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Econometrics-

Large Models Aid
GNP Forecasters

be ..

CISIon makers in government

change on any other was complicated
and yielded imprecise results.
lngl .
y In recent years to econoMuch
economic behavior has
In t .
e rIC models for forecasts of
been understood for a long time,
:c?nomic change. Reflected in this
but few statistical results were
. hIft are not only the strides made
meaningful until the widespread
econometrics but also the forma- use of computers after World
IOn of firms created to market
War II. Development of accurate
reSUlts of large-scale econometric
data collection methods paralleled
~odels. By and large, these models the development of high-speed
f aVe performed quite well in
computer equipment and opened
orecasting economic changes.
the way for major breakthroughs
I~ forecasting growth in gross
not only in forecasting economic
natIonal product in 1971 and 1972
but in analyzing changes.
change
f01'
'
W example, several large models
By making alternative assumpS ere more accurate than a running tions about previous economic con~ey of leading economists.
ditions and testing these assumpere the survey of forecasters
tions in computer simulations,
i·repared by the American Statiseconomists gained new insights
~~~ ASSOciation in conjunction
into the workings of the economic
n~ .the National Bureau of Ecosystem.
Inic Research was closer to
Recognition of the growing re~~tual GNP nine months out of the finement and usefulness of economkone model was closer 15 times.
ics was evidenced in 1969 by the
In conometrics has come as a
establishment of a Nobel Prize
fuore or less natural outgrowth of
in economics. And the first prize
Gndamentals in economic theory.
was awarded to two Europeans
thoverned by general principles
(Ragnar Frisch and Jan Tinhi at experience has shown to be
bergen) for their pioneering connegh1y dependable, economics was, tributions to the building of econore vertheless, prevented until very
metric models.
ki c~nt years from providing the
The matter of models ...
n n of detailed information
t~e~ed f?r rapid policy responses
To examine the facts of a situation,
angIng situations.
without straying from the essenapp~though its principles could be tials, economists construct simplieco ed .with reasonable confidence, fied representations of economic
the~onucs often did not provide
behavior. These representations
Sio ata needed for timely deciare called models.
th ns. Unlike sciences that allow
In studying consumption, for extr engeneration of data under conample, an economist might survey
a very large number of households
ec~ned ~aboratory conditions,
to find out why their spending patreal rrlC~ has always dealt with
int • I e sItuations. With many
terns are what they are. But he
ch errelated economic factors
would get an enormous variety of
tio~nging at the same time, isolaanswers. If he could survey memof the effects of anyone
bers of every household in the
llus'

~nd industry have turned increas-

i

X

Iness Review I June 1973

country, he would, undoubtedly,
get thousands-even many thousands-of different answers. In addition to increases in income, he
would learn that many families
listed as reasons for changes in
their spending patterns such developments as an illness, death, or
wedding in the family.
Results of such an unstructured
survey would provide little basis
for generalizations about changes
in consumer spending. But by applying general theories of economic
behavior to his study, the economist could impose a structure on
his observations. And being based
on cause and effect relationships,
this structure would allow him to
capture the implications of the
survey, making its results more
comprehensible.
Such a procedure would allow
him to determine relationships, for
example, between consumer expenditures and personal income. And
this link between income and outlays would be-even for a comparatively small number of households
-an economic model that could be
used in analyzing changes in the
spending patterns of all consumers.
An economic model becomes
econometric when mathematical
and statistical techniques are applied to the investigator's observations to quantify relationships in
the model. These relationships can
then be expressed as algebraic
equations. With an econometric
model, annual reports of income
and consumption can be related
over long periods, allowing investigators to generalize, for example,
that on average for every dollar
rise in income since World War II,
consumption has risen 93 cents.
1

Model D came remarkably close
in forecasting nominal GNP in 1971 ...
ACTUAL NOMINAL GNP

tions of individual companies, industries, or whole economies.
Much of the interest in economet-

rics focuses on models of the national economy-macro models.
MODEL A I
Basically, there are two apI !
proaches to the construction of a
macro model-a small-scale and a
large-scale approach. The small·
MODEL C
scale approach consists of1
• The identification of relation1MODEL 01
ships between such broad mea:1
sures of economic activity as
SURVEY
income and consumption, inter1
est rates and the money stock,
or interest rates and business
... but with fewer wide misses,
investment
Model B showed more consistency
• The statistical estimation of
these aggregate measures
MODEL A
• The logical combination of the I
estimated equations into mode s
I MODEL B I
Three of the relationships draW1
:
ing the closest attention have ~eeJl
MODEL C
between income and consumptIOJl,
1
interest rates and the money
MODEL 0
supply, and interest rates and
:1
vestment. If government spen.ding
SURVEY
is
added to each of these relatIonA
•
ships, the resulting model can be
AVERAGE 1
used to estimate GNP-gross na(
I
I
I
I
tional product, identified as con1,040
1,035
1,045
1,050
1,055
1,060
1,065
sumption
plus investment plus
BILLION DOLLARS
government
spending. This, then,
SOURCE: Conference Board
is a simple, small-scale model of
the domestic economy.
By building larger-scale models,
econometricians can search for
The idea "on average" reflects
He can determine, for example,
more detailed relationships in
the type of measurement being
the percentage of variation in coneconomic behavior. Instead of trYused. Unlike physical measuresumption regularly associated with ing to estimate merely total conments, which apply to objects and
changes in income. In quantifying
sumption, for example, they can
their movements, econometric
the applicable relations in ecostudy movements in each of its.
measurements apply to patterns of nomic theory, he might find that
three main components-spending
human behavior.
99 times out of 100, consumption
for durables, nondurables? and s~­
There is an element of error in
rises between 90 cents and 96
vices. And they can examme eaC
all econometric measurements that cents for every $1 rise in income.
of these components in detail.
is almost totally absent in the
With this fairly precise identificaConsumer spending on durable d
proper calculation of physical laws. tion of economic events, he is
goods, for example, can be asses?€g
But while there are unexplained
better able to predict future tenas relationships affecting spen~
variations in even the best calculadencies in consumer spending.
on automobiles, household app. tions of economic relationships, an
ances,
and other big-ticket famtlY
... and their sizes
econometrician can still estimate
items. Efforts can also be made to
the extent of variation-the size of
E conometnc
. mo deIs can be built to isolate factors causing varIa
. t'10ns
the error.
answer questions about the operain the prices of these components.

1:

u;-

2

b When these relationships have

r een reduced to the form of equaIons and estimated by their closeness of fit to actual data, the esti~ated equations can be collected
nto a single macro model of perailPIs 50 or more relationships that
wallow the prediction of not
only total consumption but also
spending on individual components, as well as the prices of each.

All forecasters undershot
in predicting nominal 1972 GNP . . .
ACTUAL NOMINAL GNP

h

MODEL A
At.

,
:

I MODEL B ',
1

MODEL C

.,
:

x

MODEL 0

Comparative advantages

..

