American Economic Association
Money, Sticky Wages, and the Great Depression
Author(s): Michael D. Bordo, Christopher J. Erceg, Charles L. Evans
Source: The American Economic Review, Vol. 90, No. 5 (Dec., 2000), pp. 1447-1463
Published by: American Economic Association
Stable URL: http://www.jstor.org/stable/2677859
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Money, Sticky Wages, and the Great Depression
J. ERCEG, AND CHARLESL. EVANS*
By MICHAELD. BORDO, CHRISTOPHER
The causes of the GreatDepression continue
to be debated 70 years later. Economists have
advanced many hypotheses: the stock market
crash of 1929 (FredericS. Mishkin [1978] and
ChristinaRomer [1990]); an autonomous drop
in consumption (Peter Temin [1976]); a dramatic increase in tariffs (Allan H. Meltzer
[1976] and Mario Crucini and James Kahn
[1996]); debt deflation (Irving Fisher [1933]);
and the nonmonetaryeffects of banking panics
(Ben S. Bernanke[1983]). Nevertheless, Milton
Friedman and Anna J. Schwartz's (1963) hypothesis, that the primary cause of the U.S.
downturn between 1929 and 1933 was that
monetary policy failed to offset bank-panicinduced declines in the money supply, probably remains the most widely subscribed
explanation.1
A major point of contention is the channel
throughwhich the monetarycollapse was transmitted to the real economy. In this paper, we
quantify the role of monetaryshocks operating
througha sticky wage channel duringthe Great
Depression in the United States. The view that
sluggish wage adjustmentplayed a key role in
accountingfor the severity of the GreatDepres* Bordo: Departmentof Economics, RutgersUniversity,
New Brunswick, NJ 08901, and National Bureau of Economic Research;Erceg: Board of Governorsof the Federal
Reserve System, Washington, DC 20551; Evans: Federal
Reserve Bank of Chicago, Chicago, IL 60690. This paper
represents the views of the authors and should not be
interpretedas reflecting the views of the Board of Governors of the Federal Reserve System, the Federal Reserve
Bank of Chicago, or other members of their staff. The
authors thank Ben Bernanke, Milton Friedman, Chris
Hanes, Andrew Levin, PrakashLoungani, Anna Schwartz,
John Taylor, Michael Woodford, and two anonymous referees for helpful comments.
1 The policy failure could reflect adherence to the gold
standard(Temin [1989] and Barry Eichengreen [1992a]);
pursuitof the real bills doctrine(Meltzer [1976] and David
Wheelock [1992]); or the death of BenjaminStrong (Lester
Chandler[1958] and Friedmanand Schwartz[1963]). Some
recent surveys of the Depression period are contained in
papers by Bordo (1989); Eichengreen (1992b); Charles
Calomiris (1993); Romer (1993); Bordo et al. (1995); Harold Cole and Lee Ohanian(1999).
1447
sion in both the United States and elsewhere has
a long history. John Maynard Keynes (1936)
and other contemporaryobserversof the period
noted that wages were sticky and this had a
negative allocative effect. To assess whether
wages were sticky, the National IndustrialConference Board (NICB) surveyed 1,718 firmsand
found that two-thirds had not adjusted their
nominal wage scales from December 1929
through December 1931 (NICB, 1932). A recent empirical literature has documented the
negative allocative effects of wage stickiness
during this period. Eichengreen and Jeffrey
Sachs (1985) found thatreal wages tendedto be
higher and industrialproduction lower among
countries that remained on the gold standard.
These results are consistent with the sticky
wage hypothesis: countries remaining on gold
were forced to maintain tighter monetarypolicies, inducing sharper price declines. Sticky
nominal wages would have producedlarger increases in real wages for the gold bloc countries, and more pronouncedoutputcontractions.
Bemanke (1995) and Bernanke and Kevin
Carey (1996) corroboratedthese results using
panel data on a more inclusive set of countries.
While the cross-country regression results
serve as an importantmotivationfor our analysis, those findings do not quantifythe extent to
which the sticky wage channel can account for
the magnitudeof the Depression and its persistence. Our analysis provides a quantitative
assessment through simulations of a generalequilibrium model. Our baseline model has a
neoclassical structure with a representative
agent, capital accumulation, and forwardlooking behavior. The model includes two key
features which allow monetaryimpulses to exert substantialreal effects. First, it assumes that
nominal wages are set in staggered and overlapping contractsof the form describedin John
B. Taylor (1980). Thus, nominal wages are
sensitive to the level of aggregate employment, but move only gradually to restore the
real wage and employment to their long-run
levels. Second, monetary shocks and their
1448
THEAMERICANECONOMICREVIEW
associated effects on the price level are largely
unanticipated.
The model simulations track a substantial
component of the decline in output and other
key macroaggregatesthat occurred during the
downturnphase of the Depression. In our baseline model, monetaryshocks account for about
70 percent of the fall in output at the Depression's trough in early 1933. The model performs particularlywell between 1929 and early
1932, consistent with the sticky wage hypothesis. However, while our model predicts that
economic activity should have stabilized at a
depressed level in 1932-1933, the simulations
fail to capture the continued decline in output
after early 1932. This suggests that other factors (includingperhapsa progressiveworsening
of the financial crisis) played a more important role in explaining the latter phases of the
downturn.
In April 1933 the United States abandoned
the gold standard. Our model simulations indicate that the subsequent remonetization
would have allowed for a considerably more
robust recovery in the absence of the National
Industrial Recovery Act (NIRA). Everything
else equal, this remonetization would have
reduced real wages enough to substantially
boost employment demand. Instead, the
NIRA codes imposed a fiat increase in nominal wages that significantly retardedthe pace
of the recovery, particularly in employment.
Thus, while the monetary expansion of 19331935 presumably mitigated the adverse effects of the NIRA-induced nominal wage
increases, the remonetization was not strong
enough to lead the U.S. economy out of the
Depression. Given that other factors, including those emphasized by Bernanke (1983),
probably also slowed the pace of the recovery, it remains for further research to provide
a convincing account of the factors that allowed for a recovery.
The remainderof this paper is organized as
follows. Section I documentsthe broadincrease
in real wages from 1929-1932. Section II outlines our baseline model with sticky wages and
examines the impulse-responsefunctions of the
model to a monetary disturbance. Section III
compares the model's implications for major
macroaggregatesto correspondingdata over the
1929-1936 period. Section IV concludes.
DECEMBER2000
24
Manufacturing Real Wage
12
/
--
12
<'
-',
g-
-'
-
--,'
\uz
,
-
-
Aggregate Real Wage
--
-_
_ _
_
_
_
_
-12
/
GNPDeflator
/
-24
_
\
Aggregate Labor Hours
-361930
FIGURE
1.
1931
1932
1933
1934
1935
1936
REAL WAGES, PRICES, AND LABOR HOURS
Note: The data are quarterlyand in percent deviations from
their values in 1929:3.
