Full text of Economic Review (Federal Reserve Bank of Atlanta) : March 1971, Volume 56, Number 3

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b y F re d e ric k R. S tro b e l
and
W illia m D. Toal
D u rin g th e past ten years, e c o n o m ic fo re ca stin g , and w ith it th e use o f
m o d e ls o f th e e c o n o m y , has gain ed co n s id e ra b le p o p u la rity b u t n o t th e
a tte n tio n it deserves. For tho se u n fa m ilia r w ith th e c o n c e p t o f e c o n o m e tric
m o d e l b u ild in g , th is a rtic le exp la ins h o w m o d e ls are c o n s tru c te d and h o w
th e y are used. O th e r readers m ay be in te re ste d in th e d iffe re n t a p pro ache s each
o f th e fiv e selected m o d e ls take, e sp e cia lly in th e d e ta ils p re sen te d in th e
A p p e n d ix . M o s t readers w ill be in te re ste d in w h a t th e m o d e ls say a b o u t
1971's e c o n o m ic o u tlo o k .

What Is Econometrics?
Theory, the Starting Point of Prediction. In o rd e r to u n d e rsta n d e c o n o m e tric s
and e c o n o m e t r i c m o d e l s / it is necessary to lo o k b rie fly at e c o n o m ic th e o ry ,
e s p e c ia lly w ith regard to p re d ic ta b ility . E co n o m e trics m ig h t best be d e s c rib e d
as a b le n d o f e c o n o m ic th e o ry , statistics, and m a th e m a tics de sig n e d to test
th e o re tic a l re la tio n sh ip s c o n c e rn in g th e e c o n o m y . The sta tistica l and
m a th e m a tica l te ch n iq u e s used in e c o n o m e tric s w ill g e n e ra lly te st to w h a t
de gre e a given th e o ry h o ld s tru e o r to w h a t e x te n t it can be re je cte d .
E con om ic th e o ry , since it is c o n c e rn e d w ith th e a ctio n s o f an e c o n o m y , is
co n c e rn e d w ith th e actio ns o f p e o p le , o fte n , m illio n s o f th e m . T he task
o f th e o ry , th e n , is to d e te rm in e w h e th e r th e a ctio n s o f these p e o p le fo llo w *
a co n siste n t o r p re d ic ta b le p a tte rn . T he key e le m e n t, here, is p re d ic ta b ility .
W h ile p e o p le m ay n o t alw ays act ra tio n a lly (as e vid e n c e d b y sales o f p ro d u c ts
th a t are k n o w n to be h a rm fu l to on e's h e a lth ), th e y w ill g e n e ra lly act p re d ic ta b ly .
Let us lo o k at a p ro b le m th a t m ig h t face an e c o n o m e tric ia n in p re d ic tin g
e c o n o m ic b e h a v io r. T he e c o n o m e tric ia n w a nts to fo re ca st a u to m o b ile sales
f o r ne xt year. T o d o th is he m ust establish a p re d ic ta b le re la tio n s h ip , based

xSee G lossary for ita licized w o rds.
R e v ie w , Vol. LVl, No. 3. Free subscription and additional copies available
upon request to the Research Department, Federal Reserve Bank o f Atlanta,
Atlanta, Georgia 30303.
M o n th ly

M O N T H L Y R E V IE W

on theory, drawn from the past, and applied to
the future. This application of past predictable
relationships to the future is the essence of
econometric forecasting.
His economic theory tells him that the amount
people as a group spend on automobiles depends
upon their income. He is reasonably confident
that personal income is going to rise by a certain
amount next year. Thus, he would expect, on the
basis of theory, that automobile sales would also be
greater. But is this theory correct and, if it is,
how much should he expect sales to rise?
To answer these questions, the econometrician
might go back to the experience of the last 20
years. Using data on income and sales, he might
determine by statistical testing that during the
last 20 years, individuals as a group spent 11
percent of their income for automobiles. The
theory that automobile sales are related to income
seems to be correct, and he has a predictable,
numerical figure expressing that relationship.
But his statistical testing also showed him that
in not every one of the 20 years did the individuals
as a group spend 11 percent of their income for
automobiles. Some years they spent much more;
and in others, much less. Apparently, something
else besides income influenced sales.
To try to find what that something else is,
the econometrician once more seeks the help of
economic theory. According to economic theory,
he might find that individuals also consider
interest rates and automobile prices along with
income when they buy automobiles. O nce more
he puts data for past years through statistical
tests and finds that automobile purchases do tend
to decrease as interest rates increase and to
decrease as automobile prices increase. The
statistical tests support the theory and give him
additional predictable relationships. As a result,
he knows that his success in forecasting automobile
sales is going to depend on knowing not only
what happens to income but also on what interest
rates and prices are going to be.
At this point the econometrician has reached
his first goal in the forecasting process. He has
established what can be called a fu n c tio n a l
re la tio n sh ip between several sets of economic
facts (often called e c o n o m ic v a riables). A functional
relationship is commonly expressed in the form
of an equation, as in the example below.

In our example, the equation for auto sales
might take the following linear form:
S = al + bP + cR
Where S = Value of auto sales, billions of
1958 dollars.

F E D E R A L R E SE R V E B A N K O F A T L A N T A




By a functional relationship, we mean that
interest rates, prices, and personal income
determine the amount of automobile purchases.
Moreover, the relationship or equation can be
quantified as seen in the next example.

In quantifying the example, values are
assigned to a, b, and c based upon statistical
observation of auto sales data in relation to
personal income, auto prices, and interest rate
data. The hypothetical equation might then
appear as follows:
S = .111 - .0015 P - .05R
W here a = .11; b = .0015; and c — - .05
This would mean that an increase in personal
income would be translated into increased sales
of automobiles in the amount of .11 times the
change in the level of personal income.
However, since the signs are negative, increases
in the second and third terms tend to reduce
the volume of auto sales. Thus, the
econometrician can now predict the value of
future auto sales, if he knows the future levels
of personal income, interest rates, and
automobile prices.

Problems of Prediction. The econometrician must
be sure that he has tested and developed a cause
and effect (functional) relationship. That is,
c o rre la tio n between two or more sets of data
may have no cause and effect relationship. As
one writer puts it ". . . take the figures that show
the suicide rate to be at its maximum in June.
Do suicides produce June brides— or do June
weddings precipitate suicides of the jilted?"2
Neither is very likely. This line of thinking is
known as spurious correlation. (It is simply the

- D arrel Huff, H o w to Lie w ith Statistics, W . W . N orton
an d C o ., N e w Y o rk , 1954.

I = The level of personal income.
P = The price index for automobiles.
R = The average interest rate charged on
auto loans.
a,b,c, = The amounts that personal income,
auto prices, and auto loan interest
rates, respectively, affect auto sales.
The values a, b, and c are called
coefficients and are given numerical
values.

43

a ssu m ptio n th a t a statistica l re la tio n s h ip exists
b e tw e e n tw o sets o f data even w h e n th e re is no
lo g ica l reason fo r th e re la tio n s h ip . O b v io u s ly ,
if th e re is no lo g ica l reason fo r th e c o rre la tio n
o f data, th e re la tio n s h ip m ay be u n re lia b le fo r
p re d ic tiv e purposes. Thus, th e e c o n o m e tric ia n
sh o u ld base his re la tio n sh ip s n o t o n ly o n statistica l
measures b u t also o n e c o n o m ic th e o ry.)
E x o g e n o u s forces pose a second p ro b le m . In a
n a tio n o r re g io n , m an y un usu al events o c c u r th a t
are u n c e rta in as to tim e o f o cc u rre n c e and as to
th e m a g n itu d e o f th e ir effects. Such factors
c o m p lic a te th e e c o n o m e tric ia n 's fo re c a s tin g p ro b ­
lem . Som e exam ples are w a r, ea rth qu ake s, o r a
sud de n, u n e xp e cte d change in co n su m e r
pre fe ren ces o r a ttitu d e s. The e c o n o m e tric ia n
m ust, th e re fo re , be ca re fu l to exa m in e his
p re d ic tiv e assu m ptio ns and m ake th e best possible
ju d g m e n t c o n c e rn in g th e course o f fu tu re
exogenous forces. Som e o f these forces, such as
G o v e rn m e n t sp e n d in g , are ro u g h ly p re d ic ta b le .
O th e r exo ge no us forces, such as ea rth qu ake s, are
la rg e ly o u t o f th e realm o f hu m an fo re s ig h t, le t
a lo n e th a t o f th e e c o n o m e tric ia n .

In d e v e lo p in g his p re d ic tiv e re la tio n sh ip s, the
e c o n o m e tric ia n is also c o n fro n te d w ith the
p ro b le m s o f com p le te n e ss and in te ra c tio n . In
refere nce to th e exa m p le c o n c e rn in g a u to sales,
th e e c o n o m e tric ia n m ust be sure th a t w h a t he
in clu d e s makes th e re la tio n s h ip as c o m p le te as
feasible. He had p re v io u s ly fo u n d th a t a u to sales
w e re re la te d to th e levels o f in c o m e , in te re st
rates, and a u to m o b ile prices. H o w e ve r, a fte r fu rth e r
e xa m in in g these e s tim a te d re la tio n sh ip s, suppose
he fin d s a s ig n ific a n t p o rtio n o f a u to sales are
u n e xp la in e d by th e th re e variables. U p o n
in v e s tig a tio n , he fin d s th a t th e level o f rap id
tra n sit fares can also in flu e n c e th e a m o u n t o f
a u to sales in a given g e o g ra p h ic area. In c lu d in g
this n e w e c o n o m ic v a ria b le , he fin d s he can
a c c o u n t fo r m o st o f th e u n e x p la in e d p o rtio n o f
his p re d ic tiv e re la tio n s h ip . If th e re are no o th e r
variab les th a t s ig n ific a n tly a ffe c t a u to sales, he
has solved his p ro b le m o f com p le te ness.
H a vin g so lve d th e p ro b le m o f com p le te ness,
th e e c o n o m e tric ia n is n o w faced w ith o b ta in in g
fu tu re data o n personal in co m e , prices, in te re st
rates, and tra n s it fares. H o w is he to o b ta in
these data? This c o n fro n ts h im w ith th e in te ra c tio n
p ro b le m . In te re st rates and a u to prices m ay n o t
as re a d ily le n d them selves to e x p la n a tio n by a
fu n c tio n a l (cause and e ffe ct) re la tio n s h ip . In o th e r
w o rd s , because o f th e d iffic u ltie s in p re d ic tin g
m o ve m e n ts in a u to prices and in te re st rates, it
is ve ry hard to establish th e necessary e q ua tion (s)
fo r each va ria b le . H ence, a ju d g m e n ta l assu m p tio n
m ust be m ade a b o u t fu tu re values o f these
variables.
H o w e ve r, assume he can establish fu n c tio n a l
re la tio n s h ip s o r e q u a tio n s to foreca st tra n s it fares

44




and personal in co m e . He m ig h t n o te th a t ra p id
tra n sit fares are tie d to th e w a ge levels o f tra n sit
w o rke rs. But, by d e fin itio n , w ages are o n e p o rtio n
o f personal in co m e . F urther, personal in c o m e is
p a rtia lly d e te rm in e d by a u to m o b ile sales, w h ic h ,
basically, he was try in g to fo re ca st in th e firs t place.
Because o f this, th e e c o n o m e tric ia n needs
a d d itio n a l fu n c tio n a l re la tio n s h ip s to foreca st
tra n sit fares, tra n s it wages, and also personal
in co m e . Som e o f these re la tio n s h ip s are
in te rre la te d — thus, th e in te ra c tio n p ro b le m . A u to
sales, w h ic h he was o rig in a lly try in g to foreca st,
a ffe c t personal in c o m e ; b u t as w e said, personal
in c o m e levels a ffe c t w a ge rates in mass tra n sit,
and, co n s e q u e n tly , tra n s it fares. T ra n sit fares, as
m e n tio n e d above, p a rtia lly a ffe c t a u to sales.
A t this p o in t, th e e c o n o m e tric ia n exclaim s,
“ H o ld it! I need an e c o n o m e tric m o d e l!"

