bank of San Francisco
9
As hypothesized by Schmidt and Gruben (1988), this
larger effect reflects the imperfect information available to
migrants and investors regarding spatial opportunities.
Because information is costly and known only with a lag,
shocks that have easily recognized impacts convey an
unusually large amount of information. The oil price rises
in 1973-74 and 1979-80 highlighted investment opportunities in the oil patch states, while the price collapse may
have encouraged potential investors to spend their limited
resources acquiring information on other regions not likely
to be hurt by the collapse.
Because resource price movements often are dramatic
and typically are expected to have differential geographic
incidence, a state's characterization as natural resourcedependent may give strong signals-whether false or
true-regarding the potential opportunities to outside factors. Dramatic price movements tend to focus attention on
a resource-rich region, thereby giving investment opportunities a better chance of attracting the needed factors than
areas that are less well known.
"Dutch Disease"
The previous aspects of the relationship between natural
resources and economic growth have stressed the ways in
which natural resource production can attract factors of
production from other regions. In this way, natural resources can be a source of non-resource industry growth.
The assumption in those cases is that the income or
wealth effects from expanding natural resource production
will spill over into non-resource production, thus diversifying the economy. In contrast, the literature in international
economics points to the potential for an expanding natural
resource sector to crowd out non-resource industries,
known in the literature as the "Dutch disease" (Laney
[1982]).
This phenomenon has been applied to cases of oil
exporting countries, in particular, although the same process affects other markets as well. In this framework, a
sudden price shock to a key export industry (such as the oil
price increases of 1973-74 and 1979-80) boosts the nominal value of the country's exports, causing an increase in
the country's trade surplus. As a result, the country's
exchange rate appreciates.
The higher exchange rate then makes imports less
expensive and non-petroleum exports more expensive.
Consequently, domestic industries that export or compete
with imports in the domestic market become less competitive. At the same time, productive factors may flow into the
petroleum sector away from other sectors, thus raising
factor costs in other sectors as firms bid for potentially
scarce labor and capital.
10
If the price shock persists, the negative effects on the
other sectors of the economy are transitional as the economyshifts toa new production mix that reflects the
heightened comparative advantage of the natural resource
industry. However, if the shock is expected to persist and,
instead, proves to be temporary, the cost in terms of
misallocated· factors, particularly irreversible investment
in plant and equipment, can offset the potential gains to the
economyof the positive price shock. Moreover, such
temporary losses ofcompetitiveness can result in the loss
ofmarket share in non-resource industries that may be
difficult to recapture. Finally, Dutch disease also can
expose country to greater risk by making its export
portfolio less diversified.
Similar forces are at work in a regional context, although there are important differences as well. Resource
price shocks can lead to increased production of those
resources, which can cause factors to be reallocated from
other industries. Moreover, factor prices can be bid upward, increasing the cost of other sectors' output relative to
other areas of the country. Thus, even though there is a
common currency, a process akin to exchange rate appreciation can occur through a rising relative cost of living.
The most important difference between regions and
nations, however, is the greater mobility of factors across
regional boundaries. Constraints are not placed on interstate movements of labor or capital, nor are constraints
placed on shipments of products. Therefore, whereas the
Dutch disease can lead to sharply higher factor prices in the
resource-dependent country, factor prices need not rise as
sharply in a region where factors can be drawn from other
regions relatively easily.
a.
Special Characteristics of Natural Resources
The preceding discussion, while couched in terms of
resource industries, is not unique to natural resources.
Many of the same relationships between resource industries and regional economies can be found in states that are
highly dependent on non-resource-based industries, such
as autos and steel.
There are, however, several areas in which natural
resource industries differ from other industries. Natural
resources lend at least three primary advantages to a
developing economy over and above simple comparative
advantage: a developed market, low initial technological
requirements, and access to capital. First, natural resources often enjoy developed markets, both domestic and
international. To an area that is sparsely developed, a
natural resource can provide a readily exportable commodity.Unlike many finished commodities, a resource
does-not require strong local demand during start-up
Economic Review I Fall 1989
phases of the operation. As long as the region has access to
transportation, trade is possible.
A •second, related advantage that accrues to natural
resource production concerns the relatively low level of
skill and capital that is required at initial stages of production. Typically, the existence of abundant resources and
access to transportation make the cost of extracting the first
units relatively 'low.> Mineral mining can be profitable
with a shovel and pan, logs can be obtained with a saw and
a river, and oil can be extracted with little more than the
technology to drill a water well. Thus, in initial phases,
successful development requires risk-taking by the available-labor supply and only modest support operations.
Production initially does not require a large collection of
highly-trained technical and professional workers.
The third advantage of a natural resource is that it
initially can produce economic rents that allow the region
to import needed capital for other ventures. Because
natural resources tend to be scarce worldwide, the price of
the resource generally will be sustained above the marginal
cost of producing it in areas where it is abundant. Trading
with more developed countries allows the region to import
capital goods that cannot be developed locally with existing
capital. Ai natural resource, therefore, can serve as a
channel through which raw materials are converted into
reproducible capital. 6
At the same time, however, dependence on natural
resource production has several disadvantages that can
threaten sustained economic growth of more developed
economies. To begin with, access to world markets makes
the region highly susceptible to fluctuations in those markets.Changes in terms of trade, embargoes, or trade
barriers can have a large impact on a resource-dependent
economy. Moreover, changes in world supplies, such as the
discovery of new reserves elsewhere, or in demand, such as
shifts in taste or technology, have immediate impacts on the
local economy. Markets are less predictable and controllable than when supply and demand are more insulated
geographically.
The downside of resource price volatility clearly has
been evident in the oil-dependent states in the 1980s.
While the rest of the nation has enjoyed a record peacetime
expansion, the oil states have experienced the worst recession in the post-depression era because of the sharp drop in
oil prices.
The problem is exacerbated by the close linkage between natural resource production and government policy.
In most countries, natural resources are heavily controlled
or owned by governments. Oil prices are determined in
large part by OPEC's decisions about production. Agricultural production is highly subsidized in most countries.
Federal Reserve Bank of San Francisco
Forestry sales are heavily dependent on decisions concerning.access to public stands of timber-decisions that often
are influenced by environmental policy considerations.
These government controls, therefore, further expose a
resource-dependent economy to political uncertainty, as
well as to the uncertainty that would normally arise in the
market.
The low skill requirements needed in many of the
extraction industries also can work to the detriment of
long-term regional growth. In many cases, boom periods
lead to rapid increases in the wages of low-skilled workers,
encouraging a migration of such workers from other areas
ofthe country. These low-skilled workers receive high
wages because of temporary output surges and concomitant shortages oflabor, rather than as a result of a steadystate measure of their opportunity cost. In periods with
slowing demand and production, these workers often become unemployed and typically are less able than skilled
workers to find other work at similar pay.
Moreover, the temporarily high returns to low-skilled
labor-which may not be perceived to be temporary by the
workers-can inhibit investment in human capital. The
value of boosting human capital through training and
education is obscured by the temporary returns to relatively low levels of such human capital. Over the long run,
a drop in the skilled labor pool can slow innovation and
productivity growth.
Finally, the theory of exhaustible resource production
typically points to declining production after some stage.
Optimal production behavior calls for spreading production over time, but even in cases where production capability first must expand, production is expected to decline
eventually, both as a share of output and in absolute levels
(Pindyck [1978]). Production tends to rise in early stages as
discoveries are made and new reserves are developed, but
finding large new deposits to replace the reserves that were
used in previous periods becomes harder over time. The
case of declining U.S. oil production in the face of rising
prices during the 1970s offers dramatic evidence of this
potential problem.
Summary
Natural resources potentially can play an important role
ina region's economy. First, a region heavily endowed with
natlIral resources can be expected to specialize in resource
production in conformity with the notion of comparative
advantage. Second, other things equal, possession of more
resources boosts the potential output of the region.
With respect to the rate of growth, however, the results
are mixed. Natural resources have at times been instrumental in providing a catalyst for rapid and sustained
11
growth. In other cases, a resource boom has crowded out
other industries to the detriment of the region's longer-term
prospects. Moreover, while natural resource industries
may provide strong growth at early stages of a region's
development, the advantages associated with those industries are more questionable as the economy matures.
III. Relative Performance and the Role of Prices
As indicated in Section II, the relationship between natural resources and differential regional economic growth is
complex. In this section, evidence from the GSP data is
presented to address two aspects of this relationship. First,
gross statistics on the relative economic performance
of resource-dependent and non-resource-dependent states
are presented. Second, simple regression results are presented to determine whether resource price shocks or
resource industries per se influence relative performance
more.
slowing construction activity, and changes in inflation
affected mineral prices and had significant impacts on
agricultural land values.
These periods also were characterized by sharp changes
in natural resource price behavior. In the early period,
natural resource prices were fairly stable. In the middle
period, however, energy prices surged. Overall, mineral,
lumber; and agricultural prices showed little trend growth
over the period, but sharp increases in precious metals
Relative Performance
For the purpose of this analysis, the 50 states are split
into two groups for each natural resource industry based on
their resource dependence. Those identified as "resource
dependent" are listed in Table 2. 7 As shown in Charts
1-5 earlier, resource dependence is highly skewed across
states. In the case of agriculture and energy, the top 10
states are selected as resource dependent. In contrast,
because the number of states with significant mining or
forestry activity is small, only the top five states are
categorized as resource dependent.
The performance of resource-dependent states has differed from that of other states during the 1964-86 period.
As shown in Table 3, resource-dependent states ("high")
grew more rapidly than did other states ("low"). Over the
whole period, the top ten resource states grew at an average
annual rate of 3.35 percent, compared to 2.56 percent
growth for the other 40 states. 8
This difference in growth rates was not constant over the
whole sample period. Resource industries, particularly the
energy sector, have faced significant changes over this
period. Accordingly, the sample period was split into three
sub-periods-1964 to 1972, 1973 to 1981, and 1982 to
1986-that are divided by the major oil price shocks in
1973-74 and 1979-80.
Although these periods are selected to capture changes
in energy industry activity, the shocks were sufficiently
traumatic to overall economic activity to make those dates
important to the other resource industries. Economic slowdowns early in each of the last two sub-periods, while not
necessarily causally related to the oil price shocks, were
associated with sharp changes in many of the industries.
Higher interest rates dampened the demand for lumber by
12
Economic Review / Fall 1989
prices at the end of the 1970s and sharpagricultural spikes
in 1973 and 1980 were important events shaping industry
activity.
As indicated in Table 3, resource industries have had
major changes in the pace of economic growth over the
whole period. In the 1973-81 period, resource states
postedgrowth thatexceededother states' growthby nearly
1.75 percentage points. Conversely, in the 1982-86 period, resource statesgrewmorethanfourpercentage points
slower than the remaining 40 states.
The.more rapid growth in the resource states was
accompanied by greater variance. The weighted average
root mean square error around trend growth was 0.57
percentage points higher for resource states than for nonresource. states. Comparing sub-periods, however, it is
clear thatthis highervariance largelyis theresultofthelast
period, <luring whichgrowth in resourceindustries slowed
Federal Reserve Bank of San Francisco
dramatically. In the earlier periods, differences in varianceswere relatively small.
Considerable differences in the relationship between
resources and growth are apparent across natural resources. Agricultural statesgenerallygrewat the samerate
as other states, with faster growth in the 1973-81 period
offsetby slowergrowth in the other periods. The variance
ingrowth· also was smaller for agricultural states during
most-time periods.
In the case of forestry, growthrates werehigher overall,
althoughthe variance was considerably higher in forestrydependent economies. As in the case of agriculture, the
middleperiod stands out as theperiod of relativegain, with
slower-than-average growthregisteredin the otherperiods.
Mineral states have out-performed the rest of the nation
in all sub-periods except the last period, and even there,
theyperformed nearly as well. The variance was higher in
13
the mineral-dependent economies, but the growth rates
were the highest registered by any resource-dependent
category.
Energy-dependent states had the most dramatic variations relative to the rest of the nation over the whole period.
Growth during the pre-1982 period exceeded that in the
nation, particularly in the 1973-81 period when oil prices
rose sharply. The collapse of oil prices in the 1982-86
period caused growth to plunge to one-quarter of the rate
of growth experienced in the rest of the country. Furthermore, after enjoying lower-than-average variance in the
early periods, the variance rose to twice the national
average in the later period. The volatility of the energy
states during this period is not surprising given energy's
relatively large share of output in these states (see Table 2)
and the large swings in prices observed in the period. 9
The data in Table 3, therefore, suggest that the betterthan-average performance of resource industries largely
was the result of gains made during the 1973-81 period.
Mining and energy states were clear winners overall,
while forestry and agriculture also registered some gains.
14
Separating Price and Share Effects
The results in Table 3 point to strong gains in resource
industries during the period of sharp gains in natural
resource prices-particularly in the prices of oil and
minerals. In this section, an attempt is made to disentangle
relative price effects from output effects associated with
the growth of resource industries apart from the price
shocks.
Ordinary least squares (OLS) and generalized least
squares (GLS) regression results are presented in Table 4
for a simple model relating prices and resource shares to
relative output growth. The data are estimated in pooled
cross-section time series form, using all 50 states and 23
time periods. In the GLS case, the data are corrected for
cross-sectional heteroskedasticity As indicated by the low
degree of explanatory power, these estimations fail to
capture most of the differences across states in economic
growth. The results are useful, however, in the sense that
they provide partial correlation statistics for share and
price variables, which are better measures than those from
simple bivariate correlations between relative growth and
prices or shares separately!"
Economic Review / Fall 1989
The dependent variable is measured as the difference
between the annual percentage changes in the level of real
GSP for each state from the national average percentage
change (formed using total·GSP summed across states).
Thus, it represents the relative growth of a state's GSP.
Share variables are formed by taking the share of a
state's GSP accounted for by the given resource industry
and subtracting from it the nation's average resource share.
For resource-dependent states, this variable is positive
and for those with below average shares, the variable is
negative.
Price variables are formed in two steps. First, the real
annual percentage change in each resource price is calculated.!' The price change for a given resource is then
multiplied by the state's relative share of that resource
described earlier. This specification is necessary because
price effects depend on a state's relative dependence on a
given resource. Clearly, oil price increases had a positive
effect on energy-exporting states and a negative effect
on energy-importing states. With this formulation, a positive coefficient indicates that a positive price shock will
boost the resource-intensive state and slow growth in nonresource-intensive states.
Finally, to account for the possibility that growth also
results from factor movements generated by differences in
per capita income, the relative per capita income of persons
in each state in 1963 (the beginning of the period) is
included. If faster growth simply is the result of a state
starting from a low base, this variable will proxy for that
effect.
Both OLS and GLS results are reported because together, they convey some sense of the robustness of the
relationships. Differences between the two estimates arise
from the treatment of variances of state growth rates,
which can differ because of a variety of factors, including
the size and diversity of the state's economy. The GLS
model estimates the coefficients after standardizing the
variances of the state, while the OLS model makes no such
correction. Both methods yield unbiased coefficients, although the standard errors can be biased in the OLS case.
Results from the regressions differ in the size and
significanceof the coefficients, but several broad characterizations can be made. First, share variables either do not
have a .significant influence on relative growth, or where
significant at the 95 percent confidence level, tend to havea
negative influence on growth. These results suggest that,
other things equal, having more natural resource production will not stimulate relative economic growth.
Price variables, on the other hand, were positive in all
cases. This result suggests that the sharp movements
in resource prices during the period did have an important, positive influence on the relative output growth of
resource-dependent economies.
The effect of the starting level of the economy, per capita
income in 1963, had a positive effect on relative growth in
the OLS case and an insignificant effect in the GLS model.
