Full text of Review (Federal Reserve Bank of Dallas) : January 1989

View original document

The full text on this page is automatically extracted from the file linked above and may contain errors and inconsistencies.

FEDERAL RESERVE BANK OF DALLAS
January,1989

•

cononuc eVlew
Oil Demand and
Prices in the 1990s
Stephen P. A. Brown and
Keith R. Phillips

Texas in Transition:
Dependence on Oil and
the National Economy
Thomas B. Fomby and
Joseph G. Hirschberg

This publication was digitized and made available by the Federal Reserve Bank of Dallas' Historical Library (FedHistory@dal.frb.org)

Economic Review
Federal Reserve Bank of Dallas
January 1989
President and Chief Executive Officer
Robert H Boykin
First Vice President and Chief Operating Officer
William H Wallace
Senior Vice President and Director of Research
Harvey Rosenblum
Vice President and Associate Director of Research
Gerald P O'Driscoll, Jr
Vice President and Economic Advisor
W Michael Cox
Economists
National and International
John K Hill
Robert T. Clair
Evan F Koenig
Joseph H Haslag
Linda C Hunter
Cara S Lown
Kenneth J Robinson

Regional and Energy
Stephen P A Brown
William C, Gruben
William T Long III
Keith R Phillips
Editors
Virginia M Rogers
Janis P. Simmons
The Economic Review is published by the Federal Reserve Bank of
Dallas The views expressed are those of the authors and do not
necessarily reflect the pOSItions of the Federal Reserve Bank of Dallas or
the Federal Reserve System
Subscriptions are available free of charge. Please send requests for
Single-copy and multiple-copy subscriptions, back issues and address
changes to the Public Affairs Department, Federal Reserve Bank of
Dallas. Station K, Dallas, Texas 75222 (214) 651~289
Articles may be reprinted on the condition that the source is credited
and the Research Department is provided with a copy of the publication
containing the reprinted material

Contents
Page 1
Current oil prices are too low to be sustained in the 1990s.
Stephen Brown and Keith Phillips forecast that by the year
2000, the price of oil (in 1988 dollars) could reach $30 to $40
per barrel. Adjusted for inflation, these prices are about 60 to
80 percent of the peak price established in early 1981
Brown and Phillips find that during the 1980s a slow
adjustment process, encouraged by OPEC actions, reduced oil
demand and put downward pressure on prices . They expect
reversal of that process in the 1990s will work with world
economic expansion to boost oil demand and prices

Oil Demand and
Prices in the 1990s
Stephen P. A. Brown and
Keith R. Phillips

Page 11
Although Texas may once have appeared immune to
national business cycles, the state's economy no longer has
such immunity. Thomas Fomby and Joseph Hirschberg
quantify the degree to which the Texas economy's responsiveness to oil prices and the national economy has changed.
They find that Texas nonagricultural employment is 62
percent less sensitive to unexpected changes in real oil prices
and 338 percent more responsive to unexpected changes in
national employment.
Fomby and Hirschberg also develop measures of the
degree of dissimilarity between the Texas and national
economies. They find that these dissimilarity measures, which
reflect differences in economic structure between Texas and
the nation, bear a closer relation to real oil prices than does
state employment.

Texas in Transition:
Dependence on Oil and
the National Economy
Thomas B. Fomby and
Joseph G. Hirschberg

Stephen P. A. Brown

Keith R. Phillips

Senior Economist and Policy Advisor
Federal Reserve Bank of Dallas

Associate Economist
Federal Reserve Bank of Dallas

Oil Demand and
Prices in the 1990s
espite excess capacity in OPEC, current oil
prices are too low to be sustained through
the 1990s. Surprisingly, a forecast of renewed
OPEC solidarity has little to do with this outlook.
Rather, increasing demand will gr:ldually push
OPEC to full capacity and prices to higher levels
during the next decade .
In fact, OPEC is only part of the explanation
for falling oil prices during th e 1980s Though
OPEC has dominated the news about the subject,
falling oil demand better explains the more than
70-percent decline in inflation-adjusted oil prices
from early 1981 to late 1988 Over much of the
19805, world oil consumption declined . Only a
decrease in demand-not a change in supply
conditions- can explai n both lower prices and
lower consumption.
The simplest explanation for the decrease in
denund is what might be call ed "non-price
conservation" -that is , technological change that
shifted demand inward indepe ndently of the
influence of prices. But this simple explanation is
not supported by the facts . Tn our recent econo metric analysis of u.s. oil demand, we found no
evidence of non-price conservation. l
Instead , we found that :1 slow adjustment
process, encouraged by OPEC, shifted o il demand
inw:lrd during the 1980s--despite world economic
expansion During the 1990s, a reversal of that
adjustment process will work w ith world econo mic expansion to boost oil demand. Our
forecast of rising demand and prices in the 1990s
rests on an understanding of that adjustment
process.

In the first quarter of 198 1, world oil consumption
was about 56 million barrels per day and the price
of oil was $4864 per barrel. (All prices cited are
the composite refiner acquisition cost for crude oil
in 1988 dollars per barreD In the first quarter of
1988, world oil consumption was again about 56
million barrels per day Yet the price of o il had
dropped to £15.47 per b:mel d espite worldwide
economic expansion . A series of events contributed to this developme nt.
The Iranian revolution and the onset of the
Iran-Iraq war reduced world oil production
between 1979 and 1981 , pushing prices up.
Because short-run oil demand is very ine lastic, the
redu ction in supply pushed prices sharply higher.
The oil consumption and price combination
that prevailed in the first quarter of 1981 could
not be sustained in the long run. In the absence
of economic growth, a sustained price of $48.64
per barrel would have eventually reduced U S. oil
consumption, for example. by about 40 pe rcentfrom 16.5 to 102 millio n barrels per day . On the
other hand , fo r U S consumers to continue to
absorb 16.5 million barre ls per day witho ut
economic growth eventually would have required
an estimated price of o nly $20.61 per barrel (See
Chart J)
We estimate that U S. consumers require
nearly a decade to adjust fully to changes in oil
prices (See box, page 7). Oil consumption responds slowly to price changes because substan-

Oil demand and prices in the 1980s

The auth ors would like to thank Phil Trostel, Ken Robinson.
Linda Hunter and Jerry ODriscoll for helpful comments
without implicating them in our conClusions

Oil demand in the late 1980s contrasts
sharply with that in the beginning of the decade .

'See Brown and Phillips (1989)

Economic Review - January 1989

Chart 1

U.S. Oil Demand in First Quarter 1981
Price (1988 dollars)
80r-,-------~----------------------~

Shorl

run

60
50
40

tial changes in the ratio of oil consumption to
output require new capital investment.
As short-run demand adjusted to prices
during the 1980s, the market price and quantity of
oil consumed were pushed down . Non-OPEC oil
producers added to the downward pressure on
price as their production increased Beginning in
1981 , however, OPEC moderated downward
pressure on prices by reducing its own production

(See Chart 2).

Nonetheless, short-run demand continued to
decline and non-OPEC oil production continued
to rise. OPEC's continued attempts to support
prices reduced its production to about 14 million
barrels per day by mid-1985, less than 50 percent
of its total capacity.
OPEC's attempts to support prices ended in
a well-publicized failure Excess capacity and the
incentive for OPEC members to cheat on quotas
led to a surge in OPEC production With demand
being very inelastic in the short run, that surge in
production caused a price break in late 1985 and
early 1986 Thereafter, OPEC was unable to
restrain its production suffiCiently to drive prices
back up to earlier levels.
Given our evidence that consumption
responds symmetrically to rising and falling oil
prices. the current price and quantity combination
is too low to be sustained in the long run 2 Even
in the absence of economic growth , the first
quarter price of S15.47 per barrel would eventually increase C .S. oil consumption, for example,
by an estimated 35 percent-from 17 to 23 million
barrels per day. On the other hand , for U.S.
consumption to remain at 17 million barrels per
day in the long run, prices must rise to an estimated $26.63, even without economic growth

---1---

CI
1

I
I

° 5~---1~0~---1~5~~2~0~--~
2~
5--~3~0~--~3~
5 --~
40
Millions of barrels per day

In the first quarter of 1981. the price of oil was $4864 per
barrel (in 1988 dollars) and US. oil consumption was 165
million barrels per day (shown as point A). At this price,
consumers would have reduced consumption to 102 million
barrels per day (point B) over the long run in the absence of
economic growth On the other hand, for consumers to continue to absorb 16.5 million barrels per day would have required an estimated price of $20 61 per barrel (point C) over
the long run without economic growth
The figure represents actual model estimates

(See Chart 3) ,

Oil demand and prices in the 1990s

?See Brown and Phi//(ps (1989)

