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U
LII
FEDERAL RESERVE BANK OF DALLAS
FIRST QUARTER 1996

Do Wages Help Predict Inflation?
Kenneth M. Emery and Chih-Ping Chang

Supply Shocks and the
Distribution of Price Changes
Nathan S. Balke and Mark A. Wynne

Policy Priorities and the
Mexican Exchange Rate Crisis
William C. Gruben

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

Economic Review
Federal Reserve Bank of Dallas

Robert D. McTeer, Jr.
President and Chiet Executive Ollieer

Tony J. Salvaggio
First Vice President and Chief Operating Officer

Harvey Rosenblum
Senior Vice President and Director of Research

W. Michael Cox
Vice President and Economic Advisor

Stephen P. A. Brown
Assistant Vice President and Senior Economist

Research Officers
John Duca
Robert W. Gilmer
William C. Gruben
Evan F. Koenig
Economists
Kenneth M. Emery
David M Gould
Joseph H Haslag
D'Ann M Petersen
Keith R Phillips
Stephen D. Prowse
Fiona D Sigalla
Lori L Taylor
Lucinda Vargas
Mark A Wynne
Mine K. YOcel
Carlos E Zarazaga
Research Associates
Professor Nathan S. Balke
Southern Methodist University

Professor Thomas B Fomby
Southern Methodist University

Professor Kathy J Hayes
Southern Methodist University

Professor Gregory W Huffman
Southern Methodrst University

Professor Finn E Kydland
University 01 Texas at Austin

Professor Roy J Ruffin
University of Houston

Editors
Stephen PA Brown
Evan F. Koenig
Managing Editor
Rhonda Harris
Copy Editor
Monica Reeves
Graphic Design
Gene Autry
Laura J Bell
The Economic Review is published by the Federal
Reserve Bank of Dallas The views expressed are those
of the authors and do nol necessarily reflecl the positions of the Federal Reserve Bank of Dallas or the
Federal Reserve System
Subscriplions are available free of charge. Please
send requests fOI si ngle-copy and multiple-copy subscriptions, back issues, and address changes to the
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Articles may be reprinted on the condition that the
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Contents
Do Wages Help
Predict Inflation?
Kenneth M. Emery and Chih-Ping Chang
Page 2

Supply Shocks
And the Distribution
Of Price Changes
Nathan S. Balke and Mark A. Wynne
Page 10

Policy Priorities
And the Mexican
Exchange Rate Crisis
William C. Gruben
Page 19

In the financial press, productivity-related wages are often
cited as an inflation indicator. For example, recently slow rates of
wage growth have been noted as a factor that will keep inflation
rates low in the future. While inflation and wage growth do appear
to be highly correlated over longer time periods, it is not clear
whether movements in wage growth precede movements in inflation, thereby providing predictive content for future inflation.
In this atticle, Kenneth Emery and Chih-Ping Chang examine
the usefulness of wage growth as a predictor of inflation, as well as
carry out a stability analysis of the relationship underlying inflation
and wages. The results caution against using wage growth as a
signal of future inflation in that wage growth has no information
content for future inflation. Furthermore, the bivariate relationship
between inflation and wage growth is shown to be unstable.

Since the early 1970s, economists have gained an increased
appreciation for the impOitance of supply shocks as sources of
fluctuations in aggregate economic activity. Yet the question of
how best to measure such shocks remains open. Traditionally,
economists have assessed the importance of such shocks by
looking at such things as the relative prices of oil or agricultural
commodities.
Recently, however, it has been suggested that changes in the
distribution of price changes for individual commodities may, in
fact, be a superior indicator of changes in aggregate supply conditions. In this article, Nathan Balke and Mark Wynne assess this
argument in the context of a very simple but well-known model
of the aggregate economy. They show that fluctuations in the rate
of technological progress across sectors are indeed reflected in
the cross-section distribution of prices, lending suppOtt to the idea
that this may be a superior measure of supply shocks. However,
Balke and Wynne raise questions about the interpretation of the
relationship between changes in the distribution of price changes
for individual commodities and aggregate inflation as evidence of
price stickiness.

Mexico's December 1994 devaluation and subsequent financial crisis came as a surprise even to some analysts who focus on
Latin American financial markets. This article outlines the events
leading up to the devaluation and discusses the tension that mounted
throughout 1994 between poliCies to address growing bankingsector problems in Mexico, the policies designed to preserve the
nation's exchange rate regime, and the pressures induced by rising
U.S. interest rates. The article concludes that-while each difficulty
impeded the resolution of the other-the explosive nature of the
ensuing crisis may have reflected a third complication, the term
structure of dollar-indexed debt.

Gauging inflationary pressures is a perennial concern for monetary policymakers and
financial market participants. For example, during 1994, the Federal Reserve tightened monetary policy in response to concerns about
building inflationary pressures and higher inflation in the future. Many people attributed the
pursuant 1995 slowdown in economic activity
largely to these Federal Reserve actions.
Unfortunately, the performance of many
once reliable guides of future inflation, such as
the growth of monetary aggregates, has deteriorated in recent years. This deterioration has
resulted from, among other things, financial
innovations that have changed the relationships between financial variables and economic
activity. The deterioration of once reliable inflation guides has led policymakers and financial
markets to monitor a broad range of inflation
indicators. Because labor costs make up more
than two-thirds of the total cost of producing goods and services in the United States,
one of these indicators is unit labor costs, or
wages adjusted for changes in labor productivity. Indeed, many analysts currently cite the
lack of accelerating unit labor costs as grounds
for believing that inflation will not increase any
time soon.
Research on the relationship between unit
labor costs and inflation has focused on whether
higher labor costs precede higher inflation, or
vice versa. In statistical jargon, the research has
focused on whether labor costs Granger-cause
inflation.1 Recent research by Mehra (1993, 1991)
that utilizes newly developed statistical techniques yields mixed results. Mehra (1993) finds
that when consumer prices serve as the measure
of prices, unit labor costs and prices are correlated in the long run. The study also finds that
this correlation is present because Granger causality is running in both directions,2 which implies that unit labor costs contain information
about future consumer prices. However, Mehra
(1991) finds that when the gross domestic product (GDP) deflator is used as the measure of
prices, a long-run correlation still exists, but its
source is Granger causality that runs only from
prices to wages. Therefore, in this case, unit
labor costs have no information content for future movements in prices.
The purpose of this article is twofold. The
first is to examine how much forecasting power
unit labor costs have for future consumer prices.
Is the attention paid to unit labor costs as an
inflation indicator justified? While Mehra finds
that unit labor costs Granger-cause consumer
prices, he does not examine the extent of unit

Do Wages
Help Predict
Inflation?
Kenneth M. Emery
Senior Economist and Policy Advisor
Federal Reserve Bank of Dallas
Chih-Ping Chang
Graduate Student
Southern Methodist University

he inclusion of unit labor costs
in forecasts of consumer price

inflation provides no significant
improvement in forecasting errors,
especially in recent years.

Figure 1

A preliminary look at labor costs and prices

Growth of Unit Labor Costs and Consumer
Price Inflation, 1957– 93

labor costs’ predictive power for out-of-sample
forecasts of inflation.3 The second purpose of
this study is to examine whether the relationship between unit labor costs and consumer
prices is stable over time.4 As with the inflationindicator properties of the monetary aggregates,
have the indicator properties of unit labor costs
deteriorated in recent years?
Our empirical strategy is to first take a
preliminary look at the raw data and the data
transformed by a filter designed by Baxter and
King (1995). Next, we carry out Granger causality tests and a stability analysis of those tests.
Finally, we examine the forecasting ability of
unit labor costs for consumer price inflation
(CPI). Our main finding is that the inclusion of
unit labor costs in forecasts of consumer price
inflation provides no significant improvement in
forecasting errors, especially in recent years.

Figure 1 plots year-over-year growth of
unit labor costs and consumer price inflation,
excluding food and energy (CPIC).5 The high
correlation between movements in labor costs
and inflation demonstrates why analysts have
paid close attention to labor costs when assessing inflation. However, what is not clear from
the figure is whether movements in labor costs
precede movements in inflation, or vice versa.
In other words, it is not clear from Figure 1
whether movements in labor costs help to
forecast future movements in inflation.
Notice also from Figure 1 that there
appears to be a potential break in the relationship between labor costs and inflation sometime during the early 1980s. The growth of
labor costs seems to be persistently lower than
inflation growth during the 1980s, and the contemporaneous correlation between the two variables appears lower.
Using a filter methodology developed by
Baxter and King (1995), we can divide labor cost
growth and inflation into their long-run and
business-cycle components. The results of doing
this are shown in Figures 2A and 2B and illustrate that labor cost growth and inflation are
correlated at both the business-cycle frequency
and in their trend, or long-run, movements.
Table 1 provides correlations from the raw data
and for the trend and cycle components for the
entire sample and for two subsamples.6 The
correlations for wages leading prices at the trend
and business-cycle frequencies (negative k s) are
positive, although higher at the trend frequency,
supporting the view that movements in wages
could help predict future movements in prices.
Additionally, these correlations seem to be con-