To the limited extent that the starting level mattered,
therefore, those states with the strongest economies at the
beginning grew faster, making the spread between state
incomes larger.
Results from the table, therefore, suggest that the superior performance of the natural-resource dependent states
shown in Table 3 may be better interpreted as the result of
sharp positive price movements during the sample period,
rather than advantages associated with resource production per se .12 To summarize, the results in Table 4 indicate
that having a large share of natural resources is detrimental
to relative growth prospects, unless the relative price of
natural resources rises.
IV. Non-Resource Industries and the Dutch Disease
In the previous section the evidence indicated that
natural-resource-based economies out-performed the rest
of the nation, although the gains appear to be the result of
price effects rather than share effects. This finding allows a
direct examination of the applicability of the "Dutch
disease" to regional economies. In this section, the data
are examined to determine whether the gains in resourcebased regional economies led to greater concentration in
resource industries-and possibly had detrimental effects
on non-resource industries-as would be consistent with
Dutch disease.
Table 5 presents average changes in the natural resource
share of state GSP over various sub-periods (weighted by
Federal Reserve Bank of San Francisco
GSP) calculated for resource- and non-resource-dependent
states. Comparing columns, it can be seen that resourcedependent states had much larger changes in the shares of
GSP contributed by the various natural resource industries
than did the non-resource-dependent states. This is not
surprising given the small shares that those industries
contribute in non-resource states.
Comparing resource industries in the resource-dependentstates, the largest changes in output shares occurred in
the energy sector. Energy's share of output droppedin each
period, with declines of three and six percentage points in
the last two sub-periods in the energy-dependent states. In
contrast, mining had less than a 0.4 percentage-point
15
change in share in the states that have the greatest output shares in that industry. Agriculture and forestry had
slightly larger changes, but those changes in shares were
less than two percentage points between any two subperiods.
As shown in the table, agriculture, forestry, and mining
shares dropped sharply during the 1973-81 period, despite
positive price shocks. Combined with the earlier information that showed those states doing far better than average
during that period, this suggests that non-resource industries in those states were the most important source of
growth-causing the resource share to fall because of the
faster growth of the other sectors.
A direct comparison of sectoral growth is given in Table
6. Comparing the first two columns, it is clear that resource
industries lagged in contributing to growth. In all casesfor both high- and low-resource-dependent states across all
categories of resources-the growth rate of the resource
industries was below that of the total state economy.
Comparing the two groups of states, the growth rates of
resource industries were relatively similar. Only in the case
of forestry states was significantly faster growth registered
in the resource sector of the high resource-dependent
states.
Non-resource industries in resource-dependent states
registered the fastest growth in all cases. Consequently, in
an apparent refutation of the Dutch disease hypothesis, the
non-resource sectors were the prime beneficiary of the
resource price shocks.
This conclusion is strengthened by comparing the
growth of non-resource industries that are directly tied to
resource production (resource processing industries such
as refining, pulp and paper, food processing, and stone,
clay, and glass) with that of other industries with less direct
ties. As shown in the last two columns of the table, nonprocessing industries ("other") had faster growth than all
processing industries except in the case of forest products.
Results from the table, therefore, suggest that the Dutch
disease did not afflict regional economies. Price effects
boosted the economy, but those prices did not result in
increased specialization in resource production and declining competitiveness of other industries.
Differences in relative factor mobility may help to
explain this difference between regional and national economies with respect to susceptibility to Dutch disease. In
the international case, factor flows are constrained by
restrictions on immigration and capital movements. Consequently, relative price shifts that encourage the movement of factors to support the resource industry take factors
away from other domestic sectors.
In the regional case, few limits are imposed on factor
16
movements. Labor and capital can flow to areas with
potential opportunities. Consequently, increased output by
the resource sector does not need to reduce factors available
to other industries, since those factors can be imported
from other regions. Costs of living can rise as labor is
attracted, but the cost increases, in tum, will stimulate
additional factor movement, such as the inflow of building
materials for additional housing.
Results in Table 6 also highlight the inelastic nature of
natural resource production. Resource industries were
unable to expand significantly even when sharp positive
price movements gave them incentive to do so. Energy
states often could not increase output as prices rose because of binding constraints on availability. In Texas, for
example, the sharp run-up in prices in 1979-80 slowed the
secular trend towards declining production and proven
reserves, but production could not rise. As a result, oil
wealth tended to be invested in other industries or regions.
Economic Review / Fall 1989
V. Conclusion
The GSP data suggest several relationships between
natural resources and relative economic performance.
Overall, the experience of the 1964-86 period indicates
that resource states grew more rapidly than non-resource
states. This faster growth was accompanied by higher
volatility in growth, however.
The faster growth and higher volatility of resourcedependent states reflects, in part, the significant volatility
in natural resource prices observed during the period,
particularly in the 1970s. Sharp price increases boosted
resource-dependent economies by providing increased investment in the economy.
Contrary to the Dutch disease, price increases in natural
resource industries boosted non-resource industries. Resource industries showed little ability to expand output
in the wake of favorable price movements and increases
in wealth. These increases in wealth, instead, were invested in non-resource industries. Thus, in the states
Federal Reserve Bankof San Francisco
with large resource industries, these non-resource industries expanded most when positive resource price shocks
occurred.
Having a large resource sector, therefore, can be beneficial to a region's growth when the industry experiences
positive price shocks. If prices fall or remain unchanged,
the slow growth (or actual decline) in resource industry
output can slow the relative growth of resource-dependent
states.
This observation suggests an important area for further
study. Why does the additional wealth generated by resource price shocks remain within a resource-dependent
region and boost local non-resource industries when investment in such industries outside the region is possible
as well? Most theories would argue that non-resource
industries in a resource-dependent economy would be
harmed, as suggested by the Dutch disease, or at least
unaffected in a world of freely-flowing capital. The answer
17
to this question may be associated with the information
cost arguments discussed earlier, but a full explanation
remains to be uncovered.
Finally, differential effects across states may diminish
over time. As noted in this study, resource industries have
become less dominant in nearly all states. Output shares
have fallen sharply, especially in the resource-dependentstates, which-should-make future regional differencesin growth less attributable to natural resource price
movements.
NOTES
1. For a discussion of the BEA GSP data, see .Giese
(1989).
2. The data in the table correspond to shares of national
output, rather than shares of national reserves of those
resources. Output and reserves tend to be correlated, but
particularly inthe case of minerals and energy, there may
be some differences in the mag nitude of the shares based
on the length oHime the resource has been extracted.
3. The GSPdata do not break forestry separately from the
industry data. Instead, BEAreportsa total for forestry,
fisheries, and agricultural services-an aggregation that
is not appropriate for this study. Because nearly all employment in forestry is in the durable goods category
"lumber and wood products," that category is used as a
proxy for the contribution of forestry to output. Although
this procedure understates the role of forestry, it is representative of that impact.
4. Often, characterizations of resource industry importance magnify the effect of the industry by counting the
employment of all persons in some way connected to
resource processing-some figures for agriculture range
as high as 25 percent of the economy. Such a claim would
be valid only if (1) those services and production were not
performed if the state did not have resource productionincluding retail sales of food-and (2) those inputs tied up
in the resource chain otherwise would be unemployed.
5. This low initial cost is not always the case, of course, as
evidenced by the high cost of developing Alaska's oil
fields.
6. This advantage is not limited to developing countries.
The Soviet Union, for example, earns much of its hard
currency to purchase machinery and supplies from sales
of gold and oil to the Western countries.
7. GSP statistics and information used in this article are
expressed in real terms unless otherwise noted.
18
8. Grovvth rates were.estimated by regressing the log of
total real GSPon a constant and a time trend for each
state. Coefficients on the time trend indicate the growth
rate, While the root mean square error from the estimation
is used to measure the variance. Averages for the high
andJow groups .are weighted by size of GSP.
9. Evidence on real oil prices is presented by Schmidt
(1988). As shown in that article, real oil prices have been
trendless over the past 115 years, although prices have
tended to be volatile. The price spikes in the 1970s,
however, were clear outliers, with unusually large deviations from the historical average.
10. The significance of the coefficients supports the inclusion of resource shares in models of regional performance. See Sherwood-Call (1988) for related work that
incorporates farm and oil variables into models explaining
deviations of state growth from national performance.
11. Prices for the resources were selected from several
sources. Lumber and oil prices are based on the wholesale price indexes for lumber and crude oil, respectively.
Agricultural prices are based on the series, "Prices Received," published by the U.S. Department of Agriculture.
Mineral prices are the weighted average of iron, copper,
lead, and zinc prices (using fixed consumption weights
derived from average consumption over the period). In all
cases, the prices are deflated by the general wholesale
price index.
12. This is particularly true in the case of energy. In the
other resource industries, although no trend growth in
prices was noted, price increases (the percentage increase) were larger in the positive direction than in the
negative direction-that is, price declines were more
gradual. Since the price variables were formed using
annual percentage changes, the positive price movements helped to explain the better-than-average performance of the resource states.
Economic Review / Fall 1989
REFERENCES
Borts, George H. "The Equalization of Returns and
Regional Economic Growth," American Economic
Review, 50, June J9.60.
____ and Jerome L. Stein. Economic Growth in a
Free Market. New York: Columbia University Press,
19.64.
Cox, W. Michael and John K. Hill. "Effects of the Lower
Dollar on U.S. Manufacturing: Industry and State
Comparisons," Economic Review, Federal Reserve
BankofDallas, March 1988.
Giese,Alenka S. "A Window of Opportunity Opens for
Regional Economic Analysis: BEA Releases Gross
State Product Data," FRB Chicago Working Paper
Series, WP-1989-3, Federal Reserve BankofChicago,
February 1989.
Gruben, William C., Joann E. Martens, and Ronald H.
Schmidt. "Interstate Shifts in Nonresidential Construction," Economic Review, Federal Reserve Bank
ofDallas, July1988.
Gruben William C. and Ronald H. Schmidt. "State and
Local Fiscal Policies, Shocks, and Interregional Migration," Presented at the Western Regional Science
Association Meetings, February 1989.
Hughes, Jonathan. "Comments on Long-Run Growth and
Development," Energy and the Southwest Economy,
Proceedings of the 1985 Conference on Energy and
the Southwest Economy, Federal Reserve Bank of
Dallas, 1985.
Laney, Leroy O. "How Contagious is 'Dutch Disease'?"
Economic Review, Federal Reserve Bank of Dallas,
March 1982.
Miernyk, William H. "Fossil Fuels and Regional Economic
Development," Energy and the Southwest Economy,
Proceedings of the 1985 Conference on Energy and
the Southwest Economy, Federal Reserve Bank of
Dallas, 1985.
Federal Reserve Bank of San Francisco
North, Douglass C. "Location Theory and Regional Economic Growth," Journal of Political Economy, 63, June
1955.
Pindyck, Robert S. "TheOptimal Exploration andProductionof Nonrenewable Resources," Journal of Political
Economy, 86, October 1978.
Plaut, Thomas R. andJoseph E. Pluta. "Business Climate,
Taxes and Expenditures, and State Industrial Growth
inthe United States," Southern Economic Journal, 51,
July 1983.
Schmidt, Ronald H. "Effects of Energy on Income Growth
in the Southwest," Energy .and the Southwest Economy, Proceedings of the 1985 Conference on Energy
and the Southwest Economy, Federal Reserve Bank
of Dallas, 1985.
____ . "Hotelling's Rule Repealed? An Examination
of Exhaustible ResourcePrlclnq," Economic Review,
Federal Reserve Bank of San Francisco, Fall 1988.
____ and William C. Gruben. "Expectations and
Regional Growth," Presented atthe Regional Science
Association Meetings, Toronto, Canada, November
1988.
Sherwood-Call, Carolyn. "Exploring the Relationships between National andRegional Economic Fluctuations,"
Economic Review, Federal Reserve Bank ofSan Francisco, Summer 1988.
Smith, Donald M. "Regional Growth: Interstate and Intersectoral Factor Allocations," Review of Economics
and Statistics, 56, 1974.
State of California. Measuring Economic Impacts: The
Application of Input-Output Analysis to California Water Resources Problems, Bulletin 210, Department of
Water Resources, March 1980.
19
Unemployment-Rate Dynamics:
Aggregate-Demand and -Supply Interactions
John P. Judd and Bharat Trehan
Vice President and Associate Director of Research, and
Senior Economist, Federal Reserve Bank of San Francisco. Research assistance was provided by Conrad Gann.
We wish to thank the members of the editorial committee,
Fred Furlong, Adrian Throop, and Carl Walsh, for valuable
comments on earlier drafts of this paper.
The unemployment rate often plays an important role in
monetary-policy deliberations, not only because policy
makers are concerned about unemployment itself, but also
because it is viewed as an important indicator of future
inflation. For example, when the unemployment rate declined rapidly to a relatively low level in recent years,
a number of Federal Reserve officials became concerned
that the economy was developing dangerous inflationary
pressures.'
One problem in evaluating the policy implications of
movements in the unemployment rate (as well as those of
other macroeconomic variables) is that these implications
often depend on one's assumptions about the structure of
the economy. Currently there is little agreement among
economists concerning the appropriate paradigm; the
Keynesian (both the traditional and "new" versions), realbusiness-cycle, and monetary-misperceptions paradigms
all have significant followings among different groups of
macroeconomists.?
The implications for monetary policy of movements in
the unemployment rate depend upon the nature of the
underlying disturbances that caused those movements.
Positive aggregate-demand shocks cause the unemployment rate to fall as inflationary pressures build, whereas
positive aggregate-supply shocks are likely to lead to afall
in both the unemployment rate and inflation. In this paper,
we employ a recently developed modeling technique to
disentangle the effects of aggregate-demand and -supply
shocks on the unemployment rate. The technique is agnostic about alternative macroeconomic theories, deriving
identifying restrictions from relatively uncontroversial
long-run, or steady state, relationships.
20
These paradigms differ in the emphasis they place
on aggregate-demand versus aggregate-supply shocks in
influencing economic activity and labor-market conditions. Real-business-cycle models ascribe a larger role to
aggregate-supply shocks, whereas Keynesian and monetary-misperceptions models place greater weight on aggregate-demand shocks. This distinction between demand
and supply factors is important because the appropriate
monetary-policy response (or lack thereof) to unemployment rate movements depends on the nature of the underlying disturbance. Positive aggregate-demand shocks cause
the unemployment rate to fall as inflationary pressures
build, and such developments could make a tightening of
monetary policy appropriate. By contrast, positive aggregate-supply shocks are likely to lead to a fall in both the
unemployment rate and the rate of inflation. Under these
circumstances, a tighter monetary policy most likely
would be inappropriate.
In this paper, we employ a recently developed modeling
technique to disentangle the effects of aggregate-demand
and -supply shocks on the unemployment rate (as well as
on other important macroeconomic variables.) The technique is agnostic about alternative theories, deriving
identifying restrictions from relatively uncontroversial
Economic Review / Fall 1989
assumptions about long-run, or steady-state, relationships.
Given the current lack of agreement about macroeconomic
theory, such models have the advantage that they eschew
over-identifying restrictions, and choose not to go beyond
the minimum number of restrictions necessary to achieve
identification.
Our empirical results suggest that for very short horizons of a few quarters, shocks to aggregate demand account for nearly all of the variance of unemployment rate
forecast errors. However, at longer horizons of twelve
quarters and more, aggregate-supply shocks playa significant role. Moreover, we find that movements in the unemployment rate that are caused by supply shocks (as defined
by our model) are positively correlated with inflation,
whereas those associated with demand shocks are negatively correlated with inflation. Thus, decomposing the
sources of unemployment rate movements into demand
versus supply shocks can be important in designing effective monetary policy.
The paper is organized as follows. Section I reviews the
relevant literature on macroeconomic modeling and discusses the rationale for the approach taken in this paper.