In the 1990s. short-run demand can be
expected to rise from the unsustainable combination of oil consumption and price that characterizes the late 1980s. Adjustment will be slow .
'\ievenheless, together with a growing world
economy. the adjustment will contribute to strong
growth in oil demand during the 1990s
Using the estimated coefficients from our
model of C.S oil demand, we constructed 21
Federal Reserve Bank of Dallas

Chart 2

A Graphical Analysis of Price and Quantity Movements in the 1980s
In early 1979, both OPEC and
Price (1988 dollars)
non-OPEC
producers
were
producing at close to full capacity
The Iranian revolution and the onset
of the Iran-Iraq war reduced oil
production from 0 0 to 0" initially
pushing the price up along the shorto
run demand curve, dodo' from Po to
P, So long as the market price and
quantity lies above the long-run
demand curve, 00, the short-run
demand curve shifts inward. As the
short-run demand curve shifts
inward, the price falls from P,
Without further changes In
production, short-run demand would
have shifted inward until a price of
P, was established
In an attempt to sustain high
prices,OPECgraduallyreducedoutput to 02 Consequently, short-run
demand shifted inward along the
long-run demand curve to d,d" establishing a price of P2 When OPEC
increased production from 02 to 03'
it drove the price down to P3 Because the market price and quantity
lie below the long-run demand curve, the short-run demand curve will shift outward
This analysis represents an abstraction of price and quantity movements in the 1980s As such,
it does not consider the long-run profit implications for OPEC behavior Nor does the analysis reflecl
the increase in long-run demand that occurred during the period

Millions of barrels per day

Chart 3

U.S. Oil Demand in First Quarter 1988
Price (1988 dollars)

50~----~--~------------------------~

Long
run

Shorl
run

30
~I

-------t--r:

I:.

I ···
I
10

Millions of barrels per day

Economic Review - January 1989

In the first quarter of 1988.
the price of oil was $1547 per
barrel and U 5 oil consumption
was 17 million barrels per day
(shown as point A) At this price,
consumers would increase consumption to 23 million barrels per
day (point B) over the long run,
even without economic growth
On the other hand, consumers
would absorb 17 million barrels a
day at an estimated price of
$2663 per barrel (point C) over
the long run, even without economic growth
The figure represents actual
model estimates

Chart 4

U.S. Oil Consumption with 2.0-Percent Annual GNP Growth
Millions of barrels per day

45~--------------------------------------------------~
40

10 ~----------------------------------------------------J
1995
1997
1999
1987
1989
1991
1993
• U.S. consumption that pushes OPEC to full capacity (See text) .

scenarios for C S oil consumption under a Yariet\
of assumptions ahout oil prices and G 1\]> growth
We examined the effects of oil prices from 510 to
540 per harrel and of real-GNP growth from 2
percent to .J percent annually \Ve assumed no
changes in energy taxation and no recessions .
The .scenarios show relatiYely little evidence of
consumption growth through 1989 (Sec Charts 4
throz./p,h 6) We project much stronger consumption growth in the early 1990s than in previou.s
years, as demand shifts outward For the 1990s.
we project potential growth rates of L S. oil
consumption ranging from a low of 2 1 percent to
9. ') percent annually (Sec Tahle 1)
If other countries behave similarly to the
t:nited States, many of our consumption projec-

tions \\ ill prm e too high to he sustained throughout the 1990s: oil consumption cannot rise above
\york! capacity to produce oil And little capacity
is Iikel\' to he added \'ith the 1m',' oil prices that
\,ill hring ahout rapid gro\\lh in oil consumption
Clearly. some of the prices \\ e used to project oil
consum ption in the 1990s arc too 1m, to survive
the decade .
Previous studies have shO\\"n that. as OPEC
is pushed to full capacity, oil prices rise .' Because
nearly all excess capacity to produce oil is in
OPEC. \\'e take "pll.shing OPEC to full capacity" to
mean "pushing \,orld oil production to full capacity ," To assess \vhat oil prices might prevail by
the end of the c'entury, we assumed that world oil
consumption \vill gro\\' at the same rate as U.S.
consumption Gi\ en this assumption, \-yorld oil
production \\ ill he pushed to full capacity when
ti.S. oil consumption rises to 20.4 million barrels
per day
Under nearly all of our scenarios, the gro\vth
in oil consumption pushes OPEC close to full
capacity het\\'een late 1992 and early 199') (See
C/J({f1s 4 throll/~h 6') . At or helow a price of 525
dollars per harrd. OPEC could reach full capacity
I

iSee Gately (1984)
'Hlgher energy taxes outSide the United States undoubtedly
mute the effect of changing crude all prices on world all
consumptIOn Nevertheless our prevIous research shows
that the long-run crude oil demand elasticities for the other six
major free-world countnes are Similar to that for the United
States See Brown and Phillips (1984)

Federal Reserve Bank of Dallas

Chart 5

U.S. Oil Consumption with 2.5-Percent Annual GNP Growth
Millions of barrels per day

45r-------------------------------------------------------,

10 ~--------------------------------------------------~
1987
1989
1991
1993
1995
1997
1999
• U.S. consumption that pushes OPEC to full capacity (See text) .

Chart 6

U.S. Oil Consumption with 3.0-Percent Annual GNP Growth
Millions of barrels per day

45 ~------------------------------------------------~S-1~0

40
35

$15

15
10~------------------------------------------------------~
1999
1987
1989
1991
1993
1995
1997
• U.S. consumption that pushes OPEC to full capacity (See texO .

Economic Review - January 1989

Table 1

Projected Growth Rate of U.S. Oil
Consumption During the 1990s
(Annual average percent)
Annqal GNP Growth Rate
Price·

2.0%

2,5%

30%

$10

8.3

8.9

$15
$20
$25
$30

6.5

7.1

5.2

5.7

4.2
3.4

4.7

95
7.6
6.3
5.3
4.5
3.8
3.2

$35

$40

2 .7
2.1

3.9
3.2
2.7

• CompOS1te refiner acquisition cost lor cTlJde oil In 1988 dollars

no later than the first quarter of 1993. By that
year, a price of $25 per barrel could prove too
low-if world oil capacity does not rise. Similarly.
with world economic growth rates between 2.0
percent and 3.0 percent, oil prices will reach $30
to $40 per barrel by the year 2000 ."

Summary and conclusion
Current oil consumption and prices are
unsustainably low-the result of a decrease in
short-run demand brought about by adjustment to
unsustainably high oil prices in the late 1970s and
early 1980s. Just as long-run adjustments in
demand put downward pressure on oil prices and
consumption from 1981 to the present, long-run

50f course, world oil capacity is unlikely to remain fixed It
grew by about 7 percent over the last decade If capacity
grows by another 7 percent, OPEC will be pushed to full
capacity when us oil consumption is 21 9 million barrels
per day In that case, the price of oil could be as much as $5
per barrel lower in the year 2000 If capacity falls 7 percent,
OPEC will be pushed to full capacity when U Soil consump·
tion is 190 million barrels per day In that case the price of
oil could be as much as $5 per barrel higher in the year 2000

adjustments in demand will put upward pressure
on prices and consumption in the 1990s, Economic expansion will add to that pressure,
After continued stagnation through mid-1989
to late 1990, forecasts constructed with our model
of u.s, oil demand suggest that world oil consumption will begin to accelerate under a variety
of assumptions about oil prices and economic
growth, By late 1992 to early 1995, strong growth
in oil consumption will push OPEC close to full
capacity, As OPEC nears full capacity, prices are
likely to rise sharply,
Under conservative assumptions about
world economic expansion, the price of oil will
rise to more than $30 per barrel by the year 2000,
With less conservative assumptions about economic growth, we forecast that prices of $35 to
$40 per barrel will prevail by the year 2000
Adjusted for inflation, these prices are about 60 to
80 percent of the peak price established in early
1981 .
Of course, these price forecasts are dependent upon a number of assumptions If world
capacity to produce oil is decreased, if OPEC
restricts its production, if oil supplies are disrupted, or if economic growth is stronger, oil
prices will be higher than we have forecast On
the other hand, if world capacity to produce oil is
increased, if economic growth is weaker, or if
energy taxation is increased, oil prices will be
lower than we have forecast. Nevertheless, rising
oil prices can be expected during the 1990s,
And as in the past, rising oil prices can be
expected to reshape world economic activity
Rising oil prices will strengthen economic growth
in energy-exporting countries while hindering
economic growth in energy-importing countries
like the United States . Within the United States,
the price rise will cut unevenly, with energyproducing regions benefitting and energy-consuming regions suffering,('

6See Brown and Hill (1988), Considine (1988), Hamilton
(1983), Moroney (1988a, 1988b), and Tatom (1988)