Figure 2A

Figure 2B

Percent
14
Inflation
Wage growth

12
10
8
6
4
2
0
–2
’57

’61

’65

’69

’73

’77

’81

’85

’89

’93

Inflation and Wage Growth Components

Trend components

Cycle components

Percent

Percent
7.5

Inflation
Wage growth

2.5
6
0
4
–2.5
2

–5

–7.5

0
’60

’63

’66

’69

’72

’75

’78

’81

’84

’87

’60

’90

FEDERAL RESERVE BANK OF DALLAS

’63

’66

’69

’72

’75

’78

ECONOMIC REVIEW FIRST QUARTER 1996

’81

’84

’87

’90

Table 1

Cross Correlation: Inflation and Wage Growth
k

1965:4– 80:4

1981:1– 94:4

1957:1– 94:4

.37
.50
.59
.56
.60
.38
.19
.04
–.09

.05
.19
.44
.45
.51
.60
.51
.27
.10

.54
.60
.68
.67
.67
.63
.54
.42
.35

.79
.85
.90
.94
.98
.90
.83
.75
.68

.58
.68
.77
.87
.96
.82
.68
.54
.41

.94
.95
.96
.96
.96
.93
.90
.87
.83

.45
.66
.81
.83
.68
.38
.03
–.25
–.40

–.35
–.20
.10
.44
.72
.84
.76
.51
.20

.31
.49
.64
.72
.65
.46
.18
–.09
–.27

Granger causality tests with the variables
in second differences will still be misspecified
if inflation growth and wage growth are cointegrated and converge to a stationary long-run
equilibrium relationship.10 If the series are cointegrated, an error-correction term must be included in the causality test. This necessity follows
from Engel and Granger’s (1987) findings that
if two variables are cointegrated, an errorcorrection model for the variables is present
and that not including the error-correction term
can lead to faulty inferences. Furthermore, cointegration between two variables implies Granger
causation in at least one direction. The presence
of cointegration provides a dynamic framework
in which an error-correction term represents
deviations from a long-run cointegrating relationship, while lagged difference terms represent short-run dynamics.
To estimate the possibility of a cointegrating
relationship between the first difference of prices
and unit labor costs, we use the Dynamic OLS
(DOLS) procedure of Stock and Watson (1993).
This procedure entails regressing one of the I(1)
variables on the other I(1) variable, and lags and
leads of the first differences of the I(1) variables.
With standard errors corrected for serial correlation, one can make valid inferences from each
coefficient estimate. The procedure is described
by the following equations:

Raw data
–4
–3
–2
–1
0
1
2
3
4
Trend components
–4
–3
–2
–1
0
1
2
3
4
Cycle components
–4
–3
–2
–1
0
1
2
3
4

NOTES: The reported coefficients show the correlation between inflation at time t and wage
growth at time t + k.

(1)

∆pt = α p + β p ∆w t +

∑γ

∆2w t −i + ⑀ pt

∆2 pt −i + ⑀wt ,

i = −k

and
(2)

sistent with a potential breakpoint sometime in
the early 1980s: for the raw data and both filter
components, the wage leading inflation coefficients (negative-signed k s) drop in the 1980s.
Additionally, for the raw data and the cycle data,
the inflation leading wages coefficients (positive
signed k s) increase during the 1980s.

∆w t = αw + βw ∆pt +

∑γ

i = −k

where p and w are the logarithms of prices and
unit labor costs and ∆ is the difference operator.
Table 3 shows the results of testing the α’s
and β’s.11 Both βp and ∆w are significant at the
1-percent level, but the α’s are only significant
with CPIC. However, the augmented Dickey–
Fuller tests for the cointegrating residuals confirm a stationary relationship between the growth
of both price measures and the growth of unit
labor costs, implying cointegration.12
We are now ready to conduct the Granger
causality tests. To examine the causal relationship between inflation and wage growth, we
estimate the following bivariate models:

Granger-causality results
Whole sample. We use both consumer
prices for all items (CPI) and consumer prices
excluding food and energy (CPIC) as our price
measures.7 Unit labor costs are for the nonfarm
business sector. As a preliminary step to the
formal causality tests, we have to determine the
stationarity characteristics of the time series.8 We
choose the augmented Dickey–Fuller method
(ADF) to conduct the tests for each variable in
levels, first differences, and second differences.
Table 2 summarizes the results and shows that
unit labor costs and both price measures are
integrated of order two, denoted by I(2).9

(3 )

(

∆2 pt = a p + b p ∆p − α p − β p ∆w
+

∑c
i =1

i =1

and

t −1

∆ pt −i + ∑ d pi ∆ w t −i + ⑀ pt
2

)

Table 2

Augmented Dickey–Fuller Test Results
(4 )

(

∆2w t = aw + bw ∆w − αw − βw ∆p
+

∑c

τ-statistics

t −1

∆2 pt −i + ∑ dwi ∆2w t −i + ⑀wt ,

CPI core
CPI
Unit labor cost

i =1

where the terms in parentheses are the errorcorrection terms and are estimated by DOLS.
The hypothesis of no causality from wage to
inflation is rejected if bp and/or all dp’s are
significantly different from zero.
Our results for the causality tests are summarized in Table 4.13 Whether wage growth
Granger-causes inflation depends on the choice
of the price series. For CPIC, wage growth is
significant at the 1-percent level, implying causality. However, for CPI, wage growth is not
significant, implying no causality.14 The results
also show that inflation always Granger-causes
wage growth, regardless of the choice of the
price series. It is noteworthy that the errorcorrection terms play a crucial role for the rejection of the no-causality hypotheses.
Granger-causality tests: A stability analysis.
There are two potential sources of instability in
the Granger causality test results presented above.
First, there may be instability in the cointegrating
relationship. Second, there may be instability in
the short-run dynamics or in the Granger regressions themselves.
To examine the stability of the cointegrating
relationship, we use Stock and Watson’s (1993)
formal test for the null hypothesis of a constant
cointegrating relationship against the alternative
of different cointegrating vectors over various
samples. In their test for structural stability, the
constant term in the error-correction term remains fixed. In contrast, our alternative test is
based on the following regression:

(5)

(

∑γ

i = −k

CPI core
CPI
Unit labor cost

– 2.68
– 2.76
– 2.11

3
4
3

15.70*
13.81
11.08

– 2.44
– 2.50
– 2.34

7
8
8

14.89
11.15
12.32

– 4.62***
– 5.03***
– 6.03***

6
8
8

17.86*
13.69
13.35

Second-differenced
CPI core
CPI
Unit labor cost

*** = Significance at the 1-percent level.
** = Significance at the 5-percent level.
* = Significance at the 10-percent level.
NOTES: The testing equations are of the form:
y t = α + (θt ) + ρy t −1 +

∑ ∆y
i =1

t −i

+ ⑀t ,

where the lag length k is determined by the Schwartz information criterion for 1 ≤ k ≤ 8.
Ljung–Box Q-statistics are used to check the serial correlation of the residuals. Q(9) and
Q(10) are reported for the levels and for the first and second differences, respectively.
All variables are in natural logs. The variables in levels are tested for trend stationarity,
and the first- and second-differenced variables are tested for difference stationarity.

Table 3

Dynamic OLS Cointegration Test: Inflation and Wage Growth

CPI
CPI core

αp

βp

Dickey–Fuller
τ-statistics

Break date (χ 22 )

.16
(.84)
.65
(14.32***)

1.03
(257.1***)
.92
(257.2***)

– 4.95***

1980:2 (15.97**)

– 4.04***

1980:4 (3.45***)

*** = Significance at the 1-percent level.
** = Significance at the 5-percent level.
* = Significance at the 10-percent level.

)( )

NOTES: (1) In the test of the significance of α and β, the reported χ21 statistics use Newey and
West (1987) robust standard errors with a truncation lag of 4. Augmented Dickey–Fuller
tests are implemented on the cointegrating residual, ∆p – αp – βp ∆w, with the lag length
of eight determined by Schwartz information criterion.
(2) To search for the significant break dates, χ22 is calculated to test for the changes in
a and b of the cointegrating residuals over the 70 percent of the whole sample. The date
is chosen based on the largest χ2 statistic. Hansen’s (1992) critical values are 16.2, 12.4,
and 1.6 at the 1-, 5-, and 10-percent level, respectively.

∆2w t −i + ⑀ pt ,

where τ is the possible break date and 1(t > τ)
is the indicator function equal to 1 if t > τ,
0 otherwise. The joint significance of θp and δp
implies rejection of the null hypothesis of a
stable long-run relationship. We conduct the
joint test for θp and δp sequentially with all possible τ’s, which covers 70 percent of the sample
period. For each τ, we get a χ2 statistic indicating the significance of θp and δp. The maximum
of the sequence of χ2 statistics yields a possible
break in the cointegrating relationship.15 It should
be noted that the conventional χ2 critical values
are invalid because the timing of the structural
change is not specified under the alternative.