Section II sets out the theoretical specification of the
model. In Section III, we discuss econometric issues that
arise .in estimating the model and the results of this
estimation, as well as their implications for the sources of
variation in important macroeconomic variables, including the unemployment rate. Also in this section we analyze
the historical evolution of the unemployment rate and the
relationship between inflation and the aggregate-demand
and -supply components of the unemployment rate. Policy
implications and conclusions are discussed in Section IV.
I. Methodological Considerations and Literature Review
Adherents of the main alternative macroeconomic theories-Keynesian, real-business-cycle, and monetary misperceptions-have very different views about the structure
of the economy. A major source of controversy concerns
the relative importance of demand and supply shocks. The
Keynesian and monetary-misperceptions theories stress
the role played by aggregate-demand shocks in inducing
short-run movements around long-run trends which are
independent of those shocks. In contrast, real-businesscycle theories emphasize the role played by technology and
labor-supply shocks in producing short-run fluctuations in
output around changing equilibrium values which are
themselves determined by factors traditionally emphasized
in neo-classical growth models.
These alternative macroeconomic theories have different implications for how monetary policy should be
conducted. For example, since Keynesians believe that
unemployment rate movements mainly are induced by
aggregate-demand factors, inflation will rise (fall) when
the measured unemployment rate goes below (above) its
natural rate. Assuming the monetary authority knows the
natural rate of unemployment, Keynesians suggest that the
observed rate of unemployment relative to its natural rate
can be a major source of information in setting policies to
control inflation.
In contrast, real-business-cycle theorists believe that
aggregate-supply shocks are the predominant sources of
change in macroeconomic variables. Under these circumstances, policy mistakes would be made if the central bank
interpreted the unemployment rate as an indicator of
aggregate-demand pressures. Further, existing real-business-cycle models generally have modeled business cycles
Federal Reserve Bank of San Francisco
as Pareto-optimal responses to exogenous shocks. Thus, in
these models, there is no role for any type of macroeconomic policy aimed at stabilizing the economy.
Macroeconomists have not been able to agree on which
theory (or combination of theories) most accurately describes the economy. Each theory implies a different set of
identifying restrictions. Thus, a certain degree of agnosticism is warranted in selecting identifying restrictions. This
agnostic approach increasingly has shown up in macroeconomic research in recent years. The use of vector
autoregressions appears to reflect this view. No identifying
restrictions are needed to obtain macroeconomic forecasts.
Of course, if these forecasts are to be interpreted in terms
of economic theory, identifying restrictions must be added.
In early applications, these took the form of assuming a
specific recursive structure for the contemporaneous correlations in the data. 3,4
The Blanchard-Quah Model
Recently, Blanchard and Quah (1989) specified a small
vector autoregression of the macroeconomy that achieves
identification by imposing relatively uncontroversial constraints on steady-state conditions, thereby avoiding the
restrictions associated with alternative theories of the
business cycle. Moreover, their model is exactly identified,
and thus avoids over-identifying restrictions that may raise
theoretical controversy. Blanchard and Quah (BQ) assume
that supply shocks (those emphasized in real business
cycle models) can have permanent effects on the level of
real activity, while demand shocks (those emphasized by
21
the Keynesian and monetary-misperceptions models) can
have only temporary effects. These assumptions are consistent with each of the three main macro paradigms.
Importantly, they are sufficient to identify certain types
of VARs incorporating important macroeconomic time
series.
Since the BQ approach is used in this paper, albeit on a
larger model, it is useful to see how their method works.
(A detailed discussion of theirmethod of identification is
provided in Appendix A.) BQ specify a VAR with two
variables: the rate of growth of real GNP (Y), and the level
of the unemployment rate (u). Two types of (unobserved)
structural disturbances, v and e, areassumed toaffect these
variables. (As discussed below, we follow BQ in identifying these disturbances with aggregate-demand and
aggregate-supply disturbances.) Equations (1) and (2) are
moving average representations of y and u interms ofthese
two disturbances. For simplicity here, we introduce dynamics intotheBQ model by including only onelagof v in
eachequation, although thefullBQmodel contains several
lags.
Yt = a.e, + b », + CjV t_ j
Ut = a2et + b2vt + C2 Vt-j
(1)
(2)
In order to study the dynamics of this system, it is first
necessary to obtain estimates of the various coefficients in
equations (1) and (2). This requires placing certain restrictions on e and v. In traditional fashion, BQ assume that e
and v are uncorrelated with each other and have unit
variance. In addition, e and v arealsoserially uncorrelated.
Given therepresentations in (1) and (2), these assumptions
imply the following identifying restrictions:
(J~=a]+b]+c]
(3a)
(J~=a~+~+c~
(3b)
(Jy"
u,
(Jy,
, u,_)
( jy,_) ,
=
u,
a ja2 + b jb2 + CjC2
(3c)
=cb
j 2
(3d)
=bc
l 2
(3e)
where (J~ is the (observed) variance of y, (J~ is the variance
of u, (Jy" u, is the contemporaneous covariance of u and y,
and the othervariances are defined similarly.
So far, there are five conditions from which we must
obtain six coefficients. One more restriction is needed to
identify the model. The traditional approach has been to
impose a recursive structure on the contemporaneous
correlations in the data (Sims [1980]). For example, one
might assume thatthe coefficient a j = 0,. thatis, shocks to
the unemployment rate do not have a contemporaneous
22
effect ontherateofgrowth ofoutput. Suchan assumption,
however, would be theoretically controversial.
BQ avoid having to assume contemporaneous causal
orderings by relying on long-run, or steady-state, restrictions. Specifically, they assume that v has no long-run
effect on output; that is,
b, + c j
= O.
(3f)
Thisrestriction, together with the conventional restrictions on variances andcovariances, is sufficient to identify
the unobserved shocks from observations on y and u. The
restriction also leads to a straightforward interpretation of
theunderlying structural disturbances: v can beinterpreted
as an aggregate-demand shock since it can have no longruneffect on y, while e can beinterpreted as a supply shock
since it is permitted permanently to affect y. In other
words, the permanent level of real GNP is determined by
realfactors. Although aggregate-demand shocks cancause
real GNP to deviate from this level, it cannot affect the
permanent level itself. By construction, neither demand
nor supply shocks have a permanent impact on the unemployment rate.
Using this method, BQ found thatdemand disturbances
hada hump-shaped effect onthetimepathofoutput, while
supply shocks had an effect that increased gradually over
time. They alsofound thatdemand disturbances accounted
for only 35% of the variance of unpredictable changes in
real output in the contemporaneous quarter, leaving 65%
for supply disturbances, while demand accounted for 13%
at a horizon of eight quarters.> In contrast, demand disturbances accounted for 100% of the variance of unpredieted changes in the unemployment rate in the current
quarter, and for 50% at an horizon of eight quarters.
The Shapiro-Watson Model
Oneproblem withBQ's analysis is thatit allows foronly
two underlying disturbances to the economy. If, as seems
plausible, theeconomy is affected by more than onekindof
supply (or demand) shock, their procedure will tend to
confound the effects of these different shocks. Based on
this reasoning, Shapiro and Watson (1988) used a system
that comprised real GNP, total labor hours, inflation and
therealinterest rate." Thisset of variables allowed them to
account for four different disturbances: two to aggregate
supply, whieh they identified as shocks to laborsupply and
technology, and two to aggregate demand, which they
referred to as IS and LM shocks, but did not identify
separately.
Shapiro andWatson (SW) found thataggregate-demand
shocks had a smallerimpact on real output than BQ did.?
Economic Review / Fall1989
Specifically, aggregate-demand shocks accounted for just
28% of the variance of the output forecast error in the
contemporaneous quarter, and 20% at an eight-quarter
horizon. In addition, they found that labor supply shocks
alone accounted for about 45% of the variance of unpre-
dieted changes in output in the contemporaneous quarter.
Thesefindings, as well as those of BQ, sharply contradict
the Keynesian and monetary-misperceptions views that
trend and cycle are neatly separable, with demand shocks
playing the dominant role over the business cycle.
II. Model Specification and Identification
In this section, we presentthe specification of the model
below, the results obtained when population is used as a
estimated in this paper. We begin with a discussion of the
measure of labor supply are much more plausible. Thus,
variables included in the model, followed by a discussion
given this paper's policy-driven focus on the unemployment rate, we have opted for working-age population.
of the equations that constitute the model, and how we
achieve identification.
We also extend the BQ and SW models by explicitly
The model includes five variables: the unemployment
incorporating a foreign variable to identify the effects of
rate, real GNP, a nominal rate of interest, a measure of
shocks originating abroad. Given the growing importance
labor supply, and a variable that measures foreign trade.
of international trade and capital flows to the U.S. econThesevariables providebroadcoverage of important types
omy, it is desirable to incorporate the independent effects
of shocks from abroad. While inclusion of the exchange
of activity in the economy, and thus should capture the
economic relationships that are important in determining
rate appears to be an obvious choice, the move fromfixedto floating-exchange rates in the early 1970s implies a
the behavior of the unemployment rate. Movements in the
unemployment rate and the interest rate are likely to be
change in the exchange-rate process that precludes sensible estimation results over our 1954-88 period. Instead,
highly correlated with two types of underlying aggregatedemand shocks, which can be thought of as being assowe includeas a foreign variable the ratio of real exports to
ciated withthe IS and LM curves of textbook macroecoreal imports.
nomic theory. The interest rate should captureshocks both
to inflation expectations and real interest rates, while
the unemployment rate should reflect aggregate-demand
The Underlying Model
shocks as theyaffectthe level of economic activity. FollowWe begin by assuming that the production technology
ing previous research, we assume that movements in real
can be described by a neo-classical growth model, so that
GNP are correlated with technology shocks, once we
the long-run level of output is determined by the capital
standardize for aggregate-demand shocks. 8
stock and labor supply? The capital stock term can be
Weuse working-age population as our measure of labor
eliminated by assuming a Cobb-Douglas production funcsupply. This variable is clearly exogenous, and therefore
tion and a constantsteady-state capital-output ratio. Thus,
guards against the possibility of confounding labor dethe steady-state level of output can be expressed as a
mand and supply. However, it has the disadvantage of
function of the steady-state levels of labor supply and
omitting the effects on labor supply of changes in partechnology.
ticipation rates and average hours worked. One obvious
The levels of labor supply and technology may be
alternative would be to follow SW andusetotallaborhours
permanently affected by labor-supply and technology
as the labor-supply variable. However, our empirical evishocks, respectively. The evolution of these variables is
dence suggests that using labor hours to measure supply
described by
causesa seriousbias; weare unablecompletely to separate
s*t = O'.s + s*t-1 + I-'
Qs (L) rrt
liS
(4)
the demand-induced changes in labor hours from those
induced by labor supply. Specifically, when we include
e*
Qe (L) lie
(5)
t = O'.e + e*
t-1 + I-'
rrt
labor hours in our model, we find that a positive laborwheres* is the log of the steady-state value of laborsupply
supply shock leads to a large, sustained decline in the
unemployment rate, an outcome that suggests a confusion
and e* represents (unobserved) technology. Thelabor supbetween labor supply and demand. Such confusion could
ply and technology shocks, !J.s and !J. e , are uncorrelated,
have a profound effect on conclusions concerning the
and the lag polynomials f3s (L) and W(L) describe the
relative importance of supply and demand disturbances in
transitory movements in s* and e* as they move to new
permanent levels.
macroeconomic time series. By contrast, as discussed
Federal Reserve Bank of San Francisco
23
Labor supply is not affected, either in the short or long
run, by any of the other variables in the system. This
assumption follows from our choice of working-age population to represent the influences oflabor supply, and yields
four of the ten restrictions we need to identify the model. 10
Both labor-supply and technology shocks can cause
short-run movements in output as the level of output
adjusts to a new steady-state value. Short-run movements
in output also can be the result of aggregate-demand
shocks. However, the two types of aggregate-demand
shocks are permitted to have only temporary effects on the
level of output. These assumptions yield two more identifying restrictions. Foreign shocks cause output to deviate
temporarily from its steady-state value, but are not permitted to have a long-run effect on output. 11 This assumption yields one more identifying restriction.
These considerations suggest the following equations
for the relationship between observed and equilibrium
values:
St
= si +
XS (L) (I-Li)
(6)
Yt = Y;+ XY (L) (l-1i, I-1T, f.1f, 1-1:1,1-1:')
(7)
where XS(L) and XY(L) are vectors of lag polynomials (in
the indicated variables) that allow for temporary deviations
from steady-state levels. Thus, this specification allows the
actual level of output to deviate from the level implied by
the Cobb-Douglas production function in the short run. As
discussed above, y* itself is a function of s* and e*. f.1f
denotes shocks originating abroad, while 1-1~1 and 1-L~2 are
the domestic demand shocks.
Statistical tests suggest that output and labor supply are
both nonstationary, and thus we take first differences of
equations (6) and (7) (see Appendix B). Substituting
equations (4) and (5) into the results yields:
St -
St-I
Yt
Yt-I
+ W (L) (I-LO + (1-L)X S (L) (I-Li)
= {XY + rY' (L) (l-1i, I-LT)
+ (1- L)XY (L) (I-Li, I-LT, f.1f, 1-1:1, 1-1:2)
= {XS
(8)
(9)
Consider now the specification of the foreign variable.
In addition to disturbances originating abroad, this variable is affected by all the domestic shocks. However, the
two aggregate-demand shocks are permitted to affect the
foreign variable only temporarily.'? These assumptions
yield two more identifying restrictions. We assume that the
long-run evolution of the foreign variable can be described
in the same wayas output, so it is included in the model in a
form similar to (8) and (9). Thus,
it -
24
h-I
= at + [3f (L) (1-1:, I-LT, f.1f)
+ (1- L)x! (L)(I-1:, I-LT, f.1f,
1-L:1, 1-L:2)(10)
Given that the interest rate appears to be non-stationary
(see Appendix B), we specify its equation in differenced
form:
. - it r-L
-X
i (L) ,(s
e t"'t'
.. f
f.L t , f.Lt,
It
II d 1
II d 2)
I""t ' I""t
(11)
Thus, all the disturbances in the model can have a permanent effect on the nominal interest rate.
There is some ambiguity about how the unemployment
rate should be included in the model. On the one hand,
there. is a large body of theoretical work in macroeconomics to suggest that the unemployment rate is stationary.13 Tests carried out over long sample periods tend to
confirm this. 14 On the other hand, as shown in Appendix
B, unit root tests suggest that the unemployment rate is
non-stationary over shorter sample periods.
This inability to reject nonstationarity in the unemployment rate over the post-war period poses a problem.
Different researchers have dealt with this problem in
different ways. BQ for instance, present results both for the
case where the unemployment rate is assumed to be
stationary and where it contains a deterministic trend.
Unfortunately, removal of a linear trend is not sufficient to
make the unemployment rate stationary. Evans (1989)
allows for an increase in the mean of the unemployment
rate beginning in 1974. As indicated in Appendix B,
allowing for this shift in the mean appears to make the
unemployment rate stationary.
Acceptance of this "solution" to the nonstationarity
problem implicitly assumes the existence of some welldefined, exogenous change in the economy that is associated with a change in the mean unemployment rate. While
some economists have pointed to the change in participation rates of women and teenagers in the labor force over
this period, the issue is by no means resolved. 15 Accordingly, we estimated two alternative versions of the model,
one that allows the mean unemployment rate to change in
1974, and one that holds it fixed over the entire 1954-88
period. The results in the two cases are similar, and so
we present only those from the specification that allows for
a mean shift. (However, we do point out instances below
in which the results from the two specifications differ
materially.)