Econometric Models and Prediction. It can easily
be seen, th e n , th a t p re d ic tio n can be a gre ater
p ro b le m than it seems. T he e c o n o m e tric ia n
in itia lly set up o n e fu n c tio n a l re la tio n s h ip to
fo re ca st a u to sales. This re la tio n s h ip o r e q u a tio n ,
w h ic h is, in fa ct, a s in g le -e q u a tio n e c o n o m e tric
m o d e l, show s h o w ce rta in " i n d e p e n d e n t "
v a r ia b le s — personal in c o m e , a u to prices, in te re s t
rates, and tra n s it fares— a ffe c t a u to sales— th e
" d e p e n d e n t " v a ria b le . In th e process, he fo u n d it
necessary to p re d ic t ce rta in in d e p e n d e n t va ria b le s;
thus, o th e r e q u a tio n s w e re necessary. For exa m ple,
he fo u n d fu tu re tra n s it fares m u st be p re d ic te d .
T h e re fo re , he ne ed ed an a d d itio n a l e q u a tio n th a t
stated th a t tra n s it fares w e re p a rtia lly d e te rm in e d
by the level o f tra n s it w o rk e rs ' wages. T his w h o le
system o f e q u a tio n s, w h e n c o m p le te d , is ca lle d
a "th e o re tic a l e c o n o m ic m o d e l." W h e n s ta tis tic a lly
tested and given n u m e ric a l values, th e th e o re tic a l
m o d e l becom es th e " e c o n o m e tr ic m o d e l." This
e c o n o m e tric m o d e l can usu ally, th e n , be solved
fo r the va ria b le th a t th e e c o n o m e tric ia n is try in g
to fo re ca st; in th e pre sen t case, th e v a ria b le is
a u to sales.
T h o u g h th e e c o n o m e tric ia n a tte m p ts to e xp la in
a n u m b e r o f variab les in th e m o d e l— e.g., a u to
sales, personal in c o m e , and tra n s it fares— he m ust
s till c o n s id e r som e variab les as o u ts id e th e realm
o f the m o d e l's e x p la n a to ry p o w e r— e.g., a u to
prices and in te re s t rates. T hose va ria b le s e x p la in e d
in side th e m o d e l are te rm e d " e n d o g e n o u s
v a r i a b l e s ; " those d e te rm in e d o u ts id e th e m o d e l
are re fe rre d to as " e x o g e n o u s v a r i a b l e s W h a t
d e te rm in e s w h e th e r a v a ria b le is exo ge no us o r
endo ge nou s? O n c e again, w e m u st c o m e back
to o u r n o tio n o f p re d ic ta b ility . If a v a ria b le is
re a d ily p re d ic ta b le , it m o st lik e ly w ill be
en d o g e n o u s. O th e r va ria b le s m ust be d e te rm in e d
o u ts id e th e m o d e l. For exa m p le , a v a ria b le such
as G o v e rn m e n t s p e n d in g in m o st m o d e ls w ill be
exogenous because its exact a m o u n t is s u b je c t

M O N T H L Y R E V IE W

to m an y u n c e rta in forces such as th e de cisio ns
o f Congress.
The e c o n o m e tric ia n is a b le to foreca st au to
sales a fte r he has m ade a ssu m ptio ns a b o u t th e
fu tu re values o f th e exogenous va riab les— in o u r
exa m ple, a u to prices and in te re s t rates. H o w e ve r,
th e e c o n o m e tric ia n m ust be aw are th a t his
fo re ca st is based on an e c o n o m e tric m o d e l th a t
is o n ly an a p p ro x im a tio n o f e c o n o m ic re a lity.
T here are tw o basic reasons fo r th is : First, th e
m o d e l m ay y ie ld an an sw e r based u p o n re la tio n ­
ships th a t, w h ile on th e average are accurate,
m ay n o t alw ays h o ld tru e fo r any in d iv id u a l
p re d ic tio n p e rio d . Second, in p re d ic tin g th e
exogenous variab les, an e rro r in ju d g m e n t m ig h t
be m ade. For exa m ple, th e fu tu re level o f in te re st
rates m ay be u n d e re stim a te d . In te re st rates c o u ld
tu rn o u t to be s ig n ific a n tly h ig h e r than e xp ected
and, thus, cause o u r m o d e l to ove rsta te th e level
o f fu tu re a u to sales.

Prediction and the Identification Problem. C o n ­
s tru c tin g statistica l tests and p ro p e rly re s tric tin g
th e th e o re tic a l re la tio n sh ip s to q u a n tify the
e c o n o m e tric m o d e l can o fte n stu m p th e
e c o n o m e tric ia n . These tasks are necessary if
a n u m e ric a l fo re ca st o f a u to sales (or any o th e r
variab le) is to be m ade. A ssu m in g th e e c o n o m e ­
tric ia n has data fo r all th e variab les he w ishes to
co n sid e r, he is s till faced w ith s o lv in g the
id e n t i f i c a t io n p r o b l e m . From a mass o f data on
a u to c o n s u m p tio n and personal in c o m e , a lo n g
w ith data o n a u to prices and in te re st rates, he
m ust screen o u t th e c o rre c t n u m e ric a l va lu e
re fle c tin g th e e ffe c t o f personal in c o m e on a u to
c o n s u m p tio n . For exa m ple, if a u to sales had
increased by 20 p e rc e n t d u rin g th e last fiv e years
and at th e same tim e b o th personal in c o m e and
tra n s it rates had increased, h o w m u ch o f th e
increase in sales is to be a ttrib u te d to each o f these
variables? As on e can re a d ily see, th e task o f
id e n tify in g th e q u a n tita tiv e re la tio n sh ip s o f the
e c o n o m e tric m o d e l w o u ld , in m ost cases, n o t be
easy.
Practical Uses of Econometric Models
Who Uses the Models and Why? D e sp ite m any
d iffic u ltie s in v o lv e d in th e c o n s tru c tio n o f e c o n o ­
m e tric m od els, th e ir use has spread ra p id ly in
th e last decade. T oda y, th e y w ill be fo u n d in
m an y facets o f in d u s try , g o v e rn m e n t, and e d u ca ­
tio n , in b o th p ra ctica l and th e o re tic a l a p p lic a ­
tions.
The p re vio u s sectio n d e scrib e d th e m o d e ls as
b e in g b u ilt a ro u n d a th e o ry o f e c o n o m ic p re d ic ­
tio n . Thus, a firm m ig h t use m o d e ls to p re d ic t
th e n u m b e r o f c o lo r T V sales next year. G o v e rn ­
m e n t e co n o m ists m ay w ish to p re d ic t th e u n ­

F E D E R A L R E SE R V E B A N K O F A T L A N T A




e m p lo y m e n t rate ne xt year. A co lle g e pro fe ssor
c o u ld use m o d e ls to teach e c o n o m ic th e o ry and
h o w it m ay be used to p re d ic t e c o n o m ic a c tiv ity
in th e real w o rld .
In a d d itio n to fo re ca stin g , a n o th e r im p o rta n t
use o f e c o n o m e tric m od els is s im u la t io n . W h ile
fo re c a s tin g asks the q u e s tio n " w h a t w ill? " s im u la ­
tio n asks " w h a t if? " For exam ple, th e g o v e rn m e n t
e c o n o m is t tries to estim a te th e 1971 u n e m p lo y m e n t
rate based on his best assum ptions o f exogenous
e c o n o m ic variab les, such as G o v e rn m e n t sp e n d in g
o r in te re st rates. He asks, "W h a t w ill th e u n e m ­
p lo y m e n t rate be if G o v e rn m e n t sp e n d in g and
in te re s t rates are such and such, w h ic h I th in k
th e y w ill be ? "
In con tra st, a s im u la tio n te c h n iq u e w o u ld
vary the assum ptions a b o u t G o v e rn m e n t sp e n d in g
and in te re st rates. T he e c o n o m is t w o u ld the n ask,
" W h a t if G o v e rn m e n t s p e n d in g w e re 10 p e rc e n t
higher? W o u ld u n e m p lo y m e n t fa ll, and b y h o w
m u c h ? " He can, th e re fo re , vary his assum ptions
on in te re st rates and G o v e rn m e n t spe nd ing , a rriv ­
in g at as m an y p ro je c te d u n e m p lo y m e n t rates as
the n u m b e r o f tim es he v a rie d his assum ptions.
He m ay the n pre sen t a list o f a lte rna tives to
G o v e rn m e n t p o lic y m akers w h o w ill in flu e n c e
th e levels o f G o v e rn m e n t sp e n d in g and in te re st
rates. In fact, th e use o f e c o n o m e tric m od els
to sim u la te th e e c o n o m ic o u tc o m e o f p o lic y d e c i­
sions has b e co m e an im p o rta n t to o l o f the
G o v e rn m e n t p o lic y m aker.

Models Examined in This Article. The fiv e e c o n o ­
m e tric m o d e ls e xa m in e d in this a rtic le pre sen t
a m ix tu re o f th e va rio u s types o f m o d e ls in
c u rre n t use. The list o f m od els and th e ir uses
d e scrib e d here is by no means exhaustive. The
m od els chosen are som e o f th e b e tte r k n o w n
m od els, o rg a n iz e d and o p e ra te d by a m ix o f
p riva te , g o ve rn m e n ta l, and e d u c a tio n a l o rg a n iz a ­
tio n s ; all had sh o w n accuracy in several e a rlie r
forecasts fo r 1970. F u rth e rm o re , th e m o d e ls'
th e o re tic a l approaches in c lu d e som e d ive rsity.
C o n sid e r also th e d iv e rs ity o f o rg a n iza tio n s
and the purposes fo r w h ic h th e fiv e m o d e ls are
used. T he W h a rto n and M ic h ig a n m od els w e re
d e v e lo p e d by e co n o m ists at e d u c a tio n a l in s titu tio n s .
The Data Resources m o d e l was d e v e lo p e d by
e co n o m ists esse ntia lly w o rk in g w ith in th e fra m e ­
w o rk o f a p riv a te c o rp o ra tio n . A ll th re e o f these
m od els em p ha size changes in real factors— such
as c o n s u m p tio n , in ve stm e n t, and G o v e rn m e n t
sp e n d in g — as th e p rim e m overs o f e c o n o m ic
a c tiv ity .3 These large m o d e ls p ro v id e d e ta ile d

3Real factors as the p rim e m o vers o f the e co n o m y
w o u ld b e m o re o ften stressed b y e co n o m ists w h o
w o u ld generally d e scrib e th em selves as Keynesians
o r n eo -K eyn esia ns. T h o se e co n o m ists w h o

w o u ld

45

e c o n o m ic in fo rm a tio n to subscribers w h o w o u ld
in c lu d e in th e ir ranks e d u c a tio n a l in s titu tio n s , banks,
m a n u fa c tu rin g c o rp o ra tio n s , retailers, and
g o v e rn m e n t agencies. T he St. Louis m o d e l was
d e v e lo p e d at th e Federal Reserve Bank o f St.
Louis and places heavy em phasis o n m o n e ta ry
factors as th e p rim e m o v e r o f e c o n o m ic a c tiv ity .
The St. Louis sta ff e co n o m ists m ake e c o n o m ic
forecasts and s im u la tio n s using th e m o d e l, b u t th e ir
forecasts are n o t o ffic ia l forecasts o f th e Federal
Reserve System. T he RCA m o d e l, w h ic h also
u tiliz e s a M o n e ta ris t th e o re tic a l a p p ro a ch , was
d e v e lo p e d by RCA and is g e n e ra lly u tiliz e d by
the firm in fo re c a s tin g d e m a n d fo r its p ro d u cts.
In a d d itio n to these m od els, th e re are m any
oth ers in use th a t p ro v id e s im ila r e c o n o m ic
in fo rm a tio n to aid th e businessm an, e d u ca to r,
o r G o v e rn m e n t p o lic y m a ke r in th e d e cisio n
m a k in g process.
A t this p o in t, it m ig h t be h e lp fu l to the
u n d e rs ta n d in g o f e c o n o m e tric m o d e ls to exa m in e a
p a rtic u la r p ro b le m o f e c o n o m ic u n c e rta in ty —
na m ely, th e p re d ic tio n o f th e u n e m p lo y m e n t rate—
and to lo o k at h o w each o f th e selected m od els
handles th is p ro b le m . For a m o re d e ta ile d d e ­
s c rip tio n o f th e m o d e ls and h o w th e y ap pro ach
m o n e ta ry p o lic y , fiscal p o lic y , and p ric e m o v e m e n t
p ro b le m s , the in te re ste d reader m ay tu rn to
the A p p e n d ix .

Predicting Unemployment. A re a d in g o f th e press
re ce n tly w o u ld leave little d o u b t in the
m in d s o f m o st observers th a t th e re is a great deal
o f u n c e rta in ty a b o u t th e forecasts o f th e u n e m ­
p lo y m e n t rate fo r 1971. Let us lo o k at the
approaches to this p ro b le m by th e m o d e ls d is­
cussed in this a rticle .

The "G ap" Models: Data Resources,
St. Louis, RCA.
These th re e m o d e ls p re d ic t th e u n e m p lo y ­
m e n t rate b a sica lly by re la tin g th e u n e m p lo y m e n t
rate to th e d iffe re n c e b e tw e e n actual o u tp u t o r
Gross N a tio n a l P ro d u ct (GNP) and p o t e n t ia l o u t p u t .
This fa ilu re o f th e e c o n o m y to p e rfo rm at its p o ­
te n tia l level is ca lle d a " g a p ." The gre ater th e
gap, th e h ig h e r th e u n e m p lo y m e n t rate.
This re la tio n s h ip (e q u a tio n ) req uire s a p re d ic tio n
o f b o th actu al and p o te n tia l G NP in o rd e r to

p la ce changes in the su p p ly o f m o n e y as the p rim e
m o v e r o f e c o n o m ic activity w o u ld fall into the
" M o n eta rist" s c h o o l o f thought. For a discu ssion
o f the m a jo r p o in ts o f view of both the Keynesian
and M o n eta rist sc h o o ls , se e W illiam N. C o x , 111,
" T h e M o n e y S u p p ly C o n tro v e rsy ," M o n th ly R e v ie w ,
Federal R e serv e Bank of Atlanta, Jun e 7969.