~oth approaches have their advanges. One obvious advantage of a
~~all ~odel is its lower cost. Anto ~er.ls ~he shorter time required
I Ulld It. While some small mod~o\h~ve taken two or three years
n ulld, they have not taken
s early as long as the truly larget~ale models. And once in operalOn, they can be maintained by
~hlY a few economists-and with
em working part time.
adAnother, possibly less obvious,
s ~antage is that when a smallt,cl· e model is complete, it can be
v eWed'
.
deta'l In. ItS entirety. Being less
COln 1 ed, Its workings are easier to
can prehend. But that fact in itself
so be a disadvantage.
b the correct relationships have
p~~n chosen and included in the
ca/ er fO.rm, a small-scale model
predict broad movements in
econo
.
acc Inic aggregates with enough
wbiu~a~y to satisfy the purpose for
not Chit was designed. But it canstrus ow many of the complex
Wit~tural relationships operating
tbi In the national economy. And
poss .ack of detail can have two
p.lbly serious consequences.
ag lrst, forecasts of specific dissu~egated factors, such as concan;r Spending on services, say,
tion ot be made. Second, prediccha~ of the effects of specific policy
in f ses, such as the recent change
andel eral grants-in-aid to state
shar·ocal governments (revenue
sibl lUg), are difficult, if not impose, to make.

It

SURVEY

... but Model B was, again,
significantly more consistent
MODEL A
1
MODEL B
1
MODEL C
1

MODEL 0
A:

.

SURVEY
AVERAGE

1,035

1,040

1,045

I

1,050

1,055

1,060

I

1,065

BILLION DOLLARS
SOURCE: Conference Board

Large-scale models, on the other
hand, offer possibilities for forecasts that are as detailed as the
model builder wants them to be.
As a result of the greater detail in
his model, a forecaster can better
advise policy makers on the likely
effects of changes in policies. He
can, for example, compare the various results of alternative assumptions, such as a 6-percent growth
in the money supply as against
an 8-percent growth.
He can also trace effects of
policy decisions throughout the
many sectors of the economy. And
changes brought on by such non-

recurring events as the recent devaluation of the dollar can be interpreted with greater precision.
Because of the complexity of the
many relationships making up the
nation's economy, large-scale models are often needed for detailed
analysis of economic trends and
fluctuations. The advantages of
these large models, however, have
their price.
Construction of a large model of,
say, 100 equations or more usually
takes many economists. Each a
specialist in a particular sector of
the economy, they are needed for
a long time to develop a usable

nUs'

llless Review I June 1973

3

model. For the results to be usable, Model C hit wide of the mark
in forecasting real GNP in 1971
computer programmers must be
hired. And large amounts of time
ACTUAL REAL GNP
on high-speed, large-memory computers are needed to make the
model workable. Even when development work is done, one person
MOO~L B I
1:
would have trouble viewing the
model as a whole. To evaluate its
MODEL
1
complex workings properly, several
M-OO-E-L-o......
economists are needed on a con:
1:
tinuing basis.
P""I. . . .

SURVEY

C

1

I

Judgment in forecasting
;1
Two problems with forecasting
must be worked out, regardless of
... and its confidence interval
the size of the model. The first
was by far the largest
arises from the economist's view
of the economy and the way he deMODEL A
velops his model to reflect its
: 1
workings. The second is the emerMODEL
B
gence of special situations, such as
the unusually large federal indiMODEL C
vidual income tax refunds this
year, that are not explicitly alMODEL 0
lowed for in the model.
The first problem results from
SURVEY I
the distinction most economists
. 1
make between two types of ecol AVERAGE
nomic factors. These factors they
I
I
I
I
I
I
I
identify as dependent and inde760
730
735
755
740
750
745
pendent variables.
BILLION DOLLARS
Some variables-such as conSOURCE: Conference Board
sumer spending, investment expenditures, interest rates, and unemployment rates-are influenced
by other factors. Changes in conjudgment-is essentially the same
the model can be used to compute
sumer spending, for example, can
for all forecasters, whether they
the
dependent
variables.
values
for
result from changes in income, maruse econometric models or not.
But while changes in indepenket interest rates, and population.
The difference is that with an
dent variables may give rise to
These factors influenced by other
econometric model, the forecas t.er
changes in dependent variables,
forces are dependent variables.
can examine explicit relationshIPs.
the assumption is that the oppoOther factors, however, are not
Where he depends only on his
site does not occur. As a result, for
determined by the model but rejudgment, the forecaster produces
an economist to make use of an
spond to influences extraneous to
an outlook based on his "feel of
eeonometric model in forecasting,
it. These factors-such as population and its composition, the level
he must first predict appropriate
the situation."
Whatever the advantages of a
of bank reserves, and the discount
future values for the independent
rate-are independent variables.
variables. Only then can the model model over judgmental forecasting, however, good judgment reIndependent variables are, in a
determine corresponding future
mains crucial to solution of the
values of dependent variables.
sense, the drivers of the model.
second set of problems confrontThis process of making assumpMany, in fact, are derived from
fiscal and monetary policy assump- tions about ongoing economic con- ing forecasters. However much
tions. Once their values are known, ditions-the exercise of professional confidence he may have in the

--

4

tnuodel, an econometrician cannot
a ow his forecasting to become
f.urelYmechanical. If the situaIon ~hanges, setting in motion
~onslderations not explicitly alowed for in the model he must
~:X:ercise great caution in interpretIng results of his studies.
For the conscientious econotnetric forecaster, knowledge of
~onditions the model does not al~w for explicitly is one of his tools.
tnodel builder must exercise as
tnuch judgment in interpreting his
~~sults as in selecting the assump. IOns used in building the model
In the first place.
Bases of comparison
La~ge-scale models have been used
serIously in forecasting for little
:ore than a decade. And although
any of the models that are used
are still being refined, the advances
~ade in their development over
th e past ten years give promise
t at large-scale models will evenf ually provide highly accurate
orecasts of economic change.
To illustrate the predictive acCuracy of four representative econ~~etric models, their 1971 and
72 forecasts of GNP were comPared with the survey of forecast'~s cO~piled regularly by the
a ~erlcan Statistical Association
nn the National Bureau of Ecotl:tnic Research. Because many of
v e forecasts included in the sur,. ,.,ey are
·qet
. based on the use of econov riC models, results of the sur13~ are somewhat "contaminated."
in ~ enough other forecasters are
111 Ct~ded to provide a fair approxitr: ~o~ of a prediction based on
dlhonal judgmental techniques.
l'e Also, because survey results
e/resent a consensus of forecastSos, they lack the precision of
111 tne of the more experienced judgth~nta.l fo.recasters. Some years,
Pe ~e mdividual forecasters outGNporm the models in estimating
. The consensus forecast is
llUs'
lness Review I June 1973

Forecasters of real 1972 GNP
also fell short of actual output .. .
ACTUAL REAL GNP

ll

L.-_M_O_Dri_L_A_.....

"

MODEL B
MODEL C

x

MODEL D

x

'--_S-:l~[""R_V_E_Y_.....1

!

. . . and they were all
about equal in consistency
MODEL A

x

MODEL B
&

MODEL C

a:

MODEL D
A
SURVEY

;I

"

AVERAGE

I

770

I

775

780

I

785

I

790

I

795

I

800

BILLION DO LLARS
SOURCE: Conference Board

used, however, merely as a benchmark for reviewing the performance of representative econometric models-not for evaluating
the performance of judgmental
forecasts.
The econometric models are all
medium to large, ranging in size
from one with 35 behavioral equations (meaning estimates of relationships between such variables
as income and consumption) and
eight definitional equations (meaning identifications of variables,
such as consumption expressed as
the sum of its components) to one
with 109 behavioral equations and

133 definitional equations. Like
the survey, all four econometric
groups make revisions in their
forecasts as the year advances.