Thus, the complete model comprises equations (8)-(11),
plus
f.Lt
= {XU + XU (L) (f.L:,
I-1T,
1JIr, 1-L:
1
,
f.L:2 )
(12)
where {XU is allowed to shift in 1974Ql. Thus, the unemployment rate is affected by all the disturbances in the
model. However, because it is entered as a level, none of
these disturbances has a permanent effect on it.
In summary, we have identified the model by restricting
Economic Review / Fall 1989
certain long-run coefficients to equal zero, and by using
working-age population, which is strictly exogenous, for
our labor-supply variable. As discussed in Appendix A, we
require ten identifying restrictions to separate the influences of each of the five shocks-two domestic demand,
two domestic supply, and one foreign-on all the variables
in the system. The assumption that population is exogenous yields four identifying restrictions. Four additional restrictions come from the assumption that the two
aggregate-demand shocks do not have long-run effects on
output and the foreign variable. One more restriction
comes from the specification that the foreign shock has no
long-run effect on U.S. output. This gives us a total of nine
restrictions. Following SW, we choose not to identify the
two aggregate-demand shocks separately. In this way, we
are able to eliminate the need for a potentially controversial tenth restriction.
HI. Estimation and Empirical Results
In this section we describe the estimation technique and
present our results. The impulse response functions and
the variance decompositions presented below provide information about the structure of the economy as estimated
by the model. We use this information to analyze the
factors that have contributed to the changes in the unemployment rate that occurred over the period from 1955 to
1988. Finally, at the end of this section, we show correlations between our measures of the aggregate-demand and
aggregate-supply components of the unemployment rate
and the rate of inflation.
Our model includes the log of the unemployment rate
and the first differences of the logs of all other variables.
Because population is exogenous, we use ordinary least
squares to estimate a regression of population growth on
six of its own lags. (A lag-length of six is used in all the
equations in the model.)
To illustrate the technique used to estimate the remaining equations, we use the real GNP equation. 16 Real GNP
is regressed on lags of all the variables in the system plus
contemporaneous values of population, the interest rate,
the unemployment rate, and the foreign trade variable. We
impose the restriction that neither the aggregate-demand
shocks nor the foreign shocks has a permanent impact on
the level of GNP by taking the difference of the relevant
right-hand-side variables one more time and reducing the
lag length by one. Thus the first difference of real GNP is
regressed on first differences of population, the second
differences of the foreign variable and interest rates, and
the first difference of the unemployment rate (in addition to
lags of first differences of real GNP). Two-stage leastsquares is used to estimate the equation because it contains
contemporaneous values of the three endogenous variables
(that is, of interest rates, unemployment, and the foreign
variable). The contemporaneous value of population and
lagged values of all variables in the model are used as
instruments.
The remaining equations are estimated in a similar man-
Federal Reserve Bank of San Francisco
nero Following our discussion above, domestic aggregatedemand variables are restricted to have only a temporary
impact on the foreign variable, while no such restriction is
placed on the domestic supply variables. No restrictions
are placed on the equations for the interest rate and the
unemployment rate. As mentioned above, the inclusion of
the level of the unemployment rate in the model implies
that no shock to the system has a permanent impact on that
rate.
The Estimated Structure of the Model
Exhibit IA shows the impulse response functions from
the model. The first two columns of the exhibit show the
response of the model's four endogenous variables to
domestic shocks, while the third column shows the effects
of shocks originating abroad. As discussed above, we
identify labor-supply and technology shocks separately,
but we do not disentangle the two underlying demand
shocks. Thus, the impulse response functions in the second
column of the exhibit represent responses to a linear
combination of the demand shocks.
Positive aggregate-demand shocks reduce unemployment and raise output and interest rates. By construction,
the effects on the unemployment rate and GNP are temporary. The effects of aggregate-demand shocks on the unemployment rate die out in about 12 quarters, while those on
output last eight to 10 quarters. At first, the ratio of U.S.
exports to imports reacts negatively to domestic demand
shocks; that is, higher domestic demand leads to a rise in
imports relative to exports. The impulse response function
then cycles, becoming positive from five to twelve quarters, at which time the effect dampens out.
Positive shocks to technology reduce the unemployment
rate. This effect lasts for about 24 quarters before substantially dying out. I? Shocks to labor supply have insignificant effects on the unemployment rate, causing it to cycle
around its original level. Positive shocks to labor supply
25
ExhibitlA
1m pul s eRes.ponse.F unet ion s
Domestic Supply Shocks
Responses of:
3
Labor Supply
1
-1
Unemployment Rate
-3
-5
-7
-9
- 11
-i-t'' "4T'' 'I""'' '12' ' ' ' ' ' ' ' 20
' ' ' ' ' ' ' ' ' 28
' ' ' "T'' 'I""'36' ' '-' ' ' '44
' ' ' ' ' ' ' ' '52
' ' ' ' ' '"T"'60T'"1
1.3
1.1
Technology
.9
.7
.5
Real GNP
Labor Supply
.3
.1
-. 1
-i-t'' "T'4 ' 'I""'' ' 12
' ' ' ' ' ' ' ' ' '20
' ' ' ' ' ' ' ' ' '28"T'' 'I""''36
' '-' ' ' ' ' ' '44
' ' ' ' ' ' ' 'I' 52
' T' ' I""T'60
"'I' "'1
19
17
15
13
11
9
7
5
3
Interest Rate
Labor Supply
Technology
1
-1
-3
....J.of..,..,..'I""'r"'I...,..,......,-,...,....,"T""I"~....,....,....,..,...,..,....I""T""I"""T""I"".,..,...,
4
12
20
28
36
44
52
60
4
3
Labor Supply
2
1
Technology
o~-I-J.~::::============--
Foreign Variable
26
-1
-2
-i-t'''"T''''I""'''''''''1''''''l''"I''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''0''T'''''''''''''''''''''
4
12
20
28
36
44
52
60
Economic Review / Fall 1989
ExhibitlA (conttnued)
Foreign Shocks
Domestic Demand
Shocks
3
1
-1
-3
...5
-7
-9
-11
1.3
1.1
.9
.7
.5
.3
.1
-.1
19
17
15
13
11
9
7
5
3
1
-1
-3
4
4
12
12
20
20
28
28
44
36
36
44
52
52
60
60
-7
-9
-11
1.3
1.1
.9
.7
.5
.3
.1
-.1
4
12
20
28
36
44
52
60
4
12
20
28
36
44
52
60
4
12
20
28
36
44
52
60
4
12
20
28
36
44
52
60
19
17
15
13
11
9
7
5
3
1
-1
-3
4
12
20
28
44
36
52
60
4
4
3
2
1
3
2
1
0
0
-1
-2
-1
-2
4
12
20
28
36
44
FederalReserve Bank of San Francisco
52
60
27
and technology permanently raiseoutput, withtheeffectof
labor-supply shocks buildingup somewhat more gradually
than the effect of technology shocks. Positivi shocks to
these two variables also permanently raise the level of
interest rates, and the ratio of exports to imports.
Positive foreign shocks temporarily raise output and
lower the unemployment rate, although the latter effects
are relatively small. These shocks also permanently raise
the interest rate.
28
Exhibit IB presents the associated variance decompositions, which show the relative importance of the various
kindsof shocksin explaining the errors made in predicting
the model's variables. At forecast horizons of up to
four quarters, variation in the unemployment rate has
beendominated by aggregate-demand shocks. Aggregatesupply shocks begin to playa larger role as the forecast horizon lengthens, reaching 15 percent at eight quarters and
25 percent at 60 quarters. These results suggest that
Economic Review ! Fall 1989
unemployment has been substantially affected both by
aggregate-demand-and -supply shocks duringthepost-war
period.
Aggregate..demand shocks arethe mostimportant factor
in. explaining variation in real GNP in the short run
(contemporaneously and at forecast horizons of one and
two quarters), accounting for 50 to 55 percent of the
variation. Technology shocks also are quite important at
these shortlags, accounting for from 28 to 35 percent. As
theforecast horizonlengthens, technology shocks beginto
dominate, as theseshocks arepermanent, while aggregatedemand. shocks are transitory. By the time the lags reach
two years, technology shocks dominate demand shocks,
with the former factor accounting for 61 percent of the
variation and the latter accounting for only 18 percent.
Labor-supply shocks beginto become important only after
two years. At the frequency of the average business cycle,
ourresultsshow a largerrolefordemand shocks relative to
supply shocks than does earlier research.
Interest rate variation is dominated at all forecast horizons by domestic demand shocks, although foreign shocks
have a noticeable effect in the long run. Domestic supply
shocks play only a small role, except at the very long lags.
At a lag of 60 quarters, labor supply accounts for 22
percent of the variation in the interest rate, while at shorter
lags, the role of this variable is quite small (under five
percent).
Theforeign variable largely is exogenous withrespect to
the otherfour variables in the model-that is, it is determined mainly by its own past behavior-at forecast horizons of up to 12 quarters. At long lags, however, labor
supply plays a significant role in the error variance of the
foreign variable, reaching 43 percent at 60 quarters. Technology and domestic aggregate demand play only small
roles at all forecast horizons.
Theeffects of the foreign variable on the U.S. economy
are relatively modest, as would be expected from the
relatively small, albeitgrowing, roleof foreign trade in the
U.S. economy. Foreign shocks playa significant role in
U.S. real GNP at short lags, accounting for 13 percent of
the contemporaneous variation and then declining in importance as the lag lengthens. Foreign shocks also have
played a significant role in U.S. interestrate movements,
accounting for 10 to 12 percent of the variation in that
variable at forecast horizons in the range of two to 12
quarters.
Historical Analysis
We how use our estimated structure to carry out two
different exercises that examine the historical evolution of
thellllemployment rate. First,to understand thefactors that
bave'eaused movements in theunemployment rate overthe
courseofthebusiness cycle, we look at the sources of our
model'sforecast errorsat a forecast horizon of threeyears.
The results of this exercise are shown in Exhibit II. By
construction, any error in predicting the unemployment
rate hastobe theresultof the unpredicted demand, supply,
and/orforeign shocks thattookplace within the three-year
forecasthorizon. We obtain the contribution of each kind
of disturbance to the forecast error for any particular
quarter by multiplying the coefficients in the impulse
response functions by the appropriate historical shocks as
measured by the model.
The•. top panel of Exhibit II shows the total error in
predicting the unemployment rate twelve quarters ahead
overtheperiodfrom 1955 to 1988. At thisforecast horizon,
the majorerrorsare closely associated withbusiness cycle
swings. The four panels below show the contributions to
Exhibit II
Historical Analysis: Decomposition
of 12-0uarter-Ahead Forecast Errors
for Unemployment Rate
Total
Errors
0.0
Labor
Supply
Foreign
52
Federal Reserve Bank of San Francisco
56
60
64
68
72
76
80
84
88
29
theseforecast errors made by theindicated shocks. Shaded
areasrepresent business cycledownturns.
Themost strikingJeature of this analysis is that aggrega.te-<iemand shocks have played by far the largest role in
unemployment. rate •movements over the course of the
business cycle inthe postwar period. Although technology
shocks. areimportant for the average quarterly variability
of the unemployment rate over the whole sample, aggregate-demand shocks appear to be more closely related to
cyclicalswings in the unemployment rate.
TechnologyshocksdO, however, contribute significantly
to the longer-run swings in the forecast errors. Forexampie, the well-known productivity surge in the 1960s is
picked. •up in•• ouranalysis as a succession of positive
tieC~~ysbocks that.led to a lower-than-predicted unemployment rate over most of the decade. Similarly the
slowdown inpr()ductivity growth in theearly to mid1970s
is pickedupasa succession ofnegative technology shocks.
The 1980s have been marked by large shocks both to
aggregate demand and to technology. Not surprisingly,
the second panel of Exhibit II shows large, negative
aggregate-demand shocks (which pushed up the unemployment rate) during theperiod from 1980 to 1982, when
the Federal Reserve oriented monetary policy around the
monetary aggregates to combat thesurge in inflation in the
late 1970s and early 1980s.
Aggregate-demand shocks then turned positive (thus
pushing down the unemployment rate)in 1983 as monetary
policy became more accommodative in the face of a continuing recession and falling inflation. In addition, fiscal
policy became highly expansionary from 1983 through
1986, with the high-employment deficitjumping sharply
in 1983 and reaching a peak in mid 1986. From1986
through 1988, aggregate demand shocks were relatively
small, although on average were slightly negative.
Technology shocks also have been important factors in
unemployment rate movements in the 1980s.1n fact,they
were about as important as aggregate-demand shocks in
raising the unemployment rate. Thiseffectwas substantial
by historical standards and lasted from early1980 through
mid 1984. Technology shocks also accounted for a good
part of the unemployment rate decline-in 1986 and 1987,
when the unemployment rate moved into a range that
contributed to the Federal Reserve's concern about future
inflation.
What might be responsible forthispatternof technology
shocks? Any suggestions would be highly speculative.lf
Several large studies on thesources of productivity change
in the U.S., for example, have failed to come up with
specific explanations for a substantial portion of that
change.'? Nonetheless, we note that the timing of the
negative technology shocks in theearly1970s andtheearly
1980s is close to the two oil price shocks, suggesting that
this factor may have been important. However, as noted
elsewhere, inclusion of oil prices causes problems in
explaining developments after 1985 (see footnote 6).
Unemployment and Inflation
We tum now to the second exercise of our historical
analysis, namely a decomposition of the unemployment
rate into its aggregate-demand and -supply components,
and a comparison of these components with the inflation
Exhibit III
The Unemployment Rate and its
Supply-Induced Component
Percent
11
9
Unemployment
Rate
7
5
62
64
66
68
70
72
74
76
78
80
82
84
86
88
*Includes the estimated mean level of the unemployment rate to allowfor comparison
with the actual unemployment rate.
Economic Review
t Fall 1989
rate. In Exhibit III, theactual unemployment rate is plotted
against the mean unemployment rate plus the contribution
ofaggregat~-supply factors. To obtain this rate, we subtractedfromthe unemployment rate both the effects of
aggr~gate-d~mand-induced changes and the effects of
shocks originating abroad.t? The difference between the
two series plotted in Exhibit III represents the effects of
aggregate-demand. pressures and foreign shocks in the
labor market. (Of the two, the latter are not very important.)iDemandpressures apparently have reduced the
unell1ploymentrate during most of the 1965-1981 period,
implying the possibility of an inflationary bias in policy.
After 1981, these pressures have been more balanced,
sometimes positive and sometimes negative.
Percent
According to economic theory, there should be a negativecorrelation between our measure of the aggregatedemand component of the unemployment rate and the rate
Qfinflation relative to inflation expectations, if our rneasur~jsvalid. This correlation arises in both the Keynesian
PhiUipsicurveand the Lucas-Barro, or monetary-misperceptions,.· Phillips curve." In the former, an aggregatedemandshockthat reduces theunemployment rate leads to
high~r inflation. In the latter, a positive aggregate-demand
shock that raises inflation above inflation expectations
(thatis,createsan inflation surprise) willleadto a decrease
intheun~mployment rate.
The expected negative correlation is shown in the top
panel.of Exhibit IV. (We have used annual averages in
Exhibit tV
Inflation and Unemployment
Percent
10
4
3
8
2
1
6
o
4
-1
-2
2
-3
o . . . . .--r-....-r-.,...,...,..,....,...,........,-.-r-r-,...,...,....,..,...,.-r"'l-.-....-r-.,...,.....-+- 4
62 64 66 68 70 72 74 76 78 80 82 84 86 88
Percent
Percent
10
4
8
3
2
6
1
4
,
2
AggregateSupply
Component ______
o
-1
o """"""""''''''''''''''''''''''''''-'-I'''"''I"".,....,..,...,.....,...,,...,........,....,-,....,...,-.-,....,...,....,.-!-- 2
62 64 66 68 70 72 74 76 78 80 82 84 86 88
Federal Reserve Bank of San Francisco
31
order to reduce the random fluctuations in the data.) Note
that we plot actual inflation rather than the difference
between actual and expected inflation. In the following
discussion, we implicitly assume a positive correlation
between actual and unexpected inflation. The top panel of
the exhibit reveals that as the aggregate-demand component of the unemployment rate fell below zero in mid-1960
through 1980, the inflation rate rose, reaching a peak in
1981. Since then, the aggregate-demand component has
fluctuated around zero, and inflation has fallen.