46




m ake a fo re ca st o f th e u n e m p lo y m e n t rate.
The actual GNP fo re ca st w ill be d e te rm in e d
w ith in each o f th e th re e m od els. P o te n tia l GNP
is exogenous in each m o d e l. Thus, care m u st be
taken in d e v e lo p in g th e best p o ssib le fo re ca st o f
p o te n tia l G N P .4 Factors c o m m o n ly co n s id e re d in
e s tim a tin g a n a tio n 's p o te n tia l o u tp u t are th e
g ro w th o f th e la b o r fo rc e , th e a c c u m u la tio n o f
ca p ita l e q u ip m e n t, and te c h n o lo g ic a l progress.

The Michigan Model's Growth Rate Equation
The u n e m p lo y m e n t rate is p ro je c te d in th e M ic h ig a n
M o d e l by firs t p re d ic tin g th e e m p lo y m e n t rate.
E m p lo y m e n t is s p e c ifie d to be re la te d to th e rate
o f g ro w th o f real o u tp u t (GNP) in th e c u rre n t and
p re vio u s p e rio d , o u tp u t p e r m a n -h o u r, and g ro w th
in the la b o r fo rce . Basically, th e m o d e l req uire s
th a t GNP a d ju ste d fo r p ric e changes g ro w at a
3 .9 -p e rc e n t rate p e r year. If th e g ro w th rate is
less than this, u n e m p lo y m e n t w ill increase.
T he u n e m p lo y m e n t rate fo r a d u lt m ales (ove r
20 years o ld ) is firs t c o m p u te d . T hen , this
is exp an de d to a to ta l u n e m p lo y m e n t rate on
th e basis o f the a d u lt m ale u n e m p lo y m e n t rate's
re la tio n s h ip to th e to ta l u n e m p lo y m e n t rate.

Wharton's Labor Market Section
T he d e te rm in a tio n o f th e u n e m p lo y m e n t rate
in this m o d e l starts w ith th e d e te rm in a tio n o f
e m p lo y m e n t in th e la b o r m a rk e t sector. H ere,
s u p p ly and d e m a n d forces are e stim a te d to
d e te rm in e th e va rio u s levels o f fa rm , m a n u fa c tu rin g ,
and n o n m a n u fa c tu rin g e m p lo y m e n t. A n u n e m p lo y ­
m e n t rate is the n c o m p u te d .
This m o d e l also co n ta in s a la b o r f o r c e p a r ­
t ic ip a t io n ra te e q u a tio n , w h ic h show s p e o p le
d ro p p in g o u t o f th e la b o r fo rc e as th e u n e m p lo y ­
m e n t rate rises. This w o u ld te n d to d a m p e n in ­
creases in th e u n e m p lo y m e n t rate.5

Back to the Uncertainty Problem
Just as p re vio u s u n e m p lo y m e n t rate forecasts
have e rre d, so th e forecasts o f th e m o d e ls
discussed are s u b je c t to u n c e rta in ty . W hy? W e

4T h e tw o so u rce s for this figure are the St. Lou is
Fed era l R eserve Bank's estim ates o f p o te n tia l C N P
an d the estim ates m a de b y the P residen t's C o u n c il
o f E c o n o m ic A dvisers.
r,Th e im p o rta n ce o f labor fo rce participa tion is that
if a p e rso n d ro p s o u t o f the labor fo rce , i.e., he sto p s
lo o kin g for a jo b , h e is n o lo n g er c o u n te d am on g
the u n e m p lo y e d ; this ten ds to re d u c e the u n e m p lo y ­
m en t rate.

M O N T H L Y R E V IE W

have seen in p re vio u s sectio ns h o w u n c e rta in ty
can e n te r a forecast. For exa m ple, co n s id e r
th e G ap M o d e ls. Here, an a ssu m p tio n was
m ade a b o u t th e fu tu re values o f p o te n tia l o u tp u t.
Since this assu m p tio n m ig h t n o t c o m p le te ly c o n ­
fo rm to the fu tu re , an e le m e n t o f u n c e rta in ty
enters th e fo re ca st o f u n e m p lo y m e n t rates.
F u rth e rm o re , th o u g h th e g ro w th o f actual o u tp u t
is p ro je c te d w ith in th e m o d e l, it, to o , is su b ­
je c t to e rro r. For instance, actual o u tp u t g ro w th
is based on assum ptions, such as th e level o f
G o v e rn m e n t s p e n d in g o r ta xa tio n , w h ic h m ig h t n o t
h o ld tru e because o f d iffic u lty in p re d ic tin g
C ong ressio na l a ctio n . M o re o v e r, a lth o u g h th e e q u a ­
tio n s o f th e m o d e l are, on th e average, q u ite
accurate, th e y m ay s till e rr fo r any sin gle fo re ­
cast.
S im ila r ele m e n ts o f u n c e rta in ty also e n te r
th e u n e m p lo y m e n t rate forecasts o f the W h a rto n
and M ic h ig a n m od els. H o w e ve r, d e sp ite the
m o d e ls ' d iffe re n c e s in th e o re tic a l a p p ro a ch , w e
sh o u ld n o te th a t th e u n e m p lo y m e n t rate
forecasts p re sen te d in th e ta b le fa ll w ith in a
ra th e r n a rro w range. W e w ill discuss th e d if ­
ferences in these forecasts in the ne xt sectio n.

The 1971 Econometric Model Forecasts
Discussion of the Forecasts. The ta b le presents th e
1971 forecasts o f th e fiv e m od els. In c o m p a rin g
these forecasts, it is h e lp fu l to keep in m in d
th e d iffe re n t assu m ptio ns a b o u t exogenous forces
and o th e r ele m e n ts on w h ic h th e forecasts are
based, as w e ll as th e s tru c tu re o f th e d iffe re n t
m o d e ls .6
T he p ro je c tio n s o f GNP (in c u rre n t do lla rs)
all fa ll in a ve ry n a rro w range, w ith th e M ic h ig a n
m o d e l p ro je c tin g th e lo w e s t an nu al le vel o f GNP
and th e St. Louis m o d e l (6 p e rc e n t m o n e y su p p ly
g ro w th ) p ro je c tin g th e high est level. The rathe r
lo w levels o f G o v e rn m e n t e x p e n d itu re s assum ed in
th e M ic h ig a n m o d e l's forecasts p a rtia lly a c c o u n t
fo r its lo w GNP. The St. Louis m o d e l (6 percent)
forecasts th e h igh est c u rre n t d o lla r GNP. The
im p o rta n c e o f m o n e y in th is m o d e l becom es
e v id e n t w h e n th e m o n e y s u p p ly g ro w th is lo w e re d
to 5 p e rce n t. T hen, th e c u rre n t d o lla r GNP
p ro je c tio n o f th is m o d e l becom es lo w e r than th e
p ro je c tio n s o f th e o th e r fo u r m od els. It m ig h t
also be n o te d th a t th e W h a rto n m o d e l's q u a rte rly
p ro je c tio n s s h o w a m o re ra p id increase in c u rre n t
d o lla r G NP in th e second q u a rte r than any o th e r
m o d e l, b u t th is is fo llo w e d by ra th e r sluggish

c T h e forecasts p re se n te d are m ainly for illustrative
>
p u rp o se s, an d the read er s h o u ld b e aware that
later forecasts b y the ab o ve m o d els are con tinu ally
b e in g relea sed, w h ich m ight o u tda te those p re se n te d
in the table.

FE D E R A L R E SE R V E B A N K O F A T L A N T A




g ro w th d u rin g th e th ird q u a rte r. This can be
e xp la in e d by th e W h a rto n m o d e l's a ssu m ptio n o f
a six-w e e k steel strike in th e th ird q u a rte r and
ra p id in v e n to ry a c c u m u la tio n in th e quarters
p re c e d in g th e strike. Both th e RCA and M ic h ig a n
m o d e ls also assume in v e n to ry b u ild u p s in th e firs t
h a lf o f 1971; h o w e ve r, n e ith e r assumes a strike.
P ro je ctio n s o n u n e m p lo y m e n t rates all fa ll
w ith in a n a rro w range exce pt fo r th e W h a rto n
m o d e l w h ic h p ro je c ts th e lo w e s t rate. The lo w
forecasts o f th e W h a rto n m o d e l can be p a rtia lly
exp la in e d by th a t m o d e l's la b o r fo rc e p a rtic ip a ­
tio n rate e q u a tio n , w h ic h , as w e have p re v io u s ly
m e n tio n e d , w o u ld te n d to d a m pe n increases in th e
u n e m p lo y m e n t rate itself. The M ic h ig a n m o d e l
p ro je cts th e high est rate. T he th re e o th e r
m od els, w h ic h all use a fo rm o f th e GNP gap
e q u a tio n to p ro je c t u n e m p lo y m e n t rates, each
p ro je c t an an nu al rate o f u n e m p lo y m e n t o f 5.8
p e rce n t in 1971. T here seems to be no consensus
a m o n g th e m od els th a t u n e m p lo y m e n t rates w ill
be gin d e c lin in g in late 1971, since Data
Resources, RCA, and W h a rto n all p re d ic t u n ­
e m p lo y m e n t rates rising in th e fo u rth q u a rte r o f
th e year.
The b e h a v io r o f prices as ty p ifie d by th e
GNP d e fla to r show s forecasts o f q u a rte rly p rice
increases (annual rates) v a ry in g fro m 2.3 p e rce n t
to 4.5 pe rce n t. Both th e St. Louis and RCA m od els
p ro je c t a ta p e rin g -o ff o f p rice increases th ro u g h ­
o u t th e year. The o th e r th re e m od els sh o w an
acce le ra tio n o f p ric e increases near m id ye a r, w ith
s lig h tly m ild e r advances o c c u rrin g to w a rd yea r-en d.
The St. Louis m o d e l, w h ic h p ro je cts th e m ost
ra p id p rice increases fo r 1971 as a w h o le , show s
prices a d va n cin g at s lig h tly m o re than a 4 -p e rc e n t
annu al rate in th e fo u rth q u a rte r. It is w o rth
n o tin g th a t b o th th e St. Louis and th e RCA
m o d e ls— th e tw o m o n e ta ry m o d e ls— p ro je c t th e
largest increases in prices in 1971.
The forecasts o f real GNP fo llo w d ire c tly fro m
p ro je c tio n s on m o n e y GNP and th e G NP p rice
index. M o n e y GNP, w h e n a d ju ste d fo r p ric e
changes, equals real GNP. Forecasts o f th e f o l­
lo w in g c o m p o n e n ts o f GNP— c o n s u m p tio n , in vest­
m en t, and net e x p o rts 7— are also given fo r som e
m odels.

Some Implications and Past Results. For m ost
categories, th e forecasts o f th e ab ove m od els all
fa ll w ith in a n a rro w range. A d e s c rip tio n o f the
exp ected b e h a v io r o f th e U. S. e c o n o m y in 1971
fro m th e forecasts m ade by these m od els in late
1970 w o u ld be su m m a rize d as fo llo w s :

7In all m o d e ls, G o v e rn m e n t sp e n d in g is d e te rm in e d
o u tsid e the m o d e l (exogen ou sly) b a sed u p o n the
b est available estim ate.