Monthly comparisons were
made, using the latest forecasts
available for each month. Because
forecasts of GNP for 1972 began
in October 1971 and were revised
through December 1972, 15 observations were available for that
year. Forecasts of current-dollar
(nominal) GNP and constantdollar (real) GNP were used.
The predictive ability of these
models was evaluated on the basis
of the accuracy of their forecasts
5

All five forecasters underestimated real GNP by remarkably
similar amounts in 1972. The total
spread between all five average
forecasts was less than $2.5 billion.
As important as accuracy is, the
lack of precision in forecasting
makes the matter of consistency
equally important. A forecast that
consistently hit fairly close-even
though it might never quite hit the
mark-could be more useful than a
forecast that, while sometimes
very close, often missed badly.
Comparison of the performances
of one model and the consensus
forecast in predicting 1971 nominal
GNP provides a case in point.
Overall, the two forecasts were
about equal in accuracy, but the
Accuracy and consistency
model provided a more consistent
All four models came close to preoutlook. The largest prediction by
dicting both nominal and real
either group was $1,051 billion.
GNP in 1971. Neither they nor the N one of the predictions by the
consensus survey produced an
model, however, was smaller than
average forecast that differed from
$1,046 billion. As a result, the
the actual value of current-dollar
spread in the model's forecasts was
GNP by as much as $3 billion$5 billion. At one point, the survey
which was remarkably close for
predicted a GNP of $1,043 billion.
an economy passing the nominal
And as a result, the spread in the
trillion-dollar mark. The best aver- survey's forecasts was $3 billion
age forecast of nominal GNP overwider than the model's.
shot the nation's total for the year
In the consistency of their real
by an insignificant $300 million.
GNP forecasts, three models perThe least accurate forecasts came
formed about equally well with
from the survey.
the survey in 1971. None of these
Two of the models tended to
four groups varied its forecasts
overestimate real GNP in 1971.
more than $7 billion.
The other forecasters were fairly
close, however, producing averages Confidence intervals
that missed real output by less
Consideration of the range bethan $1.5 billion.
tween the largest and smallest
The strength of the economy in
forecasts ignores other forecasts
1972 caught most people by surproduced during the year. If the
prise. And model builders were no
range of forecasts produced by two
exception. Performance of all these groups were about the same, the
forecasters, including the survey,
preference would, of course, be for
deteriorated that year, their outthe method that issued only one or
looks falling short of both the real
two forecasts that were off the
and nominal GNP's actually
mark instead of one that issued
reached. One underestimated nom- several bad misses.
inal GNP by an average of more
One device for taking into acthan $7 billion. But two came
count how many forecasts are close
within $2 billion.
to the extremes of the range em-

and their consistency. Accuracy
was taken to mean how close average forecasts came to the actual
value of GNP later reported by the
Department of Commerce. Consistency was taken to mean how
much individual forecasts varied
over the course of the year.
In choosing, for example, between two forecasting methods
that were equally accurate, the
preference would be for t~e one
with forecasts that were tightly
clustered around the actual value.
In the unlikely situation of two
methods with the same average
forecast values, the less consistent
would be the one with forecasts
covering the wider range of values.

6

ploys the concept of a confidence
interval. This interval is the range
of values on either side of the
average forecast and within which,
with a certain probability, the
actual value is expected to be.
The idea of a confidence interval has already been introduced in
connection with the example of an
econometric study of consumption
behavior. The statement of a hypothetical situation in which "99
times out of 100, consumption
rises between 90 cents and 96 cents
for every $1 rise in income" alludes
to a confidence interval. The interval from 90 to 96 cents constitutes an estimate of the influence
that a $1 change in income is likely
to exert on consumer spending.
Confidence intervals for the forecasts generated by these five
groups were constructed to include
actual GNP 95 times out of 100.
Again, comparisons were based .on
1971 and 1972 forecasts of nomInal
and real GNP.
While the ranges of two model
forecasts of nominal 1971 GNP
were about the same size, the forecasts of one were more closely
bunched, leaving a smaller confidence interval. In estimating real
GNP that year, both of these
groups issued forecasts with larger
confidence intervals than the survey. One provided forecasts of real
GNP spread over a smaller range
than the other. They varied mo~e
within the range, however, caUSIng
the confidence interval to be largel~
Performances in predicting 197
nominal GNP come out about the
same whether the range of fore-.
casts or the confidence interval IS
used as a basis for ranking. But
the rankings are quite different for
real GNP.
The forecast ranges used as a
criterion for consistency placed
three models in tying positions
with the survey for first place.
With forecast ranges in about the
same position, all four groups caJ!le

within $9 billion of predicting real
GNP at some time during the year.
Several forecasts were in the
lower reaches of the ranges generated by the survey and one of the
models, however. As a result, the
confidence intervals in these two
outlooks were inflated. With
broader confidence intervals, the
~urv~y and model were forced back
o thIrd and fourth positions.

Summing up
There was a persistent finding that
one model did not perform as well
as the others in predicting GNP.
And as a result-despite all the ac~uracy that has been achieved in
orecasting change with medium
~o large econometric models-the
OUr models, talcen as a group, did
not consistently outperform the
consensus of economists that base
most of their predictions on the
~PPlication of purely judgmental
l~chniq~es. Throughout 1971 and
72, this model ranked behind the
Survey in predicting both real and
n°lllinal GNP.
th One explanation might be that
Vi e model was not as closely superInsed. as the other models. EconoetrIc forecasting seems to be
Inost accurate where there is a close

nUs·lness Review I June 1973

interaction between the model and
the economists using it. By closely
supervising their model, forecasters can adjust both for minor variations in the model when it seems
to be predicting poorly and for
future events that may seem likely
but have not been allowed for in
construction of the model.
And, of course, GNP is not the
only variable of interest to decision makers. Future paths of such
variables as unemployment, prices,
investment, and interest rates are
also important. A model that performs well in predicting one set
of variables might not be as precise
in forecasting another.
-Wynn V. Bussmann
Marvin S. Margolis

7

New member banks

The Executive National Bank, Houston, Texas, a newly organized institution
located in the territory served by the Houston Branch of the Federal Reserve
Bank of Dallas, opened for business April 17, 1973, as a member of the Federal
Reserve System. The new member bank has capital of $400,000, surplus of
$300,000, and undivided profits of $300,000. The officers are: F. O'Neil Griffin,
Chairman of the Board; Larry T. Ogg, President; and Joe M. Ainsworth, Cashier.
The City National Bank of Laredo, Laredo, Texas, a newly organized ipstitution
located in the territory served by the San Antonio Branch of the Federal
Reserve Bank of Dallas, opened for business May 4, 1973, as a member of the
Federal Reserve System. The new member bank has capital of $300,000, surplus
of $150,000, and undivided profits of $150,000. The officers are: Ramiro Sanchez,
Chairman of the Board; J. D. Underhill, President; Dan M. Sanchez, Jr.,
Vice President and Cashier; and James A. Mayo, Jr., Assistant Cashier.
New par banks

The Texas Bank, Lubbock, Texas, an insured nonmember bank located in the
territory served by the Head Office of the Federal Reserve Bank of Dallas, was
added to the Par List on its opening date, April 16, 1973. The officers are:
Troy Post, Chairman of the Board; B. J. McNabb, President; Don E. Johnson,
Vice President and Cashiel'; and Conrad Schmid, Vice President.
The Wright City State Bank, Wright City, Oklahoma, an insured nonmember
bank located in the territory served by the Head Office of the Federal Reserve
Bank of Dallas, was added to the Par List on its opening date, May 1, 1973. The
officers are: L. V. Greene, President, and Edna McLaughlin, Cashier.