The bottom panel of Exhibit IV plots the aggregatesupply component of the unemployment rate and the rate
of inflation. As expected, these two series are positively
correlated. When there is a positive technology shock, for
example, inflation falls as prices adjust to a new level, and
at the same time the unemployment rate falls as firms'
demand for labor rises.
The correlations shown visually in Exhibit IV are presented more rigorously in the first two columns of Exhibit
V. The first column presents cross correlations between
past, present, and future values of inflation, on the one
hand, and the aggregate-demand component of the unemployment rate, on the other. The correlations between the
aggregate-demand component of the unemployment rate
and inflation are strongly negative, suggesting that our
measure of aggregate-demand pressure is functioning as
expected.
The second column of the exhibit presents the correlations between our measure of the aggregate-supply component of the unemployment rate and past, present, and
future rates of inflation. These correlations are uniformly
positive, which appears to validate our concept of the
aggregate-supply component of unemployment.
In the third column, we show cross-correlations between the unemployment rate minus its mean rate with past
and future values of the inflation rate. The correlations
between the aggregate-demand component of the unemployment rate and future inflation are noticeably stronger
than those between the (mean-adjusted) unemployment
rate and future inflation. Likewise, the positive relationship between past inflation and our measure of the supplyinduced changes in the unemployment rate is noticeably
stronger than that between past inflation and the unemployment rate.
Notice also that the correlations between past values of
inflation and the unemployment rate are positive. Thus, the
raw data tend to support the Keynesian Phillips curve,
which has causation running from unemployment to future
inflation, and refutes the monetary-misperceptions Phillips curve. The latter relationship implies that there should
32
be a negative correlation between past inflation surprises
and unemployment rates, rather than the positive correlation shown in column three. However, the first column of
the table shows that once the aggregate-supply shocks are
stripped away, both directions of causation are supported.
There is a negative correlation between past inflation
and aggregate-demand-induced unemployment (monetary
misperceptions) and also between future inflation and
unemployment (Keynesian).
Economic Review / Fall 1989
IV. Policy Implications and Conclusions
The aim of this paper has been to estimate the relative
importance of different kinds of disturbances in causing
movements in the unemployment rate. Towardthatend, we
have attempted to keep our model as free as possible of the
controversial identifying restrictions that are inherent in
the various competing paradigms of the macroeconomy.
We find that on average both demand and supply shocks
have been important in explaining unemployment rate
movements in the postwar period. While demand shocks
arerelatively more important in causing cyclical swings in
the unemployment rate, supply shocks playa significant
role in inducing longer-term movements. Our finding that
positive supply shocks are correlated with falling unemployment in subsequent periods casts doubt on Phillipscurve analyses, which assume that relative prices and the
unemployment rate move independently of each other.
Our historical analysis suggests that supply shocks were
important in keeping the unemployment rate low in the
1960s, and relatively high in the early- and mid-1970s. Of
particular interest right now is the role played by supply
shocks in raising the unemployment rate in the first half of
the 1980s, and then lowering it in the second half of the
decade. The relatively large role played by supply shocks
in the decline in the unemployment rate over the last few
yearscould be one reason the inflationrate has not accelerated as much as past estimates of the unemploymentinflation relationship would have led us to expect.
The analysis is relevant for policy purposes to the extent
that policy makers take the unemployment rate into accountin determining policy. Policy makers may look at the
unemployment rate in order to arrive at an estimate of
future inflation. Since movements in the unemployment
rate may be the result of either demand or supply factors,
looking at the level of the unemployment rate alone (or at
the unemployment rate relative to some fixed value)can be
misleading in particular episodes; instead, it is necessary
first-to determine the relative importance of aggregatedemand and -supply forces.
With this in mind, we consider what the model tells us
about the conditions that prevailed in 1988 (the last year of
our sample period). As Exhibit III indicates, aggregate
demand was mildly stimulatory. The unemployment rate
averaged 5.5 percent over the year. In the absence of any
demand shocks, it would have averaged 5.8 percent. The
difference between these two numbers (0.3 percent) provides a measure of aggregate-demand pressures in the
economy. A measure of the net impact of supply shocks is
obtained as the difference between what the unemployment rate would have been in the absence of demand
shocks and what it would have been in the absence of
shocks of any kind. Our model implies that in the absence
of any shocks to the economy the unemployment rate
would have settled at 6.0 percent. 22
Thus, this difference between the actual 5.5 percent rate
in 1988 and the 6.0 percent mean rate is accounted for
about equally by demand and supply shocks. Although
demand pressures do appear to have contributed to labor
market tightness in 1988, the degree of pressure probably
is not as intense as would be suggested by comparing the
prevailing rate with its 6.0 percent mean.
APPENDIX A
Identification
In this appendix we describe the identification problem
in terms of the moving average representation of a VAR.
Let the vector X, = [x., x2t . . . xnt ] denote the variables
contained in the model, where all the elements are nonstationary, but are not cointegrated. Assume that the structural representation of the model can be written as
B (L) Z,
z, =
z, = C (L)
A (L) et
(AI)
where Z, = JlXt , A(L) = Ao + AlL + A2U + A3V . . . ,
and the lag operator L is defined by Let = et- l . Further, it
is assumedthat ~At< 00, and that the structural disturbance
term e is serially uncorrelated.
Federal Reserve Bank of San Francisco
Let the estimated VAR representation of the model be
given by
=V
t
•
Multiplying both sides of (A2) by C(L)
the moving average representation
Vr
(A2)
B(L) -lleads to
(A3)
where E(v t ) = 0, and E(vsv/) = n for t = s and is zero
otherwise. (A3) is the reduced form representation of (AI),
and we have
(A4)
33
Thisis satisfied forany vt suchthat vt = S*et , and Ctl.) =
A(L)*S -I. Thus, to recover the structural representation
from the estimated VAR, we need to obtain the matrix S
which links the VAR residuals vt with the structural
disturbances e;
Theexact form of S willdepend upon thestructure ofthe
model. Under the usual assumption that the structural
disturbances are uncorrelated witheachotherandthatthey
have unit variance (that is, E( ete/) = I), the problem of
choosing the appropriate S reduces to choosing the elementsof S subject to thecondition thatS is a square rootof
n (the variance-covariance matrix of the VAR residuals).
Since n has n(n + 1)/2 unique elements and S is n*n, we
need n(n-1)/2 (that is, n2
[n(n + 1)12]) additional
restrictions in order to identify a unique S. If n = 2, for
example, n contains three unique elements while S contains four. Thus, we need one additional restriction to
identify S.
Sims(1980) suggested choosing S suchthat Sij = 0, for
all j > i, which serves tomake thesystemexactly identified.
For a two-variable system, this restriction prevents shocks
to the second variable from having any contemporaneous
effectonthe first variable. Sims' restrictions imply thatthe
underlying structural model is a recursive, simultaneous
equations model (with independent error terms), a representation that may sometimes be difficult to reconcile with
economic theory. Blanchard and Watson (1986) imposed
restrictions on contemporaneous correlations that were
explicitly derived from economic theory, and variations of
this technique have beenimplemented by Bemanke (1986)
and Walsh (1987), among others.
The technique of identification used in this paper has
been suggested recently by Blanchard and Quah. In this
technique the restrictions used to identify S can be interpreted as restrictions on the long-run effects of the associatedshocks on certainvariables. Tosee how thisworks,
assume that the vector Z, contains only two elements, so
that (A3) becomes
ZIt].
[ ZZt .
Cd L) CdL)]
[ czI(L) cziL)
[Vlt]
VZt
or
or
rIl = [Cl1(L)Sn
Ed
czlL)sn
+ c12(L)s21
+ cziL)sZI
+ CdL)S22] PI]
czlL)s12 + cziL)szz
~Zt
cn(L)s12
As discussed above, if eland e2 are assumed to be
independent of each other, only one more restriction is
needed to identify S. If it is assumed that e2 hasnolong-run
effect on xI (the first variable in the model), therestriction
takes the form
Cll
(1)
Sl2
+
Cl2
(1)
S22
=
0
Here, CII (1) is just the sum of the coefficients in the lag
polynomial cll(L). Thus, in this case identification is
achieved by choosing an S forwhich a particular weighted
sumof theelements of the second column of S is zero. The
condition that these weights be the sum of the coefficients
of the estimated lag polynomials for the first variable is
what ensures that the level of x, is independent of e2 •
Shapiro and Watson (1988) show how this restriction
can be imposed quite easily in the VAR representation.
APPENDIXB
Data and Preliminary Tests
We use quarterly data over the period 1948Q1-1988Q4
Tests for Stationarity
for our estimation.
Alldata have beenobtained from theCitibase datatape.
We tested for stationarity using the Said-Dickey test,
For population, we use noninstitutional population, 16
which is recommended by Schwert (1987). The test inyears and over, after subtracting armed forces. Theunemvolves estimating an equation of the form
j
ployment rate is the civilian unemployment rate. To make
Y t = a + !3Yt-I + I=I
.I ~\~Yt-i + e;
the GNPdata comparable, we use real GNPnet of federal
defense expenditures. The interest rate we use is the
Totestwhether theY process contains a unitroot we have
six-month commercial paper rate. Data for U.S. exports
to determine whether !3 = 1. However, under the null
and imports are from the National Income and Product
hypothesis that the process generating Y contains a unit
Accounts.
root, the ratio of the estimated value of !3 to its standard
34
Economic Review / Fall 1989
error does not have the usual t-distribution. The critical
values to be used in this case are tabulated in Fuller (1976).
Schwert (1987) demonstrates that choosing a large value
of} (as recommended by Said and Dickey) avoids the
problem of falsely rejecting the hypothesis that y contains a
unit root. In the table below we present the results for the
cases where} = 8 and} = 12. The table shows that we are
unable to reject the null hypothesis of a unit root for
population, real GNP, the interest rate, or the foreign
variable at the 10% level in either the eight-lag or the 12-lag
case.
the case of the unemployment rate, we present three
different sets of results. We are unable to reject the null
hypothesis of a unit root in the unemployment rate whether
or not we allow for a linear trend. The last column shows
the results for the case where we allow for a change in the
Federal Reserve Bank of San Francisco
mean unemployment rate beginning in 1974Ql. For the
eight-lag case, the computed test statistic is significant at
5%, while for the 12-lagcase the computed value of - 2.80
is justbelow the 5% critical value of - 2.89.
Note, however, that these critical values do not allow for
a shift in the mean under the alternative hypothesis. It is
useful to compare these critical values to those reported in
Perron (1988). Perron generalizes the null of a unit root
process to allow for a one-time change in the structure of
the series, and compares this to the alternative of a
stationary series with a discrete change in its mean. (Thus,
his null hypothesis is not strictly the same as ours.) It turns
outthatthecritical values vary with the date at which the
break occurs. For the case at hand, where the break occurs
about two-thirds of the way into the sample, the 5 % critical
value is - 3.33, while the 10% critical value is - 3.01.
35
NOTES
1. See, for example, "Records of Policy Actions of the
Federal Open Market Committee," FederalReserve Press
Release, for the eight Federal Open Market Committee
meetings in 1988.
2. See, for example, Ball, Mankiw, and Romer (1988),
Long and Plosser (1983), Lucas (1973), and Greenwald
and Stiglitz (1988).
3. Later applications used restrictions derived from economic theory. See Blanchard and Watson (1986), Bernanke (1986), Sims (1986), and Walsh (1987).
4. In another example of theoretical agnosticism, McCallum (1988) has investigated the robustness of nominalincome-targeting rules across different macroeconomic
theories.
5. These results refer to the case where no trend is
removed from the unemployment rate. Blanchard and
Quah also present results for the case where they remove
a linear trend from the unemployment rate. Removal of a
linear trend tends to increase the relative importance of
demand shocks.
6. They also included the price of oil as an exogenous
variable, on the grounds that the two recessions during
the 1970s were the consequence of the oil price shocks
during this period. Inclusion of the oil price variable is
problematic, however, since oil prices fell dramatically in
1985 without an obvious effect on real output. Shapiro and
Watson estimate their model through the end of 1985 only.
7. SW also present two sets of results: one where there is a
deterministic trend in labor hours and one where the trend
in hours is stochastic. The results discussed in the text
refer to the latter case.
8. See, for example, Blanchard and Quah (1989), Long
and Plosser (1983), and Shapiro and Watson (1988).
9. The model outlined here closely follows that in Shapiro
and Watson.
10. As described in Appendix A, once we assume that the
underlying shocks are uncorrelated and have unit variance, we need n(n-1 )/2 additional restrictions to identify a
model that contains n variables. Since n = 5 here, we
need a total of 10 restrictions.
11. This assumption implies symmetric treatment of foreign and domestic aggregate-demand shocks; that is,
neither have permanent effects on output. However, the
foreign shock also is designed to include the effects of
foreign supply disturbances. One drawback of our model
is that we are treating foreign and domestic supply shocks
asymmetrically; that is, domestic supply shocks can have
permanent effects, while foreign supply shocks cannot.
36
12. The assumption that an aggregate-demand shock
induced by monetary policy does not have a long-run
effect on the foreign variable is uncontroversial. The assumption that a fiscal-policy shock does not have a longrun effect on real exports and imports is less clear cut. See
Krugman (1985) and Mussa (1985) for discussions of
these issues and other references.
13.See Phelps (1978). For a contrary view, see Summers
and Blanchard (1986).
14. See, for instance, Nelson and Plosser (1982).
15. See, for instance, Gordon (1982) and Congressional
Budget Office (1987).
16. The reader interested in more detail is referred to
Shapiro and Watson (1988).
17. As noted earlier, we also estimated a model with no
mean shift in the unemployment rate, even though under
this specification unit root tests suggest that the unemployment rate is non-stationary. The impulse response
functions and variance decompositions for this model are
nearly identical with those presented in the text, with one
exception. The model without a mean shift in the unemployment rate ascribes a larger role to technology shocks
and a smaller role to demand shocks in determining the
error variance of the unemployment rate. Moreover (consistent with our findings in the unit root tests), the effects
of different kinds of shocks on unemployment dissipate
more slowly in the model without a mean shift than in the
model discussed in the text.
18. The list of real shocks considered by Boschen and
Mills (1988), for instance, contains changes in the price of
oil, marginal tax rates, real government purchases, working-age population, and real exports.
19. See Jorgenson, et aI., (1987).
20. More specifically, to obtain the supply component of
the unemployment rate for any given quarter, we subtract
the effect of all demand and foreign shocks that occurred
as long as 40 quarters ago. The impulse response functions in Exhibit 1A show that this is more than long enough
for the effects of these shocks to die out.
21. See Gordon (1982), Barro (1977), and Lucas (1973).
22. Prior to 1974, when we assume a mean shift, this rate is
estimated to be 4.8 percent. Also, in the model where the
mean is not allowed to shift, the mean rate of unemployment is estimated to be 5.0 percent.
Economic Review / Fall 1989
REFERENCES
Ball, Laurence, N. Gregory Mankiw, and David Romer.
"The New Keynesian Economics and the OutputInflation Trade-off," Brookings Papers on Economic
Activity, 1988:1.