47

The year 1971 will be one of gradual economic
improvement. Real economic growth will resume a
postive rate, with the economy expanding at 2.5
percent to 3.0 percent. Housing (residential in­
vestment in the table) should serve as a major
stimulus to the economy. Price increases will be
in the neighborhood of 4.0 percent, slowing from
1970's increase of 5.3 percent. However, the
labor situation is not expected to improve sig­
nificantly, with the unemployment rate expected
to remain close to 6.0 percent.8
H o w m u ch fa ith sh o u ld o n e have in the
accuracy o f th e forecasts? It m ust be k e p t in
m in d th a t th e y w e re m ade in late N o v e m b e r and
D e c e m b e r 1970. T h e re fo re , to th e e xte n t th a t the
assum ptions on w h ic h these forecasts w e re based
have changed since th e n , o r w ill change, th e
forecasts sh o u ld be m o d ifie d . M a n y u n c e rta in tie s
a b o u t 1971 exist. For exa m ple, w ill th e re be a
steel strike on A u g u st 1? H o w lo n g w ill it last?
H o w w ill Congress resp on d to th e P resident's
p ro p o s e d Federal budg et? T he answers to these
and o th e r y e t u n k n o w n q u e stio n s m ay m a te ria lly
a ffe c t th e e c o n o m y in 1971 and, thus, th e accuracy
o f these forecasts.
In th e past, th e accuracy o f th e e c o n o m e tric
m od els has been at least as g o o d o r b e tte r than
th a t o f m any " ju d g m e n ta l" forecasters. F u rth e r­
m ore , th e records o f th e m o d e ls have sh o w n
im p ro v e m e n t, and th e ir va lu e o v e r o th e r types o f
forecasts has been in th e ir g re a te r d e ta il. In
those years th a t th e m o d e ls w e re o ff, so w e re
m ost o f th e " ju d g m e n ta l" e c o n o m ic forecasters.
This was because events ha pp e n e d o r e c o n o m ic re­
la tio n sh ip s changed th a t w e re un forese en by
eco n o m ists. Thus, the m o d e ls also missed the m ark,
since th e y are, o f course, o f th e e c o n o m is t's
design.
A n o th e r reason th e m o d e ls m ay e rr is th a t
th e y te n d to in flu e n c e p o lic y decisions. For
e xa m ple, suppose th a t a set o f forecasts are
m ade w h e n th e o u tlo o k fo r th e e c o n o m y is w eak.
The forecasts sh o w this and s o m e w h a t in flu e n c e
the G o v e rn m e n t in s tim u la tin g th e e c o n o m y . The
G o v e rn m e n t's p o lic y is successful; th e e c o n o m y
does b e tte r than e xp e cte d ; and th e o rig in a l
forecasts tu rn o u t to be, te c h n ic a lly , in c o rre c t.
T h e re fo re , an e c o n o m e tric m o d e l m ust be c o n ­
tin u a lly revised and u p d a te d to a c c o u n t fo r
G o v e rn m e n ta l p o lic y actio ns, n e w th e o re tic a l
d e v e lo p m e n ts , and changes in e c o n o m ic re la tio n ­
ships. Forecasts have to be c o n tin u a lly run and
rerun th ro u g h the m od els, since th e degree o f
e c o n o m ic u n c e rta in ty a b o u t fu tu re events

8T h ese forecasts sh o u ld not be in terp re ted as official
forecasts of the Federal R eserve Bank of Atlanta
o r o f the Federal R eserve System .

48




M odel a n d P e rio d
M ichigan
1 s t Qtr.
2nd Qtr.
3rd Q tr.
4 th Qtr.
Y ear
D ata R e s o u rc e s, Inc.
1 s t Qtr.
2 n d Q tr.
3rd Q tr.
4 th Qtr.
Y ear
W harton
1 s t Qtr.
2nd Q tr.
3rd Qtr.
4 th Qtr.
Y ear
S t. L ouis— 6 % !
1st Qtr.
2n d Q tr.
3rd Qtr.
4 th Q tr.
Y ear
St. L ouis— 5 % 2
1 st Qtr.
2 n d Q tr.
3rd Q tr.
4 th Qtr.
Y ear
RCA— 6%
1 st Qtr.
2 n d Q tr.
3rd Q tr.
4 th Qtr.
Y ear
Avg. A nn. F o re c a st
Ann. R an g e— Low
High

C u rre n t
GNP

P ric e C h a n g e
(P e rc e n t, A nn.
R a te )3
i

1013.4
1034.8
1053.0
1069.5
1042.7

2.3
4.2
2.4
2.9
3.5

1017.8
1039.6
1052.0
1070.8
1045.1

2.7
2.5
4.3
2.8
3.8

1019.7
1044.9
1050.7
1067.3
1045.7

2.6
2.9
3.5
2.9
3.5

1016.1
1032.7
1057.5
1076.7
1045.8

4.5
4.3
4.2
4.1
4.4

1015.6
1030.7
1053.2
1069.7
1042.3

4.5
4.3
4.2
4.0
4.4

1013.2
1035.5
1052.9
1071.5
1043.3

4.1
4.0
4.0
3.4
4.2

1044.5
1042.7
1045.8

4.0
3.5
4.4

P e r c e n t a g e s in d ic a te d h e re a n d d ire c tly below in d ic a te
p e rc e n ta g e a s s u m p tio n of a n n u a l gro w th
ra te of th e m o n e y su p p ly .
-O m itte d fro m A verage A nn. F o r e c a s t a n d Low a n d H igh Range3C o m p u te d a s % c h a n g e in GNP im p lic it d e fla to r, 1 9 5 8 = 1 0 0 .

d im in is h e s as on e draw s clo se r to th e fo re ca st
p e rio d .

Summary and Conclusions
In sum m a ry, an e x a m in a tio n o f th e forecasts re­
veals th a t d e sp ite v a ry in g size (in term s o f
e q u a tio n s and variables) and th e o re tic a l a p p ro a ch ,
these m o d e ls te n d to agree. This fa c t o f agree­
m e n t is, o b v io u s ly , n o p ro o f o f th e v a lid ity o f
th e forecasts.
The m a jo r va lu e o f th e forecasts is th a t
th e y give th e G o v e rn m e n t p o lic y m a ke r o r th e b u s i­
ness d e cisio n m ake r a d e ta ile d set o f data a b o u t
the fu tu re o f th e e c o n o m y th a t w ill p ro v e to be
tru e if th e u n d e rly in g th e o ry and a ssu m ptio ns d o

M O N T H L Y R E V IE W

DESPITE VARYING THEORETICAL APPROACHES, ECONOMETRIC MODELS TEND TO AGREE FOR 1971
(Figures in Billions of Current Dollars Unless Otherwise Noted)

R eal G N P4

I
Real G N P G row th
(P e rc e n t, A nn.
R a te )5
C o n su m p tio n

T o tal
In v e s tm e n t

B u sin e s s
In v e s tm e n t

In v e n to ry
In v e s tm e n t
(N e t C h an g e )

R e s id e n tia l
In v e s tm e n t

N et
E x p o rts8

U n em p .
R ate
(P e rc e n t)

735.3
7 43.0
751.6
757.9
7 47.0

9.9
4.2
4.6
3 .4
3.3

643.1
655.6
666.2
676.3
660.3

139.6
143.4
145.6
147.7
144.1

103.1
103.3
103.9
105.1
103.9

2.1
4.7
4.9
4.3
4.0

34.4
35.3
36.8
38.3
36.2

4.2
3.2
5.5
5.3
4.6

6.0
6.2
6.1
6.1
6.1

735.1
746.3
747.3
7 55.4
746.0

8.7
6.2
0.6
4 .4
3.0

643.9
655.2
667.0
677.6
6 6 0.9

139.2
146.6
142.7
145.9
143.6

102.9
103.0
104.1
104.6
103.7

2.0
8.4
2.3
4.3
4.3

34.3
35.2
36.3
37.0
35.7

4.7
5 .0
5.6
5 .9
5.3

5 .9
5.7
5.6
5.8
5.8

739.7
752.3
749.8
756.7
749.6

8.0
6.8
-1.3
3.7
3.4

640.3
653.7
659.6
669.6
655.8

145.1
153.8
147.9
151.0
149.5

104.8
106.3
107.0
105.2
105.8

5.0
9.7
0.7
5.0
5.1

35.3
37.8
40.2
40.8
38.5

4.4
3 .9
4.5
4.7
4.4

5.5
4.9
5.2
5.3
5.2

733.5
737.7
747.7
753.7
7 43.2

1.8
2.3
5.6
3.3
2.3

n .a.
n .a.
n .a.
n .a.
n.a.

n .a.
n .a.
n .a.
n .a.
n .a.

n.a.
n.a.
n.a.
n.a.
n.a.

n.a.
n .a.
n .a.
n.a.
n .a.

n.a.
n.a.
n.a.
n.a.
n.a.

n.a.
n.a.
n.a.
n.a.
n.a.

5.6
5.8
5 .9
5.8
5.8

733.2
736.3
744.8
749.1
7 40.9

1.6
1.7
4.7
2.3
2.0

n.a.
n .a.
n .a.
n.a.
n .a.

n.a.
n.a.
n.a.
n.a.
n.a.

n.a.
n.a.
n.a.
n .a.
n.a.

n.a.
n.a.
n .a.
n.a.
n.a.

n .a.
n.a.
n.a.
n.a.
n.a.

n.a.
n.a.
n.a.
n.a.
n.a.

5.6
5.8
6.0
6.0
5.9

7 32.4
741.1
746.5
7 52.8
743.2

6 .2
4 .8
2 .9
3.4
2.6

638.3
650.6
6 6 4 .8
6 7 5.8
6 5 7.4

n.a.
n.a.
n .a.
n.a.
n .a.

104.9
106.9
107.9
108.5
107.1

n.a.
n.a.
n.a.
n.a.
n.a.

30.9
32.7
34.5
35.8
33.5

n.a.
n.a.
n.a.
n.a.
n.a.

5.9
5.8
5.7
5.8
5.8

745.8
743.2
749.6

2.8
2.0
3.4

658.6
655.8
66 0 .9

145.7
143.6
149.5

105.1
103.7
107.1

4.5
4.0
5.1

36.0
33.5
38.5

4.8
4.4
5.3

5.8
5.2
6.1

4C u rre n t d o lla r GNP a d ju s te d to re fle c t p ric e c h a n g e s by u se of GNP im p lic it d e fla to r, 1 9 5 8 = 1 0 0 .
“F irst q u a r te r 1971 g ro w th ra te s a re b a se d on e a c h m o d e l’s p ro je c tio n s of th e fo u rth q u a rte r 1970 levels o f real GNP. w h ic h
d iffe re d a m o n g th e m o d e ls a t th e tim e of fo re c a s t.
“E xp o rts m in u s Im p o rts.
DATES OF FORECAST RELEASE: M ichigan a n d W h arto n : N o v e m b er 19, 1970; S t. Louis: D e c e m b e r 14, 1970: D ata R eso u rc e s:
D e c e m b e r 15, 1970; a n d RCA C o rp o ra tio n : D e c e m b e r 1970.

n o t u n d e rg o radical changes. T he m o d e ls and
th e ir forecasts p ro v id e th e d e cisio n m a ke r w ith
v a lu a b le to o ls , b o th to sim u la te th e variou s effects
o f a lte rn a tiv e p o lic y de cisio n s th a t m ay be u n d e r
his c o n tro l and to eva lu a te th e e c o n o m ic effects
o f those b e y o n d his c o n tro l. These forecasts can be,
and are, c o n tin u a lly u p d a te d as n e w e c o n o m ic
facts and events u n fo ld .
A re th e 1971 forecasts pre sen te d in this
a rtic le accurate? W h ic h on e is th e m o st accurate?
A t this p o in t, it is im p o s s ib le to te ll. From w h a t w e
k n o w n o w , these forecasts a p pe ar to be reasonable,
b u t w h o can be ce rta in w h a t this year w ill bring?
N e xt year w e w ill p r in t th e actual data fo r the
forecasts p re sen te d here. Y ou be th e ju d g e !

F E D E R A L R E SE R V E B A N K O F A T L A N T A




A P P E N D IX
A CLOSER LOOK AT THE MODELS
In the follow ing descriptions, each model is examined
w ith regard to size and design. An overview of
each model's treatment of monetary policy, fiscal
policy, and price movements is also included.

The University of Michigan Model
The University of Michigan econometric model
(referred to as the DLH-lll) is a small-to-mediumsize model, consisting of 35 equations and eight
identities. These formulations are divided among
three main blocks: a Supply Block, an Expenditure
Block, and an Income Shares Block. Each block

49

describes a particular set of facts about that sector
of the economy. Therefore, the Supply Block explains
the behavior of wage rates, prices, and productivity,
economic facts of life that are inherent in determining
the amount and nature of the supply of goods and
services. The Expenditure Block develops formulations
to account for such major components of GNP as
consumer expenditures on automobiles and other
durables, business investment in plant and equipment,
and residential housing starts. The Income Shares
Block contains equations and identities that explain
such payments that provide income to individuals or
organizations, such as corporate profits, dividends,
private wages and salaries, and taxes.
In this model, monetary policy is treated in a
Keynesian framework by developing an interest rate
variable based upon movements in short- and long-term
interest rates. This interest rate variable is used to
determine those types of spending in the economy
that are related to the rate of interest. For example,
the model relates the level of Business Fixed Invest­
ment to the rate of investment in certain previous
periods and also to several selected interest rates.
An interest rate measure is also considered in deriving
an equation for the number of housing starts. Housing
starts are, in turn, used w ith tw o other variables
(including another interest rate measure) to determine
the dollar value of residential construction. The
result in the above form ulations w ould be such that
when monetary policy is eased by the Federal Reserve,
interest rates w ould fall. This w ould increase the
quantity of loans demanded for investment and
housing needs and, consequently, stimulate expenditures
in these areas. On the other hand, a tightening of
monetary policy w ould raise interest rates, which
would, in turn, discourage spending in these areas.
Fiscal policy, the conscious adjustment of taxation
and/or expenditure programs by the Government
in order to influence economic activity, is incorporated
mainly through the technique of including Government
expenditures in a definition (identity) of GNP and
then making other economic variables partially a
function of GNP. Thus, an increase in Government
spending, which increases GNP, would tend to increase
other economic variables, such as employment,
wages and salaries, personal consumption, and several
others. Taxation variables are also b u ilt into the
model to reflect increased revenues of governmental
units that w ould be affected by changes in income.
Price movements in the nonfarm sector are based
on supply factors such as the level of capacity
utilization and expected changes in unit labor costs.
Demand factors are considered by including an
unemployment variable and by assuming that a low
rate of unemployment indicates a higher degree of
demand for labor. This leads to higher expected
unit labor costs and, thus, to higher prices.