The Texas Bank of Tatum, Tatum, Texas, an insured nonmember bank located
in the territory served by the Head Office of the Federal Reserve Bank of Dallas
'
was added to the Par List on its opening date, May 5, 1973. The officers are:
Robert Cargill, Chairman of the Board; Paul P. Granbery, Jr., President; and
Tom Allbright, Vice President and Cashier.

8

Cost of Living-

Cities in Southwest
Among Least Expensive

-

Cities in the Southwest continue

~lllong the nation's least expensive
In which

to live. Figures compiled

b~ the Bureau of Labor Statistics

s o~ that in the fall of 1971, a
fatn.ily in Austin could typically
achieve an intermediate standard
~~ liVin~ fO,r $1,563 a year less than
natIon s average urban family.
I e savings in Houston and Dalas Were almost as good-$I,077
and $915, respectively.
. Although consumer prices have
~Isen sharply since then, bureau
.gures show that they have not
rIsen as fast in Dallas and Houston

1'h

-

BUdget for family of four
averages less in District cities
tHOUSAND
12
_ _ _ DOLLARS
_ _ _ _ _ _ _ _ _ __

$10,971
...... ....................... U.S.URBAN
AVERAGE
(FALL 1971)

9_
$ 8,626

as in other metropolitan areas.
And there are indications that this
is part of a continuing trend in
the Southwest.

making up a typical family budget,
none in Austin was substantially
higher than the urban average for
the nation-and most were less.
Housing, for example, usually
Costs are lower ...
takes close to a fourth o£ the
Austin was the least expensive of
budget of an urban family. And in
the 40 metropolitan areas covered
Austin, where housing cost only
in the bureau's study of urban
about 75 percent as much as in the
family budgets in 1971. Families
average city, this item alone went
in Austin typically paid only 86
far in establishing the city as the
percent as much for an intermeleast expensive of the nation's
diate standard of living as the aver- metropolitan areas.
age urban family paid. In Houston,
Only one cost component was
the average family paid only 90
higher than average in Dallas and
percent as much to achieve its
Houston. Costs of medical care
standard of living. And in Dallas,
averaged 6 percent higher in Housit paid only 92 percent as much.
ton and 16 percent higher in DalOne factor that contributes to
las. These additional expenses
the lowering of living costs in these were more than offset, however, by
the lower costs of housing. In
three cities is the absence of a
state tax on personal income.
Houston, housing cost 81 percent
of the urban average. And in DalWith only federal income taxes to
pay, families in Austin paid twolas, the cost was only 85 percent.
thirds as much in income taxes
••• and rising slower
during the study period as the avInflation in consumer prices
erage urban family in the United
reached a crescendo in 1969. Part
States. In Houston, they paid 72
of the year, prices rose at an anpercent. And in Dallas, they paid
nual rate of more than 6.5 percent.
74 percent.
The rate of increase later slowed
But costs of goods and services
considerably, however. On an analso totaled less in these three
nual basis, the rise for a 15-month
cities. Of the cost components

6_

......

PERCENTAGE CHANGES IN URBAN RETAIL PRICES

3_

(Average annu al rates)

Item

o

~_.--.

TOTAL
BUDGET

TOTAL
CONSUMPTION

SOURCE : U.s. Bureau of Labor Statistics

........
nUs'lness Review I June 1973

Food . ..... . ... .
Housing .... .
Clothing .. . .... .
Transportation .. .
Medical care .. .
All items . .

Febru ary 1973
from
November 1971
United
Dall as
States

8.5%
1.4
5.0
.2
3.8
3.4%

8.2%
3.5
1.1
1.5
3.4
3.9%

Janu ary 1973
from
October 1971
United
Houston
States

7.0%
2.8
.6
-.6
4.2
3.0%

6.6%
3.5

1.0
.2
3.3
3.4%

SOURCES: U.S. Burea u of Labor Statistics
Federal Reserve Bank of Dallas

·9

Only in transportation and medical costs do District cities not fare beUer than the nation
THOUSAND DOLLARS

THOUSAND DOLLARS

3
$ 2,638

$ 2,532

1.5 -----------------------------------------------,..... ~~.,.~.~.~........
./U .S . URBAN AVERAGE
...... ~~.'.~~~ ........ ~

(FALL 1971)

1.0 -

2-

.5-

1-

o-~""

HOUSING

o_
FOOD

-LLL

TAXES

CLOTHING

TRANSPORTATION

MEDICAL
CARE

SOURCE : U.S. Bureau of Labor Statistics

period from the fall of 1971 to
early 1973 averaged 3.4 percent.
Prices in Dallas and Houston
ran below the nation's average for
comparable 15-month periods.
Components of family budgets
contributing most to the better
price situations in these two cities
were housing and transportation.
Prices of neither item increased as
fast in Dallas and Houston as the
national average in cities. Nor did
the prices of clothing increase as
fast in Houston.
There are also indications that
the better than average performance of prices in cities of the
Southwest is part of a continuing

10

trend. For one thing, since the
previous family survey taken in
1970, the rise in consumption costs
in Austin, Dallas, and Houston has
been substantially less than the
rise in urban areas nationwide. For
another, prices have been rising
faster in large cities than in small
ones across the nation. And the
Southwest abounds in small cities.
-William R. McDonough

The BLS's budget concept

-

llu .

Budget estimates by the Bureau of Labor
Statistics apply for a family of four-a husband and wife (the man being an experienced worker 38 years old and the woman
having no outside employment), a boy 13
years old, and a girl eight. Estimates are
prepared for three standards of living-high,
low, and intermediate.
Although consumption varies with income, the budget at each level provides for
the maintenance of health, continuation of
social wellbeing, nurture of children, and
participation in community activities. The
intermediate life-style is probably the most
typical. The lower-income budget is distinguished from the intermediate by the family
performing more services for itself, using
more free recreational facilities, and living
in rented housing with no air conditioning.

Slness Review / June 1973

The high-income budget represents a manner of living that includes more household
appliances than allowed by the intermediate
budget, more use of paid services, and a
higher incidence of home ownership.
Budget estimates for various locations
show variations in the cost of equivalent
lists of goods and services, but not necessarily the same lists. Different assumptions
are made regarding food, shelter, transportation, and clothing in different areas. Because clothing needs are different in various
parts of the country, for example, estimates
of clothing costs in Boston and Houston,
say, can reflect differences not only in the
prices paid for clothing but also in the
weight and variety needed. Differences in
the costs of medical care, on the other hand,
reflect only the differences in prices.