Barra, Robert J. "Unanticipated Money, Output, and the
Price Level in the United States," Journal of Political
Economy, Vol. 86, 1977.
Bernanke, Ben S. "AlternativeExplanations for the MoneyIncome Correlation," Carnegie-Rochester Conference Series on Public Policy, 25: 1986.
Blanchard, Olivier J. and Danny Quah. "The Dynamic
Effects of Aggregate Demand and Supply Disturbances," The American Economic Review, September
1989.
Blanchard, OlivierJ. and Mark W Watson. "Are All Cycles
Alike?", in RobertJ Gordon, ed., The American Business Cycle. Chicago: University of Chicago Press,
1986.
Boardof Governors of the Federal Reserve System. "Records of Policy Actions of the Federal Open Market
Committee," Federal Reserve Press Release, for the
eight Federal Open Market Committee meetings in
1988,
Boschen, John F, and Leonard O. Mills, "Tests of the
Relation Between Moneyand Output in the Real Business Cycle Model," Journal of Monetary Economics,
22,1988.
Congressional Budget Office. Congress of the United
States, The Economic and Budget Outlook: An Update. August, 1987.
Evans, George W, "Output and Unemployment Dynamics in the United States: 1950-85," Journal of Applied
Econometrics, Vol. 4, 1989,
Fuller, Wayne A. Introduction to Statistical Time Series.
New York: John Wiley and Sons, 1976,
Gordon, Robert J. "Inflation, Flexible Exchange Rates,
and the Natural Rate of Unemployment," in Martin N,
Bailey, ed. Workers, Jobs, and Inflation. Washington
D.C,: The Brookings Institution, 1982,
Greenwald, Bruce C. and Joseph E, Stiglitz. "Examining
Alternative MacroeconomicTheories," Brookings Papers on Economic Activity, 1988:1,
Jorgenson, Dale W, Frank M, Gollop, and Barbara M,
Fraumeni. Productivity and US, Economic Growth.
Cambridge, Massachusetts: Harvard University
Press: 1987.
King, Robert, Charles Plosser, James Stock, and Mark
Watson, "Stochastic Trends and Macroeconomic
Fluctuations," unpublished paper, University of Rochester, 1987,
Federal Reserve Bankof San Francisco
Krugman, Paul R. "Is the Strong Dollar Sustainable?," in
The US. Dollar-Recent Developments, Outlook, and
Policy Options, Proceedings of a Symposium Sponsored by the Federal Reserve Bank of Kansas City,
Jackson Hole, Wyoming, August 21-23,1985.
Long, John B. and Charles I. Plosser. "Real Business
Cycles," Journal of Political Economy, 91, 1983.
Lucas, Robert E. "Some International Evidenceof OutputInflation Tradeoffs," American Economic Review, 63,
1973.
McCallum, BennettT "RobustnessProperties of a Rulefor
Monetary Policy," Carnegie-Rochester Conference
Series on Public Policy, 29, 1988.
Mussa, Michael L. "Commentary on "Is the Strong Dollar
Sustainable?," in The US, Dollar-Recent Developments, Outlook, and Policy Options, Proceedingsof a
Symposium Sponsored by the Federal Reserve Bank
of Kansas City, Jackson Hole, Wyoming, August
21-23,1985.
Nelson, Charles R, and Charles I. Plosser. "Trends and
Random Walks in MacroeconomicTimeSeries: Some
Evidenceand Implications," Journal of Monetary Economics, 10, 1982,
Perron, Pierre. "Testing for a Unit Root in a Time Series
With a Changing Mean," mimeo, Princeton University,
1988.
Phelps, Edmund S. Microeconomic Foundations of Employmentand Inflation. New York: W W Norton, 1970,
Schwert, G. William, "Effects of Model Specification on
Tests for Unit Roots in Macroeconomic Data," Journal
of Monetary Economics, 20, 1987.
Shapiro, Matthew D. and Mark W Watson. "Sources of
Business Cycle Fluctuations," NBER Macroeconomics Annual 1988, Cambridge, Massachusetts: M.l.T
Press,
Sims, Christopher A. "Macroeconomics and Reality,"
Econometrica, 1980.
____ . "Are Policy Models Usable for Policy Analysis?," Quarterly Review, FederalReserve Bankof Minneapolis, 1986.
Summers, Lawrence and Olivier Blanchard. "Hysteresis
and European Unemployment," NBER Macroeconomics Annual 1986, Cambridge, Massachusetts:
M.I.T Press,
Walsh Carl E, "Monetary Targeting and Inflation: 19761984," Economic Review, Federal Reserve Bank of
San Francisco, Winter, 1987.
37
38
Economic Review / Fall 1989
Liquidity Constraints on Consumption:
The Real Effects of "Real" Lending Policies
James A. Wilcox
Visiting Scholar, Federal Reserve Bank of San Francisco and Associate Professor, University of California,
Berkeley. I would like to thankBrian Bray, John Duca, Joe
Peek, Charles Steindel, David Wilcox, andparticipants at
the Spring 1989 Federal Reserve System Conference on
Business Conditions fortheirviews onanearlierversion of
this article. Especially helpful were the thoughtful and
constructive comments of editorial committee members,
Ramon Moreno, Bharat Trehan, and Carl Walsh.
This article argues thathouseholds are oftenprevented
from consuming as much as their permanent income
justifies. The hypothesis is advanced thatlending criteria
based on payment-to-income ratios often inappropriately
constrain borrowing and therefore consumption. The evidence indicates that the variables presumed to proxy for
payments andfor income, thenominal interest rate andthe
unemployment rate, respectively, significantly affect consumption growth in the manner suggested by thishypothesis. In contrast, there is little evidence that real interest
rates have important effects on consumption.
Federal ReserveBank of San Francisco
Personal consumption expenditures typically comprise
about two-thirds of total national spending. Not only is
consumption thesingle largest category of spending, but it
changes by large amounts. In absolute terms, the variability of consumption expenditures is as large as that of
business investment. Moreover, the variabilities of the
components of consumption (services, nondurables, and
durables) are large. The variabilities of nondurables andof
services individually are nearly as large as that of consumer durable expenditures and the variability of the sum
of nondurables and services is appreciably largerthan that
of durables.' An understanding of the movements in and
determinants of consumption anditscomponents clearly is
important for the conduct of monetary policy.
The widely accepted permanent income hypothesis posits that consumption is driven by households' wealth and
their expectations of income over the long run. Although
actual income may fluctuate, these fluctuations are hypothesized not to affect consumption unless they alter
households' expectations of their longer-run average, or
permanent, income. Instead, when households are faced
withdeviations of actual from permanent income, they are
presumed to vary borrowing andlending in orderto steady
consumption.
Considerable recent empirical research, however, based
on both macroeconomic and microeconomic data bases,
suggests that movements in actual income have sizeable
effects on consumption apart from the effect of those
movements on permanent income. Likewise, theory suggests that real, after-tax interest rates should affect consumption, but that nominal interest rates should not. The
evidence, however, indicates that exactly the opposite is
more likely to be true. Consistently, the data point to
significant nominal interest rateeffects onexpenditures for
durables, nondurables, and services, and to insignificant
real-interest-rate effects.? This is surprising indeed.
This article suggests thata single factor helps to explain
these two findings-that consumption expenditures are
reduced both by higher nominal interest rates andby the
shortfall of actual, current real income below its permanent level. These interest-rate and income effects both
result from a borrowing constraint which prevents households from obtaining sufficient credit to finance as much
39
consumption as their permanent income justifies. In particular, this borrowing, or liquidity, constraint is hypothesized to emanate from the prevailing lending practice of
granting credit subject to virtually-never-changing payment-to-income-ratio ceilings. 3
The first section presents a brief exposition of the
permanent income theory of consumption and its empirical
implications. Then it is argued that current credit-granting
practices lead to liquidity constraints that are associated
with nominal interest rates and real income. Section II
reviews the evidence from microeconomic data bases regarding liquidity constraints. It also suggests what these
constraints portend for macroeconomic data. Section III
econometrically assesses the extent to which nominal
interest rates and unemployment rates affect aggregate
consumption, apart from their effects on permanent income.The estimates suggest that each has powerful effects
on consumption expenditures. The evidence points toward
the liquidity constraint that operates through the paymentto-income ratio as the source of these important effects on
consumer expenditure. The short- and longer-run implications of these results for national spending and saving, for
economic policy, and for financial institutions are discussed in the concluding Section IV.
I. Permanent Income, Consumption, and Constraints
The permanent income hypothesis posits that consumers
base desired consumption on permanent income. The
theory can be summarized as
C = kY P
(I)
where C is the level of real, per capita consumption, k is the
(average and marginal) propensity to consume out of
P
permanent income and y is the level of real, per capita
permanent income. Permanent income, yP, is the average,
discounted income a consumer expects to receive over the
relevant horizon. In logs, (I) becomes
log(C) = log(k)
+ log(Y
P
)
(2)
Note that actual income does not appear. The permanent
income hypothesis states that actual income affects consumption only to the extent that if affects permanent
income. Consumers are presumed to have access to capital
markets, and therefore are not constrained by cash flow or
current income. Borrowing and lending are viewed as
shock absorbers for temporary fluctuations in income,
making it possible for households to maintain consumption in the face of changes in actual income that are
perceived to be temporary or are anticipated. Access to
capital markets also permits consumption to change when
permanent income changes in advance of actual income.
Permanent income represents a forecast, not a measured
quantity. When consumers use all the information that is
available at a given time to form estimates of permanent
income, those estimates will change from period to period
only as new information is received. Hence, changes in
estimates of permanent income will be unpredictable. This
is common to optimal forecasts; over time, the change in
what is expected to happen over any given future period is
random." This is embodied in
(3)
40
which shows that today's forecast of future income differs
from last period's forecast of income over the same future
period by an unforecastable amount, u. No information
available prior to the current period would help predict the
change in permanent (or forecasted) income. Otherwise, it
already would have been incorporated in last period's
estimate of permanent income.
Taking first differences of (2) and using (3) generates
~log(C) = ~log(k)
+
(4)
j.L
On the assumption that k is constant over time, the growth
rate of consumption, ~log(C), should be random. No
information available prior to the current period should
reliably predict changes in consumption growth.
This model, however, is correct only if expected, real,
after-tax interest rates are constant. When they are not
constant, theory predicts that households defer more consumption when the reward for doing so is higher. This
means that, ceteris paribus, higher interest rates last
period reduce last-period's consumption relative to current
consumption, thereby raising the growth rate of consumption:
~log(C) =
'Y
+ 8re_ 1 +
(5)
j.L
Liquidity-Constrained Consumption
One potential weakness of this permanent-income formulation is that it assumes perfect capital markets, in
which households can borrow and invest in order to
smooth consumption across time periods. If capital markets are not perfect, however, households' desired spending patterns may be "liquidity constrained" in significant
ways." Thus, the presence of liquidity constraints could
make the permanent income hypothesis an inferior explanation for aggregate consumption behavior, particularly
Economic Review / Fall 1989
since changes in borrowing flows are an empirically important factor in consumption patterns. 6
The alternative hypothesis advanced here is that liquidity constraints are indeed binding for a significant portion
of households and that the aggregate amount of liquidity
constraint is associated with unemployment and nominal
interest rates. As either rises, liquidity constraints both
bind more tightly on previously constrained households
and begin to bind on more households. Each aspect drives
consumption further below the unconstrained value that
the permanent income hypothesis predicts.
Consumers subject to liquidity constraints are assumed
to behave differently from those who are not. Their consumption generally will respond vigorously to changes in
current income or other sources of cash flow, even if those
changes are anticipated, since, by definition, constrained
households want to consume more but are prevented from
doing so by restrictions on their ability to borrow. Consumers not subject to borrowing constraints, in contrast, would
be expected to react little to anticipated, or to past income
changes since their estimates of permanent income and
their consumption plans already have adjusted to those
developments. Liquidity-constrained consumers already
may have changed their desired consumption, but actual
consumption may have to await increases in actual income
and the increased cash flow and ability to borrow that
comes with it.
The liquidity constraint impinging on an individual may
be relatively short-lived or very long-lived. Borrowing
may be constrained to less-than-optimal levels for households with expected (average, discounted) lifetime earnings that are above their actual, current earnings for
periods extending into years. Given the typical upward tilt
in the age-earnings profile, most young households would
be expected to be substantial net debtors for many years.
Kotlikoff (1988) shows that in practice, however, there is
very little net borrowing by the young, whose consumption
tracks earnings very closely at least up to age 45. And it
may be that not only the young are liquidity constrained.
Wilcox (1989) cites several studies that suggest that a
substantial portion of the elderly may be liquidity constrained. 7
Liquidity constraints resulting from the combination of
lending criteria based on current income and the usual
upward slope of the age-earnings profile then may lead to a
very important and relatively constant share of households
whose spending is constrained. For an individual, this type
of liquidity constraint may become less binding as actual
earnings approach potential earnings and as financial
assets are accumulated." The number of households sub-
Federal Reserve Bank of San Francisco
ject to this form of liquidity constraint may change as time
passes, probably slowly and in tandem with the ratio of
young to total households.
In addition to the households subject to this age-related
constraint, a changing fraction of households is likely to
experience varying degrees of liquidity constraint in response to variations in unemployment and nominal interest
rates." One reason consumers may become liquidity constrained is that lenders widely follow a practice of restricting consumer borrowing so as to keep payment-to-income
ratios below some ceiling level. A recent American Bankers Association textbook on consumer lending suggests
that a borrower's capacity to repay a loan can be measured
by the payment-to-income ratio. 10 This means that applications for credit are likely to be disapproved if the ratio of
total loan payments to income breaches a ceiling, for
example, of 40 percent. II Note that, in practice, this policy
refers to current payment-to-current income. One consequence of this policy is that lenders generally refuse to
extend credit to the currently unemployed. 12
Using a payment-to-income rule means that movements
in current income relative to permanent income can lead to
consumers being liquidity constrained. This practice suggests a capital market imperfection that may explain why
current income and cash flow affect consumption when the
permanent income theory suggests they should not.
These credit practices also suggest that the extent to
which consumption is liquidity constrained will vary with
nominal interest rates. Lending policies that predetermine
a payment-to-income ratio ceiling reduce the real amount
of credit that would be made available to a borrower as the
nominal interest rate rises.
Consider a $10,000, 48-month fully-amortizing loan.
Suppose that the real interest rate is six percent. 13 If
the expected inflation rate built into interest rates were
zero percent, the resulting six percent loan would entail
monthly payments of $235. 14 Since the actual and expected inflation rate is zero for this period, the actual
payments and their real, or inflation-adjusted, values are
both $235 per month for 48 months. The lower horizontal
line in Figure 1 shows that payment, which is level in
dollar, or nominal, terms and in real terms.
Suppose now that the actual inflation rate and the
expected inflation rate incorporated into nominal interest
rates rises from zero to five percent and that the real
interest rate remains at six percent. The resulting eleven
percent market interest rate means that the same loan now
carries a $258 payment, an increase of 10 percent. This
higher, nominal amount is shown as the upper horizontal
line in Figure 1. This repayment pattern has the same six
41
percent real return over the life of the contract. The real
burden of those higher nominal payments is shown as the
diagonal line in Figure 1. Note that relative to the constant
real payments in the zero-percent inflation case, the level
dollar payments in the five-percent inflation case, in real
terms, are higher early on and lower later in the life of
the loan.