The DRI Model of the U. S. Economy
The Data Resources model is the largest model of
the five presented in this article. There are 109
equations and 133 identities.
Monetary policy actions are basically handled
in a Keynesian fashion similar to the Michigan model.
Various types of interest rate variables are expressed

50




in the model as partial determinants of such items as
business investment, residential construction, and state
and local spending.
Fiscal policy can be simulated w ith the model
by varying Government expenditures or taxes.
Seven equations are included for this purpose.
Price changes are computed by using 8 equations
that include separate price index equations for such
variables as state and Federal Government purchases
and various classifications of personal consumption
(durable goods, nondurable goods, etc.).
As a commercial model, Data Resources offers
its users the access to a Data Bank in which 3,000
business and economic tim e series are stored. Data
Resources also maintains an "equation library,"
and has developed a set of industry equations. The
industry equations are designed to provide a bridge
between the movements of the broad aggregates
called the GNP accounts (investment, consumption,
etc.) and a specific industry such as textiles or chemicals.
A user could, therefore, estimate his own future
sales or production in relation to DRI's forecast of
the economy.

Wharton EFU Model
This model ranks among the large econom etric
models. It contains 47 equations and 29 identities.
Besides being used for prediction of a large number
of economic variables, it is also used for various
monetary and fiscal policy simulations.
Money's role in the W harton model is surprising.
Though the final effect of monetary policy on GNP
is about the same as it is in the long run fo r a
comparable fiscal policy, the effects in the first year
are quite small, because of the lags involved, when
compared w ith fiscal policy. In fact, in the model,
the long-run effects of a change toward easier monetary
policy w ill actually increase the unem ployment rate
slightly as businesses find it relatively cheaper to
substitute capital for workers. A rapid growth rate
of GNP appears to be the main benefit of an expan­
sionary monetary policy in the W harton model.
Accordingly, policy makers, when using the model,
w ould tend to emphasize fiscal policy over monetary
policy because of its positive em ployment effects.
Through simulations this model also analyzes the
effects of alternate fiscal policies. However, there is no
specific fiscal sector in the model, since taxes and
transfers are incorporated into most of the spending
equations. Also, Government expenditures for purchases
o f goods and services and fo r defense are considered
exogenous.
The model contains seven price equations plus a
price identity to determine the GNP price deflator (the
index of price change). However, the manufacturing
price equation seems to be the key equation, since
manufacturing prices are used along w ith other demandrelated variables to determine all other prices in the
model. The manufacturing price equation has as
independent variables, unit labor costs and capacity
utilization, along w ith lagged values of manufacturing
prices. Thus, demand-pull, cost-push, and wageprice spiral influences are present and affect prices.

M O N T H L Y R E V IE W

The St. Louis Federal Reserve Model
This model is what is technically called a re d u c e d
form model. In this particular model, the total
spending (GNP) equation— the heart of the model—
is an equation w ith changes in money stock and
changes in "high-em ploym ent Federal expenditures"
as the independent as well as the exogenous
variables. This equation, though sufficient for
predictive purposes is not, because of its summary
nature (reduced form), conducive to exact policy
simulations. High-employment Federal expenditures
are expenditures on goods and services plus transfer
payments (mainly unemployment benefits) adjusted to
full employment levels.
As is well known, the St. Louis model places
the greatest emphasis on monetary policy. In this
model, it is monetary actions that play the strategic
role; fiscal policy actions especially for periods over
a year in length have almost no effect on output or
prices. However, the exact structure of the money
sector and the fiscal sector is impossible to identify
because only the reduced form of the model is
given.
Price changes are transferred through the model
by two variables— demand pressure and price expecta­
tions. Demand pressure is determined by the difference
between the change in total spending and the potential
change in output— the greater this difference, the
greater the demand pressure. Price expectations are
determined in the model by past price changes and a
measure o f resource utilization— the unemployment
rate. Thus, price changes are basically determined by
demand forces. However, including the lagged values
for demand pressure does tend to pick up the effect
of input price changes and the effect of changes in
costs of production.

similar to the variables in the St. Louis model's
price equation.
GLOSSARY

Coefficient: A numerical or alphabetic symbol placed
before a variable; often referred to as a parameter
Correlation: The degree to which variables move togeth­
er; the extent to which movements of the variables are
related
Dependent Variable: The variable that is determined
by the movements of another variable(s)
Econometric Model: An equation or system of equations,
statistically tested, representing the operations of eco­
nomic forces in the "real w o rld "
Endogenous: Those variables or facts explained w ithin
the framework of an economic model
Exogenous: Those variables or facts not explained w ith ­
in an economic model but imposed upon the model
by outside forces
Functional Relationship: A statement of cause and ef­
fect; that is, a relationship that shows how one or more
variables affect another variable
Identification Problem: The problem of assigning coef­
ficient values to the equations of an econometric model
by using the correct statistical tests
Identity: A statement of de finition; a relationship that
holds true at all times
Independent Variable: The variable that initiates change;
it determines the value of other variables
Labor Forces Participation Rate: Ratio of the total labor
force (including armed services) to the total noninstitutional population
Lagged Values of Variables: Values of variables pertain­
ing to previous time periods. For example, consumption
expenditures today might be related to both today's
income and yesterday's income. Yesterday's income is
a lagged variable.
Potential Output (Potential Gross National Product):

RCA Econometric Model
The RCA econometric model is a monetary model
similar in form to the St. Louis model. However,
it is what the authors of the model call an "o utsid e-in "
model. It starts w ith an equation that relates GNP
to exogenous monetary and fiscal factors (similar to
St. Louis total spending equation); it then determines
the components of GNP, such as consumption of
durables and nondurables and private investment— both
residential and business. Also, certain microeconomic
variables, such as color TV sales, are estimated for
internal use. Overall, the model has 13 equations, 6
identities, and a price-expectation equation.
As w ith the St. Louis model, monetary influences
are the strongest on current dollar GNP. The RCA
spending equation does not have as many lagged
terms as the same equation in the St. Louis model;
hence, though the immediate effects of monetary and
fiscal policy can be determined, the delayed effects
of such policies cannot. Again,-the effects of many
precise types of fiscal or monetary policies cannot be
estimated, since the spending equation is a reduced
form equation.
The GNP price deflator is determined as a function
of expected price changes, changes in GNP, change
in potential real GNP, and the gap between potential
and real GNP. Therefore, this equation's variables are

F E D E R A L R E SE R V E B A N K O F A T L A N T A




The value of the final output of goods and services
produced in a year if all resources (land, labor, and
capital) are fully utilized
Reduced Form: An equation relating an endogenous
variable to several exogenous and lagged endogenous
variables
Simulation: The process of imposing various hypothetical
conditions on a model in order to observe their effects
on certain variables
V a ria b le : A quantity that may assume a succession of
values, which need not be distinct
B IBLIO G R APH Y
Anderson, Leonall C., and Carlson, Keith M., "A M one­
tarist Model for Economic Stabilization," R eview ,
Federal Reserve Bank of St. Louis, April 1970, pp. 7-25.
Evans, Michael K., and Klein, Lawrence R., Th e W harton
E co n o m e tric Forecastin g M o d e l, Philadelphia: U ni­
versity of Pennsylvania, 1968.
Hymans, Saul H., and Shapiro, Harold T., T h e D H L -Ill
Q uarterly Eco n o m e tric M o d e l o f the U. S. E co n o m y ,

Ann Arbor: University of Michigan, 1970.
Morrison, G. R., "A Monetarist Approach to Predicting
Business Fixed Investment," Business E co n o m ics, Sep­
tember 1970, pp. 47-52.
Te ch n ica l Sum m ary, Lexington, Massachusetts: Data Re­
sources, Inc., 1970.

51




M

P

t h

i s

s

i s s

d

d

l i n

g

u

r r e

a

e

C

i p

p

i

i n

A

g

n

1

9

a

i n

s

7

0

:

t

t

b y W i l l i a m N . C o x , III

In a n e a r l ie r a s s e s s m e n t o f M is s is s ip p i's e c o n o m y , w e d e s c r i b e d
h o w t h e s t a t e 's i n c r e a s i n g i n d u s t r ia li z a ti o n w a s b r in g i n g
w i t h it a n i n c r e a s i n g i n t e r d e p e n d e n c e b e t w e e n M is s is s ip p i's
e c o n o m ic fo rtu n e s a n d th o se of th e n a tio n .1 T h e sa m e
a rtic le w e n t o n t o p o i n t o u t t h a t th is w a s p a r t i c u l a r l y t r u e
fo r t h e s t a t e 's g r o w i n g m a n u f a c t u r i n g s e c t o r , m a n y o f w h o s e
firm s d e p e n d o n m a r k e t s o u t s i d e t h e s ta te . As g o e s t h e
n a t i o n , s o g o e s M ississip p i. O r s o w e m i g h t e x p e c t .
For t h e n a ti o n a l e c o n o m y , 1 9 7 0 w a s a d i s a p p o i n t m e n t .
It w a s a y e a r in w h i c h e m p l o y m e n t a n d i n c o m e a n d
p r o d u c t i o n b a r e l y g r e w , e s p e c i a l l y in c o m p a r i s o n w i t h t h e
b o o m y e a r s o f t h e la te 1 9 6 0 's . In v i e w o f t h e i n c r e a s i n g
i n t e r d e p e n d e n c e b e t w e e n M ississip p i a n d t h e n a t i o n , w e
w o u l d e x p e c t t o f in d t h a t 1 9 7 0 a ls o c a m e as a l e t d o w n fo r
M is s iss ip p i's e c o n o m y . A n d , in fa c t, it w a s .
This a s s e s s m e n t o f M is s is s ip p i's 1 9 7 0 e c o n o m i c p e r ­
f o r m a n c e s h o u l d b e t e m p e r e d , t h e r e f o r e , b y r e a liz in g w h a t
h a p p e n e d n a ti o n a ll y a n d b y r e c o g n i z i n g t h a t M ississip p i
h a d t o p a d d l e a g a in s t t h e t h r u s t o f a n a d v e r s e n a t i o n a l
c u r r e n t . In d o i n g this, M ississip p i m e t w i t h c o n s i d e r a b l e
success.
1W illiam N. C o x , III, "M ississip p i: Industrialization Brings In te rd e p e n d e n c e ," M o n th ly
R e vie w , Federal R eserve Bank o f Atlanta, M ay 1968.

M O N T H L Y R E V IE W

M is s is s ip p i’s key e co n o m ic in d ica to rs . . .
. . . co m pared favorably w ith the n ation ’s in 1 9 7 0
% G row th, 1969-70

0.0

Nonfarm Employment

Farm Employment

2.0
-2.0

*Personal Income

0.0

'U.S.
7.5

y////y/z//tti*
V.

. . . .
^M is s is s ip p i

IQ
M anufacturing Payrolls

Farm Receipts

3.0

♦ B ased on 3 q trs . d a ta

B e t t e r T h a n N a t io n a l
W o rse T h a n U su al

M ississipp i p a d d le d su cce ssfully against th e c u rre n t
in 1970. A lth o u g h th e state's e c o n o m ic pace was
s lo w e r tha n it had been d u rin g th e late Sixties,
it, ne vertheless, g e n e ra lly o u tp e rfo rm e d th a t
o f th e n a tio n .
W h a t happens to n o n fa rm e m p lo y m e n t is o f
p rim e im p o rta n c e to M ississipp i because o f the
c o n tin u in g need to p ro v id e a d d itio n a l jo b s fo r
w o rk e rs c o m in g o ff th e fa rm .2 M o re n o n fa rm jo b s
d id b e co m e ava ila b le in 1970— 2 p e rc e n t m o re
than in 1969— b u t th e increase was d is a p p o in tin g ly
b e lo w th e 4 -p e rc e n t annu al g ro w th re co rd e d in
th e 1960's. Even so, M ississip p i's 2 -p e rc e n t g ro w th
lo o k e d g o o d w h e n c o m p a re d w ith th e n a tio n a l
n o -g ro w th s itu a tio n .
In M ississip p i, m a n u fa c tu rin g e m p lo y m e n t
a c tu a lly fe ll a b it in 1970, re fle c tin g sm all decreases
in b o th d u ra b le and n o n d u ra b le sectors. In th e

2W illiam N. C o x , III, "M ississip p i N onfarm Jobs in the
the Sixties: A Sn ea k P rev ie w ," M o n th ly R e v ie w , Federal
R eserve Bank of Atlanta, N o v e m b e r 1969. N onfarm jo b s
are a p rim e target o f the state's Balance A gricu lture
w ith In du stry (B A W I) program .