I. Real Wages, Prices, and Employment
In a monetary explanation of the Great Depression with sticky wages, an importanttransmission mechanism is the initial rise in real
wages following an unexpected monetary contraction.Duringthe initial downturn,real wages
rose markedlyboth in the aggregate as well as
across industriesand worker skill levels.
To see this at the aggregate level, Figure
1 plots quarterlydata on aggregate and manufacturingreal wages, aggregatelaborhours, and
the GNP deflator over the 1929-1936 period.
Each series is representedas a logarithmicpercent deviation from its 1929:3 level. The real
wage data are derived by deflatingnominal average hourly earnings by the GNP deflator.2
Figure 1 displays the sharpprice decline that
occurredin the downturnphase of the Depression, accompanyingthe substantialrise in real
wages. Real wages rose from August 1929
2
The aggregate labor hours measure is derived as the
productof total employmentof full-time equivalentworkers
(U.S. Departmentof Commerce, Survey of CurrentBusiness [SCB]) and Edward Denison's (1962) estimate of
annual hours worked per full-time equivalent worker. The
aggregate nominal average hourly earnings measure is derived by dividing total compensationin all industries(SCB)
by aggregatelaborhours.These series are interpolatedfrom
an annualto a quarterlyfrequencyusing a cubic spline. The
nominal manufacturingwage is an average of monthly
payroll hourly earnings in 25 manufacturingindustries,
compiled by the NICB (M. Ada Beney, 1936). The GNP
deflatorwas constructedby Nathan S. Balke and Robert J.
Gordon (1986).
VOL.90 NO. 5
BORDO ET AL.: MONEY,STICKYWAGES,AND THE DEPRESSION
throughMarch 1932. In annualizedterms, real
manufacturingwages rose 4.3 percent compared with a 1.6-percent increase over the
period 1920-1929. Between early 1932 and
mid-1933, real wages graduallydeclined in the
face of massive unemployment. Labor hours
fell 35 percentbelow their pre-Depressionlevel
by the cyclical trough in early 1933.
The sharp rise in real wages after mid-1933
primarilyreflects the implementationof the National IndustrialRecovery Act (NIRA), which
was passed by Congress in June 1933. The
NIRA attemptedto boost consumer purchasing
power by raising real wages through administrative fiat. The NIRA was implemented
through the passage of codes that specified
minimum-wage schedules and other wage
guidelines for each industry, and imposed
industry-specificcaps on the length of the workweek. The sluggish recovery in hours in 19331934 suggests that the NIRA significantly
retarded the potentially stimulative effects of
the price-level rise that occurredover the same
period.
Thereis considerableevidence thatthe rise in
aggregate real wages over the 1929-1932 period reflected an increase in the costs of hiring
constant-qualityworkers.The rise in real wages
from 1929 to 1932 was broadly based across
manufacturingindustries.Table 1 presents real
wage growth in 25 manufacturingindustries
over the ten quarters from 1929:3 through
1932:1. The industry real wage data are obtained by deflating industry average hourly
earnings (Beney, 1936), by the GNP deflator.
The first column refers to real wage growth
for "all workers" in an industry. For the 25
industries-All Industries, real wages grew by
10.8 percent(or 4.3 percentannualizedover the
10 quarters).Real wages declined in only one
industry,Lumberand Millwork. For six industries, the rise in real wages was in single digits;
the remaining18 industriesexperienceddoubledigit growth. Of course, these growth rates depend on the price index used to deflate nominal
wages. Using the wholesale price index (WPI)
for finished goods may better reflect product
wages for the manufacturingsector. Since the
WPI fell by 28.5 percent over the period comparedto the 21.5-percentfall in the GNP deflator, real product wages may have risen even
more sharplythan reportedin Table 1.
1449
TABLE 1-REAL WAGE GROWTHBY INDUSTRY
AND SKILLLEVEL
Percent change in real
wages, 1929:3 to 1932:1
All
Male
Male
workers skilled unskilled
All Industries
AgriculturalImplements
Automobile
Boot and Shoe
Chemical
Cotton
Electrical
Foundries
Machines, Machine Tools
Heavy Equipment
Hardware,Small Parts
OtherFoundryProducts
Furniture
Hosiery and Knit Goods
Iron and Steel
Leather,Tanning, Finishing
Lumberand Millwork
Meat Packing
Paint and Varnish
Paper and Pulp
Paper Products
Printing,Book, Job
Printing,News, Magazines
Rubber
Silk
Wool
Memo: GNP deflator
Memo: WPI finished goods
10.8
5.8
10.4
1.2
9.7
4.9
19.8
11.1
19.0
10.8
13.0
12.9
11.6
5.3
12.4
11.7
-10.2
10.1
16.3
10.2
12.1
25.7
16.2
17.0
7.6
11.4
-21.5
-28.5
9.1
5.0
8.1
6.0
9.4
6.1
11.7
12.7
18.3
10.5
13.8
10.5
9.4
-7.1
2.2
7.4
-5.7
15.1
23.3
7.2
19.0
20.9
15.8
16.5
4.7
18.5
7.0
6.5
15.3
14.0
14.8
8.5
17.7
7.6
16.0
15.2
11.4
12.6
16.9
16.2
-12.3
5.1
-10.7
6.9
20.7
12.5
14.2
21.1
15.1
10.0
20.7
8.1
Note: Industryreal wage data are industrynominal average
hourlyearnings(Beney, 1936), deflatedby the GNP deflator
(Balke and Gordon, 1986).
We also consider how these real wage increases were distributedacross skilled and unskilled male workers.Columns 2 and 3 indicate
that real wages rose broadly across both categories of workers.Using the GNP deflator,Table 1 shows that there are only four instances of
falling real wages in the skill-level data. For
several industries there is some evidence that
the industry average real wage increase is biased upwards due to a shifting composition
towards skilled workers.3 Wage increases in
Electrical;Ironand Steel; Leather,Tanning,and
3 Skill composition issues like these motivate Stanley
Lebergott (1990) and Robert Margo (1993) to caution
against drawinginferences about real wage behaviorduring
the interwarperiod from aggregate wage data.
1450
THE AMERICANECONOMICREVIEW
Finishing;and Printing,Book, and Job are consistent with this pattern.For most of the industries, however, the three categories of wage
changes are reasonablyclose, suggesting no significant skill composition biases.
Firm-level survey data corroborates the
evidence from industry-level average hourly
earnings data. The National Industrial Conference Board (NICB) conducted a survey of
1,718 firms regarding their wage adjustments
between December 1929 and the survey date,
April 1932 (NICB, 1932). The majorityof firms
in the survey were in manufacturingindustries
(1,503). Nevertheless, the sample also included
finance companies, railroads, public utilities,
mining companies, and companies engaged in
wholesale or retail trade.The survey found that
straight-line reductions in wage scales for all
employees were the most common means of
reducing the nominal wages of blue-collar
workers. Over 90 percent of the firms had not
adjusted their nominal wage scales from their
December 1929 level by the end of 1930, with
almost two-thirds of firms not adjusting wage
scales as late as September 1931. Since the
price level fell over this period, the NICB survey results are consistent with the evidence of
the broad increase in real wages across industries and comparable skill levels found in the
Beney data.4
It is thus empirically plausible that the monetary contractionsthat occurredbetween 19291933 were transmittedto the real economy via
an increase in real wages. We now turn to a
quantitative account of this channel for the
Great Depression.