Level nominal payments during a period of inflation
imply falling payments in real terms over time. An increase
in expected inflation leads to an immediate one-time
increase to higher nominal payments. The problem is that
the onset of inflation does not have the same effect on
household incomes. Incomes generally rise gradually as
the level of prices rises. They do not jump by the same 10
percent in the first month that payments do. Thus, an
increase in nominal interest rates stemming from an increase in the inflation premium would raise the initial real
burden of this loan by virtually 10 percent. Suppose that a
lender followed a payment-to-income rule and that the
borrower would be permitted in either inflation scenario to
borrow an amount that would imply a $235 payment. In the
zero percent inflation case, a loan of $10,000 would be
granted; in the five percent inflation case, a loan of only
$9,087 would be granted.
This happens even though households and financial
institutions both may think that they are adjusting for
inflation. By basing their decisions on the ratio of two
(nominal or real) flows, which often leads to an inflationadjusted magnitude, they may be attempting to make a
"real" decision. They are not. The reason is straightforward: the dollar payment per dollar of credit extended rises
with the nominal interest rate. The only type of loan
repayment schedule currently available provides for level
Real
Payment ($)
dollar repayments. As time passes and inflation raises the
level of wages and prices, those level payments constitute
falling real payments.'> Since later nominal repayments
will be less in real terms, earlier ones must be greater to
preserve the same average real payment and real rate of
interest.
Over time, lenders might be expected to make lending
policy parameters "realistic" to maintain optimal real
borrowing limits. In practice, adjustments in lending policy take place so slowly that the aggregate amount of
consumption that is liquidity constrained is likely to rise
with the level of inflation. To the extent that nominal.
interest rates respond to (expected) inflation, payment-toincome ceilings would have to rise and fall with inflation to
avoid tightening of liquidity constraints. It does appear
that, on average, consumer credit parameters may have
become somewhat looser in higher inflation periods. It
does not appear, however, that they became tighter as
inflation fell over the past ten years. In any event, lending
parameters seem to be adjusted slowly enough, if ever, that
as nominal interest rates move in response to inflation,
more households become subject to these interest-raterelated restrictions.
The extension of loan maturities may reflect an attempt
to overcome the high, initial, real payments brought on by
inflation-related increases in nominal interest rates. The
evidence does seem to be that loan maturities have consistently lengthened over the past four decades, but that seems
to have gone on apart from the rise and fall of inflation.
Regardless, attempting to solve the real-payment-tilt problem with longer maturities is indirect and inefficient since
longer loans have lower, but still level, dollar payments.
Figure 1
Nominal and Real Loan Payments *
260
250
240
230
220
2 1 0 -h-..,...,...,...,...,...,...,...,...,...,..,...,..,........,..,...,...,.....,..,...,....,..,..,..,...,....,..,..,,..,..,....,..,..,,...,..,....,...,..................................
12
24
36
48
*Basedon $10,000, 48-month, fully-amortizing loan with a 6% real interestrate.
42
Economic Review / Fall 1989
II. Micro Evidence
One potential way to measure the extent of aggregate
liquidity constraint is to look at data on loan applications disapproved." However, the results of such a study
will almost certainly understate the degree of liquidity
constraint since borrowing constraints related to actual
income may not be imposed only by lenders but also (selfimposed) by households. To the extent they recognize that
lenders impose payment-to-income restrictions, households are likely to adjust current borrowing behavior as a
hedge against temporary declines in actual income in the
future. This may explain why, for example, so many
households purchase lines of credit through credit card
fees. Such lines may provide access to the credit that
lenders otherwise would refuse to extend under circumstances that would lead a borrower to seek it. By tempering
their debt accumulation and by maintaining lines of credit,
households effectively have credit-access insurance.
Likewise, households may not bother applying for credit
when they (accurately) forecast that they do not fall under
lenders' payment-to-income ceilings. Most households are
likely to believe (accurately) that their prospects for obtaining additional credit are dim when they are unemployed,
for example, and therefore may not even apply. Finally,
even though the constrained may end up obtaining credit,
the amount they borrow will be less than if they were not
constrained. In the mortgage market, for example, it is
common for potential borrowers to seek prior estimates
from lenders directly or indirectly of the maximum mortgage they would qualify for and then to adjust home
purchases accordingly.
As an alternative approach to assessing the extent to
which consumers are liquidity constrained, a number of
empirical studies have investigated the consumption behavior of individual households. The important question
for our purposes is whether liquidity-constrained consumers are numerous enough or receive a large enough share of
aggregate income that aggregate consumption patterns are
importantly affected. Below, I briefly review the results of
studies of the spending patterns of individuals with that
in mind.
Hall and Mishkin (1982) used data from the early 1970s
on income and consumption of food by individual families. They conclude that 80 percent of these actual consumption expenditures appear to move in the manner
prescribed by the liquidity-unconstrained, permanent income hypothesis. Their results further suggest that much
of the deviation of actual from unconstrained consumption
is .due to the inability to borrow to overcome temporary
income shortfalls. Hall and Mishkin conclude that "food
Federal Reserve Bankof San Francisco
consumption behaves as if constraints on borrowing were
relatively unimportant."
Hayashi (1985) analyzed consumption behavior of two
groups of consumers in the early 1960s: those who had
high savings and those who did not. The hypothesis was
that consumers who had accumulated wealth were unlikely
to be constrained in their consumption behavior since they
could self-finance more consumption by saving less, or
even by dissaving. In contrast, those who did not have high
savings were more likely to find their consumption restricted if liquidity constraints did, in fact, exist.
When the empirical model that best tracked the consumption of the high savers was used to predict the
consumption of the low savers, Hayashi found that low
savers tended to spend less (and save more) than the model
forecasted. The rationale given for that result is that
liquidity constraints prevented the group with low accumulated savings from consuming as much as they otherwise
would have chosen; their inability to borrow precluded
spending. The group whose consumption seemed most
constrained was young households. That result is consistent with the typical upward tilt in age-earnings and in agewealth profiles.
Mariger (1986) also concluded "that liquidity constraints are quite prevalent." Again employing the early
1960s data set, his estimates suggest that about twenty percent of families were liquidity constrained. These families
accounted for about one-sixth of aggregate consumption.
Zeldes (1988) uses food consumption data collected
from the late 1960s through the early 1980s to assess
whether binding liquidity constraints have been widespread. Like Hayashi, he splits the individual-family data
set in two. The specific criterion is whether the family has a
non-negligible wealth-to-income ratio. He then estimates
whether either group seems to exhibit consumption behavior that is consistent with the presence of borrowing
constraints. He finds that, as Fitzgerald and Hemingway
first conjectured, the rich are different.'? Their consumption displayed no indication of being constrained by an
inability to borrow, whereas that of the group with the low
wealth-to-income ratio did.
These studies each find that a minority of households has
beeninfluenced by either binding current, Or potentiallybinding future, liquidity constraints. Taken together, these
studies seem to make a compelling case for the practical
importance of liquidity constraints. First, the fraction of
households deemed to be constrained was a substantial
minority. Finding that about 20 percent of consumers had
been constrained implies serious deviation from the un-
43
constrained model of aggregate consumption. Since the
unconstrained consumers are able optimally to smooth
their consumption, it may be that the remaining consumers
account for a very large fraction of total consumption
variability. 18
Second, of all the items in the household budget, expenditure for food would seem to be one of the least likely to
be liquidity constrained. Food consumption is usually considered very income inelastic; it is the last purchase to
be sacrificed when income falls. Evidence that the food
expenditures of twenty percent of all households are constrained suggests that expenditures on the remaining categories of expenditure may be vastly more affected. 19
III. Macro Evidence
Given the evidence from studies of the spending patterns
of individual households, this section investigates whether
liquidity constraints have important effects on aggregate
consumption behavior. According to the liquidity-unconstrained version of the permanent income hypothesis of
consumption, no lagged values of any variable should help
predict the growth rate of consumption. Table I provides
evidence to the contrary. It presents the results of regressing consumption on the first four lags of (personal disposable) income. Rows I, 2, and 3 present the results of using
lagged income to predict total consumption expenditures
(C), the sum of consumer expenditures on nondurables and
on services (CNS), and consumer expenditures on durables (CD), respectively. All variables in Table I are expressed as real, per capita, seasonally-adjusted percentage
changes at annual rates. Current growth rates are based on
current relative to prior-quarter levels.
The first two lagged income coefficients tend to be
sizeable and significant. Lags three and four tend to be
smaller and negative. Not surprisingly, t;IE reaction of
durable goods expenditures to lagged income is considerably different from that of nondurables and services. For
44
nondurables and services, it is reasonable to take both the
size and the timing of the consumption services that flow
from them to be the same as expenditures on them. When it
comes to durables, such an assumption is patently unreasonable.s? A $20,000 expenditure this quarter for a new
automobile is almost entirely investment and little consumption. The continuing flow of consumption services
from past durable goods purchases, therefore, suggests that
a positive response of durable goods expenditures to income is likely to be followed by negative ones. That is what
Row 3 shows. After large and significant positive responses to the first two lags of income, large, negative
responses appear.
The F-statistics in each row test whether the lagged
values of income significantly help predict consumption.
Since each of the calculated F-statistics exceeds the .05
significance level (critical value of 2.37), I conclude that
each of the measures of consumption is predicted by
lagged, actual-income movements. 21 This predictability
of consumption growth argues against the simplest version
of the permanent income theory as a sufficient explanation
for consumption.
Economic Review I Fall 1989
The results in Table 1, however, do not point to the
reasons the permanent income hypothesis might be violated. The results presented in Table 2 suggest that lagged
income and lagged consumption appear to affect consumption to the extent that each serves as a proxy for current
income. Table 2 shows the results of arbitrarily splitting the
sample period into decades and testing whether lagged
values of consumption and income help predict consumption and income. (Reported F-statistics above 2.65 in Table
2 indicate statistical confidence above the. 95 level.) When
current income is predicted by its own lags, as in the 1950s
and, to a lesser extent, the 1960s, lagged income serves as
an effective proxy for current income. Those are also the
periods when consumption is predicted by lagged income.22 When income is not predicted by past income, as
in the 1970s and 1980s, lagged income does not serve as an
effective proxy for current income. In those periods, consumption is not predicted by past income.
Similar findings pertain to lagged consumption. When
actual income is predicted by, and therefore effectively
proxied by, lagged consumption, as in the 1980s, consumption is significantly related to its own lags. When
lagged consumption does not serve as an effective proxy
for current income, lagged consumption does not significantly predict current consumption. The exception to this
pattern is that, in the 1970s, when income is not predicted
by lags of consumption, lagged consumption still helps
predict consumption. 23 These results indicate that it is not
lagged income or lagged consumption per se that affects
consumption. Instead, what they suggest is that consumption reacts to current income to an extent greater than is
warranted by the permanent income hypothesis.
Testing for Liquidity Constraints
The finding that consumption does not correspond to the
predictions of the simplest version of the permanent income hypothesis does not necessarily invalidate it. As
noted earlier, consumption growth also may be affected by
changes in the real interest rate and be completely in
accord with the permanent income hypothesis. The apparent violation reported above may reflect that effect. However, as I argue, an alternative explanation for the results
in Tables 1 and 2 is that consumption is importantly affected by liquidity constraints associated with payment-toincome ceilings on borrowing.
This hypothesis implies that changes in the nominal interest rate (0, as distinguished from changes in the real
interest rate, and changes in the unemployment rate (U),
which proxy for changes in current income, affect the
growth rate of aggregate consumption expenditures on
nondurables and services, apart from their effects on permanent income.24 Suppose that the propensity to consume
out of permanent income apart from the liquidity constraints associated with i and U is k*, that the actual propensity to consume is k, and that the two are related by:25
k = k*e(aU+[3i)
(6)
On the assumption that (X and ~ are negative, equation (6)
embodies the hypothesis that, as either the unemployment
rate or the nominal interest rate rises, consumption is
reduced relative to the level implied by permanent income.
The reason is that as either rises, liquidity constraints
will bind on more households and more consumption per
household. Taking logarithms of equation (6), then substituting into equation (1), and allowing for the real interest
rate effects discussed earlier produces:
D.log(C) = 'Y
+
8r:"'1 + (XD.U
+
~D.i
+
f.L
(7)
Table 3 shows the results of estimating equation (7) and
some variations of it. To ensure consistent estimates and
valid statistical inference of the effect of each of these
right-hand-side variables on consumption growth, an instrumental variables estimation technique was used. 26
This approach addresses two problems. First, the current
changes in unemployment and interest rates (and presumably in almost all other macroeconomic variables) are very
likely to be correlated with current revisions to permanent
income and are, therefore, correlated with the equation's
error term.
Second, even if households are continuously obeying the
permanent income hypothesis, quarterly-average measures of consumption growth will be correlated with the
previous period's consumption growth, which is deter-
Federal Reserve Bank of San Francisco
45
mined by the previous period's new information. This
apparent violation of the permanent income hypothesis
emanates from time-averaging of data, not from a violation
of the theory. The induced autocorrelation implies that,
when time-averaged data are employed (as they are here),
the consumption equation error term will be correlated
with one-period-lagged variables, such as the one-periodlagged expected real interest rate that equation (7) suggests
is relevant. Thus, one-period-lagged variables are not valid
instruments.
The variables used as instruments are a constant term
and lags two through five of the first differences of the
unemployment rate, the expected inflation rate, the auto
loan rate, the Treasury bill rate, and the real, after-tax
Treasury bill rate.?" Expected real interest rates were
derived by subtracting the one-year expected inflation rate
from the nominal interest rate. 28
The top and bottom halves of Table 3 use as the
46
consumption measure the sum of expenditures on consumer nondurables and services (CNS) and expenditures
on consumer durables (CD), respectively. Row 1 presents
results from estimating equation (5), the modified (unconstrained) permanent income hypothesis, using the real,
after-tax Treasury bill interest rate as the measure of the
lending rate that consumers face."? As can be seen from
these results, the real interest rate does not have a significant effect on consumption. Row 2 adds the two presumed
determinants of the degree of economy-wide liquidity
constraint: the unemployment rate and an interest rate at
which consumers can borrow. The borrowing rate that
households face is taken to be the nominal, before-tax,
interest rate on auto loans. Row 3 includes both the
borrowing and the lending interest rates households face.
The lending rate is taken to be the Treasury bill rate. The
borrowing and lending rates that consumers face did not
always move closely over this period, in part due to
Economic Review / Fall 1989
regulations, in part due to maturity differences. The estimates.show that the borrowing rate clearly is the dominant
financial force.>" That the entire interest-rate impact takes
place through the nominal borrowing rate is just what a
payment-to-income constraint leads us to expect.
Consistently, the estimated effect of (lagged, expected)
real interest rates on nondurables and services expenditure
is negative, small, and statistically indistinguishable from
zero. This coincides with most earlier research and holds
regardless whether liquidity constraint proxies are included .. By contrast, the liquidity constraint proxies are
large and significant. 31
Rows 5-8 use expenditures on durables as the dependent variable. These results are qualitatively similar to those
for nondurables and services. The unemployment rate and
interest rate coefficients are large and clearly statistically
significant in each row. Again, the borrowing rate dominates the lending rate. The real-rate effects are positive and
much larger than for nondurables and services, but never
approach statistical significance.
The estimates in Table 3 also embody some equalities
that support the. payment-to-income-constraint hypothesis. Since the coefficients for durables are about five times
as large as those for nondurable and services spending and
since the level of the latter category is about five times as
large as that of the former, the estimated reduction in
spending due to a rise in either the unemployment rate or
the nominalinterest rate is about the same for both spending categories.
Percent of
Unconstrained
Consumption
Second, equality of the interest-rate- and unemployment-rate-spending elasticities cannot be rejected. Tests of
that hypothesis for expenditures on nondurables and services.and for expenditures on durables generate t-statistics
of -0.8 and 1.8, respectively, each of which is below
the critical value for a .05 significance test. 32 These results
are consistent with the hypothesis concerning payment-toincome constraints since equal percentage changes in the
interest rate and in the unemployment rate should have
(approximately) the same effect on the payment-to-income
ratio and therefore on the degree of liquidity constraint.