Federal Reserve Bank of Dallas

Estimating U.S. Oil Demand'
We modelled U.S. oil
consumption as a function of
the general level of economic
activity, the share of output in
the industrial sector, and past
and present real prices of
crude oil. To allow for lags in
price, but be parsimonious in
estimating the model, we restricted the effects of price on
consumption to a polynomial
distributed lag. We used statistical tests to optimize the
number of lags on price at 38
quarters and the degree of
polynomial at 9.
We estimated the model
in natural logs, so that the
coefficients can be interpreted as elasticities. With
quarterly data from the first
quarter of 1972 through the
first quarter of 1988, we
estimated the coefficients
shown in Table B1.
As indicated by the

adjusted R2 and the overall Fvalue for the regression, the
model fits the data well. With
the exception of the industrial
production variable, the coefficients are significant and of
the right sign. A low F-value
found for the polynomial restriction indicates that the restriction is not objectionable.
The estimated coefficients for price and its lags
indicate a short-run (same
quarter) price elasticity of oil
demand of -0.08 and a longrun price elasticity of demand
of -0.56. At 1.13, the estimated coefficient for GNP
indicates an income elasticity
of demand that is not significantly different from one. The
coefficient for industrial production's share of GNP is not
different from zero at the 5percent level of significance.

*For a more detailed discussion of the model, see Brown
and Phillips (1989). For a
copy of this technical paper,
write Research Department,
Federal Reserve Bank of
Dallas, Station K, Dallas, TX
75222.
Though similar, our
approach improves upon that
used by Gately and
Rappoport (1988). They
used ad hoc methods to
select lag length and the
degree of polynomial, while
we used statistical procedures. In addition, their
model was estimated with
annual data and suffers from
a very high degree of autocorrelation in the residuals.
Our model was estimated
with quarterly data and shows
no significant autocorrelation.

Table 81
Estimated Coefficients for U.S. Oil Consumption
Oil price
in periods
t-1 to t-38

Intercept

Oil price
in period t

Coefficient

2.01

-0.08

-0.48

t-statistic

2.62

-5.64

70.22**

Summary Statistics:

Overall F-Value
Adj R2
Durbin-Watson
F-value for polynomial

Real
GNP

Industrial
production
share of GNP

1.13

-0.23

11.80

-1.73

77.86
0.93
1.69
0.54

"The statistic reported for the lagged values of the oil price is an F-statistic.

EconOlnic Review - January 19H9

References
Brown, S. P. A., and John K. Hill (988), "Lower
Oil Prices and State Employment,"
Contemporary Policy Issues 6 Quly): 60-8.
Brown, S. P. A., and Keith R. Phillips (989), "An
Econometric Analysis of U .S. Oil Demand,"
Research Paper 8901, Federal Reserve Bank of
Dallas, January.
Brown, Stephen P . A., and Keith R. Phillips
(984), "The Effects of Oil Prices and
Exchange Rates on World Oil Consumption,"
Federal Reserve Bank of Dallas Economic
Review, Quly): 13-21.
Considine, Timothy]. (988), "Oil Price Volatility
and U.S. Macroeconomic Performance,"
Contemporary Policy Issues 6 Quly): 83-95.
Gately, Dermot (984), "A Ten-Year Retrospective:
OPEC and the World Oil Market," Journal of
Economic Literature 22 (September): 1100-14.
Gately, Dermot, and Peter Rappoport (988), "The
Adjustment of U.S. Oil Demand to the Price
Increases of the 1970s," The Energy Journal 9
(April): 93-107.
Hamilton, James D. (983), "Oil and the
Macroeconomy Since World War II,'Journal
of Political Economy 91 (April): 228-48.
Moroney, John R. 0988a), "Energy, Capital, and
Technological Change in the United States,"
unpublished paper, Department of
Economics, Texas A&M University, October.
___ 0988b), "Output and Energy: An
International Analysis," Working Paper 88-32,
Department of Economics, Texas A&M
University, November.
Tatom, John A. (1988), "Macroeconomic Effects of
the 1986 Oil Price Decline," Contemporary
Policy Issues 6 Quly): 69-82.

Federal Reserve Bank of Dallas

Thomas B. Fomby

Joseph G. Hirschberg

Assoclate Professor of Economics
Southern Methodist University
Consultant
Federal Reserve Bal'lk of Dallas

Assistant Professor of Economics
Southern Methodist. University

Texas in Transition: Dependence
on Oil and the National Economy

s the oil patch goes, so goes the Texas
economy," according to the conventional
economic wisdom of the day. Another popular
wisdom would have it that "where the national
economy goes, the Texas economy need not
follow ." Although casual inspection of the Texas
economic data tends to SUppOlt these wisdoms, a
closer examination of the data-while allowing
for structural change in the Texas economy from
one Texas business cycle to the next-indicates
that the conventional wisdoms probably no longer
hold 1
This article evaluates these wisdoms
critically. The article finds that the Texas economy is probably losing its independence vis-a-vis
the national economy and , at the same time,
appears to be growing less dependent on fluctuations in the price of oil. In fact , in comparing the
Texas economy over the period 1974:Ql-1983:Q1
with the Texas economy over the period
1983:Q2- 1988:Q1, our analysis indicates that, as
of the latter period, the Texas economy is approximately 62 percent less sensitive to unexpected changes in real oil prices while being 338
percent more sensitive to unexpected changes in
nonagricultural employment in the rest of the
United States
The reduced sensitivity of the Texas economy to oil price shocks and its increased openness vis-a-vis the economy of the rest of the
nation are probably largely due to the fact that
during the first period, Texas mining and manufacturing became 35 percent more specialized
because of substantial growth in the oil and gas
extraction industry. In the latter period, largely
because of retrenchment in the oil and gas
Economic Review-January 1989

extraction industry, Texas mining and manufacturing became progressively more similar to the
same sectors in the rest of the United States, to
the point of being as similar to the rest of the
United States as in 1975. 2
In the second section, an economic history
of Texas in the 19705 and 19805 is presented and
casually inspected for the oil dependence and
"maverick" traits in the Texas economy. Some
data presented in the third section suggest that the
Texas economy has been and continues to be in
transition from an economy dependent on oil to
one that more closely resembles the economy of
the rest of the nation. In the fourth section, a
small vector autoregressive model of the Texas
economy is constructed that emphasizes the
interplay between employment in the state, oil
prices, and the national economy as measured by
employment in the United States excluding Texas.
Finally, some conclusions suggested by this
research are presented in the last section of this
article.

'
r

The discussion here probably overstates the case that the
above wisdoms are conventional and represent the majority view of watchers of the Texas economy On the contrary,
after watching the recent performance of the Texas economy, many may hold the perspectives uncovered by this
article However, selling up a straw man view that could
easily be derived by inspecting the data casually offers a
good counterpoint for a beginning discussion

No attempt is made here to provide a theoretical framework
for analyzing either the role of regional sectoral shocks in
restructuring a regional economy or the nature of that
restructuring For discussions along this line, see Neumann
and Topel (1984) and Schmidt and Gruben (1988)

The conventional wisdoms

Chart 1

Real Price of Oil and Texas Nonfarm Employment
The supposition that oil prices affect the
Texas economy seems beyond question In fact,
the price of oil is a leading indicator of the Texas
economy. In Chart 1 the price of oil as measured
in 1982 dollars is plotted, along with the number
of workers employed in the state (excluding the
agricultural sector), for the period 1974-88.l In
general, during this time, increases in employment
have followed increases in oil prices but with
some delay. Likewise, declining oil prices have
led to lower employment. The lead relationship
that the real price of oil exhibits vis-a-vis Texas
employment is clearly demonstrated following the
major decline in oil prices beginning in the first
quarter of 1981. One year later, Texas employment peaked and declined along with declining
oil prices. Subsequent leads, though shorter and
less definitive, are apparent. After the price of oil
plateaued in 1982, Texas employment began to
recover in 1983 and continued to do so until
shortly after the next major decline in oil prices in
late 1985 through 1986. Texas employment has
since recovered slightly, reflecting the previous
slow correction of oil prices upward beginning in

The real price of oil used in thiS study was calculated by
dividing the domestic refiner acquisition cost of crude oil
(from the US Department of Energy) by the implicit GNP
price deflator (Bureau of Economic Analysis, US Commerce Department) Texas employment data came from
the U S Bureau of Labor StatistiCS All data are seasonally
adjusted at their source except the Texas employment
data, which were seasonally adjusted by means of the X-II
procedure
The refiner acquisition cost of crude oil was used here
because the data are the most readily available consistent
series on oil prices Although some oil price data exist prior
to 1974, most are for specific grades of crude all, rather than
for a composite of crude types

Because economic deci-

sions are likely to have been made for Texas on the basis of
more than one crude grade, we decided that a composite
measure was a better choice for the present study
4

We are not trying to imply by Chart 1 and our interpretation
of it that the price of oil is the only determinant of Texas
employment For example Cox and Hill (1988) show that
Texas manufacturing is moderately dependent on the value
of the dollar and international trade

1982 dollars

Millions of employees

45r----------------------------------,75
(Quarterly)

...+......·······~·······I··~····
Employment

....
.......