11

Federal Reserve Bank of Dallas
June 1973

Statistical Supplement to the Business Review

metal products, and stone, clay,
Total credit at weekly reporting
banks in the Eleventh District rose and glass products. Among producers of nondurable goods, subsharply in the five weeks ended
stantial gains were reported for
May 23. With a moderate decline
in total deposits, banks were forced petroleum refining, printing and
publishing, and apparel. All manut.o reduce their investment portfofacturing industries exceeded yearlios and increase their borrowings
earlier
production levels.
from nondeposit sources-particuAll four categories of mining relarly in the Federal funds marketported increases in output for
to finance an especially large exApril, led by metal, stone, and
pansion in loan demand.
earth minerals. Nevertheless, proAll major types of borrowers
duction of both natural gas and
used their bank credit lines more
natural
gas liquids was below April
than usual. Business loan demand
1972 levels, and crude petroleum
wa~ particularly strong, as corpooutput showed only a slight yearratIOns continued to borrow to fito-year increase. Utilities gained
nance inventory expansion. Real
0.2 percent in April as both elecestate loans rebounded shru'ply
and natural gas distribution
tricity
from their rather low growth rate
rose
slightly.
of recent months, and consumer
loans increased somewhat more
Registrations of new passenger au~han usual. The sizable expansion
tomobiles in Dallas, Fort Worth,
In loan demand led banks to suband San Antonio deHouston,
stantially reduce their holdings of
14
percent in April from an
creased
both U.S. Government securities
unusually
high
level in March.
and other securities.
Total deposits declined less than Total registrations were 27 percent
higher than in April 1972. CumuUsual, as net withdrawals of delative
registrations for the first four
(and.depo~its were below normal
months
of 1973 were 24 percent
or thIS penod. Large negotiable
than
for the same period
greater
C
. D's rose moderately, and reportin
1972.
~ng banks slightly increased their
orrowings in both the Eurodollar
Department store sales in the Elevand commercial paper markets.
enth District were 20 percent
higher
in the four weeks ended
Jhe s.easonally adjusted Texas in26
than in the comparable peMay
ustrlal production index rose
riod
last
year. Cumulative sales
sharply in April to a level 6.0 perthrough that date were 13 percent
cent above a year before. Increased
greater than in the corresponding
~anufacturing output again properiod of 1972.
ylded the primary impetus as minIng and utilities rose only slightly.
Seasonally adjusted total employManufacturers of both durable
ment
in the five southwestern
~nd nondurable goods posted
states
eased slightly in April, the
Increases in production for the
first
decline
in nine months. Em~onth. The increase in output of
ployment
remained
3.5 percent
. Ul'able goods was paced by signifabove
a
year
before,
however. AlICant gains in primary metals, nonthough
the
labor
force
also conelectrical machinery, fabricated

tracted slightly, the unemployment rate edged up to 3.8 percent
from 3.7 percent in March. This
was still well below the 4.3-percent
rate for April 1972.
Cutbacks in agricultural and
manufacturing employment were
responsible for the overall drop as
nonmanufacturing employment
rose slightly. Increases were reported in finance, trade, services,
and government. There were substantial employment declines in
construction and mining, while
transportation and public utilities
had only a slight decrease. N evertheless, employment in all industries held above year-earlier levels.
Agricultural activities in the five
states of the Eleventh District
gained momentum in May after a
slow start due to excessive moisture
in the early spring. Flooding in
Louisiana continued to hamper
planting, but in Texas and Oklahoma, planting was nearing average
completion levels. Wheat and oat
crops in the District states were
reaching maturity, and early yields
were above average.
Range and pasture conditions in
the four western states were excellent in early May. Livestock conditions were also improving as drier
weather relieved feedlot stress.
Texas and Arizona had more than
2.7 million head of cattle and calves
on feed on May 1. Compared with
year-earlier levels, this represented
gains of 17 percent in Texas and 5
percent in Arizona. Both states,
however, had fewer head on feed
than at the start of April as marketings exceeded placements during the month.
The outlook for farm income
this year in the District states re(Continued on back page)

CONDITION STATISTICS OF WEEKLY REPORTING COMMERCIAL BANKS

Eleventh Federal Reserve District
(Thousand dollars)
May 23,
1973

ASSETS

Apr. 18,
1973

May 24,
1972

Federal funds sold and securities purcha sed
under agreements to res ell • .••••.• . ••..• ••••

Other loans and discounts, gross ... ....... .... . .
Comm ercial and Industrial loans ... .... .. ....•
Agricultural loons, excl uding
certincates of interest •••. .• • .. • •.•• .••.••.

ecc

1,152,3 10
9,392,873

779,872
7,725,646

266,843

275,467

196,667

319
57,524

42
57,132

1,160
56,823

5,155
523,052

4,976
523,415

2,688
456,639

193,845
678,780
1,349,137
28,396
60,919
1,017,755

196,519
7 10,321
1,291,179
40,678
64,805
1,004,712

120,964
562,237
1,004,214
21,086
30,996
859,782

500
1,115,613
3,970,615

0
1,051,448
4,115,174

0
932,993
3,622,480

186,256
0

1,003,077
167,081
0

902,993
9,608,806
4,310,968

------4, 172,1 79
3,479,397

loan s to brokers and d ea lers for
purchasing or ca rrying :

U.S. Gove rnm ent securities ... ......... . ... .
Other securities ..... ....... ... .. . ...... . .
Oth er loon s for purcha sing or carrying:

U.S . Government securities ... ............. .
Oth er securities .... .... .. ... ... .... .... . .
Sales Anance, personal flnance, factors,
and oth er business credit companies . .. ... .

Other .. .. . ........... ..... .. ..... . .. ..
Real estate loon s. ... . •.•. ..... .• .. . .. .... .
loons to dom estic comm ercial bonks.. .•....•..
loons to foreig n bonk s..•• .. .. . .............
Consumer instalment loan s. ....... . " ..•.. , ..
loons to foreign governments, offlcial
institutions, centrol banks, and international
institutions . .................. , ...... , . . ,
Other loans. ..... ...... .. . ...... .. ....... .
Total investments .. .... ... .... ... ........... .
Total U.S . Government securities . •...... .. .•..
Treasury bills . ..............•...........
Treosury certiflcates of indebtedness . ...... .
Treasury notes and U.S. Governm ent
bond s maturing:
Within 1 year ....... ................. .
1 year to 5 years .... .. .. • ...... • ....•.
After 5 years . .. .. ....... ... ... .... .. .
Obligation s of stat es and political subdivisions:
Tox warrants and short-term notes and bill s•••

910,944
140,973
0

---982,507

135,513
470,732
163,726

132,559
507,676
156,016

159,575
509,024
167,397

213,896
2,601,395

2Bl,307
2,538,877

144,290
2,229,086

8,581
235,799
1,445,551
872,795
116,686
416,235
16,B04

96,723
215,760
1,429,253
901 ,095
109,451
401,751
12,361

23,104
222,923
1,378,532
803,356
99,834
421,266
11,895

774,310

750,099

568,858

TOTAL ASSETS.... . ..................... 18,124,795

18,264,367

15,411,739

All other ................... . . ........ . .

not consolidated) .. ... . . . .. .. .. . . ........ . .

May 24,
1972

I

Total deposits • ••. . •. . .•.•.••• .•••..••..• ••.. 13,424,522

13,561,605

12,011,452

6,864,101
4,657,615
739,366
144,667
1,178,805

7,024,075
4,883,857
551,641
246,844
1,193,571

6,531,479
4,439,995
525,420
200,919
1,243,014

i

2,6 13
44,444
96,591
6,560,421

3,720
43,872
100,570
6,537,530

5,372
34,900
81,859
5,479,973

1,185,088
3,55 1,008
1,692,612
28,815
90,178

1,183,188
3,487,900
1,722,901
28,723
91,448

1,164, 179
2,843,814
1,335,659
23,261
91,160

12,600
120

13,250
10,120

20,800
1,100

2,581,296
201,279
556,372
160,578
13,970
1,186,778

2,481,318
372,306
500,115
160,762
13,951
1,174,310

1,658,093
34,521
443,520
138,697
17,697
1,107,759

IB,264,367

15,411,7~

Total d emand d eposits•.•.... .... .. . " . . .. "
Individuals, partn ership s, and corporations . . ..