FEDERAL RESERVE BANK OF DALLAS

Ljung–Box Q-statistics

First-differenced

∆pt = α p + β p ∆w t + θ p + δ p ∆w t 1 t > τ
+

Lag order (k )

Levels

i =1

)

Hansen (1992) derives a SupF test for parameter
instability in the context of cointegrated regression models. The far right column in Table 3
displays the SupF statistics and the selected break
dates. Based on Hansen’s asymptotic critical values, we discover a significant shift in regime.
Depending on the price series, the break dates

ECONOMIC REVIEW FIRST QUARTER 1996

Table 4

Causality Tests for Bivariate ECM
∆ pt = a p +b p (∆p − α p − β p∆w )
2

t −1

an error-correction term.17 The bottom line is
that the Granger causality results from all three
of these models turn out to be the same. However, because the original specification (the first
ECM model) has a better fit across all samples,
both in terms of R 2 and in superior forecasts that
follow, we report in Table 4 results only from
this model. Consequently, for the whole sample,
the results of the Granger causality tests are
unaffected.
Again, the whole sample results were that
unit labor costs Granger-cause CPIC, but not
CPI. Interestingly, the results for the subsamples
in Table 4 indicate that wage growth Grangercauses CPIC in the pre-1980 sample only, not in
the post-1980 sample. In neither subsample do
wages cause CPI.18 However, across all samples
and for both price measures, prices cause wages.19
Summarizing, the in-sample causality tests
indicate that changes in wage growth have information content for changes in core consumer
price inflation, but not consumer price inflation.
However, the information content for changes
in CPIC appears to disappear after 1980. The
information content of changes in both measures of inflation for future changes in wages
appears to be much more robust. We now turn
to an examination of the extent of the information content that wages have for inflation in
out-of-sample forecasting exercises.

+ ∑ cpi ∆ pt −i + ∑ d pi ∆ wt −i + ⑀pt
2

i =1

∆2wt = aw + bw (∆w − αw − βw ∆p )t −1 + ∑ cwi ∆2pt −i + ∑ dwi ∆2w t −i + ⑀wt

Price equation
Null hypothesis

bp = 0

dpi = 0 (i = 1 ,2,3,4)

bp = 0 and
dpi = 0 (i = 1,2,3,4)

(p = CPI core)
1957:1– 94:4
1957:1– 80:4
1981:1– 94:4

15.5 (.000)***
14.0 (.000)***
.27 (.609)

4.23 (.003)***
2.99 (.024)**
1.29 (.289)

5.23 (.000)***
3.79 (.004)**
1.04 (.407)

1.80 (.182)
.07 (.796)
.59 (.446)

1.42 (.230)
.97 (.428)
.72 (.585)

1.57 (.172)
.81 (.546)
.65 (.663)

bw = 0

cwi = 0 (i = 1,2,3,4)

bw = 0 and
cwi = 0 (i = 1,2,3,4)

13.8 (.000)***
5.56 (.021)**
1.3 (.002)***

1.30 (.274)
1.86 (.125)
2.56 (.051)*

6.41 (.000)***
4.46 (.001)***
7.15 (.000)***

33.7 (.000)***
28.9 (.000)***
12.7 (.000)***

.82 (.516)
.69 (.603)
1.02 (.409)

1.6 (.000)***
9.35 (.000)***
3.41 (.010)**

(p = CPI)
1957:1– 94:4
1957:1– 80:2
1980:3– 94:4
Wage equation
Null hypothesis
(p = CPI Core)
1957:1– 94:4
1957:1– 80:4
1981:1– 94:4
(p = CPI)
1957:1– 94:4
1957:1– 80:2
1980:3 – 94:4

*** = Significance at the 1-percent level.
** = Significance at the 5-percent level.
* = Significance at the 10-percent level.

Forecasting exercises

NOTES: The error-correction term in both equations are estimated by Stock and Watson’s
Dynamic OLS with leads and lags equal to eight. F -statistics for the Granger causality
tests are reported together with their p -values in the parentheses.

Out-of-sample forecasts. The forecasting
exercises consist of horse races between
autoregressive univariate forecasts of inflation
and forecasts obtained from the ECM models,
which include unit labor costs.20 The objective is
to examine the reduction in forecast errors obtained by including the information content of
wages. We carry out forecasts for the level of
inflation and wages at forecast horizons of one,
four, and eight quarters and for the three samples
examined above.
The out-of-sample forecasts provide the
real test of how forecasters would have done in
real time using productivity-adjusted wages to
help predict inflation. The first type of forecast
we conduct is to use the ECM model estimated
for the 1958–89 sample and then ask how it
does in helping predict inflation from 1990
through 1994. As with the in-sample forecasts,
we compare the root mean square errors (RMSEs)
of the ECM model with a univariate autoregressive
model. Each quarter, we update the parameter
estimates of both models as the forecasts proceed through the 1990s.
The results are shown in the top section of

are 1980:4 and 1980:2. Therefore, the potential
break point apparent in Figure 1 is supported by
the formal stability tests.
In sum, the data suggest instability in the
cointegrating regression. We now turn to the
question of whether this instability affects the
nature of the short-run dynamics between
changes in wage growth and changes in inflation. In other words, does this instability affect
the Granger causality tests? For this purpose, we
estimate three Granger causality specifications
over the two subsamples and the entire sample.16
Two specifications are error-correction models
(ECM). In the first ECM, the error-correction
term is estimated from equations 1 and 2. In the
second ECM model, the estimated cointegrating
regression is estimated allowing a change in
both the constant and the slope across the two
subsamples. The third model does not contain

Table 5

Out-of-Sample Root Mean Square Forecast Errors
Table 5. For CPI inflation, the use of the ECM
model actually results in a higher RMSE than the
univariate model at all forecast horizons. For
CPIC inflation, the use of the ECM model results
in modest reductions in RMSEs, particularly at
the four-quarter forecast horizon. Note that
neither CPI nor CPIC inflation helps forecast
wages, as the RMSEs in five out of the six
forecasts actually increase with the ECM model.
Because the evidence suggests that there
may have been a break in the wage–inflation
relationship in the early 1980s, we also conduct
out-of-sample forecasts in which the models are
estimated using data only from the post-1980
period. In these forecasts, we initially estimate
the models over the 1981–89 period and then
conduct forecasts for the 1990 –94 period. Again,
the parameter estimates are updated as the forecasts progress through the 1990s. The results are
shown in the middle section of Table 5. They
indicate that breaking up the sample does not
result in an improvement for the ECM model. In
fact, the ECM model does worse.21 Notice, however, that the RMSEs from the univariate models
are the smallest of all the models considered.
Therefore, the results indicate that for forecasting CPI and CPIC inflation during the 1990s, the
use of only the post-1980 data results in lower
forecast errors. Additionally, the inclusion of
wage growth actually results in larger errors.
Figure 3 plots the forecast errors from both
models. Finally, the forecasts of wages indicate
that the inclusion of CPIC inflation reduces the
forecast errors at four- and eight-quarter horizons but the inclusion of CPI inflation does not
result in lower forecast errors.
As a final exercise, we look to see whether,
during the late 1970s, wages helped forecast

Inflation
Forecast horizons

ECM

AR(4)

Wage growth

(Percent
change)

ECM

AR(4)

(Percent
change)

Initial in-sample estimation: 1957:1– 89:4
Forecasting periods: 1990:1– 94:4
(CPIC)
1
4
8

.800
.908
1.442

.808
1.015
1.563

(1.10)
(10.56)
(7.76)

2.000
2.445
3.034

2.082
2.411
2.947

(3.96)
(– 1.37)
(– 2.97)

(CPI)
1
4
8

1.337
1.739
1.829

1.293
1.710
1.803

(– 3.36)
(– 1.72)
(– 1.43)

2.221
2.686
3.047

2.082
2.411
2.947

(– 6.67)
(–11.39)
(– 3.42)

Initial in-sample estimation: 1981:1– 89:4
Forecasting periods: 1990:1– 94:4
(CPIC)
1
4
8
(CPI)
1
4
8

.879
.866
.856

.814
.785
.710

(– 8.07)
(– 10.33)
(– 20.65)

2.185
1.901
1.893

2.086
2.168
2.260

(– 4.77)
(12.35)
(16.24)

1.339
1.568
1.328

1.268
1.439
1.172

(– 5.58)
(– 8.95)
(–13.26)

2.213
2.188
2.099

2.086
2.168
2.260

(–.88)
(–.88)
(7.12)

Initial in-sample estimation: 1957:1–77:4
Forecasting periods: 1978:1– 81:4
(CPIC)
1
4
8

2.836
3.102
4.041

3.085
3.564
4.206

(8.08)
(12.97)
(3.92)

3.987
5.017
4.313

3.859
4.442
4.838

(– 3.33)
(–12.93)
(10.86)

(CPI)
1
4
8

2.413
4.102
4.925

2.358
3.967
5.038

(– 2.31)
(– 3.41)
(2.24)

2.930
5.526
4.823

3.859
4.442
4.838

(24.07)
(– 24.39)
(.33)

NOTES: The forecasts for the ECM were formed using VAR(4) (including error-correction term
and a constant). The entries in the table refer to the root mean square forecast error. To
forecast inflation and wage growth for out-of-sample periods, we reestimate both ECM
and AR model by updating the in-sample periods. For example, the four-quarter-ahead
forecast for inflation and wage growth for 1991:1 is constructed by the models estimated
over the period 1957:1– 90:1 or 1981:1– 90:1.

Figure 3

Four-Quarter Ahead Out-of-Sample Forecast
Of CPI Core Inflation, 1990 – 94
inflation. In these forecasts, we estimate the
models using data from the 1958 –77 sample and
then conduct out-of-sample forecasts for the
period 1978 –81. The results are shown in the
bottom section of Table 5. For CPI, the use of
the ECM model does not result in improved
forecast errors. For CPIC, the use of the ECM
model does result in an improvement, especially
at the one- and four-quarter forecast horizons.
At the four-quarter horizon, the use of the
ECM model results in a 13-percent reduction in
RMSE. Figure 4 plots the forecast errors. The
results for inflation as a predictor of wage growth
are mixed.