II. The Basic Model with Wage Contracts
In our model, a representativeagent makes
consumption and investment decisions in a
forward-lookingmannerthat is consistent with
utility maximization subject to an infinitehorizon budget constraint.We introducenominal rigidity by assuming that wages are set in
4Christopher Hanes (2000) provides complementaryevidence by industryon the numberof months that manufacturers took to cut nominal wages after the onset of the
Depression. In 9 of 20 industries, firms accounting for 99
percent of the industry's employment waited at least 12
months before cutting nominal wages.
DECEMBER2000
Taylor contracts that last four quarters.In the
shortrun-the period before laborcontractscan
be renegotiated-households are assumed to
supply all labor demanded at the given wage
rate, so that the quantityof labor actually hired
is determinedby the demandfor labor schedule
of the representativefirm.
A. Households
The representativehousehold seeks to maximize a utility functional of the form:
(1)
Eol jtU(Ct,
Pt)
t=:O
where ,B is the discount factor, C is real consumption,M is end-of-periodnominal cash balances, and P is the price level. The periodutility
function is given by:
(2) U(Ct,Ip )
in Ct + (1 -
__)ln
To focus attentionon the role of nominal wage
contracts in determininglabor movements, we
abstractfrom the household's labor-supplydecision by omitting labor from the utility function. As we discuss below, we assume: (1) that
the household has a time-invariant,targetvalue
of labor hours denoted by L, and (2) that the
process of wage adjustmentimplied by the Taylor contractsspecificationis consistent with the
household supplying this quantity of labor in
the long run. At any point in time, however,
labor hours will vary from L depending upon
variationsin the demand for labor.5
S Our model effectively abstracts from households'
short-run labor-supply decisions. However, a wage contracting scheme similar to the Taylor specification considered below can be derived by assuming that households
have some degree of marketpower in the labor market,and
set wages in a staggered and overlapping fashion (as in
Erceg [1997], Jinill Kim [1998], or Erceg et al. [1999]).
Such a framework,however, requiresa considerablymore
complicated labor-marketsetup. Our quantitativeanalysis
of these alternativeframeworks,which include endogenous
labor supply, did not yield appreciablydifferentresults, and
so we do not report them.
VOL.90 NO. 5
BORDO ET AL.: MONEY,STICKYWAGES,AND THE DEPRESSION
Households accumulate nominal assets according to the following law of motion:
1451
where tfp is a stochastic discount factor. The
productionfunctionF() is assumedto be CobbDouglas:
(3) Bt = Bt-1 + (Rt-1Bt-1 + WtLt
(6)
+ JtKt + t + Xt)
-
(Ptct + Ptit + Mt- Mt-)
whereB is nominalbond holdings,R is the nominal interestrateon bonds, Wis the nominalwage
rate,L is totalhoursworked,J is the rentalpriceof
capitalin nominalterms,K is the capitalsupplied
to firms,7ris nominalfirmprofits,X is lump-sum
cash transfersfromthe government,andI is gross
real investment.The first term in parenthesesin
equation(3) is householdnominalincome.Household incomeconsistsof interestincomeon bonds,
wage income, income from capital,firm profits,
and lump-sumcash transfersfrom the government. The second term in parenthesesis household nominal expenditures,which consists of
spendingon consumptionand investmentgoods,
and on accumulatingcash balances.
Householdspurchaseinvestmentgoods in order to increase their stock of physical capital
(which they in turn rent to firms):
(4)
Kt+1= (1
-
8)Kt + It.
The household's optimization problem is to
choose Ct, Mt, Bt, and Kt+1 to maximize (1),
subjectto constraints(3) and (4). In choosing its
optimalplan, the household's expectationabout
its currentand futureemploymentpath depends
on expectationsabout real wages and the labor
demand of firms.
Firms rent labor and capital services from
households at the prevailing nominal wage Wt
and capital rental price Jt. Because firms also
face quadraticcosts of adjustingtheir labor input, the representativefirm seeks to maximize a
profit functionalof the form:
max EoE qit{P,F(Kt, Lt, Lt-1)
t=O
-
WtLt
-
-
JtKt}
qLt (
-
Lt
If qL =0, labor hours are not costly to adjust
and the firm's problem is a static one. In this
analysis the firm views all changes in labor
hours as equivalent, whether they occur as
changes in employment or the length of the
workweek.6
C. Taylor Overlapping Wage Contracts
In the Taylor contracts specification, the labor force may be regardedas divided into equalsized cohorts of workers, with the number of
cohorts equal to the length of the contract period. With the contractintervalset equal to four
quarters,this means that there are four cohorts
of workers.During the beginning of each quarter, one of the four cohorts agrees to a nominal
wage that it will receive for the following year.
Following Taylor (1980), the contract wage xt
depends on the geometric average wage Wt of
all cohortsin the economy that will prevail over
the (four-quarter)life of the contract,as well as
on the evolution of hours worked. Specifically,
the contractwage of the cohortrenegotiatingits
wage at time t is given by:
(7) ln(xt)=
+Et
B. Firms
(5)
F(Kt, Lt, Lt-1)
koln(Wt) + y(L, -
_n(Wt+)
ty y(Lt+2 -L)
L)
+
- L) + 421n(Wt+2) +
+ -y(L,+1
431n(W,+3) + y(Lt+3 L)
where y > 0 and Wtdenotesthe geometricaverage wage of all cohortsin the economy at time t,
(8)
Wt =
iXktt-
t-2 t-3
6
Workweekvariationsin manufacturingindustrieswere
substantial.Bernanke (1986) provides a model of interwar
labor marketsin which employment and hours worked per
week are not perfect substitutes. Also, the wage data in
Section I show some variationby skill level. See Matthew
D. Shapiro(1986) for an analysis with adjustmentcosts for
productionand nonproductionworkers, as well as capital.
1452
THEAMERICANECONOMICREVIEW
where we take Xi = 0.25, for all i. With Xi =
0.25, repeated substitution of (8) into (7)
yields:
(9) ln(xt)
S 1n(xt-3) + 1 n(xt-2) + 4ln(xt-1)
+
Et1
l
n(xt+,) + -ln(xt+2) +
1n(xt+3)+ ykLo(Lt+k
-L)
J
As in Taylor (1980), two features of equation
(9) are noteworthy. First, contract wages have
an inertial or backward-looking component;
that is, past contract wages appear in the
equation. This inertial component reflects that
the current contract wage depends on the current average wage across all cohorts, which
itself is an average of contract wages set in
the past that are still in effect. This inertial
component of wages tends to "anchor" the
current wage. Second, the only channel forcing nominal wages to adjust is the gap between currentand future expected labor hours
and the target value of L. Accordingly, the
parameter y is of crucial significance, determining how quickly wages adjust to changing
labor-market conditions.