25
20
15

6.5

... .'

.....•.

.'
55

.....'

50
4 .5

~.--~

10
51974

40
1976

1978

1980

1982

1984

1986

1988 3 .5

Sources of Primary Data : U.S, Department of Commerce, Bureau of
Economic Analysis .
U.S. Department of Energy .
U S. Department of Labor, Bureau of Labor
Statistics.

the third quarter of 1986. Thus, surely the Texas
economy has been dependent on oil prices ."
The second conventional wisdom about the
tendency for the Texas economy to move independently of the national economy is casually
supported when comparing the business cycles of
the Texas economy with those of the nation as a
whole, The Texas coincident index recently
developed by Phillips (1988) can be used to
delineate Texas' economic past into well-defined
upturn (recovery) periods and downturn (recession) periods \Vith the peaks and troughs in the
Texas coincident index as indicators of Texas
business cycle turning points. since 1971 the
Texas economy has traveled through four upturns,
spanning the periods August 1971-July 1974, June
1975-August 1981, May 1983- 0ctober 1984, and
the present period beginning March 1987, Three
downturns in the Texas economy occurred over
the periods July 1974-June 1975, August 1981May 1983, and October 1984-April 1987, These
downturns are represented by the shaded areas in
Chart 2
In contrast. the nation 's economy has sustained over the same period, as classified by the
~ational Bureau of Economic Research. four
upturns dated November 1970- November 1973,
Federal Reserve Bank of Dallas

March 197'5-January 19HO, July 19HO-July 1981,
and November 19H2 to present; and three downturns dated November 1973-March 197'5,
January-July 1980, and July 1981-November 1982.
The Texas dmvnturns (on the left) and the US .
downturns (on the right) are rerresented by the
juxtaposed shaded areas of Chart 3
The supposition of a maverick Texas economy comes largely from the divergent performances of the Texas and U.S. economies over the
1970s and 19HOs' The national downturn of
l\()\'Cmber 1973-March 197'5 was matched by a
Texas downturn hut with a delay of roughly eight
months . In addition, the Texas economy proved
to be more resilient, in that the duration of the
Texas downturn was arproximatcly five months
shorter. Cnlike the national economy, the Texas
economy did not sustain a recession during 19HO,
though the growth of the Texas coincident index
did come to a virtual standstill. Evidently, the
Texas economy was buoyed by significant
increases in oil prices at the time (See Chart 1)
\XThat was a leg-iron for the national economy
turned out to be a counterweight for the Texas
economy.
FU11her distinct rerformances occurred but
,vith the Texas economy carrying the leg-iron
The July 1981-Novemher 19H2 national downturn
was roughly matched by a Texas downturn, but
Chart 2

Texas Coincident Economic Indicator Index
(January 1981 = 100)
120 ~--------------------------------~

Chart 3

Comparison of Texas and U.S. Business Cycles
Since 1972
Texas

1972
1973
1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988

United States

1972
1973
••••••••

1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987

____________~______________~1988

Note: Recessions are denoted by shaded areas .
Sources: U.S Department of Commerce. Bureau of Economic Analysis .
Federal Reserve Bank of Dallas.

the Texas economy was not as resilient this time
and recovered six months later than the U.S.
economy Quite rrobably the extended duration
of the Texas downturn during August 19H1-May
1983 resulted because of the substantial fall in oil
prices at the time (See Chart 1) Since November
19H2, the U.S. economy has heen enjoying the
second longest upturn since the end of World
War II. In contrast, the Texas economy faltered
during the period October 19H4-Arril 19H7, again
evidently because oil prices fell further beginning
in 19H4. These divergences in performance seem
to support the contention that the Texas economy, because of its dependence on oil, is likely to
exhibit business cycle behavior that is often
distinct from that of the rest of the nation .

100
80
60

The present comparisons of Texas business cycles with
those of the U S economy are made somewhat imprecise

by the fact that the simple methodology used here to

determine turning points in the Texas economy is not the
same methodology used by the National Bureau of EconomiC Research (NBER) to determine the turning points in
the national economy In fact. the NBER uses a more

0 1972 1974 1976 1978 1980 1982 1984 1986 1988
Note: Recessions are denoted by shaded areas
Sou rce: Federal Reserve Bank of Dallas.

Economic Review-January 1989

eclectiC approach whereby a consensus is sought among
many economic variables. there being no simple definition
of consensus

Chart 4

Texas Employment in Oil and Gas Extraction
Thousands of employees

350~--------------------------------------------------,
(Monthly)

300

250
200

150

100 ,...._ _- - -

5O~------------------------------------------------~
1970
1972
1974
1976
1978
1980
1982
1984
1986
1988
Source : U S Department of Labor, Bureau of Labor Statistics .

Chart 5

Texas Employment in Oil and Gas Extraction as a Percentage
of Texas Mining and Manufacturing Employment
Percen1

24r----------------------------------------------------,
(Calculated from monthly data)

22
20

10 ~------------------------------------------------~
1970
1972
1974
1976
1978
1980
1982
1984
1986
1988
Source of Primary Data: U S. Departmen1 of Labor, Bureau of Labor Statistics

Federal Reserve Bank of Dallas

Indications of transition
in the Texas economy
Since the major break in oil prices in 1981,
the state's oil and gas extraction industry (Standard Industrial Classification Code 13) has been
retrenching. Employment in this industry, expressed both as the number of workers employed
and as a percentage of the Texas mining and
manufacturing work force, is plotted in Charts 4
and 5. 6 Although employment in the industry
from 1983 to mid-1984 responded positively to
what at the time appeared to be a leveling of oil
prices, employment has otherwise been in steady
decline since 1982 until a recent leveling began in
1987. Likewise, the industry'S share of Texas
mining and manufacturing employment has fallen
from a high of 22 percent in 1982 to approximately 15 percent, which was typical in 1978.
Given the smaller absolute size of the oil
and gas extraction industry and its supposedly
leaner work force, a given percentage change in
oil prices is likely to result in a smaller percentage
change in the industry'S work force than was the
case in the early 1980s, when the industry was
highly leveraged to meet what appeared to be an
ever-increasing demand for Texas petroleum
products. The sensitivity of overall Texas employment to oil price changes is likely to be less than
before because the oil and gas extraction industry's employment now represents a smaller
fraction of mining and manufacturing.
Quite separate from the above retrenchment
argument, once oil prices moved to more realistic
levels after 1981, reduced expectations about
future price increases were likely to prevail in
subsequent Texas business cycles. Related industries (for example, construction, real estate, and
banking) could react more slowly to movements
in oil prices, if nothing else out of self-defense
and the desire to confirm new plateaus in oil
prices. Thus, the argument can plausibly be made
that, with respect to employment, the Texas
economy is probably less sensitive to oil price
fluctuations now than it was in the early 1980s.
These arguments, of course, bring into question
the conventional wisdom that "as the oil patch
goes, so goes the Texas economy."
But what about the maverick status of the
Texas economy? A closer examination of mining
Economic Review-January 1989

and manufacturing employment in Texas by twodigit SIC Code industries indicates that, since the
oil price break of 1981, the Texas economy has
been slowly evolving toward an employment
composition that more closely mirrors the employment composition of the mining and manufacturing sectors of the rest of the nation. Table 1
contains three time-specific snapshots of the
composition of mining and manufacturing employment in Texas and the rest of the United
States. The first snapshot is that of the employment composition in the first quarter of 1975,
when oil prices were beginning to rise. The
second snapshot is dated the second quarter of
1982, when oil was king in Texas and 21.85
percent of Texas mining and manufacturing employment resided in the oil and gas extraction
industry. This employment percentage represents
an all-time high. Obviously, the Texas economy
was highly specialized then and quite susceptible
to declines in the price of oil. The third snapshot
presents the composition of mining and manufacturing employment as of the third quarter of 1988.
To offer perspective, the average composition of
mining and manufacturing employment for Texas
and the rest of the United States over the period
1975:QI-1988:Q3 is contained in the last display
in Table 1.
The snapshots of Table 1 convey a picture
of the Texas economy becoming more specialized
as oil prices rose. Then, with the oil price shock
of 1981, the oil and gas extraction industry began
a retrenchment. Thereafter, the Texas economy
has progressively become less specialized and, as
a result, has come to resemble more closely the
economy of the rest of the nation.
To formalize the measurement of the degree
to which the Texas economy has differed from
the economy of the rest of the United States, we
present two "dissimilarity" measures that provide
summary characterizations of the disparity between the proportions of employment in the