Percent
6
5.5

Actual
ECM
AR

5
4.5
4
3.5
3
2.5
2
1.5
1990

1991

1992

1993

1994

FEDERAL RESERVE BANK OF DALLAS

ECONOMIC REVIEW FIRST QUARTER 1996

Notes

Figure 4

Four-Quarter Ahead Out-of-Sample Forecast
Of CPI Core Inflation, 1978 – 81
Percent
1

15
14

Actual
ECM
AR

13
12

11
10

9
8
7
6
5
1978

1979

1980

1981

In summary, out-of-sample forecasts offer
little support that the growth of unit labor costs
substantially helps forecast inflation, especially
in recent years. For forecasting inflation during
the 1990s, of the models we consider, a univariate
autoregressive model of inflation using only post1980 data results in the smallest forecast errors.
The out-of-sample forecasts for the late 1970s
indicate that wage growth did modestly help to
forecast CPIC inflation.

Conclusions
Many analysts have heralded the slow
growth of unit labor costs during recent years as
a harbinger of continued low inflation. In this
article, we investigate the usefulness of labor
costs as a predictor of inflation. Earlier studies
have focused on in-sample causality tests. Our
in-sample causality tests indicate that, during the
pre-1980 period, wage growth did have information content for future core inflation (CPIC)
but not overall CPI inflation. During the post1980 period, however, this information content
has disappeared. Additionally, we find that the
evidence of inflation causing wage growth is
quite robust across samples.
In contrast with earlier studies, we also
investigate out-of-sample forecasts of inflation
using labor costs in an error-correction model.
Out-of-sample forecasts offer the ultimate test of
whether wages help predict future inflation. For
recent years, the out-of-sample forecasting exercises offer no evidence that wage growth contributes to any reduction in forecast errors
compared with univariate autoregressive models
of inflation. Therefore, when assessing future
inflation developments, these results suggest that
policymakers and analysts should put little weight
on recent wage trends.

We would like to thank Nathan Balke, Joseph Haslag,
and Evan Koenig for helpful comments and suggestions. Any remaining errors are our own.
The Granger causality test is simply a statistical
methodology for showing whether a variable contains
information about subsequent movements in another
variable.
However, Mehra finds that the presence of this bidirectional causality is sensitive to how inflation is modeled.
The motivation for Mehra’s work is to examine the
hypothesis that prices are marked up over productivity-adjusted labor costs, a central proposition of the
expectations-augmented Phillips curve model. If that
hypothesis is correct, then long-run movements in
prices and labor costs must be correlated, and shortrun movements in labor costs should help predict
short-run movements in prices. Therefore, Mehra’s
results are consistent with the markup hypothesis for
consumer prices but not for the implicit price deflator.
Any instability in the wage-price relationship could also
be a source of instability in the price markup hypothesis and the Phillips curve (see Mehra 1993).
Unit labor costs are for the nonfarm business sector.
The figure for consumer prices including food and
energy is qualitatively similar. The formal analysis in
this study is carried out using both measures of
consumer prices.
The 1980:4 breakpoint was chosen arbitrarily on the
basis of looking at Figure 1.
We use consumer prices both because they are perhaps the most closely watched measure of underlying
inflation and because of Mehra’s finding that labor costs
do have information content for future consumer prices.
A time series is nonstationary if it has a time-varying
mean and/or variance. Nonstationarity of a series
violates an assumption underlying many statistical
inferences and can lead to “spurious regression phenomenon,” first described by Granger and Newbold
(1974). One commonly used way of removing nonstationarity is to take first differences of the series.
In other words, second differencing is required for
stationarity. Mehra (1991) reports similar results,
while Mehra (1993) finds consumer prices to be I(1).
Throughout the analysis that follows, we check the sensitivity of our results to the finding that prices are I(2).
The concept of cointegration, first proposed by
Granger and Weiss (1983), is fundamental to the use
of the error-correction model. Engel and Granger
(1987) show that a model estimated using differenced
data will be misspecified if the variables are cointegrated and the cointegrating relationship is ignored.
Cointegration of two series means that they are nonstationary and tend to move together such that a linear
combination of them is stationary. Cointegration is
sometimes interpreted as representing a long-run
equilibrium (steady-state) relationship.

Since the standard errors are corrected for serial
correlation, the χ2 statistic is appropriate to test
whether the α’s and β’s are significant.
The ADF test is applied to the long-run relationship. In
other words, ∆ p – αp – βp ∆w and ∆w – αw – βw ∆ p.
The Schwartz information criterion always implies a lag
length of four or less. Since the results are not sensitive
to the choice of lag length (k = 2, 4, or 8), we report

Chong, Yock Y., and David F. Hendry (1986), “Econometric Evaluation of Linear Macro-Economic Models,” Review
of Economic Studies 53 (August): 671–90.

only those from the model of k = 4.
This result is consistent with Mehra (1991), which
models consumer prices as I(2). However, Mehra
(1993) models prices as I(1) and finds significant
causality. As a robustness check, we also find causality for the whole sample and both price measures if we
model prices as I(1).
This maximum χ2 statistic is sometimes called the
Quandt likelihood-ratio statistic, which tests for a break
in any or all of the coefficients.
Formally, the rejection of the null of a stable cointegrating vector implies two alternatives: no cointegration
and therefore no error-correction model, or an errorcorrection model in which there is assumed to be two
cointegrating vectors, one from each subsample.
Another important implication of Hansen’s test is that
the lack of cointegration is a special case of the
alternative hypothesis, so the SupF test can also be
viewed as a test of the null of cointegration against the
alternative of no cointegration. If the SupF rejects the
null, one may conclude that the standard model of
cointegration, including its implicit assumption of
long-run stability of the cointegrating relationship,
is rejected by the data.
For both CPIC and CPI, the results for the subsamples
do not change when prices are modeled as I(1). Thus,
only the whole sample results for the CPI are sensitive
to whether prices are modeled as I(1) or I(2).
Formally, the results from the two subsamples are
conditioned on there being a structural break in the
ECM. However, it should be noted that rolling formal
stability tests cannot reject the null hypothesis of
stability. Our choice to examine the results for the post1980 sample was made on the basis of rejecting
stability of the cointegrating relationship. Additionally,
the results from the Baxter and King filter analysis and
Figure 1 motivated us to examine the results for the
post-1980 period.
The ECM and autoregressive models with four lags
perform superior to alternative lag lengths.
Encompassing tests due to Chong and Hendry (1986)
confirm this finding.

Gordon, Robert J. (1985), “Understanding Inflation in
the 1980s,” Brookings Paper on Economic Activity,

Engel, Robert F., and Clive W. J. Granger (1987), “Cointegration and Error-Correction: Representation, Estimation,
and Testing,” Econometrica, March, 251–76.

1, 263 – 99.
——— (1982), “Price Inertia and Policy Ineffectiveness
in the United States, 1989–1980,” Journal of Political
Economy, December, 1087–1117.
Granger, Clive W. J. (1988), “Some Recent Developments
in a Concept of Causality,” Journal of Econometrics,
September/October, 199 – 211.
———, and Paul Newbold (1974), “Spurious Regression
in Econometrics,” Journal of Econometrics, July, 111–20.
———, and A. A. Weiss (1984), “Time Series Analysis
and Error Correcting Models,” in S. Karlin, T. Amemiya
and L. A. Goodman (eds.) Studies in Econometrics, Time
Series and Multivariate Statistics (New York: Academic
Press).
Hansen, Bruce E. (1992), “Tests for Parameter Instability
in Regressions with I(1) Processes,” Journal of Business
and Economic Statistics 10 (July): 321– 35.
King, Robert J., James H. Stock, and Mark W. Watson
(1995), “Temporal Instability of the Unemployment –
Inflation Relationship,” Federal Reserve Bank of Chicago
Economic Perspectives, May/June, 2 –12.
Mehra, Yash P. (1993), “Unit Labor Costs and the Price
Level,” Federal Reserve Bank of Richmond Economic
Review, Fall, 35 – 52.
——— (1991), “Wage Growth and the Inflationary Process: An Empirical Note,” American Economic Review,
September, 931– 37.
Newey, Witney K., and Kenneth D. West (1987), “A
Simple Positive Semi-Definite Heteroskedasticity and
Autocorrelation Consistent Covariance Matrix,” Econometrica, May, 703 – 8.

References

Stock, James, and Mark Watson (1993), “A Simple Estimator of Cointegrating Vectors in Higher Order Integrated
Systems,” Econometrica, July, 783 – 820.

Baxter, Marianne, and Robert G. King (1995), “Measuring
Business Cycles: Approximate Band-Pass Filters for Economics Time Series,” Working Paper, no. 5022 (Cambridge, Mass.: National Bureau of Economic Research).