D. Money Stock Evolution
The model economy abstractsfrom a banking
sector and the endogenous creation of money.
Thus, the stock of money is assumed to be
exogenously determined (and is referred to as
the money shock). The growthrate of the stock
of money is assumed to follow a first-order
autoregressionof the form:
(10)
(11)
gt+
=
go
+
gt= ln(Mt)
pgt
-
+
DECEMBER2000
E. Solution Method and Calibration
The approximatedynamic behavior of the
model is obtainedby first log-linearizingthe appropriatenonlinearstochasticdifferenceequations
aroundthe steady-statevalues of the state variables, and then applyingthe methodof Oliver J.
Blanchardand CharlesKahn (1980).7 The equations determiningthe evolutionof the state variables includethe Taylorcontractswage equation
(9), the firm'sfirst-orderconditionfor laborhours,
and the representativehousehold's first-order
equationsfor real balancesand capital.
Our model is assumed to hold at a quarterly
frequency.The discount factor ,B is set to 0.99,
implying an annualreal interestrate of about 4
percent.The preferenceparameter,u has no effect
on the dynamicsof the log-linearizedsystem,so it
is unnecessaryto calibrateits value. The capital
share parameter0 is set equal to 0.3 and the
quarterlydepreciationrate6 to 0.025, in the range
of typicalestimatesof these parameters.We considertwo alternativevalues of the adjustmentcost
parameter,namely, qL = 0 (our baseline value),
and qL = 0.7. The latterchoice impliesthatif the
representativefirm faced a 1-percentpermanent
increasein its real wage (holdingits capitalstock
fixed),it wouldmakehalf of its totaladjustmentin
just over one quarter'stime, and about70 percent
withintwo quarters.Daniel S. Hamermesh(1993)
surveys the literatureon the dynamic costs of
adjustinglabor, and concludes that the evidence
supportsan adjustmenthalf-lifebetweenone and
two quarters.8The money supply parametersgo
and p are estimated using observationson the
growthrateof Ml over the 1922:2-1928:4period
(regardedas a "typical"periodduringwhich expectations about money growth would be
formed). Least-squaresestimation yields go =
0.0039, p = 0.33, and (
= 0.0126.9
Et+I
ln(Mt41)
where the innovation E+ iS independentand
identicallydistributedN(0, o-). Ourformulationis
in accordancewith the recentliteratureinvestigating the stickywage channel(BemankeandCarey,
1996): equal-sizedinnovationsto the quantityof
money shouldhave similareffects on real activity
irrespectiveof theirsource.
7Because the money stock grows at a nonzero rate on
average, the state variables representing nominal magnitudes-namely the nominal wage and price level-are
scaled by the stock of money. Thus, the system is stationary
in the transformedvariables.
8 Hamermesh (1993) reviews 38 articles based upon
monthly and quarterly data. Only Bernanke's (1986)
study covers the interwar period, and his estimate of the
adjustment half-life is under one quarter. This lies between our baseline and adjustment cost parameterizations.
9 The residualsdisplay no evidence of serial correlation,
and higher lag orderswere not statisticallysignificant.Also,
VOL.90 NO. 5
BORDO ET AL.: MONEY,STICKYWAGES,AND THE DEPRESSION
3.0 -
3.0 -
2.5 2.5
2.5 Outputwith no
adjustmentcosts
2.0-20-
2.0
1.5 -
0.5
1
Outputwith
adjustment costs
"
/
-
.0
1.0
-
1.0
1453
0.5
-.
-
,
'-
0.0I
0.0
1
5
9
13
17
1
9
5
13
17
QuartersElapsed
QuartersElapsed
FUNCTIONS
FIGURE2. IMPULSE-RESPONSE
FOROUTPUT
Notes: The panels display the response of outputfollowing a positive money growth innovation, for specifications with and
withoutadjustmentcosts. The responsesare measuredas percentdeviationsfrom steady state. The dashedlines are 95-percent
confidence bands aroundthe Taylor contractsmodel responses (solid line).
We also estimate the wage-contract sensitivity parameter y by least squares. In particular, we choose y to minimize UV, the
sample average squared deviation (v,) between the observed real wage Wdata and the
simulated real wage series implied by our
model w7odel:
the real wage implied by the log-linearized
model:
( 13)
(13)
g0i P)
w7model(y
( /Y, go,
Wt
00
=
E BW,,k(Y go,
P)E(g,g)
k=O
t=
T
1
(12)
(mV
(v
-T
-
T
=min
'
-E
wdata -
model
A
A
p)2
t=1
are
The money growth innovations E( gp)
O,
derived from equation (10) using data on the
actual rate of money growth over the 1929:41933:2 estimation period, and our parameter
estimates O and A.The gap v, between the real
wage data and simulated real wage is assumed
to be mean-zero measurementerror.10We estimate y = 0.0037 when qL = 0, and y = 0.0093
when qL =0.7.
The estimation is restricted to the pre-NIRA
period (1929:4-1933:2). The real wage data
is the manufacturingreal wage series
Wtiata
shown in Figure 1. The simulated real wage
series wtode is constructedby inputtingestimates of the money growth innovation E( go,
A) into the moving average representationfor
Figure 2 displays model impulse-response
functions (IRFs) following an innovationin the
growth rate of money. The innovationoccurs in
timeperiod1, andis scaledto inducea cumulative
the results discussed below are not particularlysensitive to
substitutingthe growthrateof M2 for Ml. The dataon monetary aggregates are taken from Friedman and Schwartz
(1963).
10 Both the real wage data and simulated real wage are
assumedto be at steady state in 1929:3. We also assume that
the truemoney growthinnovation Et and measurementerror
v, are mutually and serially independent,under which condition the least-squaresestimate of -yis consistent.
F. Impulse-ResponseFunctions
1454
THEAMERICANECONOMICREVIEW
2.5
1.00
0.75
0.5-
DECEMBER2000
12.0
L
PriceLevelLaoHur
Hours
1.51.0
0.50__
0.0
0.25 -
_
_
_
_
0.5
_
_
_
_
-
-0.5
NominalWage
__
Real Wage
-1.0
0.002
6
10
14
18
QuartersElapsed
2
6
10
14
18
QuartersElapsed
FIGURE3. IMPULSE-RESPONSE
FORPRICES,WAGES,AND HOURS
FUNCTIONS
Notes: The panels display responses following a positive money growth innovation.The responses are measuredas percent
deviations from steady state and are derived from the baseline model.
rise of 1 percentin the moneystock.The left panel
shows the outputIRF for the baselinemodel with
no adjustmentcosts, and the right panel corresponds to the specificationwith adjustmentcosts
(qL = 0.7 and y = 0.0093). In the baselinecase,
the peakresponseof output-about 1.5 percentoccurs in the initial period. The output IRF is
humped-shapedin the specificationwith adjustment costs, with the initial responseonly about
half as large as in the baseline. However, the
quantitativedifferencesbetweenthe specifications
narrowsubstantiallyaftertwo quarters.