There was a major labor strike in the oif and gas extractIOn
industry during the first quarter of 1980 Because the strike
had an effect that was not typical of the basic trend in the
industry at the time, we chose to smooth Charts 4 and 5 by
replacing the actual data with interpolated values

Table 1

Composition of Mining and Manufacturing Employment
in Texas and Rest of United States at Given Times
1975:01
Industry (SIC Code)

Texas employment
(Percent)

Mining except oil and gas (10, 12, 14)
Oil and gas extraction (13)
Food and kindred products (20)
Tobacco products (21)
Textile mill products (22)
Apparel and other textile products (23)
Lumber and wood products (24)
Furniture and fixtures (25)
Paper and allied products (26)
Printing and publishing (27)
Chemicals and allied products (28)
Petroleum and coal products (29)
Rubber, miscellaneous plastics products (30)
Leather and leather products (31)
Stone, clay, and glass products (32)
Primary metal industries (33)
Fabricated metal products (34)
Industrial machinery and equipment (35)
Electronic and other electric equipment (36)
Transportation equipment (37)
Instruments and related products (38)
Miscellaneous manufacturing industries (39)

0.726
13.326
9.291
0.000
0.722
7.270
2.866
1.662
1.897
4.891
7.251
4100
2150
0.699
3.622
4.103
8.197
10.800
6.354
7.021
1.916
1.135

Rest of U.S. employment
(Percent)
2.239
1.083
8.538
0.411
4.503
6.224
3.117
2.171
3.460
5.738
5.237
0.828
3.221
1286
3.322
6.399
7.702
11 .146
9.199
9.020
2988
2.167

1982:02
Industry (SIC Code)

Texas employment
(Percent)

0.763
21.848
7059
0.000
0.387
4.783
2.643
1.157
1611
4.909
5.983
3.021
2.336
0.757
3214
3.346
7032
13.271
7.337
5.751
1.842
0.951

Rest of U.S. employment
(Percent)
2260
2313
8.159
0.367
4.005
5.863
2.991
2.217
3.416
6.405
5.315
0.840
3576
1.129
2.866
4.809
7.219
11 364
10.256
8943
3708
1 979

Federal R('s erve Bank of Dallas

Table 1-Continued

Composition of Mining and Manufacturing Employment
in Texas and Rest of United States at Given Times
1988:03
Texas employment
(Percent)

Industry (SIC Code)

0.837
15.378
8.446
0.000
0.353
4.786
2.971
1.428
2299
6531
6.599
2.808
2.921
0.740
3.758
2.550
6 .697
9.018
10.464
8.316
1931
1 168

Rest of U ,S employment
(Percent)
1.600
1291
7 .990
0 .268
3.756
5.405
3.764
2.724
3.470
7792
5 ,191
0.707
14.405
0.722
2.832
3 .950
7.220
10.696
10.455
10.204
3 .619
1.940

Average for 1975:01-1988:03
Industry (SIC Code)

Texas employment
(Percent)

0.768
17.194
8.137
0000
0.475
5.633
3.072
1.459
1.883
5369
6.531
3.452
2 .550
0.688
3.683
3250
7157
11 110
8230
6528
1.771
1060

SOURCE OF PRIMARY DATA: US Bureau of Labor Statistics.

Economic Review-January 1989

Rest of U.S. employment
(Percent)
2.021
1.530
8.169
0.350
4.169
5.985
3440
2.389
3.434
6.429
5 .144
0.798
3.714
1.064
3.027
4 .987
7.440
10836
10.074
9.551
3.423
2.027

various two-digit Texas mining and manufacturing
industries and the proportions of em pl oyment in
the same industries for the rest of the nation The
first dissimilarity measure used here is based on
the so-called expected information measure
proposed by Theil (1972).
DSl I =fE us.t.; ln[:IS I/ ],
;=1

TX, 1. /

where Ell.>/ i denotes , at time t, the propo rtion of
total mining and manufacturing e mpl oyment for
the rest of the United States represented by the ith
industry in the mining and manufacturing sector.
The term E TX., i is Similarly defined for Texas The
natural logarithmic function is denoted by In, and
n denotes the number of industry classifications in
the mining and manufacturing sector used in this
study, namely 22. As in the classifications of
Table 1, these industries consist of the 20 twodigit industries in manufacturing (SIC Codes
20-39) and, in mining, the oil and gas extraction
industry (Code 13) and the rest of mining (the
collectio n of Codes 10, 12, and 14)
The second dissimilarity measure used here
is of the form of a goodness-of-fit measure analogous to the goodness-of-fit statistic used in
examining the closeness of an emp irical distribution to a postulated theoretical distribution .- The
goodness-of-fit dissimilarity measure is

rest of the United States and as ETX/ i and E(ls., i
become more equal, the dissimilarity measures
become smaller and approach zero , the lower
limit for both measures that would denote perfect
coincidence between the employment proportions
for Texas and the rest of the United States.
Greater dissimilarity between Texas and the rest
of the natio n thus reflects greate r speC ialization
within the Texas economy and a greater reliance
on a few key industries for continued growth.
These dissimilarity measures, along with the
real price of oil , are plotted in Charts 6 and 7,
beginning with 1975 The story to ld by these
measures is roughly the same. Texas mining and
manufacturing became increasingly more specialized (dissimilar) as oil prices rose through the
early 1980s, to the extent of being 35 percent
more spec ia lized than in 1975. The Theil information-based dissimilarity measure, DSl " indicates
an almost immediate impact of the 1981 drop in
oil prices on the specialization in the Texas
economy; the goodness-of-fit diSSimilarity
measure , DS2" indicates a delayed movement
away from specialization beginning in late 1983.
Both measures, nevertheless, indicate that the
Texas economy is currently much less specialized
Chart 6

Information-Theoretic Dissimilarity Measure
of the Texas Economy and the Real Price of Oil
(Calculated from quarterly data)

~(E7V
. - E 1/..·>,/,
=- £....
/\.1 1
C

EU5

. )

I ;

where the notation is the same as before H
These dissimilarity measures have the common characteristics that, as the proportions E 1X , i
and Eus li become more unequal , the dissimilarity
measures become larger. In contrast, as the Texas
economy becomes more like the economy of the

DS\

1982 dollars

r----------------------------------, 45

20
15

For a discussion of goodness·of-fit tests, see, for example,
Hogg and Craig (1970)

'Sherwood-Cal! (1988) uses the same measure (except for
sign) to determine the relative diversities of the state economies in the United States and the effect such diversity has
on the strength of the linkage of a state's economy to the
national economy

0
19 ~-19~7~6~-19~7~8~~19~8~0~1~9~8~2--1-9~8-4~1~9~8~
6--1-9.J
88

Sources of Primary Data U.S Department of Commerce, Bureau
of Economic Analysis .
U.S , Department of Energy.
U.S _Department of Labor, Bureau
of Labor Statistics

Federal Reserve Bank of Dallas

as a result of the 1981 fall in oil prices and is
approximately as similar to the economy of the
rest of the United States as it was in 1975, before
the substantial increase in the price of oil in the
late 1970s. In this sense, the Texas economy is
much less reliant on its oil and gas extraction
sector than it was in 1981.

interest that occur at time t. A trivariate vector
autoregression of
Y, Z) with each variable
entering with equal lag length, I, is represented by

A V AR model of the Texas economy

(2) Y, =

The previous examination of the Texas
economy's dependence on oil prices and independence of the national economy proceeded by
heuristic means-that is, visual examinations of
time series plots, trends, and lead and lag relationships. A more sophisticated econometric examination of the interplay between Texas nonagricultural employment, oil prices, and nonagricultural
employment in the rest of the nation would be
desirable .
One approach that has proven useful in
uncovering systematic relationships between regional and U.S. economic time series is the
estimation of vector autoregressions (VARs)Y To
establish some notation, let XI' YI , and ZI denote
observations on three economic variables of
Chart 7

Goodness-of-fit Dissimilarity Measure of the
Texas Economy and the Real Price of Oil
(Calculated from quarterly data)
1982 dollars
2 . 2r---------------------------------~45

2.1
Oil price

2 .0

r..
: . ...

;

1.8

1.5

14~--------------------------------~ 5

1976

1978

1980

1982

1984

1986

1988

Sources of Primary Data: U.S. Department of Commerce, Bureau
of Economic Analysis.
U.S. Department of Energy
U.S. Department of Labor, Bureau
of Labor Statistics.