FEDERAL RESERVE BANK OF DALLAS

ECONOMIC REVIEW FIRST QUARTER 1996

In a recent paper, Ball and Mankiw (1995)
propose a new measure of supply shocks. Specifically, they advocate a measure of the skewness of price changes across sectors as a superior
alternative to existing measures of supply shocks,
such as the relative price of oil. Ball and Mankiw
begin by showing that various measures of the
skewness of the distribution of relative price
changes across industries in the producer price
index (PPI) are positively correlated with the
rate of increase in the overall PPI. They further
argue that these measures of skewness are better measures of supply shocks than more traditional measures such as the relative prices of
food and energy when used in simple, shortterm Phillips curve-type relationships. Their interpretation of the relationship between skewness
and aggregate inflation relies heavily on the
existence of menu costs associated with changing prices at the firm level. They further argue
that since menu cost models were designed to
explain monetary nonneutrality, these models
“…gain [scientific] credibility from their ability to
fit the facts regarding inflation and relative-price
changes.”
This article builds on the analysis of Ball
and Mankiw by exploring in some detail the
dynamics of relative price changes in a simple
dynamic general equilibrium model. We begin
by providing further evidence of a robust
statistical relationship between the skewness
of the distribution of individual price changes
and inflation. We then ask what sort of relationship would we expect to see in a model in
which all prices are free to adjust instantaneously. We show that when a simple general
equilibrium model with no menu costs is calibrated to match certain features of the real
world, it is possible to find a significant relationship between the skewness of individual
price changes and aggregate inflation. Thus, our
results cast some doubt on Ball and Mankiw’s
interpretation of the correlation between the
skewness of the distribution of price changes
and aggregate inflation as supportive of menu
cost models.

Supply Shocks
And the Distribution
Of Price Changes
Nathan S. Balke
Associate Professor
Department of Economics
Southern Methodist University
Mark A. Wynne
Senior Economist and Policy Advisor
Federal Reserve Bank of Dallas

his article explores in some

detail the dynamics of relative
price changes in a simple
dynamic general
equilibrium model.

Relative price changes as aggregate
supply shocks
Ball and Mankiw begin their analysis by
discussing a simple model in which menu costs
associated with changing prices cause firms to
adjust nominal prices only in response to large
relative price shocks. The existence of menu
costs implies the existence of range of inaction
over which firms do nothing to change their
prices in response to shocks. In Ball and Mankiw’s

disaggregation. At the four-digit level of disaggregation, the number of component series rises
from 213 in 1949 to 343 in 1989. Ball and
Mankiw then document the relationship between
the distribution of the changes in these several
hundred price series and the overall inflation
rate (as measured by the PPI).
Their data analysis reveals a number of
interesting facts. First, there is considerable variation in the distribution of price changes over
time. For example, in 1987 the distribution is
fairly symmetric, while in 1973 it is skewed
sharply to the right and in 1986 it is skewed
sharply to the left.1 Not surprisingly, both 1973
and 1986 were also years in which there were
significant oil price shocks, with oil prices rising
dramatically in 1973 and falling dramatically
in 1986.
Ball and Mankiw document a statistically
significant relationship between various measures of skewness and the overall inflation rate.2
They also show that the skewness of the distribution of price changes tends to dominate the
standard deviation of the distribution as an explanatory variable for inflation. This result is
robust to their use of any of three measures of
skewness.
In the analysis presented below we will
examine the relationship between the distribution of price changes and aggregate inflation in
the context of a multisectoral model that is
calibrated to match certain characteristics of the
U.S. economy. Considerations of tractability
prevent us from considering a model with
more than a small number of sectors. In fact, we
work with a version of the real business cycle
model proposed by Long and Plosser (1983) that
has only six sectors. Before proceeding, then,
it is important to verify that the empirical regularities observed by Ball and Mankiw in the
prices that make up the PPI are also present
when we consider more aggregated measures
of prices.
The six sectors Long and Plosser use to
calibrate their model are agriculture, mining,
construction, manufacturing, transportation and
trade, and services and miscellaneous. Table 1
presents some summary statistics for inflation
rates (as measured by the implicit gross
domestic product (GDP) deflators for these
sectors) over the period 1949–93. The table
reveals a number of interesting facts about the
time series behavior of sectoral inflation rates.3
First, there are notable differences in the average rates of inflation across the six sectors over
the sample period, ranging from a low of just
under 2 percent per year in agriculture to a

model, the adjustment to large but not to small
shocks results in a positive correlation between
the skewness and the mean of the (cross section) distribution of price changes. Ball and
Mankiw argue that a flexible price model would
predict no such relationship. In the flexible price
model, a large positive relative price shock is
likely to be offset by small declines in the prices
of other commodities. Ball and Mankiw examine
the cross section distribution of several hundred
prices and indeed find a positive correlation
between skewness and the mean of the distribution. They interpret this as evidence in favor of
the menu cost model.
Yet, as we show below, it is possible for a
flexible price model also to generate this positive correlation between skewness and the mean
of the distribution of price changes. Ball and
Mankiw’s assertion that a flexible price model
cannot generate this correlation is probably correct for the case in which a large number of
sectors are experiencing shocks that are independent of one another and are of the same
relative magnitude. This situation would cause
relative price changes for the different commodities to be relatively independent of each
other also. Yet, in reality, prices across commodities are not independent; shocks in one
sector tend to affect prices in other sectors.
Furthermore, a few very volatile sectors, such as
food and energy, may be responsible for most
of the observed volatility in the distribution of
price changes. As a result, the correlation between the average inflation rate and the skewness of the distribution of price changes found
in the data may arise just because of the importance of a few large price shocks.
Below we consider the implications for the
data of a modified version of the general equilibrium model due to Long and Plosser (1983).
The model is modified slightly to include a
numeraire role for money. The model has complete price flexibility and multiple sectors. Among
the key characteristics of this model is that a
shock in one sector can spill over to other
sectors. We show that as the sectors become
more interrelated, it becomes easier for the flexible price model to generate a positive correlation between the skewness of the distribution of
price changes and aggregate inflation.

The data
Ball and Mankiw look at the relationship
between the distribution of prices in the producer price index (PPI) on an annual basis over
the period 1949– 89. The advantage of looking at
the PPI is that it is available at a high degree of

FEDERAL RESERVE BANK OF DALLAS

ECONOMIC REVIEW FIRST QUARTER 1996

Table 1

Statistics on Inflation Rates by Sector, 1948–93
(Annual data)

Agriculture
Mining
Construction
Manufacturing
Transportation and trade
Services and miscellaneous

Mean

Variance

1.99
3.28
4.01
5.23
3.41
4.94

114.34
8.39
210.74
21.53
7.02
4.45

A simple dynamic general equilibrium
model with multiple sectors
A logical starting point for an investigation
of the relationship between the distribution of
price changes across sectors and aggregate inflation is the equilibrium business cycle model of
Long and Plosser (1983). A great virtue of this
model is that it has multiple sectors, but more
importantly, the calculation of decision rules is
simplified because of restrictions on preferences
and the rate of depreciation of capital. The
original version of this model was a “real”model
in every sense of the word, in that there was no
role for money.
For our purposes we would like to extend
the model to include money as a numeraire.
Benassy (1995) has recently proposed a version
of the Long and Plosser model that incorporates money by including real balances in the
utility function. While introducing money into
the model in this way is not entirely satisfactory,
it is well-known that this specification is functionally equivalent in certain circumstances to
the more popular cash-in-advance and shopping-time formulations of the demand for real
balances. However, Benassy works with a singlesector variant of the Long and Plosser model,
and it is far from straightforward to extend his
analysis to a multiple-sector setting (the essence
of the problem that arises in this regard is the
absence of a single correct measure of the price
level in a multisector environment). An alternative is to introduce money via some sort of
cash-in-advance constraint on either purchases
of consumption goods (or some subset thereof)
or purchases of capital goods, or both. However, it rapidly becomes apparent that it is no
longer possible to calculate simple closed form
decision rules in either of these cases.
We opt instead to introduce money in a
somewhat novel manner. Specifically, we assume that consumers are obliged to hold some
fraction υ of their consumption purchases during each period in the form of cash at the end of
the period. Thus, we posit the following constraint on household choices:

high of nearly 5.25 percent per year in manufacturing. Second, there are dramatic differences
in the variances of the individual inflation rates
across sectors, from a low of 4.45 in services
and miscellaneous to a high of 210.74 in construction.
Table 2 presents some simple regression
results for the relationship between the rate of
inflation as measured by the fixed-weight GDP
deflator and measures of the distribution of prices
across six sectors of the U.S. economy. The first
column shows the results of regressing the rate
of inflation on its own lagged value, while the
second column shows the results of adding an
unweighted measure of skewness to this basic
regression. Comparing columns 1 and 2, we see
that skewness has significant explanatory power
–
for the inflation rate: the R 2 increases from 0.52
to 0.66, and all of the coefficients in the second
regression are significant at the 1-percent level.
Column 3 shows what happens if we use a
weighted measure of skewness instead. We ob–
tain an even higher R 2 and again all coefficient
estimates are significant at the 1-percent level.
The results in this table compare favorably with
the results reported in Tables IIIA and IIIB of
Ball and Mankiw. In fact, working with our
more aggregated price data, we find an even
stronger statistical relationship (in the sense of
–
higher R 2 ) between the skewness of the distribution of price changes and the aggregate inflation rate.
To summarize, it is clear that we can obtain the same strong statistical relationship between the skewness of the distribution of price
changes and aggregate inflation looking at only
six prices as Ball and Mankiw do looking at
several hundred prices. The relationship uncovered by Ball and Mankiw seems robust, and our
decision to focus on just six sectors does not
seem to be a gross violation of the spirit of their
analysis. Our objective in what follows is to see
to what extent we can replicate the facts about
the relationship between skewness and inflation
as documented here in the context of a simple
dynamic general equilibrium model.