The dashed lines in each panel are 95-percent
confidence bands for the IRFs. The confidence
bands are constructedusing a bootstrapmethod
that takes into account the uncertaintydue to
the estimationof go, p, and y OurMonte Carlo
procedure repeatedly draws time series of
money growth innovations (Et) and real wage
measurementerrors(v,) to constructa series of
500 estimates of go, p, and y, which in turnare
used to generate 500 impulse-response functions." The width of the confidence bands in
" In Figure 2, our 95-percent confidence bands are the
upperand lower 2.5 percentilesfrom 500 bootstrappedIRFs.
(Lutz Kilian [1999] discusses the advantages of percentile
intervals.)For each iterationi, we draw a time series of 27
independently and identically distributed(i.i.d.) N(O, Ur)
money innovations Eit, for t = 1922:2-1928:4. Inputting
si,t into equation(10) evaluatedat our point estimatesyields
a synthetic money growth series gi,t. Separately, we con-
the firstfew quartersafter an innovationreflects
almost entirelyimprecisionin our estimate of p.
For any given innovation,high drawsof p imply
a relatively large cumulativerise in the stock of
money, and hence in output. The upper confidence band is associated with high values of p,
and the lower band with low values of p. The
wage-sensitivity parametery has little effect on
the initial outputresponse. However, the persistence of outputvaries inversely with y, because
a high y implies more rapidwage adjustmentto
the employment gap. Accordingly, the lower
band declines more rapidly in the medium term
because it is associated with higher values of y
in the bootstrap.For example, the lower bandof
the outputresponse is only aboutone-quarterof
its initial value by the ninth quarter,while the
upper band is about half its initial value.
The left panel of Figure 3 displays IRFs for
struct a synthetic real wage data series w"ta for t =
1929:4-1933:2. For each iteration i, we set data =
Wtde (i, go, p) + Vi t. The term Wt g?deo(, g
p) is the
simulatedreal wage when all parametersare set equal to the
estimates reported in Section II, subsection E, and corresponds to the "true"real wage (in this bootstrapprocedure)
which is only observed with measurementerrorvi,t [drawn
from N(O, (r2)].
Given synthetic time series { gi,t
wdata }1=l, we estimate 500 sets of parameters(%j,goi0 i)
using Kilian's (1999) method to bias-correct estimates of
gOi and Pi. The impulse-responsefunctions are scaled by
1 - A to induce a 1-percentcumulative rise in the money
stock for the median Monte Carlo draw of Pi.
VOL.90 NO. 5
BORDO ET AL.: MONEY,STICKYWAGES,AND THE DEPRESSION
the price level and nominal wage, while the
right panel shows the IRFs of the real wage and
labor hours. For parsimony, only IRFs for the
baseline model are shown. The IRFs illustrate
the familiar mechanism through which monetary innovations induce an output expansion in
the Taylor contracts model. A monetary innovation causes the price level to jump, while
nominal wages respond more gradually. The
persistentdecline in the real wage shown in the
right panel induces a rise in labor hours, and
hence in output. The effects of capital deepening are evident from the eventually positive
response of the real wage, and the fact that the
output response (the left panel of Figure 2) is
more persistentthan that of labor hours.
1455
6
4
2
0-2-
-6
.
-8 -
1930
FIGURE
4.
1931
. . .
1932
. .
1933
1934
1935
MONEY GROWTH INNOVATIONS,
1936
1929-1936
Notes: The data are measuredin percent. The dashed lines
are 95-percentconfidence bands aroundthe estimatedinnovation series.
III. EmpiricalResults
ning in 1930:1. The time path of the money
innovations is obtained by evaluating equation
(10) at the estimatesof 4Oand p, and the dashed
In this section, time-series plots of the price
lines are 95-percentconfidencebands.As Friedlevel, real wage, output, consumption, investman and Schwartz(1963) discuss, the monetary
ment, and labor hours generated from model
decline was initially associated with a modest
simulationsare comparedto historicaldata.The
fall in credit supplied by the Federal Reserve.
historical data on output, consumption,and inThe monetarycontractionintensified following
vestment are based on quarterly national inthe first banking crisis in October 1930, as the
come accounts data constructedby Balke and
Federal Reserve failed to offset declines in the
Gordon (1986). Because our model does not
money multiplierthat were drivenby a progresinclude a governmentor an external sector, the
sive loss of confidence in the financial system.
Figure 5 compares model simulations of the
Balke-Gordon consumption data are adjusted
to include a measure of public consumption. price level, the real wage, output, labor hours,
consumption, and investment to the historical
Similarly, the investment series includes both
data. The panels display simulations from two
government investment and net exports. The
model specifications: the baseline case of no
governmentinvestment data are from the U.S.
Departmentof Commerce(1990), and are interadjustmentcosts, qL = 0.0, and the specification with adjustmentcosts, qL = 0.7. Focusing
polatedfrom an annualto a quarterlyfrequency.
The modelis simulatedover the 1929:4-1936:4 first on the baseline, panel A of Figure 5 shows
that the progressive monetary contraction
periodby inputtingestimatesof money growthinnovationsfrom equation(10) into the state-space should have led to a series of largely unanticiof the linearizedmodel.The simula- pated declines in the price level.'2 The model
representation
accounts for a sizeable fraction of the price
tionsassumethatall modelvariables(includingthe
level in
decline through 1931, but substantiallyundergrowthrateof Ml) areat theirsteady-state
1929:3.Becausethe simulateddataarerepresented states the decline at the business-cycle trough.
in "percentdeviationfrom steady-state"
In panel B, the simulatedreal wage series peaks
form,historicaldata are presentedas a logarithmicpercent in 1932:1 at around 10 percent above its
deviationfromtheir1929:3level.
A. Data and SimulationMethodology
B. The DownturnPhase: 1929-1933
Figure 4 shows that there was a series of
negative innovations to money growth begin-
12
James D. Hamilton(1992) and MartinEvans and Paul
Wachtel (1993) found evidence that these price declines
were largely unanticipated.But this is subjectto contention.
See Stephen G. Cecchetti (1992) for an opposing view.
1456
THEAMERICANECONOMICREVIEW
DECEMBER2000
A. Price Level
10 5
25 20 -
-30
-15 -
-5
-10
-15
-20
-25
-30
Be
10
'
-
-
Agea
-Data
---10
Wag
-Bsln
-35
1930
-25
ManufacturingWage
ne Costs
/' Bsln
/Adjustment/
'
~
-
B. Real Wage
1932
\ <
X
25 -
1934
1936
,
C. Output
/
|
Data
'
1932
Xustment
1934
1936
Costsa
/
D. Labor Hours
40
\
j
-
30
~~~~~~~~~~Baseline
-
'
20
193210
Aodele
1930
-2? Xl40 -
X
0
-15
N
\
1
0
-10
Costs
Adjustment
ajdjustmentCostsc
cro
t
-25 --20-
~~'~'
~-'
Data
-30
-50
. . .