Economic Review-January 1989

ex,

(1)

= a o + a 1 x t _ 1 + .. , + a, x t _,
+ A Y t - 1 + ... + f3, Y t - ,
+ 11')

° °
0

.z;-1

1 X t_ 1

+£1

Y t-

+ ... + 11',

.z;-, + U t '

+ ... + o,x t _,
+ ... + £, Y I-,

+ Y 121-1 + ... + y, 21-, +

VI '

and

(3)

.z; = 80 + 81 X I _ 1 + ... + 8, x t _ ,
+7)1

Y t-

+ 111

.z;-1

+ ... + 7), Y t - ,
+ ... + 11, .z;-, +

WI '

The regression coefficients to be estimated are
represented by the a's, Ws, and so on, while u l' VI'
and WI represent the unobserved errors of the
respective equations. The system consisting of
equations 1, 2, and 3 is estimated by applying the
method of least squares, equation by equation.
Because the regressors of the three equations are
identical, the least squares and seemingly unrelated regression estimates coincide. Thus, even if
the errors u,' VI' and WI exhibit contemporaneous
correlation, the method of least squares is fully
efficient relative to estimation of seemingly
unrelated regressions .
The adoption of equal lag length for each
variable in each equation is more of a convenience than a necessity. Allowing each variable
in each equation to enter with different lag
lengths (lj' 12 , and so on) would increase substantially the number of possible model specifications
that would have to be examined.lO Choosing 1 to
be as large as any lag length in the "true': model
ensures that the least squares estimates of the
equal-length VAR parameters will nevertheless be
unbiased and consistent. A cost of the equal lag

For applications of VARs in regional and macroeconomic
forecasting and simulation, see Anderson (1979) and
Utterman (1986)

In a tovariate system assuming a maximum lag length M,
the equal lag length specification requires a search over

length specification, however, is the potential loss
of efficiency in estimation due to the need to
estimate an unnecessarily large number of parameters. Because the present study is exploratory in
nature, the equal lag length specification is
adopted here.
The variables chosen for examination were
quarterly time series observations on the real price
of oil , Texas nonagricultural employment, and
nonagricultural employment in the rest of the
United States for the period 1974:Q1 through
1988:Ql. The length of the data is limited by the
unavailability of the real price of oil before
1974:Ql.
A preliminary investigation of the data
indicated that each series was nonstationary in its
mean, with the variability of each series increasing
somewhat in levels. In addition, inspection of the

only M + 1 specifications. 1=0. 1. 2.
. M In allowing for
unequal lag length but with a maximum of M. the number of
specifications that must be examined increases to 3(M + 1?
In the case of a reasonable specification of M = 4 in the
presence of quarterly data. five specifications must be examined for the equal lag length. and 375 for the unequal lag
length
" To determine if the logarithms of the three series contain a
unit root. augmented Dickey-Fuller equations including a
time trend (to model the apparent drift in all series) were
estimated Varying the number of augmented terms between zero and four. the Tr statistics for each variable and
over all equations were always greater than -3 50. the onetailed 5-percent critical value for 50 observations Thus,
each logged variable appears to have a unit root For
discussion of the Tr statistic and its critical values, see Table
852, P 373, in Fuller (1976) and see Dickey and Fuller

(1979)
To test for co-integration, the residuals of level equations in
the logarithms of the variables were examined and tested for
unit roots using augmented Dickey-Fuller equations of the
form found in Engle and Yoo (1987) See equations 19 and
20. p 152 The ~ statistics over all possible normalizations
of the co-integrating equation and for all Dickey-Fuller equations having between zero and four augmented terms were
greater than -3 75, the one-tailed 5-percent critical value for
50 observations See Table 3, p 158 Thus, the three logged
variables of interest appear to constitute a multivariate system with three unit roots and no co-integrating vectors
The RA TS computer program was used to calculate all the
empirical results presented in this study See Doan and
Litterman (1981 )

series for co-integrability indicated no need to
model equations 1 through 3 using error correction terms, as in Engle and Granger (1987).11
Given these findings, it was decided to model
each series in percentage growth form by taking
differences in the natural logarithms of each
series. Using the above notation, the variables XI'
Y" and z, chosen for the VAR of the Texas economy were , respectively, the quarterly percentage
change in the real price of oil, Texas nonagricultural employment, and nonagricultural employment in the rest of the nation.12
With respect to choosing a lag length, I, for
the VAR system, a pragmatic approach was taken.
The maximum lag length considered was 1 = 4.
The averages of the adjusted R 2's of the three
equations in the VAR system, computed using the
entire span of the data C1974:Ql-1988:Q1), were
0.469, 0.527, 0.532 , and 0.370, respectively, for
equal lag lengths 1= 1, 2, 3, and 4. Though the
second- and third-order systems fit the data
equally well while providing better fits than the
first- and fourth-order systems, it was decided to
focus on the results of the second-order systemthis system being smaller and thus much easier to
present in the subsequent discussion. Despite this
choice of Simpler model, the qualitative results
produced by the third-order system are the same
as those reported for the second-order system.
Now we turn to the empirical analysis of the
second-order system, using the entire data set
1974:Ql-1988:Q1. The estimated model is
reported in Table 2, along with F-tests of the joint
significance of the various variables in each
equation . For example, the first F-test reported
for the first equation, where x, is the dependent
variable, examines the joint significance of x,_J
and X,_2 in explaining the variation in x,. The
second F-test examines the joint significance of
Y,- l and Y,-2 in explaining the variation in x,. The
rest of the F-tests are Similarly defined. A BoxPierce Q test of the residuals of each model
indicates that they are white nOise , implying that
no systematic variation in the equations remains
to be explained by additional parameters. Thus,
overall, the reported second-order system seems
to fit the data quite well.
The F-tests reported in Table 2 indicate
(1) the real price of oil is exogenous, in that it is
independent of fluctuations in nonagricultural
Federal Reserve Bank of Dallas

Table 2

Estimation Results for Second-Order Texas VAR System,
1974:01-1988:01
Dependent variable: x (percentage change in real price of oil)
Variable

x
x
Y
Y

z
z

Constant

Lag
1
2
1
2
1
2
0

Coefficient
0.7054
-0 .3351
-1.3525
3.4371
3.4566
-4.8703
-0.9983E-2

Standard error
0.1409
0.1500
2.7013
2.4344
2.8048
2.7500
0.1696E-1

Significance level
8305E-5
.3027E-1
6189
.1645
.2239
.8304E-1
.5589

R' = .2982; 0(21) = 8.2618 (P= .9939).

Tests for joint significance, dependent variable
Variable
F-statistic
Significance level
x
12 .5536
4283E-4
Y
1.3646
.2654
z
1.5693
.2188

Dependent variable: y (percentage change in Texas nonagricultural employment)
Variable

x
x
Y
Y

z
z

Constant

R' =

Lag
1
2
1
2
1
2
0

.6316; 0(21)

Coefficient
o 2228E-1
-0.1883E-2
0.6462
0.4234E-1
0.2655
-0.1715
0.1602E·2
=

21 .5900 (P

Standard error
0.8759E-2
0.9322E-2
0.1678
0.1512
0.1742
0.1708
0.1053E-2

Significance level
1430E-1
.8407
.3554E-3
.7807
.1342
.3205
.1351

.4234).

Tests for joint significance, dependent variable
Variable
F-statistic
Significance level
x
36283
.3424E·1
Y
19 1440
.8287E-6
z
1.1684
.3197

Dependent variable: z (percentage change in nonagricultural employment
in rest of United States)
Variable

x
x
Y
Y

z
z
Constant

R' =

Lag
1
2
1
2
1
2
0

.6503; 0(21)

Coefficient
-0.8330E-2
0.3635E-2
0.3849
-0.4805
0 .7226
-0 .1366E-1
0.2387E-2
=

'15.6182 (P

Standard error
0 .6849E-2
0.7289E-2
0.1312
0.1182
01362
01336
0.8241 E-3

Significance level
.2299
6202
.5170E-2
.1828E-3
.3004E-5
.9190
.5701 E·2

.7907) .