(1)

M t ≥ υ∑ Pi ,tC i ,t ,
i =1

where Mt denotes the stock of nominal money
balances held at the end of period t and
N

∑ Pi ,tCi ,t denotes nominal consumption expen-

i =1

ditures during period t, with Pi ,t denoting the
price of good i at date t, and Ci ,t denoting the
quantity of good i purchased for consumption

Table 2

Inflation and the distribution of price changes, 1948–93
purposes at date t. The existence of this constraint can be thought of as arising due to the
need to, say, maintain some minimum level of
cash balances in a bank account to facilitate
consumption purchases made with inside money.
It will turn out that money does not play a very
important role in our economy.
The rest of the model is relatively standard.
Households. The economy is populated by
a large number of identical consumers, each of
whom has preferences summarized by the following utility function:
U =

(2)

∞

∑ β u (C , L ),
t

i =1

≥

i ,t

i =1

i ,t

.715**
(.102)

.642**
(.088)

.609**
(.080)

.011**
(.003)
.004**
(.001)
.52

.66

.72

1.54

1.78

2.02

held over to the next period, Mt .
The remaining constraint that the consumer
faces is on the allocation of available time,
N

i =1

which states that the sum of leisure and time
worked in each sector cannot exceed the total
amount of time available, which we normalize
to 1.
The consumer’s problem is to maximize
the objective function given in equation 2 above
subject to the budget constraint (equation 4), the
cash constraint (equation 1), and the constraint
on the allocation of time (equation 5), taking as
given the prices at which he or she can purchase
consumption and capital goods and the wage
and rental rates at which labor and capital services are sold to the business sector.
Firms. Production possibilities in the i ’th
sector are given by the following production
function:

j =1

i =1

+ ∑ Pj ,t ∑ K i , j ,t + M t ,

where Wi ,t denotes the (nominal) wage in sector
i at date t (which in equilibrium will be the
same in all sectors), Hi ,t denotes hours worked
in sector i at date t, R i,j,t denotes the nominal
rental rate on type j capital employed in sector
i in period t, Ki,j,t –1 represents the quantity of
type j capital employed in sector i during
period t, (that is, capital in place at the end of
period t – 1) and µt represents the gross rate of
increase in the money stock at date t. The
sources of funds each period are wage income
N

where Yi,t denotes output of the i ’th good
at date t, Zi,t is a random variable or productivity shock that denotes the state of technology
in the i ’th sector at date t, Hi,t denotes hours
worked in the i ’th sector at date t, and Ki,j,t
denotes the quantity of output of the j’ th industry employed in the i’ th industry at date t.
The parameters of the production function, bi
and ai,j are assumed to satisfy bi > 0, ai,j > 0 and

and a transfer from the government that is
directly proportional to nominal money holdings held at the end of the previous period,
µt Mt –1. The uses of funds each period are conN

∑ Pi ,tCi ,t , purchases of

bi + ∑ ai , j = 1 for i = 1, 2, …, N. The produc-

i =1

new capital equipment,

i ,j

j =1

i =1 j =1

sumption expenditures,

Yi ,t = Zi ,t H ib,t ∏ K ia, j ,t −1 ,

(6 )

∑Wi ,t H i ,t , income from capital ∑ ∑ Ri , j ,t K i , j ,t −1

i =1

Lt + ∑ H i ,t = 1,

(5)

i =1 j =1

∑P C

Lagged inflation

NOTE: Standard errors are in parentheses.

H i ,t + ∑ ∑ Ri , j ,t K i , j ,t −1 + µt M t −1

.014**
(.004)

*,** denotes significance at the 5-percent and 1-percent levels, respectively.

where θi ≥ 0 , ∀ i. If θi = 0 for some i ≥ 1 then
that commodity has no utility value to the consumer.
The budget constraint of the representative
consumer is given by
N

.013**
(.004)

Durbin–Watson statistic

i =1

i ,t

.010*
(.005)

Constant

–
R2

u (C t , Lt ) = θ0 log(Lt ) + ∑ θi log(C i ,t ),

∑W

(3)

Weighted skewness

where 1 > β > 0 is the discount factor, Ct =
(C1,t ,C 2 ,t ,…,CN,t ) / is an N × 1 vector of commodities consumed at date t, and Lt denotes
leisure at date t. The point-in-time utility function is furthermore assumed to have the following specific functional form:

(4 )

(2)

Unweighted skewness

i =0

(3 )

(1)

j =1

i =1

∑ Pj ,t ∑ K i , j ,t , and funds

FEDERAL RESERVE BANK OF DALLAS

tion side of this model can be viewed in two

ECONOMIC REVIEW FIRST QUARTER 1996

sector, as summarized by the level of output
produced in each sector.
To better understand why the allocation of
labor across sectors (and total labor or leisure) is
independent of the current state of the economy,
consider the condition determining the equilibrium allocation of labor.4 This condition states
that the value of the marginal product of labor in
each of its alternative employments should equal
the wage rate, where all prices and wages are
denominated in utility units. The wage rate is
simply the marginal utility of leisure. The question then is, Given an initially optimal allocation
of labor across sectors, would a change in either
the available capital stock or the state of technology change either side of this equation? Given
the specification of preferences we are working
with, the marginal utility of leisure at any point
in time depends only on the labor–leisure allocation at that time, so any effect of the capital
stock or technology on the optimal allocation
must come about through changes in the value
of the marginal product. Consider the effect of a
higher than expected realization of the technology. For a given allocation of capital to a particular sector, one effect of the technology shock
would be to raise the marginal physical product
of labor. However, working against this, the
technology shock will put downward pressure
on the price of the sector’s output, lowering the
value of the marginal product of labor. It just so
happens in this case that these two effects offset
each other, leaving the value of the marginal
product unchanged. In other words, the optimal
allocation of labor to the sector is unaffected by
realizations of the technology shock. Similar reasoning applies to determining the effects of
greater availability of capital in a sector.
We can use the equations above to write
dollar-denominated prices in our extended
model as

ways. We can think of each sector as producing
both consumption and capital goods that are
used in every sector, with the capital depreciating at a 100-percent rate. Or, we can think of
each sector as producing consumption goods
and goods that are used as intermediate inputs
in the production of other goods. The two interpretations are equivalent.
The firm’s optimization problem is to maximize profits, taking as given the available technology, the price at which output can be sold
and the prices or rental rates of the labor and
capital inputs.
Equilibrium. Straightforward manipulation
of the first order conditions for the household
and firm maximization problems allows us to
obtain the following decision rules:
⎛θ ⎞
C i ,t = ⎜ i ⎟Yi ,t ,
⎝ γi ⎠

(7 )

(8 )

Lt =

θ0 (1 + ν(1 − β))
θ0 (1 + ν(1 − β)) +

∑γb

i i

i =1

(9 )

H i ,t =

γ ibi
N

, and

θ0 (1 + ν(1 − β)) + ∑ γ jb j
j =1

(10 )

⎛ βγ a ⎞
K i , j ,t = ⎜ i i , j ⎟ Y j ,t ,
⎝ γj ⎠
N

where γ j = θ j + β∑ γ i ai , j . The derivation of
i =1

these rules is explained in more detail in the
Appendix.
Some comments are in order. As noted,
the simple form of these decision rules stems
from the particular assumptions we have made
about preferences, production possibilities and
the durability of capital. Equation 7 shows that
consumption of each type of good is simply a
constant fraction (θi /γi ) of the available output
of that type of good, with the fraction being a
complicated function of the parameters of the
underlying preferences and technology. Likewise, equation 10 shows that the amount of
each sector’s output that is allocated for use
in production in other sectors is a constant
fraction (βγi ai,j /γj ) of the available output. Perhaps more surprisingly, equations 8 and 9 show
that total leisure and the allocation of effort to
each sector are independent of realizations of
the productivity shocks and are also independent of the endogenously chosen “state” of each

(11)

Pi ,t =

1
ν

γi

Mt
.
Yi ,t

∑θ

j =1

That is, nominal prices are directly proportional
to the nominal money stock.5 It is straightforward to show that these prices are also directly
proportional to the utility-denominated prices
calculated by Long and Plosser.
Dynamics. The dynamic behavior of this
economy is implied by the technology as summarized by the production functions, along with
the decision rules for the inputs to the production processes. It is convenient to write the
system in logarithmic form as follows,

(12)

y t = k y + Ay t −1 + z t ,

(16 )

⎛N ⎞
= mt − m − log ⎜ ∑ γ i ⎟
⎝ i =1 ⎠

where we adopt the convention that lower case
letters denote the logarithms of the corresponding upper case variable. Thus, yt is the N × 1
vector (log(Y1,t ),log(Y2,t ),…,log(YN,t )) /, ky is an
N × 1 vector of constants, and zt is the N × 1
stochastic vector (log(Z1,t ),log(Z2,t ),…,log(ZN,t )) /.
Since our primary focus in this article is on the
evolution of the distribution of prices, we will
also need to specify a stochastic process for the
log of the nominal money stock, mt .
The evolution of prices is given by
(13)