1930
.
. .
1934
.
1932
.
.
1936
ata
Baein
Baselin
-40 -
.
-50 -.
E. Consumption
5 -
.
1930
.
. ..
1932
1934
.
.
. .
1936
F. Investment
12080
0*
-/
-
-
~~~~~~~~~~~~~~40
\,Adjustment Costs 7
__
7Baeine
-10
8
-20 -Dat
-25
Costs1'
-0Adjustment
-15 -
-120 . .
..1930
.
. . .
1932
.
. . . .
1934
.
.-160
1936
Baseline
-/-Data
1930
1932
1934
1936
FIGuRE5. THEEFFECT
OFMONEY
1929-1936
SHOCKS,
Notes: The data are percentdeviations from their values in 1929:3. The simulationsare percentdeviations from steady state.
Baseline refers to the Taylor contracts model simulations without adjustmentcosts. AdjustmentCosts correspondsto the
model with adjustmentcosts.
pre-Depressionlevel, close to the observed real
wage increase of 11 percent.
The baseline model does extremely well in
capturingthe general patternof decline in output, labor hours, consumption, and investment
from 1929 throughearly 1932. The continuing
pattern of negative monetary shocks through
1931 produces a smooth, steep decline in simulated output (panel C); the actual path of output is strikingly similar. The simulated output
pathdeclines 35 percentthroughthe firstquarter
of 1932, comparedwith 32 percent in the data.
The baseline model predicts a larger decline in
labor hours than the data's decline in panel D.
Since the productiontechnology (6) implies a
labor elasticity of 1.43 with respect to output,
the relatively close match for outputin panel C
foreshadowsthe overpredictionfor labor hours.
In panel E, the model clearly accounts for the
fall in consumption. In the first six quarters,
simulated consumption does not fall quite as
much as actual consumption.In the model, the
householdprefersto smooth consumptionin the
face of monetary shocks that are expected to
have transient real effects. By 1931 and into
1932, however, the arrival of additional negative monetary shocks reduces the feasibility of
maintaining the higher levels of consumption
VOL.90 NO. 5
BORDO ET AL.: MONEY,STICKYWAGES,AND THE DEPRESSION
that were plannedduringthe initial downturnin
1929-1930. The baseline model indicates an
initially steeper decline in investment than in
the historical data (panel F). However, the
model performswell in trackingthe investment
decline throughmid-1932.
Turningto the periodbetween early 1932 and
mid-1933, the baseline model predictsthat economic activity should have stopped declining
and remainedat a depressed level. Two opposing factorscontributeto stabilizingoutputin the
simulations. On the one hand, simulated real
wages begin to fall because the low levels of
employmentdepress contractwages. This stimulative effect on labor demand, however, is
offset by the continued fall in the capital stock
thatdepresseslaborproductivity.Consequently,
the gap fails to narrowbetween the actual real
wage and the real wage at which firms would
demand the full employment level of labor
hours L. The baseline model's account of
1932:1 to 1933:2 is somewhat at variance with
the historicaldata:the economic downturnwas
exacerbatedover these six quarters,as shown in
Figure 5.
Bernanke (1986) and Bemanke and Carey
(1996) find evidence that adjustmentcosts were
importantduring the Depression. Figure 5 also
reports the model simulations when changing
labor hours is costly (qL =0.7). With adjustment costs, firms are more reluctantto reduce
the workforcebecause each negative monetary
shock is expected to have a temporaryeffect.
Althoughthe sheer accumulationof these "temporary"shocks during 1929-1932 reduces labor demand severely, the cumulative effect
through 1933 is smaller with adjustmentcosts.
As a result, the model with adjustmentcosts
generally tracks labor hours better than the
baseline model. The baseline model captured
output and consumption through 1933 fairly
well, so introducingadjustmentcosts does not
help in panels C and E. The model's prediction
for the price level in 1933 improves when adjustment costs are introduced.In this case, the
less depressed consumptionprofile results in a
higher demand for money than in the baseline,
implying a comparativelysharperdecline in the
price level.
Overall,the model providesa reasonablyaccurate account of the joint behavior of the price
level, real wage, and majormacroeconomicag-
1457
gregates during the downturnphase, despite its
simple structure.Panel A of Table 2 summarizes
our results for the path of output. Ninety-fivepercent confidence bands for each statistic are
reported,based upon the bootstrapproceduredescribedearlier.13Throughoutthe downturnphase,
the confidencebands aroundthe outputpath are
reasonablyuniform,in the rangeof plus/minus3
to 6 percentagepoints for the baseline. Panel B
gives the percentage of the observed peak-totroughchange (1929:3-1933:1) explainedby the
model for key variables,that is, the ratio of the
percentchangein the simulatedseries to the percent change in the correspondingdata series.
PanelB indicatesthatmoney shocks can account
for roughly70 percentof the outputdeclinein our
baseline specification,with a marginof errorof
about 13 percentagepointson eitherside. Money
shocksaccountfor nearly50 percentof the output
declinein the specificationwith adjustmentcosts,
with a slightly smallermarginof error.The table
also shows that monetary shocks can explain
or moreof the fluctuationin the real
three-quarters
wage, laborhours,and consumptionin our baseline specification,and somewhatover half of the
decline in the price level and investment.
These estimatesprovide considerablesupport
for the empirical relevance of the sticky wage
hypothesis duringthis period. Of course, allowing for additional dynamic complications may
furtherimprovethe model's ability to matchthe
data. For example, while the model specification with adjustmentcosts does well in tracking
the decline in labor hours, introducing labor
hoarding might improve the model's ability to
accountfor the magnitudeof both the decline in
labor hours and output.14
13
Recall that for each iteration i, a model-simulated
wage series w`todel(.i,
goi,
Pi) is generated. Simultaneously, the other variables are simulated through 1936:4,
and percentilebands are constructedafter 500 Monte Carlo
draws.