Tests for joint significance, dependent variable = z
Variable
F-statistic
Significance level
x
0.7396
.4827
Y
8.2714
.8360E-3
z
27 .9609
.1000E-7

Economic Review-January 1989

employment in Texas and in the rest of the United
States; (2) Texas nonagricultural employment is
heavily dependent on its own past history, as well
as past movements in real oil prices, though
seemingly independent of u.s. nonagricultural
employment; and (3) nonagricultural employment
in the rest of the nation is heavily dependent on
its own past and to a lesser extent on nonagricultural employment in Texas. Stated another way,
the real price of oil is determined outside the
system (equations 1 through 3) and, in turn, feeds
into the Texas economy by way of employment.
Though employment in the rest of the United
States is largely self-determined, it evidently can
be affected by the fluctuations in Texas employment that have causes affecting employment in
other states as well. On the other hand, Texas
employment seems to be impervious to fluctuations in U.S employment.
Treating the structure of the Texas economy
as unchanged from 1974 to the present, one
comes to the impression that the real price of oil
has been and remains a very crucial determinant
of the Texas economy and that the Texas economy operates independently of the national
economy. As suggested earlier, however, the
Texas economy has undergone structural changes
in response to the oil price shocks of 1981 and
1986. In particular, there has been substantial
retrenchment in the oil and gas extraction industry, making it a less significant portion of Texas
economic activity. The Texas economy now more
closely resembles the rest of the U.S. economy

Rather than throwaway some of the data at the fringes of
the exact turning points of the two Texas business cycles,
and thereby lose valuable degrees of freedom, it was
decided to include the observations for the dates January
1974-May 1975 in the first business cycle period, instead of
starting at the June 1975 trough in the Texas coincident
indicator Similarly, the observations for the dates April
1987-March 1988 were included, though they actually
follow the ending trough of the second Texas business
cycle Thus, the term business cycle period is adopted
here

" The appropriate critical values for the Chow test are 225
for a 5-percent level of significance and 3 12 for a I-percent
level of significance The degrees of freedom for the Fstatistic are 7 for the numerator and 40 for the denominator

with respect to the employment composition of its
mining and manufacturing sectors.
Obviously, the transition in the Texas
economy has been a gradual one. An observer
would be hard-pressed to designate an exact date
that might serve as a dramatic break between the
"old" Texas and "new" Texas economies. Despite
this difficulty, it was decided to analyze the Texas
V AR by major business cycle periods. With the
Texas coincident economic indicator and the real
price of oil as guides, the first business cycle
period was defined to span the period 1974:Q1
through 1983:Q1; the second business cycle
period was defined to span 1983:Q2 through
1988:Q1. 13 The logic here is that once the Texas
economy has lived through a major correction in
oil prices-as represented by the first period-it
would take on a more defensive posture in the
second period and, by so doing, become more
insulated from subsequent oil price fluctuations.
To test for a significant structural change in
the Texas VAR over the two defined periods, each
equation of the VAR was subjected to a Chow
(960) test The resulting F-statistics for the oil
price, Texas employment, and rest of U.S. employment equations were 1.509, 3.413, and 1.089,
respectively, indicating that the Texas employment equation underwent a significant structural
change between the two periods.14
The nature of the structural change in the
Texas employment equation is revealed in the
estimation results reported in Table 3. The
estimation results for the first period,
1974:Ql-1983:Q1, are presented in the upper
portion of the table while those for the second
period, 1983:Q2-1988:Q1, are reported in the
lower portion. In contrast to the impression
conveyed by the comprehensive estimation results
for the Texas employment equation reported in
Table 2, Texas employment is now more dependent on the national economy, as represented by
the substantial joint significance of nonagricultural
employment in the rest of the United States in the
latter period . The real price of oil and Texas
employment's own past history, though still
significant at the la-percent level, have lost
significance relative to employment in the rest of
the nation
Thus, though still dependent on the price of
oil, the Texas economy is probably less so than
Federal Reserve Bank of Dallas

Table 3

Estimation Results for Texas
Nonagricultural Employment
(Dependent variable = y)

1974:01-1983:01
Variable

Lag

x
x
Y
Y
z
z

1
2
1
2
1
2
0

Constant

Coefficient

Standard error

0.2763E-1
0.4616E-2
0.9509
- 0.5063
0.1681
01012
0.4028E-2

.1411E-1
.1562E-1
.2149
.2373
.1767
.1755
.1809E-2

Significance level
6062E-1
7698
1428E-3
.4215E-1
.3499
.5688
.3456E-1

.6752; 0(15) = 7.5542 (P = .9404).

Tests for Joint significance, dependent variable = y
Variable

F-statistic
2.9342
103211
1.2042

x
y

Significance level
.7028E-1
.4682E-3
.3155

1983:02-1988:01
Variable

Lag

x
x
Y
Y
z
z

1
2
1
2
1
2
0

Constant

Coefficient

Standard error

0.2684E-1
-0.1065E-1
0.4783E-1
0.5173
2.3187
-1 .3852
-0.6083E-2

.1071E-1
9763E-2
.2246
.2318
.6077
.4535
.4186E-2

Significance level
.2627E-1
.2950
.8346
4389E-1
.2143E-2
.9220E-2
.1699

.7284; 0(10) = 7.2394 (P= .7026) .

Tests for joint significance, dependent variable = y
Variable

x
y

F-statistic
3.1516
3.3882
8.2107

Significance level
.7656E-1
.6541E-1
.4946E-2

NOTE: x = percentage change n real price of oil
y = percentage change Texas nonagricultural employment.
z = percentage change nonagricultural employment in rest of United States

Economic Review-January 1989

Table 4

Impulse and Cumulative Responses
of Texas Nonagricultural Employment to
One-Standard-Deviation Shock in Real Price
of Oil and in Nonagricultural Employment
in Rest of United States, 1974:01-1988:01
Responses to
oit price shock
Period

Responses to U.S.
employment shock

Impulse

Cumulative

Impulse

Cumulative

OOOOE+O
.1906E-2
2226E-2
1732E-2
1070E-2

.0000

.6935E-2

.OOOOE+O
.1103E-2
.1118E-2
.8423E-3
.4072E-3

0000

2
3
4
5

.3471 E-2

6
7
8
9
10

.6471 E-3
.4355E-3
.3284E-3
.2614E-3
.2159E-3

.8824E-2

.8947E-4
-.7491 E-4
-.1286E-3
-.1287E-3
-.1089E-3

.3119E-2

11
12
13
14
15

.1854E-3
.1631E-3
1437E-3
1250E-3
.1071E-3

.9548E-2

-.8484E-4
-.6310E-4
-.4629E-4
-.3462E-4
-.2699E-4

.2863E-2

16
17
18
19
20

.9086E-4
.7655E-4
6429E-4
.5394E-4
4527E-4

.9879E-2

-.2201 E-4
-.1853E-4
-.1586E-4
-.1364E-4
-.1170E-4

.2782E-2

21
22
23
24
25

.3802E-4
.3197E-4
.2692E-4
2267E-4
.1911E-4

.1001 E-1

-.9992E-5
-.8482E-5
- 7168E-5
- 6039E-5
-.5079E-5

.2745E-2

26
27
28
29
30

.1610E-4
.1357E-4
.1140E-4
.9639E-5
8121E-5

1007E-1

-.4270E-5
- 3591 E-5
- 3021 E-5
-.2543E-5
- 2142E-5

.2729E·2

NOTE: All responses are in terms of quarterly growth rates

Federal Reserve Bank of Dallas

before, owing to retrenchment in the oil and gas
extraction industry. Possibly because of its
greater resemblance to that of the rest of the
nation, the Texas economy is now a more open
economy, in that the economies of the other
states are now playing a greater role in affecting
the Texas economy.
To emphasize further what appears to have
been a significant shift in the nature of the Texas
economy over the past two Texas business cycles,
an impulse response function analysis of the
Texas VAR was conducted, first over the entire
period and then with respect to the two business
cycle periods defined. Impulse response functions give the dynamic responses of the VAR
system's three variables (x, y, z) to a shock to the
system. The mathematical details of impulse
response functions are discussed in the Appendix.
With the estimation period taken to be the
entire data set 1974:Ql-1988:Q1, the impulse and
cumulative responses of Texas employment to a
one-standard-deviation shock (0.0855) in the
growth rate of real oil prices and to a onestandard-deviation shock (0.00415) in the growth
rate of nonagricultural employment in the rest of
the United States are given in Table 4. To illustrate, consider a "surprise" 8.55-percent quarterly
increase in the real price of oil at time t = l.
Then, given the second-order VAR estimated for
the entire period and reported in Table 2, Texas
nonagricultural employment will, on average,
grow at a 0 19 percent faster quarterly rate at time
t = 2 than would otherwise be expected. The
additional unexpected quarterly increases for
subsequent periods arising because of the given
oil price shock are 0.22 percent, 0.17 percent, 0.10
percent, and so on. The cumulative effect of such
a shock after 30 periods is 1.007 percent in
unexpected growth.
Obviously, given the perspective of the
entire sample, an unexpected increase in oil
prices results in substantial unexpected increases
in Texas nonagricultural employment. In a similar
manner, given the perspective of the entire
sample, a "surprise" 0.415-percent quarterly
increase in nonagricultural employment in the rest
of the United States implies that additional
unexpected quarterly increases in Texas employment for periods 1 onward would be 0.11 percent, 0.11 percent, 0.084 percent, and so on, with
Economic Review-January 1989