F E D E R A L R E SE R V E B A N K O F A T L A N T A




d u ra b le goods secto r, M ississipp i registered o n ly
a Vi -p e rc e n t e m p lo y m e n t d e c lin e , w hereas
th e n a tio n e xp e rie n ce d a decrease o f
pe rce n t.
Here again, M ississipp i p a d d le d against an adverse
n a tio n a l c u rre n t and, here again, had som e success
in d o in g it. In n o n d u ra b le m a n u fa c tu rin g e m p lo y ­
m e n t, the state and th e n a tio n b o th posted d e clin e s
o f a b o u t o n e p e rcen t.
C o n s tru c tio n p ro v id e d a b ig e m p lo y m e n t b o o st
in 1970. Jobs in th a t se cto r g re w a s u rp risin g
6 1 p e rce n t, c o n tra s tin g w ith a n a tio n a l d e c lin e
/2
o f 2 V 2 pe rce n t. T w o special factors he lp ed.
The G u lf Coast was engaged in p o s t-C a m ille
re b u ild in g , and th e re was a bu rst o f a c tiv ity
s u rro u n d in g th e In g a lIs -L itto n shipya rd at
Pascagoula.
A lth o u g h fa rm e m p lo y m e n t in M ississippi d id
n o t g ro w d u rin g 1970, th is was b e tte r than the
n a tio n 's 2 -p e rc e n t d e c lin e in farm jo b s d u rin g th a t
year and th e state's V2 -p e rc e n t d e c lin e in 1969.
Farm cash receipts rose o n ce again in 1970, la rge ly
because o f n a tio n a l increases in liv e s to c k prices.
For a change, a n a tio n a l c u rre n t was flo w in g the
rig h t w ay. C ro p receipts ed ge d d o w n s lig h tly ,
also in lin e w ith th e n a tio n a l m o ve m e n t.
Personal in c o m e is a b o u t as g o o d an in d ic a to r
o f o ve ra ll e c o n o m ic a c tiv ity as is ava ila ble. In
M ississipp i, it rose a b o u t 7 p e rc e n t in 1970, a b o u t
equal to the n a tio n a l increase o f 7V2 p e rcen t.
The m a n u fa c tu rin g p a y ro ll p o rtio n o u tp a ce d the
n a tio n 's , up 2 V2 p e rc e n t fo r M ississipp i and up
1 p e rc e n t fo r th e U. S.

53

A C lo s e r L o o k
Let us re tu rn fo r a clo se r lo o k at th e cru cia l
ca te g o ry o f n o n fa rm e m p lo y m e n t. M ississip p i's
n o n fa rm jo b g ro w th in 1970 (2 pe rcen t) was
h ig h e r than th e n a tio n 's (0 p e rce n t). A
sta te m e n t o f th is k in d can som e tim es be m is­
le a d in g , h o w e ve r. If a state p e rfo rm s b e tte r
than th e n a tio n does in n o n fa rm jo b g ro w th , it
m ay be because jo b g ro w th in all, o r ne arly all,
in d u strie s ran ahead o f jo b g ro w th in those
in d u strie s n a tio n a lly .
B ut th e re m ay be a n o th e r reason; na m ely,
th e e m p lo y m e n t m ix is d iffe re n t at th e state
level than it is at th e n a tio n a l level. A n
extrem e exa m p le serves to illu s tra te this
p o in t. S uppose th a t all o f a state's n o n fa rm
jo b s are in a sin gle in d u s try . Suppose, to o ,
th a t n a tio n a lly , to ta l n o n fa rm e m p lo y m e n t
gro w s 5 p e rc e n t b u t, th a t in in d u s try X,
e m p lo y m e n t a c tu a lly shrinks 10 p e rce n t. T hen if
n o n fa rm jo b s in th e state m e re ly h e ld steady,
it w o u ld be an im p ressive o ve ra ll p e rfo rm a n c e —
im p ressive d e sp ite th e n a tio n 's 5 -p e rc e n t increase
in to ta l e m p lo y m e n t.
A d m itte d ly , th is exa m p le is fa r-o u t. But m o re
s u b tle d is to rtio n s o f th e same so rt are in tro d u c e d
w h e n e v e r w e ig n o re sta te -n a tio n a l d iffe re n ce s

in e m p lo y m e n t m ix. T he m o re these m ixes d iffe r,
th e g re a te r th e p o s s ib ility th a t naive
co m p a riso n s w ill p ro v e to be in a ccu ra te .
O n e w a y o f a v o id in g th is e m p lo y m e n t-m ix
tra p is to m ake in d u s try -b y -in d u s try c o m p a riso n s.
T he ta b le does this fo r M ississip p i. It suggests
th a t 1970 jo b g ro w th in a lm o s t all o f M ississip p i's
in d u strie s o u tp a c e d n a tio n a l g ro w th in those
same in du stries. R e tu rn in g to o u r o rig in a l
th e m e , w e can say w ith re n e w e d c o n fid e n c e
th a t M ississipp i was successful in p a d d lin g
against th e cu rre n t.
T here is a n o th e r w a y o f a llo w in g fo r d iffe re n c e s
in s ta te -n a tio n a l e m p lo y m e n t m ix. W e can fig u re
a n o th e r h y p o th e tic a l g ro w th rate fo r n o n fa rm jo b s
b y using the in d u s try -b y -in d u s try p e rce n ta g e g ro w th
fo r M ississipp i and c o m b in in g it w ith th e
e m p lo y m e n t m ix fo r th e U. S.
W h e n this is d o n e , w e fin d th a t M ississip p i's
n o n fa rm jo b g ro w th w o u ld have been h ig h e r tha n
it a c tu a lly was if its e m p lo y m e n t m ix in 1970
had been th e same as th e n a tio n 's . T he
hypothetical increase, using th e n a tio n a l jo b m ix,
was a b o u t 4 p e rc e n t (co m p a re d w ith an actual
increase o f o n ly 2 p e rce n t). In o th e r w o rd s ,
M ississip p i's past e m p lo y m e n t m ix, w ith its
gre a te r c o n c e n tra tio n o f s lo w e r g ro w in g in d u strie s,
h a n d ic a p p e d th e state's n o n fa rm e m p lo y m e n t
g ro w th in 1970.

PERCENTAGE CHANGE IN EMPLOYMENT
1969-70
Miss.
1.0
T o ta l E m p lo y m e n t.........
0.0
............................
Farm
2.0
N o n fa rm ..........................
6.5
C o n s tru c tio n .........
1.5
M in in g .....................
. . . - 1.0
M a n u fa c tu rin g . . .
. . - 0.5
D u ra b le G o o d s . .
3.0
L u m b e r ..........
. . . - 6.5
F u rn itu re
2.0
T ra n s p o rta tio n E q u ip m e n t . . .
N o n d u ra b le G o o d s ........................ ..
- 1.0
F o o d ................
3.0
A p p a re l .........
... - 2.0
T ra n s p o rta tio n . . .
3.5
T rade ........................
2.5
Finance, Insurance, Real Estate. . .
4.0
1.5
Services ...................
2.0
G o v e rn m e n t .........

54




U.S.
1.0
- 2.0
0.0
- 2.5
0.5
- 3.5
- 5.5
- 4.5
- 5.0
-11.5
- 1.0
0.0
- 2.0
1.5
1.5
3.5
3.0
2.0

M O N T H L Y R E V IE W

B e t t e r T h in g s t o C o m e ?

It is almost unquestionable that better times are on
the way. In retrospect, it seems likely that last
year's performance will be viewed as subpar. For
1971, there is a big reason for optimism, since the
national economy is now showing signs of
responding to more expansive monetary and fiscal
policies. Mississippi will not have to fight as strong
an adverse current as in 1970. But there are more

grounds for optimism than just simple interpolation
of our expectations about the national economy.
For one thing, reductions in mortgage rates and
increases in housing starts, nationally, have a
disproportionate effect on Mississippi because of
the importance of the state's lumber industry.
Some of this stimulus is already materializing.
For another thing, the contracts awarded to
Ingalls-Litton for 47 ships can be expected to
keep the Gulf Coast area humming through 1975.
Finally, one can reasonably expect that devastating
visits like the one from Hurricane Camille
are the exception rather than the rule. I

B a n k

and Ethel M. Miller, cashier. Capital, $400,000;
surplus and other capital funds, $350,000.

A n n o u n c e m e n t s

THE COLLIER COUNTY BANK

FEBRUARY 16, 1971
Naples, Florida

FEBRUARY 1, 1971

GULF COAST BANK
Abbeville, Louisiana

Opened for business as a par-remitting nonmem­
ber.

Opened for business as a nonmember. Officers:
Mrs. Mamie B. Tooke, president and chairman;
Ward E. Boehner, Jr., executive vice president;
and John H. Druffel and Addison B. Miller,
vice presidents. Capital, $600,000; surplus and
other capital funds, $600,000.
FEBRUARY 22, 1971

FEBRUARY 4, 1971

FLORIDA BANK OF COMMERCE

BARNETT BANK OF SEMINOLE COUNTY,
NATIONAL ASSOCIATION

Clearwater, Florida

Altamonte Springs, Florida

Opened for business as a nonmember. Officers:
Gordon R. Williams, president; Norman H.
Mueller, executive vice president and chief
executive officer; O. C. Neal, senior vice presi­
dent; Reginald S. Wareham, vice president;

Opened for business as a member. Officers:
George W. Foster, president; Mary Ellen Sicoutry,
cashier; and Gwendolyn O. Inness, assistant
cashier. Capital, $400,000; surplus and other
capital funds, $200,000.

F E D E R A L R E SE R V E B A N K O F A T L A N T A




55

B A N K IN G S T A T IS T IC S
B illion $

- 27

DEPOSITS
-

Net Demand*

-2 4

11

- 10
/V
/
— 7.5

-2 1
/V
- 14

Loans (net)*

-6 .5

- 13

-5 .5

r\j

Investments'*

— 4.6

-6
i ii ii Ii ii ii ii i iii Ii ii i i i i

i i ii iIii ii i i i i ii i Ii •i i ii i

J

J

D J

J

DJ

J

D J

J

1969

1971

1970

1969

— 4.2
J

D J

1970

1971

LATEST MONTH PLOTTED: JANUARY
Note: All figures are seasonally adjusted and cover all Sixth District member banks.
‘ Daily average figures **Figures are for the last Wednesday of each month.

SIX T H

B

A

D IST R IC T

N

K

I

N

G

N

O

T

E

S

C H A N G E S IN T E R M LOAN C A T E G O R IE S
M illion $

~

23 Large Banks

-

1968

40

/
1969

/

-20

1970

jM
I
Durable
Goods Mfg.

Nondurable
Goods Mfg.

56




M ining

W holesale & Transportation, C onstruction
Retail Trade
Comm., P. U.

- 10
Services

M O N T H L Y R E V IE W

D IS T R IC T T ER M L E N D IN G T U R N S D O W N

D u rin g 1970, business te rm loans at th e larger
D is tric t c o m m e rc ia l banks d e c lin e d $29 m illio n —
a d ro p o f 3.8 pe rce n t. This sm all d e c lin e contrasts
sh a rp ly w ith th e gains in 1969 and 1968— years
w h e n te rm le n d in g ad van ced $124 m illio n and
$86 m illio n , re sp e ctive ly.1
T erm le n d in g is g e n e ra lly less responsive than
s h o rt-te rm le n d in g is to th e shifts in ba nk le n d in g
p o lic ie s and changes in th e re la tiv e cost and
a v a ila b ility o f c re d it fro m n o n b a n k sources. For
exa m ple, a fte r ra p id ly in crea sing d u rin g th e firs t
h a lf o f 1969, s h o rt-te rm business loans at large D is­
t r ic t banks ta p e re d o ff d u rin g th e second ha lf. T erm
loans, on th e o th e r hand, d id n o t d e c lin e u n til ea rly
1970, w h e n business de m an ds eased fo r ba nk
c re d it in gene ral, thu s e n a b lin g banks to c u t back
th e ir te rm loans. Even th o u g h m an y D is tric t bankers
m ay have w a n te d to c u t back th e ir te rm loans
ea rlie r, a c o n s id e ra b le lag is o fte n in v o lv e d b e fo re
p re v io u s ly esta blished lines o f c re d it are d ra w n
d o w n by business firm s.
Last year's weakness in te rm loans was spread
across m ost types o f c o m m e rc ia l and in d u s tria l
b o rro w e rs . In p re vio u s years w h e n c o n su m e r
s p e n d in g was strong , w h o le sa le rs and retailers
m ade c o n s id e ra b le use o f te rm loans. Because
sales w e re sluggish last year, h o w e v e r, te rm loans
to w h o le sa le rs advanced o n ly s lig h tly , and those
to retaile rs d e c lin e d ne arly $14 m illio n . Except fo r
m a c h in e ry and tra n s p o rta tio n e q u ip m e n t m a n u fa c­
tu rin g — c h ie fly in F lo rid a — te rm loan a c tiv ity to
d u ra b le goods m a n u fa ctu re rs also fa ile d to in ­
crease.
Banks in G eo rg ia and F lo rid a u su a lly p ro v id e
th e b u lk p o rtio n o f th e D is tric t's c o n s tru c tio n loans
exte n d e d fo r lo n g e r than a o n e -y e a r p e rio d . Last
year, th e banks in G e o rg ia sh a rp ly re d u ce d such
loans w h ile banks in F lo rid a ad van ced th e ir le n d ­
in g o n ly s lig h tly .
Likew ise, at banks in th e G u lf Coast states, the
use o f in te rm e d ia te - and lo n g -te rm c re d it fo r the
e x p lo ra tio n o f c ru d e p e tro le u m and na tural gas
and fo r p e tro le u m re fin in g d e c lin e d — by ne arly
$14 m illio n — o v e r th e same p e rio d . Except fo r the
d ro p in le n d in g to p e tro le u m refin ers, th e loans to
n o n d u ra b le go od s m a n u fa ctu re rs, h o w e v e r, d id
n o t change in th e D is tric t.