⎛N
⎞
+ log ⎜ ∑ γ i exp( yi − yi ,t )⎟ ,
⎝ i =1
⎠
where bars over variables denote steady-state
values, which we pick as the base year.
Calibration. Long and Plosser calibrate their
model to six sectors of the U.S. economy using a
consolidated version of the twenty-three sector
input–output table for 1967 (U.S. Department
of Commerce, 1975). This yields an estimate of
the A matrix for the model above. Given an
estimate of the A matrix, the vector of coefficients b is recovered from the assumption of

pt = k p + ι N mt + y t ,

where pt = (log(P1,t ),log(P2,t ),…,log(PN,t )) /, kp
is a vector of constants and ιN is an N × 1
vector of ones. An important point to note
from this expression is that the money stock
only affects the mean of the distribution of
prices across sectors and not any of the higher
moments (such as the standard deviation or
skewness).
Measures of the aggregate price level. We
can easily calculate a variety of measures of the
aggregate price level that correspond to the
measures commonly used to gauge inflation in
the real world. Three such measures are defined
in the Appendix. We will concentrate on just
one of them, a fixed-weight measure of the
aggregate price level that corresponds to the
fixed-weight GDP deflator. We construct a fixedweight GDP deflator starting from the definition

constant returns to scale, i.e., bi = 1 − ∑ ai , j . To
j =1

calibrate the vector θ, we note that the decision
rules for consumption of each type of good
imply that θi = γ i

(14 )

PGDPFt =

i ,t i ,b

i =1
N

∑P

i ,b i ,b

i =1

⎛ 0.33
⎜
⎜ 0
⎜ 0
A=⎜
⎜ 0
⎜ 0
⎜⎜
⎝ 0

That is, the value of the fixed-weight GDP deflator at date t, PGDPFt , equals the ratio of the cost
of purchasing the base-year market basket of
N

final output at current-year prices, ∑ Pi ,tYi ,b , to
i =1

the cost of purchasing the base-year market
basket of final output at base-year prices,
N

i =1

11 above, we obtain
PGDPFt =

Mt
Mb

∑γ

γ iYi ,b

i =1

Yi ,t

∑

i =1

which can also be rewritten in logs as

FEDERAL RESERVE BANK OF DALLAS

0
0.33
0
0

0
0
0.33
0

0
0
0
0.33

0
0
0
0

0
0

0.33
0

⎞
⎟
⎟
⎟
⎟
⎟
0 ⎟
⎟
0.33⎟⎠
0
0
0
0

and θ = (0.167, 0.167, 0.167, 0.167, 0.167,
0.167) /. We assume that the technology innovations hitting each sector are i.i.d. with zero
mean and unit variance. A priori, we expect that
there will be no relationship between measures
of the cross-section distribution of prices and
aggregate inflation in this economy. We think
that Ball and Mankiw have an economy such as
this in mind when they question the ability of a
flexible price model to generate the correlations

∑ Pi ,bYi ,b . Making substitutions using equation

(15)

C i ,t
. We can obtain estimates
Yi ,t

of the share (γi ) of each sector’s output in aggregate output from the 1967 input–output table.
The same table also allows us to estimate the
fraction of each sector’s output that was allocated to consumption that year, which together
with the estimate of γi allows us to obtain an
estimate of θi . Finally, we set the discount factor
β equal to 0.95 and the parameter ν equal to 1.
Experiment 1. Our first experiment examines the behavior of inflation and the distribution of prices in an economy with six sectors but
with no input–output relations between the
sectors and with each sector subject to i.i.d.
shocks of equal variance. Thus, we set

∑P Y

pgdpft ≡ log(PGDPFt )

ECONOMIC REVIEW FIRST QUARTER 1996

The innovations ⑀t = (⑀1,t , ⑀ 2,t , ⑀ 3,t , ⑀4,t , ⑀ 5,t , ⑀6,t ) /
are assumed to be i.i.d. with variances σ 2 =
(1.01, 1.26, 0.25, 0.54, 0.19, 0.03) × 10 – 3.
Experiment 4. For our fourth experiment,
we estimate a simple VAR for the technology
innovations in each sector. Again, we assume
total factor productivity follows the process

between skewness and inflation that are found
in the data.
Experiment 2. For our second experiment,
we calibrate the A matrix and the θ vector along
the same lines as Long and Plosser, but retain
the assumption of i.i.d. shocks. Thus, we set
⎛ 0.4471
⎜
⎜ 0.0000
⎜ 0.0029
A=⎜
⎜ 0.0618
⎜ 0.0017
⎜⎜
⎝ 0.0174

0.0033
0.0935
0.0104
0.0340

0.0146
0.0427
0.0003
0.0050

0.0004 0.0166
0.0212 0.0595

0.1591⎞
⎟
0.4854⎟
0.0893⎟
⎟
0.1267⎟
0.1246 0.1040 0.3249⎟
⎟
0.1998 0.0871 0.3805⎟⎠

0.2093 0.0999
0.1744 0.0549
0.4189 0.1209
0.4576 0.0611

z t = Pz t −1 + ⑀t
where now the matrix P is given by
⎛ 0.231
⎜
⎜ 0.006
⎜ 0.044
P =⎜
⎜ 0.188
⎜ 0.083
⎜⎜
⎝ 0.065

and θ = (0.003465, 0.000162, 0.022046, 0.139968,
0.089811, 0.15012) /. The economy of this experiment differs from that of the first experiment mainly in allowing for complicated
input–output type relations between the various
sectors.
Experiment 3. For our third experiment, we
retain the specifications of the A matrix and θ
vector used in the second experiment but calibrate the technology shocks to the actual postwar data. Thus, we estimate Solow residuals for
each sector as

− (1 − αi ) log(ki ,t −1 ) − αi log(ni ,t )

where log(yi,t ) is the BP-filtered log of output
in sector i produced during period t, log(ni,t )
is the BP-filtered log of full time equivalent
employees in the i ’th sector during period t,
log(ki,t –1) is the BP-filtered log of the net (real)
capital stock in the i ’th sector as of the end of
period t – 1 (i.e., capital available in the i ’th
sector at the beginning of period t), and αi is
the average value over the sample period
(1947–94) of labor’s share in the i ’th sector
(defined as the ratio of nominal compensation
of employees in the i ’th sector to nominal GDP
in that sector). For the BP filter, we used the
parameter values up = 2, dn = 8 and K = 3.6
We assume that total factor productivity in
the model evolves according to
z t = Pz t −1 + ⑀t

0.0
0.0
0.458
0.0
0.0
0.0

0.0
0.0
0.0
0.127
0.0
0.0

1.031

0.872

0.468
0.458
0.074
0.075
0.066

0.344
0.438
0.188
0.020
0.166

1.831⎞
⎟
2.334⎟
0.022⎟
⎟
0.230⎟
0.092⎟
⎟
0.079⎟⎠

Results
For each experiment, we calibrate the
model as described above and simulate it for
fifty periods one hundred times. We use the
time series on prices generated in each of the
one hundred simulations to run the regressions
described in Table 2. Table 3 reports the average
value over all one hundred simulations of the
regression coefficients on the weighted and
unweighted measures of the skewness of the

where we use OLS to estimate the matrix P as
0.0
0.024
0.0
0.0
0.0
0.0

0.205
0.948
0.515
0.438
0.348
0.075

and again the innovations ⑀t are assumed to be
i.i.d. with variances σ 2 = (0.75, 0.87, 0.20, 0.41,
0.13, 0.02) × 10 – 3.
Each of these experiments introduces progressively more interaction between the sectors
and allows for greater diversity in the shocks
hitting the sectors. In the first experiment, there
is no interaction and the shocks hitting each
sector are completely independent of one another. The second experiment allows for interaction through input–output relationships but
retains the assumption of independent shocks.
The third experiment allows for interaction
through input–output relationships and allows
for serially correlated shocks in each sector. The
fourth experiment allows for input–output type
interaction between sectors and for serial correlation in the state of technology across sectors.
One final comment on the experiments. In
each of the four experiments reported here, we
hold the money stock constant, so that the only
source of fluctuations in the model economies
are technological innovations. This technique
allows us to completely isolate the effects of
what Ball and Mankiw call “supply shocks” on
the relationship between the distribution of price
changes and aggregate inflation.

zi ,t ≡ log(Zi ,t ) = log( yi ,t )

⎛ 0.109
⎜
⎜ 0.0
⎜ 0.0
P =⎜
⎜ 0.0
⎜ 0.0
⎜⎜
⎝ 0.0

0.285
0.116
0.031
0.127
0.068
0.004

0.0
0.0
0.0
0.0
0.106
0.0

0.0 ⎞
⎟
0.0 ⎟
0.0 ⎟
⎟
0.0 ⎟
0.0 ⎟
⎟
0.329⎟⎠

Table 3

Estimated coefficients on skewness in inflation regression
distribution of prices at each date for an inflation
regression in which we use the rate of change
of a fixed-weight measure of the GDP deflator
as the measure of inflation. For comparison,
we also report the coefficients estimated using
actual data.
Moving down the rows of Table 3, we see
that in the basic economy with no interaction
between sectors and i.i.d. shocks of equal variance hitting each sector, we are unable to generate a significant role for skewness in explaining
the inflation rate. Note that the coefficient estimates are the same for the weighted and
unweighted measures of skewness as all sectors
are identical by construction. Moving to the
economy of experiment 2, there is still no role
for unweighted skewness in explaining inflation, but the weighted measure is now significant. Recall that this economy differs from that
in experiment 1 only in that it allows for input–
output type relationships between all of the
sectors. For the economies of experiment 3 and
experiment 4, the weighted measure of skewness helps explain inflation. However, in none
of our experiments is the unweighted measure
correlated with inflation in a statistically significant sense.
Summarizing our results, it is clear that
we are able to replicate to a surprising degree
the key correlation between skewness and inflation Ball and Mankiw find in the data. Most
importantly, we are able to do so in the context
of a simple equilibrium model with fully flexible
prices, thus raising questions about Ball and
Mankiw’s interpretation that this correlation results from the existence of menu costs. In Balke
and Wynne (1995), we also document other
aspects of the relationship between the distribution of price changes and the overall inflation
rate and show that skewness seems to be a
leading indicator of aggregate inflation. Our
model has less success in replicating this feature
of the data.