the cumulative effect of such a shock being 0.273
percent.
As indicated by the previous Chow tests, the
Texas economy appears to have undergone a
structural change between business cycles. To
follow up on this theme, the impulse response
functions for the two separate periods were
computed and compared with the response
functions for the entire sample period. By way of
an abbreviated presentation, the 30-period
cumulative responses of Texas nonagricultural
employment to a 0.0855 shock in oil prices (the
same used in Table 4) and to a 000415 shock in
nonagricultural employment in the rest of the
United States (again the same used in Table 4) for
the two separate business cycle periods,
1974:QI-1983:Ql and 1983:Q2-1988:Ql, are
reported in Table 5. For the purpose of comparison, the cumulative responses for the entire
sample period are reported in Table 5 as well.
The cumulative responses reported in Table
5 (as well as the F-tests in Table 3) clearly indicate
that, compared with behavior in the first Texas
business cycle, the Texas economy appears now
to be somewhat less responsive to oil price
shocks while being much more responsive to
shocks in U.S. employment. In fact, comparing
the 30-period cumulative responses of Texas
nonagricultural employment to shocks over the
two periods shows that, as of the second period,
the Texas economy is approximately 62 percent
less sensitive to real oil price shocks while being a
surprising 338 percent more sensitive to shocks in
nonagricultural employment in the rest of the
United States.

Conclusion
The recent instability of the Organization of
Petroleum Exporting Countries does not necessarily bode well for Texas, but it should be of less
concern now than back in the early 1980s.
Results obtained here indicate that , even though
negative oil price shocks still imply reduced
growth in Texas employment, their present
cumulative effect is substantially less than the
cumulative effects of the early 19805.
Analysis of the Texas economy during the
period 1974:Ql-1983:Ql conveys a picture of a
fragile economy, probably somewhat leveraged,
25

that was highly sensitive to oil price shocks. As
of 1981 , however, the Texas economy hegan to
make accommodations for the continuing weakness in the price of oil.
The oil and gas extraction industry began to
retrench by cutting employment and production.
with inefficient workers and production probably
the first to be eliminated The industry is now
much leaner. At the same time , the Texas economy, partially because of the smaller extraction
sector, became less specialized and took on a
mining and manufacturing employment composition much more similar to that of the rest of the
nation. Through this process it appears that
Texas has become a more open economy.
Fluctuations in nonagricultural employment in the
rest of the United States play a much more
important role than in the past in determining
nonagri cultural employment in Texas As goes
the nation , so will Texas likely follow. Thus, the
health of the national economy should now be
more of a concern to Texans than in the past
Fortunately, the Texas economy is no longer a

one-horse c haise Por this , Texans can currently
be thankful.
In the future, oil is likely to play an everdeclining role in the Texas economy. As the
Texas economy becomes larger over time, a
future expansion in the oil industry, even of the
magnitude exh ibited in the heyday period of
1974-81, \vill playa smaller role, proportionately,
in providing employment in the state. Moreover,
given the experiences of the 1981 and 1986 oil
pri ce shocks. oil-related investment is likely to be
more circumspect Thus. the rapid growth in
Texas nonagricultural employment that previously
has heen fueled largely through abundant natural
resource end()\\'Illents is much less likely to be
forthcoming in the future . This is not to say that
the state is not fortunate to have such endowments. nor that it will not later benefit from their
presence: rather. Texas growth in the future will
be dependent on other sources more so than
befo re Texas growth can no longe r he taken for
granted

Table 5
The 30-Period Cumulative Responses
of Texas Nonagricultural Employment to
One-Standard-Deviation Shock in Real Price
of Oil and in Nonagricultural Employment
in Rest of United States
1974:011983:01

1983:021988:01
(Percent)

1974:011988 :01

Cumulative response to oil price shock

0.755

0.287

1.007

Cumulative response to U.S. employment shock

0.602

2.637

0.273

30 periods

NOTE: The shocks equal the respective standard deviations of oil price and U S. nonagricultural
employment that were calculated for the entire sample period 1974:01-1988:01
All responses are in terms of quarterly growth rates

Federal Reserve Bank of Dallas

References
Anderson, Paul A. (1979) , "Help for the Regional
Economic Forecaster: Vector Autoregression,"
Federal Reseroe Bank of Minneapolis Quarterly Review, Summer, 2-7.
Cox, W. Michael, and John K. Hill (1988), "Effects
of the Lower Dollar on U.S. Manufacturing:
Industry and State Comparisons," Federal
Reserve Bank of Dallas Economic Review,
March,I-9
Chow, Gregory C. (960), "Tests of Equality
Between Sets of Coefficients in Two Linear
Regressions," Econometrica 28 (July):
591--605.
Dickey, David A., and Wayne A. Fuller (1979),
"Distribution of the Estimators for Autoregressive Time Series with a Unit Root, " Journal of
the American Statistical Association 74 (June):
427-31.
Doan, Thomas A., and Robert B. Litterman (1981),
User's Manual, RATS Version 4.1 (Minneapolis: VAR Econometrics).
Engle, Robert F., and C. W. J. Granger (987),
"Co-integration and Error Correction: Representation, Estimation, and Testing," Econometrica 55 (March): 251-76

Neumann, George R., and Robert H. Topel (984),
"Employment Risk, Sectoral Shifts and Unemployment" (Research supported by U.S.
Department of Labor, Office of the Assistant
Secretary for Policy, October).
Phillips, Keith R. (988) , "New Tools for Analyzing
the Texas Economy: Indexes of Coincident
and Leading Economic Indicators," Federal
Reserve Bank of Dallas Economic Review,
July, 1-13.
Schmidt, Ronald H., and William C. Gruben
(988), "Expectations and Regional Growth"
(Paper presented at the Thirty-fifth North
American Meetings of the Regional Science
Association, Toronto, Ontario, 11 November).
Sherwood-Call, Carolyn (988), "Exploring the
Relationships Between National and Regional
Economic Fluctuations, " Federal Reserve Bank
of San Francisco Economic Review, Summer,
15-25
Theil, Henri (1972), Statistical Decomposition
Analysis, with Applications in the Social and
Administrative Sciences (Amsterdam: NorthHolland).

Engle, Robert F., and Byung Sam Yoo (1987),
"Forecasting and Testing in CO-integrated
Systems," Journal of Econometrics 35 (May,
Annals Issue): 143-59.
Fuller, Wayne A. (976) , Introduction to Statistical
Time Series (New York: Wiley).
Hogg, Robert V , and Allen T. Craig (1970) ,
Introduction to Mathematical Statistics, 3d ed.
(New York: Macmillan)
Litterman, Robert B. (1986), "Forecasting with
Bayesian Vector Autoregressions-Five Years
of Experience," Journal of Business & Economic Statistics 4 (January): 25-38.

Economic Review-January 1989

Appendix

The Mathematics of Impulse Response Functions

The vector autoregression represented by equations 1 through 3 is
rewritten in moving-average form as

where Y r == (xr' Yr , Zr)'; Ad' d == 0, 1,2,
. . . ,is the delay-d response coefficient
matrix of dimension 3x3; and U r-<l == (ur-<l'
vr-<l' wr-<ll'. Assume the usual normalization Ao ==
where denotes a thirdorder identity matrix. Thus , the response
Y r at time t = k to an initial shock s at time
t == in the U error process is Ak . s.
Furthermore, the response at step k to a
unit shock in equation i (i = 1, 2, 3) is just
the i th column of the Ak matrix. In a
similar manner, the cumulative effect, after
d ' periods, of an initial and sustained
shock s in the error process is just

'3'

ILoAd

Of interest here are the impulse
responses of Texas employment to an oil
price shock and to a shock to the national
economy . The shock, say 5 1' to the real
price of oil multiplied by the first column of
the estimated response coefficient
matrices Ad' d = 0, 1, 2, . . . ,would
provide the vector of intertemporal
responses of oil prices, Texas employment, and rest of U.S. employment to an
51 innovation (surprise) in real oil prices.
Similarly, the first column of S 1 . L.% ~11 Ad
would provide the vector of cumulative
responses to an innovation in the real
price of oil after d' time periods. The
impulse and cumulative response
coefficients for a shock, say 52' to national
employment are analogously defined
using the third columns of Ad and
Ad

r.:': (1

Federal Reserve Bank of Dalla~

Full text of Review (Federal Reserve Bank of Dallas) : January 1989

FRASER