1io h n M . G o d fre y , "T e rm L e n d in g : A Lagging R e sp o n ­
d e n t to M o n eta ry Restraint," this R e v ie w , June 1970,
pp . 80-83. This article sh o u ld also b e c o n su lte d for a
m o re co m p le te d e scrip tio n o f the banks in c lu d e d and
the d efin ition o f term b u sin ess loans and loan ca te ­
gories.

B U S I N E S S T E R M LO A N S

1
1967

i
1968

i
1969

M illion $

i
1970

1971

F lo rid a banks ty p ic a lly a c c o u n t fo r ne arly h a lf
o f th e D is tric t's in te rm e d ia te - and lo n g -te rm loans
to service typ e businesses. In 1970, h o w e ve r, these
banks re p o rte d m o re than a $ 1 2 -m illio n d e c lin e in
loans fo r firm s p ro v id in g lo d g in g , am u sem ent,
re cre a tio n , and o th e r nonbusiness services. In c o n ­
trast, service loans advanced in A la ba m a, so u th e rn
Louisiana, and s o u th e rn M ississippi.
The lack o f g ro w th o f te rm loans d u rin g 1970—
a year fo llo w in g a p e rio d o f re strictive m o n e ta ry
p o lic y — is s im ila r to th e p e rio d fo llo w in g the
" c r e d it c ru n c h " o f 1966. T hen, as in 1970, banks
d id n o t be gin e xp a n d in g th e ir te rm loans im ­
m e d ia te ly fo llo w in g th e s h ift in m o n e ta ry p o lic y .
G e n e ra lly, banks firs t a tte m p t to re b u ild th e ir li ­
q u id ity b e fo re b e in g w illin g to a p p ro ve loans w ith
m a tu ritie s ave rag ing th re e -to fiv e years. C o rp o ra te
b o rro w e rs w ill w a it u n til business a c tiv ity picks up
b e fo re th e y feel c o m p e lle d to seek a d d itio n a l bank
c re d it to be used fo r b u ild in g up th e ir w o rk in g
ca p ita l and fo r m a kin g a d d itio n s to p la n t and
e q u ip m e n t. A fte r th e m o n e ta ry strin g e n cy in 1966,
m o re than 18 m o n th s passed b e fo re te rm le n d in g
tu rn e d up, thus p ro v id in g som e reason fo r n o t ex­
p e c tin g a tu rn a ro u n d in te rm le n d in g u n til la te r
this year.
T here are o th e r factors th a t p o in t to an u p tu rn
in te rm le n d in g d u rin g th e la tte r m o n th s o f 1971.
T erm loan advances are ty p ic a lly strong est in the
last six m o n th s o f th e year. If business a c tiv ity is
stro n g and these patterns re o ccu r, the n w e sh o u ld
see n o tic e a b le gains in te rm le n d in g at th e larger
D is tric t banks d u rin g th e last h a lf o f 1971.
JO H N M . GODFREY

F E D E R A L R E SE R V E B A N K O F A T L A N T A




57

S ix t h

D is t r ic t

S t a t is t ic s

S e a s o n a lly A d ju ste d
(All d ata are in d e xe s, 1 9 5 7 -5 9 = IO O , u n le s s in d ic a te d o th e rw ise .)

Latest Month

One
Two
Month Months
Ago
Ago

One
TWO
Month Months
Ago
Ago

One
Year
Ago

5.0
40.5

5.5
39.8

5.3
40.2

3.8
40.5

334
244
265

336
240
257

332
233
258

248

356
171

337
124

346
286

353
172

180
172
181
137
87

180
171
181
129
90

180
174
181
130
90

176
178
176
143
84

3.9
41.6

4.2
40.5

4.2
41.3

2.7
40.3

421
300

420
294

408
292

386
259

260
171

262
93

258
129

266
189

Jan.
Jan.
Jan.
Jan.
Jan.

152
136
161
149
49

152
137
160
148
50

152
135
160
145
48

154
145
159
155
49

Jan
Jan.

4.0
40.4

4.2
39.5

4.0
39.4

3.0
39.6

Member Bank L o a n s ............................... Jan.
Jan.
Member Bank D e p o sits ...........................Jan
Jan.
Bank D ebits**............................................ Jan
Jan.

362
257
349

369
252
339

357
252
340

348
230
317

M anufacturing P a y r o l l s ...................... Jan.
Farm Cash R e c e i p t s ...........................Dec.

230
160

229
167

229
187

218
136

134

132

132
119
134
118
49

134
124
139
137
49
5,4

41.9

42.0

43.2

42.5

304
203
233

298

295
198

221

277
176
214

One
Year
Ago

SIXTH DISTRICT

: Month
Unemployment Rate
(Percent of Work F o rc e J t..................Jan.
Avg. Weekly Hrs. in Mfg. (Hrs.) . . . Jan.

INCOME AND SPENDING
264
155
143
208

260
129
128
164

261
167
124
175

258
150

321
324

341
338

323
327

305
289

154
152
Nonfarm E m p lo y m e n tt.......................... Jan.
Manufacturing ..........................................Jan.
145
145
Nondurable G o o d s .......................... Jan.
136
136
.................................................... Jan.
121
120
Food
T e x t i l e s ...............................................Jan.
110
110
Apparel ...............................................Jan.
174
175
P a p e r ....................................................Jan.
127
127
Printing and Publishing . . . Jan.
157
157
C h e m ic a ls ..........................................Jan.
139
141
Durable G o o d s .................................... Jan.
159
159
Lbr., Wood prods., Furn. & Fix. Jan.
108
106
Stone, Clay, and Glass . . . Jan.
131
130
138
138
Primary M e t a ls ............................... Jan.
Fabricated M e t a ls ..........................Jan.
173
173
Machinery, Elec. & Nonelec. . Jan.
254
256
Transportation Equipment . . Jan.
186
186
Nonmanufacturing
............................... Jan.
157
155
C o n s tru c tio n .................................... Jan.
143
132
Transp., Comm., & Pub. U tilitiesJan.
135
135
T r a d e .................................................... Jan.
149
147
Fin., ins., and real est.....................Jan.
166
165
S e r v ic e s ...............................................Jan.
175
175
Federal G o vern m ent.....................Jan.
126
125
State and Local Government . Jan.
191
190
Farm Em ploym ent..........................................Jan.57
56
Unemployment Rate
(Percent of Work F o rc e lt.....................Jan.
4.6
4.8 r
Insured Unemployment
(Percent of Cov. E m p .).......................... Jan.
3.0
2.9
Avg. Weekly Hrs. in Mfg. (Hrs.) . . . Jan.
40.7
40.1
Construction C o n tra c ts * .......................... Jan.
214
263
R e s id e n tia l.................................................... Jan.
224
318
All O th e r......................................................... Jan.
205
217
Electric Power Production** . . . . Dec.
165
164
Cotton Consum ption**............................... Dec.102
97
Petrol. Prod, in Coastal La. and Miss.**Jan.
303
309
244
242
Manufacturing P ro d u c tio n ..................... Dec.
Nondurable G o o d s .................................... Dec.
210
210r
Food
...............................................
Dec.
171
170
T e x t i l e s ....................................................Dec.
235
236r
....................................................Dec.
264
264
Apparel
P a p e r ......................................................... Dec.
197
198
Printing and Publishing . . . . Dec.
164
166
C h e m ic a ls ...............................................Dec.
267
270
Durable G o o d s ..........................................Dec.
285
281
Lumber and W o o d ..................................Dec.
171
171 r
Furniture and F ix t u r e s ..................... Dec.
183
184
Stone, Clay and G l a s s .....................Dec.
171
168
Primary M e t a ls .................................... Dec.
198
196
Fabricated M e t a ls ............................... Dec.
244
242
Nonelectrical Machinery . . . . Dec.
365
340r
Electrical M ach in ery.......................... Dec.
626
624
Transportation Equipment . . . Dec.
345
341

152
144
136
120
110
175
126
156
141
158
106
130
138
174
258
179
154
131
134
148
165
175
126
188
54

153
150
138
118
116
176
130
154
143
168
109
134
135
180
265
211
153
146
133
148
161
172
126
181
55

4.8

3.5

3.0
40.3
221
244
202
166
101
311
246
209
169
235
265
196
167
269
291
169
184
169
202
241
358
657
360

2.3
40.3
358
280
424
167
103
232
239
206
162
229
256
203
171
261
280
166
186
172
202
247
361
558
353

. Dec.
Instalm ent Credit a t Banks* (Mil. $)

122
202

FINANCE AND BANKING
Member Bank L o a n s ............................... Jan.
Member Bank D e p o s i t s ...................... Jan.
Bank D e b i t s * * ........................................ Jan.

306

210

FLORIDA*
INCOME

EMPLOYMENT AND PRODUCTION

258

212
301

372
311

362
299

342
289

254

252
204
292

225
186
276

210
289

EMPLOYMENT
Nonfarm Employm entt
...................... Jan.
M anufacturing
....................................Jan.
Nonm anufacturing . .
..................Jan.
C o n s t r u c t io n ....................................Jan.
Farm E m p lo y m e n t....................................Jan.
Unemployment Rate
(Percent of Work F o rc e J t.................. Jan.
Avg. Weekly Hrs. in Mfg. (Hrs.) . . . Jan.
FINANCE AND BANKING
Member Bank Loans ................................Jan.
Member Bank D e p o sits...........................Jan.
Bank D ebits**....................................
. Jan.
GEORGIA

M anufacturing P a y r o l l s ...................... Jan.
Farm Cash R e c e i p t s ............................... Jan.
EMPLOYMENT

FINANCE AND BANKING
Loans*
369
305

M anufacturing P a y r o l l s ...................... Jan.
Farm Cash R e c e ip t s ............................... Dec.

Unemployment Rate
(Percent of Work Forcelt . . .
Avg. Weekly Hrs. in Mfg. (Hrs.) .
FINANCE AND BANKING

EMPLOYMENT
Nonfarm E m p lo y m en tt...........................Jan.
M anufacturing
....................................Jan.
Nonmanufacturing
...........................Jan.
C o n s t r u c t io n ....................................Jan.
Farm E m p lo y m en t....................................Jan.
Unemployment Rate
Jan.
(Percent of Work ForceJt . . .
Avg. Weekly Hrs. in Mfg. (Hrs.) .
. Jan.

12
2

138
132
52
6.5

10
2
135
12
2
49
6
.6

6
.8

FINANCE AND BANKING

Bank Debits*/*

201
210

M ISSISSIPPI
INCOME
Manufacturing P a y r o l l s ...................... Jan.
Farm Cash R e c e i p t s ...............................Dec.
EMPLOYMENT
Nonfarm Employmentt . . . .
M anufacturing
......................

58




Jan.
Jan.
Jan.
Jan.
Jan.

233
150

224
114

228
114

228
143

INCOME
M anufacturing P a y r o l l s ...................... Jan.
Farm Cash R e c e i p t s ............................... Dec.

298
142

297
146

297
131

274
118

133
133
134
118
57

132
133
131
99
57

132
133
131

134
127
133
127
57

EMPLOYMENT
Nonfarm E m p lo y m en tt...........................Jan.
M anufacturing
....................................Jan.
N o n m a n u fa c tu rin g ............................... Jan.
C o n s t r u c t io n ....................................Jan.
Farm E m p lo y m en t....................................Jan.

154
161
151
172
48

152
160
149
160
47

152
160
149
159
46

152
161
148
183
47

. 10
0
53

M O N T H L Y R E V IE W

Ons
Two
Month Months
Ago
Ago

One
Year
Ago

4.7
40.0

4.5
40.4

5.1
40.0

3.9
40.9

468
307
298

470
305
296

460
301
298

425
277
284

Latest Month

Latest Month

One
TWo
Month Months
Ago
Ago

One
Year
Ago

EMPLOYMENT

Unemployment Rate
(Percent of Work ForceJt . . . . . Jan.
Avg. Weekly Hrs. in Mfg. (Hrs.) . . . Jan.