14 We have also examined the sensitivity of our simulation resultsto two alternativewage-contractstructures.In an
earlier version of the paper (Bordo et al., 1997), we found
that four-quarterFischer-style contracts (Stanley Fischer,
1977) did not account as well for the depth of the downturn
in 1932-1933, reflecting that the real effects of monetary
injections are confined to the contractperiod. On the other
hand, we found that a Calvo-style contractstructure(Guillermo Calvo, 1983) was flexible enough to yield results
similar to those reported in Figure 5. Jeffrey C. Fuhrer
and George R. Moore (1995) criticize both Taylor- and
1458
THEAMERICANECONOMICREVIEW
TABLE 2-THE
DECEMBER2000
EFFECT OF MONEY SHOCKS IN THE TAYLOR WAGE-CONTRACTING MODEL
A. Percent Changes in Outputfrom 1929:3
Selected quarters
Data
1930:1
-9.0
1931:1
-20.2
1932:1
-32.1
1933:1
-47.9
1934:1
-35.8
1935:1
-29.1
1936:1
-20.8
Model without
adjustmentcosts
Model with
adjustmentcosts
-7.9
(-13.2, -4.4)
-15.0
(-18.5, -10.7)
-35.3
(-41.3, -28.9)
-33.2
(-39.1, -26.9)
-17.0
(-25.4, -7.7)
8.9
(1.1, 16.9)
13.6
(5.9, 21.1)
-3.6
(-5.1, -1.9)
-10.6
(-12.7, -7.8)
-23.4
(-27.7, -19.0)
-22.3
(-27.6, -17.6)
-12.4
(-19.1, -7.5)
7.7
(2.1, 12.6)
12.9
(5.8, 21.7)
B. Percentageof Actual Change in Key VariablesExplained by the Model, 1929:3-1933:1
Variables
Output
Price level
Real wage
Labor hours
Consumption
Investment
Model without adjustmentcosts
Model with adjustmentcosts
69.5
(56.1, 81.6)
53.7
(43.9, 62.9)
80.7
(52.6, 108.0)
113.5
(90.1, 135.0)
73.4
(59.3, 87.6)
61.7
(49.8, 71.8)
46.6
(36.7, 57.6)
71.6
(63.9, 79.2)
75.2
(52.1, 100.1)
75.7
(57.9, 96.8)
48.7
(38.5, 59.7)
41.9
(31.7, 52.7)
Notes: Numbers in parenthesesare 95-percent confidence intervals. Percentagesin panel B are derived as the ratio of the
percent change in the simulated series to the percent change in the correspondingdata series, multiplied by 100.
C. The Recovery Phase: 1933-1936
As Figure4 shows, money growthinnovations
were predominatelyexpansionaryby the end of
1933 and through1934. This remonetizationcoincided with Roosevelt's decision to leave the
gold standard.In fact,thepricelevel rose about10
percent in the year following the Depression's
trough.Figure5 shows thatthe wage-contracting
model implies thatoutputand laborhoursshould
have turnedup sharplyby the end of 1933. Both
variablesshouldhave returnedto theirpriorbusi-
Calvo-style contractsas undulyrestrictivein fittinginflation
persistence. Allowing for more flexible inflation dynamics
than provided by these specifications is an interestingavenue for future research.
ness-cycle peak at the end of 1934. Indeed, the
model's predictionsmatchRomer's(1992) explanationfor the economic recoveryquite well: the
economicrecoverywas due to expansionarymonetarypolicy and gold inflows.
The historical record, however, is different.
Following the business-cycle trough in 1933:1,
the path to recovery was slow. By the last
quarter of 1934, output remained 31 percent
below its 1929 business-cycle peak, only about
8 percentagepoints higher than in 1932:4. Labor hours also recovered slowly, remaining 27
percentbelow their 1929 peak. The implications
of the baseline wage-contracting model are
sharplyat variancewith the data's sluggish pattern of recovery. Adjustment costs have very
little effect in slowing the returntowardssteady
state. Agents perceive the monetary shock as
VOL.90 NO. 5
BORDO ET AL.: MONEY,STICKYWAGES,AND THE DEPRESSION
facilitating a returnto the steady state. Hence,
losses in the current period associated with
changinglaborhoursare largely offset by future
adjustmentcost savings.
A strikingdeviation between the model's account of this period and the historical data is
seen in Figure 5 for real wages. Our measure
of real wages for all industries rose 6 percent
between 1933:3 and 1934:4, while the manufacturingreal wage rose over 12 percent. According to the model simulations, the real wage
should have fallen about 15 percent over the
same period. This divergence provides an essential clue as to why the remonetizationfailed
to generatea robustrecovery.In both the model
and the data, real wages had risen above their
steady-statevalue duringthe 1929-1933 downturn. But the fall in the capital stock severely
depressed labor productivity.Thus, increasing
labor hours towards their steady-statelevel required the real wage to fall enough to accommodate the decline in labor productivity. In
particular,the real wage needed to fall significantly below its steady-statelevel.
The NationalIndustrialRecoveryAct (NIRA)
interfered with this equilibrating process by
forcing a largely fiat rise in wages. In the year
following the passage of the NIRA in June
1933, over 450 industry-specific codes were
passed and made into statutes.The NIRA codes
typically set nominal wage schedules for workers of various skill levels within an industry,as
well as an industryminimum wage.15
While the NIRA set nominalwages explicitly,
we simulatethe effects of the NIRA under the
assumptionthatthe NIRA exogenouslyfixed real
wages, at least in the mediumterm.This simplificationseems appropriateinsofaras the explicit
goal of the NIRA was to increaseconsumerpurchasing power by increasingthe real wage."6In
our model simulations,firms and householdsad15
The largerincrease in the manufacturingreal wage in
Figure 5 is not surprising,given that the coverage of the
NIRA was more encompassing in manufacturingthan in
other sectors, and the relatively greaterinfluence of unions
in manufacturing.
16 Thus, ourmodelingstrategyabstracts
fromthe effects of
price-level uncertaintyduring the NIRA period. Price-level
uncertaintywould accountfor differencesbetweenex post real
wages and real wages expected at the time the NIRA codes
were established.However,our presumptionis thatsignificant
differences between expected and actual real wages would
1459
just their labordemandand the capitalstock endogenously (as detelmined by the first-order
conditionsfor laborandcapital),takingaccountof
this exogenously imposed real wage path. We
performtwo simulationsto gauge the potentially
negativeeffects of the NIRA.
In the first case, our analysis assumes that
agents have "limited" perfect-foresight about
the exogenous evolution of real wages beginning in 1933:2. At time t, agents know the
realizationsof the real wage thatwill occur over
the next four quarters:wt through wt+3. We
equatethese wages with the observed aggregate
real wage series displayed in Figure 5. Beyond
four quarters,agents at time t expect futurereal
wages Wt+S= wt+3 for 3 < s < S. Since we
allow for modest total factor productivity
growth (of 1 percent per year) in this analysis,
period S is the date when the detrended real
wage returnsto its steady-statelevel.17 Agents
expect detrendedreal wages for periods t + S
and beyond to remainat their steady state. This
process is repeated at time t + 1 and beyond.
We refer to this as the NIRA case.
Panel A of Figure 6 displays a simulationof
the effects of the NIRA on outputfor the NIRA
case.18 The simulated output series contracts
substantiallyfurther,declining an additional 13
percent by 1935:4 from its 1933:1 level. The
NIRA-induced output contraction largely reflects a fall in the labor-capitalratio due to the
massive real wage hike. The effects on output
are magnified by an additional, albeit more
gradual,decline in the capital stock.19
have led to furtherrevisionsin nominalwage schedulesin the
hope of achievingthe desiredlevel of the real wage.
17 The detrendedreal wage is w,/A,, where A, is total
factor productivity. Since the level of real wages is set
exogenously in this experiment,allowing for trendproductivity growth diminishes the expected long-run negative
effects.
18 The 1929:3-1933:2 simulatedoutputis from the baseline Taylor contracts model. The two simulated series in
panel A take as initial conditions the level of the capital
stock and labor hours from the baseline model.