States and political subdivisions . .........••
U.S. Gov ern ment . . .. . . . ...... . .... ... . ..
Banks in th e Unit ed States •.. .•.. ..... ... . .
foreign:
Governm ents, ofAcial institutions, central
banks, and international institutions .... ..
Comm ercial banks . ......... . . .. .. . ....
C ert ifl ed and offlc ers' checks, etc .•• ........ .
Total time and savings d eposits•••• . .... .. ... .
Individuals, partn ershi ps, and corporations:

---- ----

Savings deposits •• •••• .••..••..•••.•. ••

Loans to nonbank Anancial institutions:

Oth er bond s, corporat e stocks, and securities:
Certiflcates representing participations in
federal ag ency loan s... ...............•
All other (including corporate stocks} •.•.. ....
Ca sh items in procon of coll ection ... . ... . . .. ... .
Reserves with federal Reserve Bank . ........... .
Currency and coin .. •... ... ........ . .. .......
Balances with banks in the Unit ed States . •...... .
Balanc es with banks in foreig n countries ... ... . .. .
Oth er a ssets (including investm ents in subsidia ries

Apr.18,
1973

May 23,
1973

lIA81l1T1ES

Oth er tim e d eposits ... ...•...•.........
States and political su bdivisions .... ........
U.S. Governm ent (includ ing postal saving s) . ..•
Bank s in the Unit ed Stat es .. .. ....... .... ..
Foreig n:
Governm ents, offlcial institutions, central
banks, and international institutions •. ....
Commercia l banks ... •.............•.•.
Fe deral funds purchased and securities sold
under agreeme nts to repurcha se .... . ... ..... .
Oth er liabilities for borrow ed mon ey .... ........
Oth er liabilities ..... ... ........• ..... •.•.••. .
Reserves on loan s... ......•...•..••..........
Rese rv es on se curities . . . ...... .. ...... .. ......
Totol capital accounts . ••..... .. .........•. .. .

TOTAL LIABILITIES, RESERVES, AND
CAPITAL ACCOUNTS .. .......... . .... .. 18,124,795

----

Eleventh Federal Reserve District

DEMAND DEPOSITS

TIME DEPOSITS

Total

Adjuste d!

Governm ent

Total

Savings

1971 . April •.• •••
1972. April •..••.
May .. . ...

11,555
12,470
12,268
12,320
12,529
12,420
12,619
12,866
12,844
13,439
13,636
13,270
13,203
13,237

7,982
8,696
B,530
8,553
B,694
8,824
8,933
9,034
9,321
9,688
9,802
9,516
9,454
9,550

227
314
384
280
289
226
254
264
222
289
317
379
395
331

9,575
10,938
11,075
11,233
11 ,304
11,441
11,492
11,618
12,009
12,26 1
12,501
12,811
13,038
13,249

2,361
2,640
2,660
2,688
2,714
2,717
2,744
2,770
2,786
2,812
2,815
2,817
2,B48
2,855

August .•.. .
September.

October •••
Novembe r ••
D ecemb or • .

1973, January ••••
Fobruary •. .

Apr. 25,
1973

Mar. 28,
1973

Apr. 26,
1972

loans and discounts, gross .. ......... . . . . .
U .S. Government obligations . ............ .
Other securities .. ••........... . ...... . ..
Reserves with Fe deral Reserve Bank . . .. .. . .
Co sh in vault .•. . . ..................•...
Balances with bonks in the United States • ...
Balance s with banks in foreign countries e .•. .
Co sh items in process of collection ..•• . .....
Oth er o sse tse . .......••..•.•...........

18,357
2,444
6.015
1,390
334
1,217
14
1,606
1,373

18,065
2,525
5,832
1,380
321
1,246
13
1,585
1,336

14,987
2,399

TOTAL ASSETSe ........... ... . . ..... .

32,750

32,303

28,419

Item
ASSETS

Demand d eposits of banks .. ... .• ...... . .

Other demand deposits ••......•..•.•.••.
Total deposits ...... .. . . ......... .... .
Borrowing s . .. . ..•.. ... .. .. . . .... .. .. ..

Other liabllities e ••• •.. • •••• •. ••. .. • •.•..

Total capital accountse . .... ..........•..

TOTAL LIABILITIES AND CAPITAL
ACCOUNTSe •••••..•..•••.........
e-Estlmatad

1,548
11,466
13,302
26,316
3,011
1,174
2,249

1,645
11,431
13,13B
26,214
2,790
1,066
2,233

March • . ••.
April ..... .

I
{
I

I

-- r

1. Other th a n thos e of U.S . Gove rnment and dom estic commarclal banks, lesS
cash Ite ms in process of collecllon

s.o48

1,633
303
1,166
12
1,761
1,11 0

LIABILITIES AND CAPITAL ACCOUNTS
Time deposit s .. .................. ..... .

-

Date

June • •••• .

(Million doll ars)

I
I

-

(Averages of dally figures. Million dollars)

Jul y .......

Eleventh Federal Reserve District

I

DEMAND AND TIME DEPOSITS OF MEMBER BANKS

U.S.

CONDITION STATISTICS OF ALL MEMBER BANKS

1

1,692
10,591
10,950
23,233
1,905
1,342
1,939

RESERVE POSITIONS OF MEMBER BANKS

Eleventh Federal Reserve District
(Averages of dally figures. Thousand dollars)

Item

4 weeks end ed
May 2, 1973

Total reserves held.... ........ .. .
With Fe deral Rese rve Bonk .. • . . .
Currency and coin ...... ..... ..
Re quired reserves .... . • • . • • . . • • . .
Exce" r.serves. • • • . . • • • • • . • • • . • .
Borrowings. • . • . . • • • . . . • . . . . . . . .
Free reserves ........ .... . ......

1,767,926
1,478,645
289,281
1,759,252
8,674
124,547
-115,873

4 weeks end e d

Apr. 4, 1973
1,753,796
1,468,761
285,035
1,747,194
6,602
95,053
-B8,451

----2

4 woeks end d

MOy3,~
1884,497
1:619,28~
265,2 1
1,859,1 ~~
25,3 7
3,18
22,1 40

-----------------------------------------------~

I

I
r

I

BANK DEBITS, END-OF-MONTH DEPOSITS, AND DEPOSIT TURN OVER

SMSA's In Eleventh Federal Reserve District

-

(Dollar amounts In thousands, seasonally adjusted)
DEBITS TO DEMAND DEPOSIT ACCOUNTSI
DEMAND DEPOSITSI
Percent change

Standard metropolitan

(Annual-rote

March

statistical are a

basis)

1973

April
1972

4 months,
1973 from
1972

$ 12,174,829
4,8BB,555
14,962,688
1,103,584
2,910,463
9,063,782
14,623,007
7,769,005
3,245,854
1,316,550
8,907,427
638,436
175,726,944
10,967,242
3 1,566,424
3,553,836
159,915,822
2,573,678
1,344,844
7,709,460
3,347,550
2,394,260
2,277,032
1,868,964
26,185,862
1,330,190
1,965,143
2,908,435
4,400,737
3,289,072

2%
-I
-4
-4
-7
-4
10
-I
9
-2
15
4
4
I
-5
- I
-2
15
-3
-6
6
-6
II
0
2
-16
0
-2
0
-2

35%
22
10
19
15
25
17
18
33
14
23
32
19
19
12
25
16
35
27
34
30
14
18
II
18
4
18
II
27
16

30%
26
16
7
17
27
II
14
20
II
12
29
15
18
14
19
19
25
21
30
24
15
II
17
15
9
13
19
21
12

18%

17%

AR IZONA. Tucson
LOUISIANA,

~h~~:~~~r; ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ : ~ ~ ~ ~ : ~ : : : ~ : ~

NEW MEXICO, Roswell ' . .... ... ... . . ............ . ....