154
154
154
189
58

149
153
147
162
55

149
152
147
157
53

151
157
148
175
59

4. 6
40.3

4. 7
39.9

4. 9
39.6

3. 9
39.9

354
233
294

Nonfarm Employm entt..........................
Manufacturing
....................................
Nonmanufacturing...............................
C o n s tru c tio n ....................................
Farm Em ploym ent....................................
Unemployment Rate
(Percent of Work ForceJt . . . .
Avg. Weekly Hours in Mfg (Krs.) . . Jan.

FINANCE AND BANKING

366
232
283

347
230
277

325
203
262

FINANCE AND BANKING
Manufacturing Payrolls
.......................... Jan.
Farm Cash R e c e ip ts .....................................Dec.

250
124

254
156

247

12
2

. Jan.
. Jan.

240
116

•For Sixth District area only; other totals for entire six states
“ Daily average basis
^Employment and payroll figures for Florida have been adjusted to new bench mark data.

fPreliminary data

r-Revised

N.A. Not available

Sources: Manufacturing production estimated by this Bank; nonfarm, mfg. and nonmfg. emp., mfg. payrolls and hours, and unemp., U.S. Dept, of Labor and cooperating
state agencies; cotton consumption, U.S. Bureau of Census; construction contracts, F. W. Dodge Div., McGraw-Hill Information Systems Co.; petrol, prod., U.S. Bureau of
Mines; industrial use of elec. power, Fed. Power Comm.; farm cash receipts and farm emp., U .S .D A Other indexes based on data collected by this Bank. All indexes
calculated by this Bank.

D e b it s

to

D e m a n d

D e p o s it

A c c o u n ts

In su re d C o m m e rc ia l B a n k s in th e S ix th D istric t
(In T h o u s a n d s of D o llars)
Percent Change

Percent Change

Jan. 1970 from

Jan. 1971 from
Dec.
1970

Jan.
1971

Jan.
1970

Dec.
1970

STANDARD METROPOLITAN
STATISTICAL AREASt
Birmingham
. .
Gadsden
. . . .
Huntsville
. . .
M o b i le .....................
Montgomery
. .
Tuscaloosa
. . .

. 2,083,890
73,376
222,143
675,764
409,314
133,040

2,301,729
78,170
247,584
685,712
464,322
141,018

2,011,268
70,472
236,079
672,506
386,720
129,262

Ft. Lauderdale—
Hollywood
. .
Jacksonville . . .
Miami
.....................
Orlando
. . . .
Pensacola
. . .
Tallahassee . . .
Tampa—St. Pete. .
W. Palm Beach

. 1,300,248
1,970,677
. 4,259,391
912,935
310,234
227,769
. 2,607,638
.
779,562

1,219,240
2,256,347
4,708,264
1,015,083
323,472
235,605
2,513,506
778,517

1,282,452
1,980,985
4,041,729
856,716
254,932
193,015
2,496,828
797,560

A l b a n y .....................
A t l a n t a .....................
Augusta
. . . .
Columbus . . . .
Macon .....................
Savannah
. . .

132,642
. 7,959,200
348,906
294,466
368,535
360,829

139,921
8,860,144
349,627
334,155
403,731
401,162

124,207
7,708,432
327,949
282,919
332,604
357,495

Baton Rouge . .
Lafayette . . . .
Lake Charles . .
New Orleans . .

.

818,632
185,579
182,210
. 3,163,482

821,797
183,502
181,423
3,263,373

165,190
848,208
1,015,360
626,635
1,893,039

Biloxi—
Gulfport
Jackson
. . . .
Chattanooga
. .
Knoxville . . . .
Nashville . . . .

.

- 9
- 6
-10
- 1
- 12
- 6

+ 7
-1 3
- 9
-10

+ 4
+ 4
- 6
+ 1
+ 6
+ 3
+ 1
- 0
+ 5
+ 7

- 4
- 3
+ 4
+ 0

+22

- 5

+ 7
+ 3
+ 6
+ 4

-10
- 0
- 12

- 9

+18
+ 4
- 2

-10

+ 11
+ 1

823,050r
184,699
192,231
2,974,312

- 0
+ 1
+ 0
- 3

- 0
+ 1
- 5
+ 6

175,372
1,002,898

158,581
864,755

- 6
-1 5

+ 4
- 2

967,578
675,991
2,089,748

871,470
586,013
1,897,973

+ 5
- 7
- 9

+ 17
+ 7
- 0

OTHER CENTERS
Anniston
.....................
D o t h a n ..........................
Selma
..........................

82,280
99,464
50,022

B a r t o w ..........................
Bradenton
. . . .
Brevard County . .
Daytona Beach
. .
Ft. Myers—
N. F t Myers . . .

42,252
116,936
242,944
132,733
167,564

83,962
97,772
61,235r

80,379
89,184
50,983

- 2
+ 2
-1 8

+ 2
+12
- 2

44,734
112,171
260,735
117,367

49,175
114,391
269,981
117,082

- 6
+ 4
- 7
+ 13

-14
+ 2

168,481

146,096

-

’Includes only bank* in the Sixth District portion of the state

F E D E R A L R E SE R V E B A N K O F A T L A N T A




1

Jan.
1971

Jan.
1970

-10

+ 13
+15

tPartially estimated

Dec.
1970

Jan.
1970

Dec.
1969

Jan.
1969

- 1 1

+ 7
- 2
+16
- 7

142,031
219,125
60,358
106,681
27,576
603,813
199,376
1,324,278
99,139

117,729
190,555
42,867
105,970
27,811
518,958
211,768
1,343,323
106,736

140,987
62,307
124,563
16,652
85,058
47,302
26,195
28,631
97,017
67,488

165,884
66,985
143,529
20,078
102,292
53,693
25,358
36,248
109,461
75,693

108,258
60,583
112,753
16,749
105,835
44,215
24,036
28,874
96,742
68,774

- 2 1
- 1 1
- 1 1

.
.
.
.
.
.

16,175
184,390
8,487
49,728
53,448
17,783
37,948

17,332
178,611
10,471
52,117
50,461
17,469
33,251

18,134
179,255
9,166
44,979
47,715
18,472
35,321

- 7
+ 3
-19
- 5
+ 6
+ 2
+14

- 4
+ 7

.
.
.
.

82,403
51,614
78,971
41,565

81,821
55,208
82,676
48,739

58,595
55,295
85,509
45,232

+ 1
- 7
- 4
-1 5

+41
- 7
- 8
- 8

. . . .
. .
. .
. . . .

74,914
57,419
35,534

96,473
64,457
38,601

79,896
51,457
26,881

-2 2
- 1 1
- 8

-

Bristol
..................... . .
Johnson City . . . .
. .
Kingsport . . . .

100,248
119,151
170,397

113,427
115,135
200,414

94,967
103,101
172,872

- 12

+ 6
+16
- 1

Gainesville
. . .
Lakeland . . . .
Monroe County
.....................
Ocala
St. Augustine . .
St. Petersburg . .
Sarasota
. . . .
Tampa .....................
Winter Haven . .

.
.
.
.
.
.
.
.
.

.
126,089
.
186,054
.
49,791
.
98,079
.
25,037
.
625,022
.
193,117
. 1,355,608
.
106,689

Athens .....................
Brunswick
. . .
Dalton .....................
Elberton
. . . .
Gainesville
. . .
Griffin
.....................
LaGrange . . . .
Newnan
. . . .
Rome
.....................
Valdosta
. . . .

.
.
.
.
.
.
.
.
.
.

.
.
.
.
.
.
.
.
.
.

Abbeville . . . .
Alexandria
. . .
B u n k i e .....................
Hammond
. . .
New Iberia . . .
Plaquemine . . .
Thibodaux
. . .

.
.
.
.
.
.

. . . .
.
.
. .
.
. .

Hattiesburg

Meridian
. .
Natchez
. .
Pascagoula—
Moss Point
Vicksburg . .
Yazoo City .

District Total
Alabamat
Florida*
.
Georgia*
.
Louisiana**
Mississippi**
Tennessee**
* Estimated

. . . . . 45,911,505
. .
. . .
. . .
. .
.
. .

. .
.
.
. .
. .
. .

.
.
.
.
.
.

5,302,593
15,692,109
12,024,317
5,477,434
1,923,879
5,491,173

-1 5
-18
- 8
- 9
+ 4
- 3
+ 2
+ 8

- 10
+20

- 9
+ 1
- 0

-15
- 7
-13
-17
-17

+30
+ 3

- 12

+ 7
+ 9
- 1
+ 0
- 2

+ 3

+ 3
-1 5

+ 10
- 1
-2 0

- 1 1

+ 3
- 7
+ 11
+ 12

6
+ 12

+32

48,572,723r 44,263,517r

- 5

+ 4

5,680,695r 5,201,051
16,410,773 15,232,468
13,153,350 11,485,262
5,286,580r
5,407,682
1,926,278
2,153,311
5,766,912
5,131,878

- 7
- 4
- 9
+ 1

+
+
+
+
+

- 1 1

- 5

2

3
5
4
0

7

r-Revised

59

D is t r ic t

B u s in e s s

C o n d it io n s

The Southeastern economic picture has brightened a bit. Nonfarm employment picked up in January;
the gain was fairly evenly shared among different industries and states. Auto sales have strengthened
somewhat. However, construction contract dollar volume in January was down from December's un­
usually high level. The index of farm prices remained depressed. At the banks, weak demand for busi­
ness loans combined with declining short-term interest rates in February to produce the ninth prime
rate cut in less than a year.
Preliminary data indicate that nonfarm employ­
ment rose in three of the four reporting District
states in January. O n ly F lo rid a sh o w e d e m p lo y m e n t
losses, w ith th e largest de clin e s o c c u rrin g in
tra n s p o rta tio n , service, and tra d e e m p lo y m e n t.
G eo rg ia , Louisiana, and M ississip p i re co rd e d gains
in n o n m a n u fa c tu rin g e m p lo y m e n t. Both m a n u fa c ­
tu rin g e m p lo y m e n t and w e e k ly ho urs ed ge d u p ­
w a rd .

Further interest rate reductions occurred in
February. E ffe ctive F ebruary 13, th is Bank lo w e re d
its d is c o u n t rate by 1 p e rce n t, w h ic h p la ce d th e
A
c u rre n t rate at 4 - 3 p e rce n t. In th e fo llo w in g w e ek,
A
m o st D is tric t banks a n n o u n c e d a n o th e r 1 -p e r­
A
ce n t re d u c tio n in th e ir p rim e le n d in g rate, b rin g ­
in g th e cost o f ba nk c re d it m o re in lin e w ith o th e r
s h o rt-te rm rates. C o m m e rc ia l and in d u s tria l loan
d e m a n d re m a in e d w e ak at th e la rg e r banks, w h ile
le n d in g in v o lv in g secu ritie s to bro ke rs, dealers,
and o th e rs ad van ced th ro u g h th e firs t p a rt o f
February. S tron g in flo w s o f tim e and savings d e ­
posits at m e d iu m - and sm all-size banks sh o w no
signs o f le ttin g up.

For the first time in five months, unit auto sales
for January passed the year-ago level. In January,

consum ers d id n o t m ake m o re use o f in s ta lm e n t
c re d it at c o m m e rc ia l banks. T he d o lla r v o lu m e
d ro p p e d m o re fo r e xte nsio ns o f n e w c re d it tha n
fo r repaym ents. H ence, to ta l c o n s u m e r c re d it o u t­
sta n d in g d e c lin e d s lig h tly .

January figures reveal a drop in the dollar volume
of both residential and nonresidential construction
contract awards. N e verthe less, p re lim in a ry data
suggest a c o n tin u a tio n o f large savings in flo w s
at D is tric t savings in s titu tio n s in January, p u ttin g
fu rth e r d o w n w a rd pressure o n m o rtg a g e rates.
M a n y savings and loan associations are b u ild in g u p
th e ir liq u id ity p o s itio n s s u b s ta n tia lly and have been
b u y in g m o re m ortga ge s fro m o u ts id e th e ir d ire c t
le n d in g areas.

In spite of freezes that damaged the unharvested
crop, citrus prices declined further in January.
Prices o f m ilk , eggs, hogs, an d to b a c c o jo in e d th e
d o w n w a rd tre n d . B roilers, b e e f ca ttle , vegetables,
and gra in crop s e xp e rie n c e d p ric e increases, h o w ­
ever. T his o ffs e t o th e r p ric e d e clin e s and re su lte d
in little n e t change in th e ge ne ral a g ric u ltu ra l p ric e
level. Severe w in te r w e a th e r c o n trib u te d to in ­
creased e x p e n d itu re s fo r liv e s to c k fee d, re d u c in g
p ro fits in th e reg io n.

NOTE: Data on which statements are based have been adjusted whenever possible to eliminate seasonal influences.

60




MONTHLY REVIEW
March 1971