Third, when Rows 4 and 8 of the table are re-estimated
using the two individual components of the nominal interest rate, the expected real rate and the expected inflation
rate, the estimated coefficients on the components are
close to that on their sum. Statistical tests of this equality
within each spending category do not call for rejection; the
respective t-statistics were 0.6 and 1.7, considerably
below the critical value for confidence at the .95 level.
Thus, it seems that it is not the expected inflation rate nor
the real rate component of the nominal interest rate, but the
nominal interest rate in toto, that affects consumer spending. This finding is consistent with the hypothesis advanced here that the nominal interest rate affects payments
and through this channel, affects spending.
Equation (6) can be rewritten as
(8)
The heavy line in Figure 2 plots the constraint for nondur-
Figure 2
Constraints on Consumption
of Nondurables and Services
40
35
Combined
Constraint
30
25
20
15
10
Unemployment
Rate Constraint
5
Q-+--.-r-l...............-..--.-.....,...,...,...,....,...,....,...,...,.."'1'""T"T"T-.-r-r-r--.-.""T""l....................,....,.-,-,...,...,...,
1955
Federal Reserve Bank of San Francisco
1960
1965
1970
1975
1980
1985
47
Percent of
Unconstrained
Consumption
Figure 3
Decomposition of the
Nominal Interest Rate Constraint
30
Nominal
Interest-Rate
Constraint
25
20
Expected Real
Interest-Rate
Constraint
15
10
5
Expected
Inflation-Rate
Constraint
o
-5
- 1 0 -f---,--.-........-,--.-.--.-.--.-.--.-........-,--.-.--.-......,.....,--.-.--.-.--.-.--.-......,.....,--.-.--.-.,...,....,....,...,,.....,
1955
1960
1965
1970
abies and services spending implied by the estimates in
Row 4 of Table 3. This constraint shows the joint effects on
the propensity to consume out of permanent income over
time due to changes in unemployment and interest rates. In
the 1970s, this constraint rose and then declined briskly
after 1980. Apparently, the effects of rising nominal interest rates have offset those of falling unemployment rates
over the last few years, stalling the decline in the constraint.
The separate effects of unemployment and interest rates
are also plotted in Figure 2. The estimates suggest that the
constraint related to the nominal interest rate generally has
had a much larger effect on spending than that related to
the unemployment rate. The reason for this may be that
many more households are affected by payment changes
than by income changes.
1975
1980
1985
Equation (9) separates k, into its components, the effects
of changes in expected real rates, k., and of changes in
expected inflation, kp '
k,
1
= e13; = e13(r + p) = e13re 13p=kr kp
(9)
All three are plotted in Figure 3. Figure 3 reflects the
dominant role of expected inflation in interest rate movements and consequently in changes in the constraint. The
secular rise of expected inflation until the 1980s is estimated alone to have increased the constraint by about 15
percentage points. Since the early 1980s, lower expected
inflation and lower real rates have combined to reduce
liquidity constraints operating through nominal interest
rates, thereby freeing consumers to spend more.
IV. Interpretations and Implications
The evidence presented here points to large and reliable responses of aggregate consumption expenditures to
changes in unemployment and in nominal interest rates.
This is consistent with the view that large numbers of
households find themselves liquidity constrained. I argue
that this constraint emanates from households being constrained in their ability to borrow as a result of lenders'
payment-to-income restrictions.
These borrowing restrictions become more binding as
nominal interest rates rise. Given that the major factor
driving nominal rates has been expected inflation, households become increasingly liquidity constrained as expected inflation rate rises. This prevents households from
48
carrying out their utility-maximizing consumption. This is
an example of a large, real cost of expected inflation, one
that may help account for households' generally inexplicable antipathy to expected inflation.
The presence of, and even the possibility of future,
liquidity constraints means that aggregate spending will
behave very differently than is predicted by models that
ignore such constraints. The impacts of fiscal and monetary policies, short-run or long-run, will depend upon the
amount and nature of liquidity constraints faced by various
households. Since the degree of liquidity constraint is
likely to differ systematically by group, assessment of
these policies should allow for differential effects on the
Economic Review / Fall 1989
young and the old, the rich and the poor, the homeowner
and the renter. Without allowance for these distributional
considerations, the workings of the economy and the
effects of policies are likely to be misunderstood.
In the absence of liquidity constraints, changes in fiscal
policy that the public perceives to be temporary would not
be expected to affect consumption much, especially that of
the young. In the presence of liquidity constraints, however, the change in cash flows can greatly alter households'
ability to achieve desired consumption, especially that of
the young. A temporary income tax reduction, for example, might change permanent income very little, but might
raise consumption virtually dollar-for-dollar, as consumers
found themselves less bound by liquidity constraints. 33
The widespread presence of liquidity constraints also
affects the effectiveness of monetary policy, since nominal
interest rate changes have large effects on expenditures. In
fact, in contrast to much recent theorizing, anticipated
monetary policy may have larger effects than unanticipated
policy, since it is the anticipated part of inflation that
primarily affects nominal interest rates.
The substantial response of aggregate consumption to
nominal-interest- and expected-inflation rates suggests
that a large number of households are genuinely bound by
the cash-flow constraint associated with those factors. This
paper has not addressed the reasons that such constraints
persist. To the extent they are associated with institutional
and psychological factors that can be overcome, the financialservices industry can satisfy a heretofore-constrained,
enormous household demand for credit. The easiest way to
tap this unmet demand is to introduce financial instruments
the payments of which are geared to the upward tilt in
household income associated with long-run aggregate productivity increases, long-run individual real income increases, and, especially, increases in the average level of
prices.
NOTES
1.The variabilities referred to are the standard errors of the
estimate generated by separately regressing the ratio of
each spending component (in real terms) to detrended
real GNP on a linear trend with the 194702-198803
quarterly data. Detrended real GNP consists of the exponentiated fitted values obtained by regressing the log of
real GNP ona constant, a linear trend, and the square of
the linear trend.
2. For example, a nominal interest rate variable is significant when added to the FRBSF econometric model's
current equation for consumer expenditures on nondurables and services. The durables expenditure equation
already has a nominal rate variable included, in order to
handle credit rationing effects. For a typical example of
significant estimated nominal interest rate effects on aggregate consumption, see Blinder and Deaton (1985).
3. Consumers and households are referred to interchangeably, as are liquidity or borrowing or financing
constraints. I do not attempt to ascertain why lenders have
chosen their lending criteria or why they so seldom
change them.
4. Of course, the optimal forecast may be that actual
income will change. During a period of perceived-to-betemporary unemployment, the optimal forecast will be that
income will rise on average over time. The optimal forecast of what average income will be over a fixed period will
be revised as new information arrives, but the direction
and size of those revisions will not be predictable. Optimal
forecasts have the property that no one, including the
forecaster, can forecast how the forecast will change.
5. Liquidity constraints may affect not only households.
Econometric estimates of business investment spending
long have found substantial effects of various financial
variables which can be interpreted as indicating whether
Federal Reserve Bankof San Francisco
firms are likely to face liquidity constraints. Recent microeconomic evidence concurs with the earlier aggregate
estimates that support the significant role played by these
constraints on business borrowing. Given the size of the
economic units involved and the relatively more collaterizable nature of the assets being financed by businesses,
it is easy to imagine that individual households also might
be subject to financing constraints.
6. Household expenditures are financed with some combination of current income, changes in gross household
debt, and changes in gross household savings (assets).
To illustrate the typical financing pattern, three regressions were performed. In each, the change in personal
consumption expenditures was regressed on the change
in one of the methods of financing. The data were monthly,
current-dollar, seasonally adjusted at an annual rate, and
covered the period 1975:03-1988:10. Each regression
contained one regressor but no constant term. Income
was calculated as the sum of the consumption and net
saving measures. Consumption was taken to be total
personal consumption expenditures. The flow of net savingwas taken to be personal saving. The flow of debt
was taken to be the net (extensions minus repayments)
change in consumer installment credit.
The results show that an additional dollar of consumption
typically is financed with an estimated $0.62 of additional
income, $0.13 of reduced (gross) saving, and $0.25 of
additional debt. Although aggregate household assets
exceed household liabilities, the distribution of financial
assets is very skewed, with most households owning very
few. One consequence is that, per dollar change in consumer spending, the change in the flow of credit is about
twice as large as that of (gross) saving. Thus, changing
credit flows are an integral part of changes in consumption. These estimates also hint that the borrowing, rather
49
than the lending, rate that households face may be more
relevant for household spending.
7. Applying lending criteria based on current income to
recipients of social security income, which is tied to the
CPI by statute, seems especially perplexing.
8. Credit-granting processes that do not use age as a
determining factor then may inadvertently discriminate
against the young.
9. Tobin and Dolde (1971) discuss some types of liquidity
constraints and, through simulations, assess their effects
on consumption. Walsh (1986) models the fraction of
consumers whose consumption is liquidity constrained
due to an inability borrow against future income. The
fraction is affected by the level of wealth and also fluctuates with actual aggregate income. He concludes that it is
inappropriate in such circumstances to treat aggregate
consumption as being the outcome of fixed shares of
constrained and unconstrained households.
10. See Beares (1987). One example in Beares (1987)
shows how the average maturity of a consumer's loans
can be lengthened in order to reduce the payment-toincome ratio and thereby enable the lender to extend
credit to a loan applicant that it would otherwise turn
down. This does not mean that a payment-to-income
ceiling is the sole criterion. Various consumer lending
textbooks refer to various criteria, e.g., the six "c's'' of
credit. The literature and discussions with consumer lending officials do suggest that lending policies set ceilings
on payment-to-income ratios, and rely much less on debtto-income ratios. In spite of that, it is apparently common
in this industry and its literature to refer to debt-to-income
ratios when meaning payment-to-income ratios.
11. Such rules may have developed from either households' or financial institutions' fear of default. Households'
recognition that they may be subject to future constraints
may also influence their current behavior.
12. The unemployed referred to here are those who would
not generally be deemed to be guaranteed re-employment. We mean to exclude the seasonally unemployed
~~d those on definitely temporary layoff, for example, but
It IS not apparent that much is made of this distinction in
the credit process.
13. Suppose, for the sake of this example, that income
taxes are irrelevant.
14. In this example all dollar amounts have been rounded
to the nearest dollar. Calculations were based on unrounded amounts.
15. It is commonly, and mistakenly, thought that adjustable
rate loans cure this problem. They do not. Such loans
allow payments to vary with the interest rate, and therefore
!ndir~ctly with the inflation rate. In a period of steady
inflation, they do not, however, imply constant real payments over the repayment period. Similarly, graduated
payment mortgages provide for payments that are lower
initially and rise for a short period. The rate of increase of
those payments is not tied to the inflation rate. After the
initial period, payments typically are constant in nominal
50
terms for the remainder of the term of the mortgage, and
theretore likewise fall in real terms if there is inflation.
16. The American Bankers Association's annual Retail
Bank Credit Report for the years 1979, 1980, and 1981
(only)does contain a table headed "Reasons why borrowers m~y fail to qualify for financing during (that year)."
The candidate reasons were inadequate income, insufficient equity, inadequate income management, and other.
For large banks, the percentage judged to have "inadequateincome" rose over those years from 31 to 43 to 47
percent.
17. F. Scott Fitzgerald: "You know, Ernest, the rich are
different from us."
Ernest Hemingway: "Yes, they have money." (attributed)
l? Optirnatsmoothinq is not ~he same as total smoothing.
Since permanent Income vanes, optimal consumption will
too.
19..Hall and Mishkin state that their results for food imply
nothing about the behavior of other expenditures.
20. It is, however, reasonable to question how durable
much of what is classified in the national income accounts
as services and nondurables is.
21. Table 1 shows the ability of lagged income to predict
consumption. That contrasts with the results presented by
Hall (1978), whose sample period ended with 197701
data. When my sample period is terminated at 197701, I
get results much like his (F-statistic = 2.19). Re-estimating with a sample that is just one year longer, however,
implies rejection of the unpredictability hypothesis. Both
longer and shorter samples generally lead to rejection of
the unpredictability hypothesis. Variables other than income also may lead to that result.
22. From now on, unless otherwise noted, consumption
refers to the sum of nondurables and services, expenditures on which correspond most closely to the flow of
consumption services. Total consumption service flow
would also include the flow of services from the outstanding stock of consumer durables. Since our objective here
is to explain expenditures, we consider expenditures on
only these two consumption categories.
23. This. hints that there are factors other than changes in
current Income that are associated with the violation of the
permanent income hypothesis.
24. The unemployment rate is used here to proxy for
aggregate income. Flavin (1985) concludes that the unemployment rate is superior empirically to a measure of
the deviation of actual income from its permanent value in
accounting for consumption.
25. Even if there were no interest- and unemploymentrate-related liquidity constraints, those arising from the
age-earnings profile might exist. In what follows, I take k*
to be equal to one.
26. For a more complete discussion of these issues, see
Hall (1988) and Campbell and Mankiw (1987).
27. Specifically, I used the investment yield to maturity on
a three-month Treasury bill. The finance rate on 48 month
Economic Review / Fall 1989
automobile loans comes from DR!. Until 1984, I used the
average marginal personal income tax rate on interest
income from Peek and Wilcox (1987). The rates for 1984,
1985,and 1986 have been calculated in the same manner.
The rates for 1987 and 1988 are assumed to be 27 and 25
percent, respectively. For each quarter I used the corresponding annual rate. The tax rate for the upcoming
quarter is applied to the interest rate for the current
quarter.
28. The expected inflation data are taken from the Livingstan survey, which records expectations each June and
December. I used those values for the second and fourth
quarter Observations, respectively. First and third quarter
observations were obtained by interpolating between
second and fourth quarter observations.
29. Using lags of the explanatory variables required shortening the sample slightly. Starting the sample after the
Korean War, omitting 1975 (due to the one-time income tax
rebate), and omitting 1980 (due to the imposition and
removal of credit controls) each seemed to make little
difference to the estimates.
30. To the extent that credit rationing effects stemming
from adverse selection become more severe as openmarket nominal interest rates rose, I would have expected
the Treasury bill yield better to have explained consumption. The reason is that if banks do not raise loan rates
commensurately with open market rates, the open market
rate would likely better capture the combined constraint
effects of bank rules (as already proxied by the borrowing
rate) and of credit rationing (as captured by the difference
between the lending and borrowing rates). In fact, these
estimates provide little evidence to support that effect.
Federal Reserve Bank of San Francisco
31. An alternative functional form for k is one which is
linear, as opposed to the linear-in-Iogarithms form in (6).
Specifying k = k* + aU + 13i necessitates using a
nonlinear instrumental variables estimation technique.
Doing so produces qualitatively similar results to those
presented in Table 3. Estimates of the linear form were
also obtained while including the squares of the unemployment rate and of the interest rate. This allowed the
datatosuggest whether liquidity constraints bind more, or
less, than proportionately as rates rise. Significantly negativecoefficients estimated for the squared unemployment
and interest rate terms would imply that an acceleratingconstraint hypothesis fit the data better. The squared
terms turned out to be negative, but insignificant. Thus no
strong conclusion about the appropriate functional form
for kemerged.
32. Since these consumption equations do not deliver
constant elasticities, the test is for equality of the elasticities at the respective sample means of the unemployment and interest rates.
33. Even a permanent, anticipated, balanced-budget shift
in tax policy could affect aggregate spending. One way to
do so would be to have some parameters of the tax code
be age-specific, perhaps by making average income tax
rates rise with the age of the taxpayer. To the extent that
young-household cash flows were enhanced and balanced by reductions for older households, the average
amount of constraint would be loosened.
51
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