TEXAS,

~~i~~~i~:

: : : : : : : : : : : : : : : : : : : : : :: : : :: : : : : : : : :
Austin ................... .. . . .... .... . ... . . .
Beaumont- Port Arthur-Oronge •••.. .....•.• . ..•.•
Brownsville·Harlingen-San Benito .. .. ......... .. ..

Bryan-College Stalian .•.•• •. ••. ...... . . .. .. .. .

'~I~~1;Irr[ iii; ;;i;;;;i:.~:~:~:~::;::

Galv es ton-Texa s City ............. ..... .. . .....
Houston • . ••• . .••..•••• ..••.. . ....... . . .. . ...

Killeen-Temple .............. . ......... . . .....
loredo ...... .. . ........... ...... . . .........
Lubbock . ••••. . ...•.••••.... . ...•. .. . .. . ....
McAlion-Pharr-Edinburg ••••.. ..•. •.•.• .•. •.• . •.
Mid land ........................ . .. . . .......

.:; :

m:~~~~~'LL ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~

Texarkana (Texas -Arkan sas ) ....... . ... . ...... . .

--

~~fft~: ~~Iis:.:.::::

:::::: : : : : : : : : :: :::: : : :::::

rotal_30 centers .• • •..••••. .. ......................

Annual rote
of turnover

April 1973 from

April
1973

1%

$524,929,675

Apri l 30,
1973
$339,76B
118,334
331,207
46,941
132,047
218,077
433,090
285,901
121,369
58,511
279,650
40,089
3,007,786
302,151
887,064
130,621
3,373,435
115,988
60, 188
216,007
167,904
163,481
96,320
92,0 15
917,396
80,666
92,976
129,667
156,362
144,805
$12,539,816

Apri l
1973

March
1973

April
1972

36.0
40.5
46.9
24.0
21.8
41.7
33.3
27.4
27.7
23.0
31.9
15.7
59.7
34.9
36.1
27.5
47.9
22.5
22.8
36.0
20.1
15. 1
23.3
21.3
29.0
16.5
21.6
22.9
28.8
23.4

36.1
41.5
51.1
24.8
23.1
44.2
28.6
28.0
26.2
23.7
27.5
15.1
58.2
34 .8
39.1
28.2
49.3
19.9
23.9
38.5
19.6
16.3
21.2
22.5
28.3
19.8
22.6
24.0
28.7
24.6

29.9
36.6
47. 1
21.8
22.6
40.0
32.3
24.4
25.5
23.3
27.6
14.4
56.0
33.3
36.1
23.5
46.0
18.7
22.5
30.9
18.8
13.8
18.2
21.9
27.7
17.6
19.6
23.2
24.6
22.1

42.4

42.5

40.1

~. geposlts of Individuals, partnerships, and corporations and of slales and political subdivisions
. Ounty basis

CONDITION OF THE FEDERAL RESERVE BANK OF DALLAS
BUILDI NG PERMITS

('Thousand dollars)

......

May 23,
1973

Item

April 18,
1973

VALUATION (Do llar amounls in thousands)

Ma y 24,
1972

Percent change

-------------~~------------~~-----------------240,525
236172
350,529
,
o
48,060
213,869
o
a
0
44,566

rota I gold cer' IA colo reserves . . . . . . . . . . . . . . .
loa
Oth"S ~o mem b e r banks.... . ... ... .........
Feder oans . . ................ .. ... ..•. t. .
u.s eGa I ag ency obligal/ons. . .. . ......... . ..
rot~1 ove rnm e nt securllies.. .. .. . . .. . . . .....
Me bearnlng a sse ts •.... " . . . . . . . . . . • . . . . •

Fedlll or bank reserve de posits . .. .. .. .... . ..
eral Reserve notes in actua l circulation . .. . .

56,9 11
3,409,457
3,5 14,428
1,490,53 1
2,280,50 1

57,2 14
3,3 18,590
3,589,673
1,485,961
2,265,558

3,200,855
3,245,42 1
1,421,267
2,111,849

---------------------------------------------

April 1973
NUM8ER

$ 16,066

$68,663

73
452

31 1
1,704

2,169
4,148

8,093
37,774

-3
-10

-64
-35

-42
94

80
201
516
239
105
357
1,631
25
5 17
366
49
2,706
37
198
95
135
122
88
1,722
37
57
194
74

284
596
2,024
724
394
1,333
5,805
85
2,012
1,446
225
9,74 1
210
688
350
427
399
337
7,080
136
205
788
3 15

1,340
5,552
23,966
2,043
3,857
4,448
22,778
76
18, 176
11 ,055
1,073
55,558
470
9,079
1,4 19
1,3 17
237
869
21,053
942
522
1,660
669

10,943
18,722
9 1,037
9,8 18
11,181
23,166
111,920
1,167
54,578
48,562
4,390
272,878
9,0 11
32,585
5,793
5,294
1,900
3,789
82,621
2,409
1,610
16,358
6,758

-64
54
-31
7
284
4
-32
-75
39
-40
- 54
-39
-94
-2 1
118

-47
199
27
-54
593
-I

63
I II
II

Total-26 cities •• • 10,726

39,949

$210,542

$941,020

Tucson •• •..•• .

Amarillo •.... .

Austin ..• . . . . .
Beaumont • . ...
Brownsville .. ..

Corpus Chrisl/ •.
Da ll as ....... .
January-April

~re a and typ e

FIVE S
Sr Ar~~ITH WESTERN
~.'ld onl'l· '1' • : • • • . • • • . . • •
No
a building...... .
No nre~ldentia l building. • • .
UNlr nbullding construcl/on....
ReEI~ STATES ... ... . . ... .
N' enl/al building. • • • . . .
N~~r.s id .nlial building . • • .
~Iding conslruction....

Apri l
1973

March
1973

February
1973

1973

1972r

Denison . . . .. . .

EI Pa .o ...... .
Fort Worth •...
Ga lves ton .. .. .
Houston ••.. .. .

954
477
282
195
8,8 14
4,512
2,634
1,668

1'51310
2
439
138
8,644
4,643
2,707
1,294

826
460
248
117
6,839
3,277
2,229
1,333

3,837
1,930
1,348
559
31,063
15,656
9,954
5,452

3,612
1,774
940
898
27,005
12,866
7,878
6,26 1

I. Arl
' .... Fle z~na, Louisiana, New Mexico, Oklahoma, and Texas
NO'T v sed
SOU E: Details may not add to totals because of rounding.