19The NIRA case simulations over 1933-1935 are not
especially sensitive to reasonable modifications in: (i) the
assumed perfect-foresightdurationof four quarters,or (ii)
the assumed process of long-runreal wage adjustmentafter
period S. On the latter assumption, our analysis assumes
that detrended real wages revert monotonically to their
steady-statelevel. The labor-capitalratio returnsto its prior
steady state, but the new steady-state levels of labor and
THEAMERICANECONOMICREVIEW
1460
A. AlternativeNIRAWage Paths
B. High ProductivityGrowthUnder NIRA
1~~~~~~~~~~~~~~0*
10
00
DECEMBER2000
X
__
_
____
__
-20 -
D
_
-20 -
-50 1930
1931
1932
1933
1934
1935
Adjustme50n
-30
1493
<
Costs /
Adjustment
\
N
1930
1931
Costs
1933
1934
1935
PERIOD:THE EFFECTSOF NIRA AND PRODUCTIVITY
GROWTHON OUTPUT
FIGURE6. THE RECOVERY
Note. Each panel displays percent deviations from 1929:3 values (data) or from steady state (model).
This simulationexaggeratesthe effects of the
NIRA on outputif the observedreal wage series
overstatesthe legislation's impact on the cost of
hiring a constant-qualityworker. In fact, it is
likely that the NIRA introduced upward bias
into wage measures based on average hourly
earnings.The NIRA increasedincentives to underreporthours worked, and induced compositional effects that shifted labor demand toward
high-skilled workers.20
Accordingly, our second simulationattempts
to put a minimum bound on the effects of the
NIRA. The simulation assumes that agents expected the NIRA to freeze real wages at their
1933:2 level. Since the legislation's goal was to
increase real wages and incomes, it seems reasonable to assume that agents expected the pro-
capital are lower. We have also studied long-run wage
adjustments that return labor and capital to their prior
steady-state paths. These unreportedsimulations actually
imply a sharperfall in the capital stock over the 1933-1935
period, and hence a slightly larger output fall. Agents perceive the currentNIRA wage hikes as having less effect on
theirpermanentincome, and hence they reducetheir current
consumptionless.
20 First, as Michael Darby (1976) observes, NIRA regulations on both the wage and maximum work hours encouraged employers to "cheat"by redefininghours worked
more narrowly,e.g., to exclude breaktimes. Second, NIRA
often set minimum wages at a high fraction of national
averagewages for each industry.As CharlesFrederickRoos
(1937) discusses, this had a disproportionateeffect on lessskilled workers during the recovery. A compositional shift
towards high-wage, skilled workers would impart an upward bias to industry measures of the real wage derived
from aggregate hourly earnings data [see Claudia Goldin
and Margo (1992)].
gramto at least stabilize wages. We refer to this
as the Fixed wage case, with the alternative
expectations assumption constituting the only
difference from the NIRA case.
Figure 6 shows that output would have remained roughly flat in the Fixed wage case,
recovering only slightly from its 1933:1 level.
Thus, even if the NIRA only kept real wages
from falling, such a policy would have significantly impededrecovery.The key insight is that
real wages needed to fall due to the drastic
decline in the capital stock from 1929-1932.
The simulations of our baseline model (Figure
5) indicate that a recovery required a 10- to
15-percent reduction in real wages from their
mid-1933 level. Unfortunately, NIRA raised
nominal wages precisely at the time that real
wages needed to fall. Ouranalysis indicatesthat
NIRA contributed substantially to the persistence of the Depression.21
The question remains as to what factors allowed the economy to recover. A recent literature has identified productivity improvements
as playing a substantialrole. There is industry
evidence that productivity increased markedly
in the recoveryphase of the Depressionbecause
of shakeoutsof inefficientproducers.For example, Timothy F. Bresnahanand Daniel M. Raff
(1991) find that automobileproductionby 1935
had returnedto its 1929 level, although there
21
Michael M. Weinstein (1981) analyzes the effect of
the NIRA on nominal wages and outputin the context of an
IS-LM model, and also finds that the NIRA significantly
depressed output.
VOL.90 NO. 5
BORDO ET AL.: MONEY,STICKYWAGES,AND THE DEPRESSION
was a considerable change in the mix of producers. Most of the additionaloutput occurred
throughan expansionof establishmentsthathad
relativelyhigh productivitylevels in 1929. New
firms with a similarly high level of efficiency
also contributedto this expansion in output,but
to a lesser extent. The industry evidence is
supportedat an aggregatelevel by Cecchettiand
Georgios Karras(1994). In an identified vector
autoregression,these authors find that supply
shocks account for a large fraction of output
growth during the recovery phase of the Great
Depression.
To quantify a role for productivity growth
during this period, we modify our analysis of
the NIRAcase to allow productivityto increase
at a constant4 percentper year, ratherthan the
1 percent reported in the Figure 6, panel A,
simulations. The simulation is displayed in
panel B, with and without adjustmentcosts. The
simulations capture the slow recovery path of
the historical output data through 1935. Evidently, if the observed aggregate real wage series adequatelycapturedthe effects of the NIRA
on the cost of hiring a constant-qualityworker,
strong productivity growth would have been
necessary to accountfor recovery in the context
of our simulations.Of course, to the extent that
our real wage dataoverstatethe NIRA's effects,
somewhat weaker productivitygrowth than the
4 percent assumed here could account for the
recovery.
IV. Conclusions
The importantrole that sticky wages played
in propagatingnegative monetaryshocks during
the 1929-1933 downturn was recognized by
Keynes (1936) and other contemporaryobservers. Our model with Taylor wage contractsaccounts for much of the joint decline in output,
consumption, labor hours, investment, and the
price level from 1929-1932. Our analysis indicates that contractionarymonetary shocks accounted for between 50 and 70 percent of the
decline in real GNP at the Depression's trough
in 1933:1. While nonmonetaryfactors almost
surely added to the severity of the downturn
in the United States, our focus on monetary
factors complements cross-sectional international evidence: countries that remainedon the
gold standard suffered more persistent eco-
1461
nomic downturns.Unfortunately,the converse
was not true in the United States: even though
Roosevelt abandonedthe gold standardin 1933
and a remonetizationbegan, a robust economic
recovery did not ensue. Instead, our analysis
finds that exogenously mandated wage codes
due to the National Industrial Recovery Act
interfered with the economy's natural equilibrating mechanism. Real wages were not allowed to fall, and the pathto recoverywas slow.
Our analysis of the tepid U.S. recovery
places blame squarely on the shoulders of the
nonmonetary government authorities. As long
as real wages were legislatively mandated at
levels well above the marginal product of
labor that would prevail at full employment,
monetary expansion alone could not lead to
recovery. In the United States, institutional
changes seem crucial for confronting the discontinuity between the downturn and recovery phases of the Great Depression. To the
extent that countries tried different nonmonetary measures to escape the Depression, further international comparisons can shed light
on this explanation.
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