FlCE: F. W. Dodge Division, McGraw-Hili Information Systems Company

.4 months,
1973 from
1972

2,330

ARIZONA
LO UISIANA
Monroe- West
Monroe . .. . .
Abilene . . . ... .

........

Apr.
1972

650

Area

TEXAS

(Million dollars)

Mar.
1973

.4 mos,
1973

Shreveport ... .

VALUE OF CONSTR UCTION CONTRACTS

from

.4 mos.
1973

April
1973

laredo . ... . . .
Lubbock .• . . . .
Mid land •.•...
Odessa . • . .. ..
Port Arthur ... .
Sa n Ango lo • • . •
San Antonio ..
Sherman . ....
Texarka na . ..
Waco . . ... . .

.
.
.
•

Wichita Fall ••••

April

1973

102%

79%

5

-5

-83
115
155
-56
13
-24
173
115
-89
-49
150
-46
-44
-42

-75
-69

-25

-6
-70
36
-16
89

-25%

-55
3%

-10%

7
159
-I I
-27

-7
-19
11 7

-7
28
92
92
-38
-63
26
48
- I
-34
-51
51
32
9%

1
DAILY AVERAGE PRODUCTION OF CRUDE OIL

LABOR FORCE, EMPLOYMENT, AND UNEMPLOYMENT

(Thousand barrels)

Five Southwestern States1
Percent change from

Area

FOUR SOUTHWESTERN
STATES .. . . .............
l ouisiana .... ...... .... . .
New Mexico .............

Oklahoma .. . .. .. .. . . ... .
Texas ........... . ..... .

Gulf Coast ............
West Texa s ... . ... . . ..
Ea st Texas (p roper) .... .
Panhandle ••••.. . .. ....

Rest of sta te ....... ....

UNITED STATES ...... . .....

April
1973

Ma rch
1973

April
1972r

6,778.8
2,359.0
275 .2
546.0
3,598.6
727.8
1,81 4.9
248.2
60.8
746.9
9,3 42.5

6,751.3
2,370.3
276.3
553.6
3,551.2
711.7
1,796.1
244 .7
59.3
739.4
9,316.4

6,925.5
2,4 16.5
310.7
576.4
3,621.9
748.2
1,750.5
218.4
70.0
834.8
9,489.7

March
1973

0.4%
-.5
-.4
- 1.4
1.3
2.3
1.0
1.4
2.5
1.0
.3%

April
1972
-2.1%
-2.4
- 11.4
-5.3
-.6
-2.7
3.7
13.6
- 13.1
- 10.5
-1.6%

r- Revised
SO URCES: American Pe trol e um Institute
U.S. Bureau of Mines
Federal Rese rve Bank of Da ll as

i

I

(Seasona lly adjusted)
Percent chang e

April 1973 from

Thou sands of p ersons
It em

Civilian labor force ... ... . ..
Totol em pl oyment ..•.. ... .. .
Total un empl oymen t •.. .•..••
Unemployme nt rote •. .••...•
Total nonagricu ltural wage
and salar y em pl oy ment . .. .

Ma nufacturing . .. .... ....

Durabl • ..... . ... ......
Nondurable ....... . .. . .
N onma nufacturing • •...•.•
M ining .... . ...... . ... .
Construction .. . ... . . ...
Tran sportation cnd
public utiliti es • •......
Trad e . . . .. . .. .. . .... .
Finan ce ... •..••..• . .•.
S!lrvice ••.. ••..• . ..•• •
Governm ent • ....•...••

April
1973 p

March
1973

April
1972r

8,849.3
8,511.2
338.1
3.8%

8,854.2
8,522.9
331.3
3.7%

8,592.5
8,225.1
367.4
4.3%

7,0 18.3
1,223.8
679.9
543.9
5,794.5
232.5
486.2

7,0 16.9
1,2 28.4
680.6
547.7
5,788.6
234.2
490.3

6,726.9
1,1 7 1.9
639.0
532.9
5,555.0
230.9
450.8

.0
- .4
-.1
-.7
.1
-.7
- .8

4.3
4.4
6.4
2. 1
4.3
.7
7.9

476.6
1,679.3
379.6
1,146.0
1,394.4

477.3
1,674.6
377.2
1, 143.8
1,391.1

462 .3
1,607.0
354.5
1,096.6
1,352.9

- .1
.3
.6
.2
.2%

3. 1
4.5
7.1
4.5
3.1%

Mar .

1973

Apr.
1972

-0.1 %
3.0%
- .1
3.5
2.1 -8.0
'.1 ' -.6

1. Arizona, Louisiana , New Mexico, Oklahoma, and Texas
2. Actual change
p-Pre llmlnary
r-Revlsed
NOTE: Oetalls may not add to tota ls because of rounding.
SO URCES : S ta te e mployme nt age ncies
Fe de ral Reserve Bank of Da ll as (seasona l adjus tme nt)

INDUSTRIAL PRODUCTION
(Seasonally adjusted Indexes, 1967

Area and type of index

= 100)
April
1973p

March
1973

February

1973

Ap ril
1972

137.2
14 2. 1
156.8
131.5
119.0
161.2

135.3
139.8
154.5
129.3
11 7.6
160.9

134.6r
139.0r
154. 1
128.2r
11 7.2r
159.1r

129.4
130.5
141.9
12 2.3
119.3
158.8

WINTER WHEAT PRODUCTION

TEXAS
Totol industrial production •. .. ..

Manufacturing . •. .... .. . .. .....
Durable . . . ............ . .... .

Nondurable . . .. . . ... ..... .. . .
Mining •.• ..... . .. ...... . .• . .. .
Utilities .... .. . ....•..... . •... .

UNITED STATES
Totol in dustrial p ro duction .. ....
Ma nufacturing ... ... .. ..... .. . .

Durable . ................•...
Nondurable . . . ... .. . .........
Mining •.......... . . .... ..... . .
Utilities ..... . ...... . . .. .. .. .. .

123.0
122.8
118.6
128.8
107.1
153.0

121.8
121.5
116.9
128.2
107.8
150.9

121.1 r
120.6r
116.2r
126.9,
109.1r
150.4r

p-Prelimlnary
r- Re vised
SOURCES : Board of Governors of th e Federal Rese rve System
Federal Reserve Bank of Dall as

mained good as cash receipts from
farm marketings continued at record levels through the first quarter.
Total receipts stood near $1.9 billion, 24 percent ahead of the same
period last year. Livestock receipts
t otaled about $1.2 billion, a gain
of 24 percent over a year before,
and crop receipts totaled over $700
million, up 25 percent.

112.8r
111.8r
105.8r
120.3r
109.0r
140.2r

..

(Thousand bu s he ls)
1973,
ind icat ed
Area

May 1

1972

Arizona ... ..... ... ...... ... .

Texas ..................... .

13,090
550
8,D92
141,960
83,200

11,390
690
4,335
89,700
44,000

11,764
805r
3,840r
72,OOOr
31,416

Total. ................... .

246,892

150,115

119,8 25 r

Loui si an a . ••. .•.. . .•..• . ..•..
N ew Mexico . ...... . ... ... . . .

Oklahoma ......... . ... .•. . ..

r- Revlsed
SOURCE: U.S. Department of Agriculture

1971

----