Unweighted skewness
Data

.011**
(.003)

.004**
(.001)

Experiment 1

.038
(.182)

Experiment 2

–.037
(.222)

.297**
(.102)

Experiment 3

.000
(.003)

.005**
(.002)

Experiment 4

.001
(.004)

.007**
(.003)

*,** denotes significance at the 5-percent and 1-percent levels, respectively.
NOTE: Standard errors are in parentheses.
SOURCE: Authors’ calculations.

(in the sense of adjusting their prices) to large
shocks but not to small shocks. We show that in
the context of a simple dynamic general equilibrium model with no costs of adjusting prices it is
possible to observe the same correlation between the skewness of the distribution of price
change and the overall inflation rate. Our model
is driven solely by supply shocks in the form of
technological innovations.
The analysis in this article leaves a number
of issues open for further research. First, it would
be interesting to document in a more thorough
fashion the behavior of the distribution of price
changes over the business cycle and its relationship to aggregate activity and aggregate inflation. It would also be interesting to extend the
analysis above to a model with more sophisticated dynamics and a more important role for
money. Finally, it would be interesting to extend
the model outlined above to allow for a limited
degree of price stickiness (say along the lines of
Ohanian, Stockman, and Kilian 1994) and see
how much nominal rigidities can contribute to
explaining the relationship between the distribution of price changes and inflation in an
equilibrium model. Some of these issues are
addressed in Balke and Wynne (1995).

Conclusions
In this article, we explore the relationship
between shifts in the distribution of prices and
the aggregate inflation rate in the context of a
simple dynamic general equilibrium model with
multiple sectors. The idea that changes in the
distribution of relative price changes might have
implications for the overall inflation rate was
first proposed by Ball and Mankiw (1995). A
crucial part of the story that they tell is that firms
face significant adjustment costs associated with
changing nominal prices. The existence of these
so-called menu costs means that firms respond

FEDERAL RESERVE BANK OF DALLAS

Weighted skewness

Notes

We thank Evan Koenig and Finn Kydland for comments
on an earlier draft. Whitney Andrew provided excellent
research assistance.
The skewness of a distribution is defined as E [(x– µ)3]/σ 3,
where µ is the mean of the distribution of X and σ is the
standard deviation.
Ball and Mankiw look at two measure of skewness in
addition to the conventional measure defined in note 1
above. The first, intended to measure the mass in the

ECONOMIC REVIEW FIRST QUARTER 1996

References

upper tail of the distribution relative to the mass in the
lower tail, is defined as
−x

∫

Balke, Nathan S., and Mark A. Wynne, 1995, “A general
equilibrium analysis of the relationship between relative
price changes and aggregate inflation,” manuscript in
progress, Federal Reserve Bank of Dallas.

∞

∫

AsymX = rh(r )dr + rh(r )dr ,
−∞

where r is defined as the relative price change defined as the four-digit industry inflation rate minus the
average of the four-digit industry inflation rates (i.e.,
rit = ∆ log(pi,t ) −

Ball, Laurence and N. Gregory Mankiw, 1995, “RelativePrice Changes as Aggregate Supply Shocks,” Quarterly

Journal of Economics, February, 161–193.

∑ ∆ log(p
N

i,t

) and h (r ) is the density of r.

i =1

The tails are defined as relative price changes greater
than X percent or less than –X percent. Their second
alternative measure of skewness is
Q =

4
5

∫

∞

−∞

Baxter, Marianne, and Robert G. King, 1995, “Measuring
Business Cycles: Approximate Band-Pass Filters for
Economic Time Series, NBER Working Paper No. 5022,
February.

r •rh(r )dr .

Benassy, Jean-Pascal, 1995, “Money and wage contracts
in an optimizing model of the business cycle,” Journal of
Monetary Economics, 35, 2, April, 303–315.

This variable measures the average of the product of
each relative price change and its own absolute value.
Formal tests for nonstationarity indicate that all of the
sectoral inflation rates with the exception of agriculture
are nonstationary, meaning that in samples of infinite
size the variances of these series will be undefined.
However, this is not necessarily a problem from our
perspective as the data in Table 1 are simply presented to illustrate differences in the rates of change
of prices in different sectors.
See also Long and Plosser (1983, 49–50).
Note that in the basic Long and Plosser model, nominal GDP (denominated in utility terms) is a constant.
In our extended model, nominal GDP (denominated
now in terms of dollars) is directly proportional to the
money stock.
The BP filter is explained in Baxter and King (1995).

Long, John B., and Charles I. Plosser, 1983, “Real
Business Cycles,” Journal of Political Economy, vol. 91,
no. 1, 39–69.
Ohanian, Lee E., Alan C. Stockman, and Lutz Kilian,
1994, “The effects of real and monetary shocks in a
business cycle model with some sticky prices,” mimeo
University of Pennsylvania.
U.S. Department of Commerce: Bureau of the Census,
1975, Historical Statistics of the United States: Colonial
Times to 1970, Washington, D.C.: U.S. Government
Printing Office.

Immediately following Mexico’s December
20, 1994, devaluation of the peso, some observers expressed detailed support for the move.
They conjectured that it would calm financial markets that had showed some signs of
volatility.
Mexican officials themselves treated the
devaluation as if it would have a stabilizing
influence. In presenting the devaluation, they
announced that the country’s erstwhile crawling
peg regime would remain in place, and that
the devaluation would represent only a change
in the weak-side edge of Mexico’s exchange
rate band.
In the United States, some analysts also
accepted Mexico’s initial devaluation as equilibrating. Then Acting International Monetary Fund
Director Stanley Fischer noted Mexico’s initiatives as “an appropriate policy response to recent market developments.…” 1 MIT Professor
Rudiger Dornbusch, who had for some time
been advocating a Mexican devaluation, was
quoted as saying that now was the time for
“smart money” to move into Mexico, with the
currency at appropriate levels.2
But instead of inducing stability in financial markets, the initial devaluation triggered a
run on the currency. Foreign currency reserves
fell markedly. Mexican interest rates rose rapidly. The exchange rate moved well beyond
what advocates of Mexican devaluation
had said they thought sufficient.
While subsequent exchange rate and
interest rate reactions to Mexico’s initial devaluation raised questions about how financial
markets operate, financial events preceding
Mexico’s initial devaluation of the peso also
offered anomalies. Financial markets typically
sense impending devaluations. This time, despite concerns voiced occasionally that the
peso was overvalued, reports were widespread
that the timing had surprised financial markets3 and even that investors felt betrayed.4
This article considers factors that led to the
devaluation, examines why it seemed to surprise markets, and addresses financial market
behavior in the wake of the peso devaluation. It
is useful to consider factors that led to devaluation in the framework of the so-called impossibility theorem. Recall that this theorem claims
that policy authorities cannot simultaneously and
continuously follow the three objectives of free
capital mobility, fixed exchange rates, and an
independent monetary policy.
I detail why policymakers might reasonably conclude that Mexico could possibly
pursue limited episodes of monetary indepen-

Policy Priorities
And the Mexican
Exchange Rate
Crisis
William C. Gruben
Research Officer
Federal Reserve Bank of Dallas

his article considers factors that
led to Mexico’s December 1994

devaluation, examines why it seemed
to surprise markets, and addresses
financial market behavior in the
wake of the peso devaluation.
It is useful to consider factors that
led to devaluation in the framework
of the so-called impossibility theorem.

FEDERAL RESERVE BANK OF DALLAS

ECONOMIC REVIEW FIRST QUARTER 1996

dence—even if the impossibility theorem ultimately could not be denied—and why the possibility could have been permitted to become a
reality. Nevertheless, when witnessing a financial panic like Mexico’s, it may require effort to
recall that exchange rate devaluation is a matter
of choice. A central bank can always maintain a
pegged exchange rate. The price is contraction
in the monetary base or, equivalently, a persistently high interest rate.5
This article outlines the rise of priorities
that came to dominate the preservation of the
Mexican exchange rate regime. Specifically, already high real interest rates, resulting increases
in nonperforming loan rates, and the implications of all of these factors for commercial bank
solvency seem in part to have motivated credit
creation at times in 1994 while the United States
was tightening credit.

long-term rates seem to influence Mexican longterm interest rates.6
To the extent that these data suggested
limited financial integration in short-term markets, Mexico may have perceived itself able to
pursue a relatively independent monetary policy
in the short run. In any case, as will be detailed,
Mexico did pursue a monetary policy in the
second half of 1994 that was inconsistent with
the United States’ increasingly restrictive approach
to money market intervention.
The implications of the U.S. long-term
debt to Mexican long-term debt relations suggest that Mexico’s monetary policy could not
remain independent in the longer term—at
least not in a pegged exchange rate regime.
Once the United States moved its long rates,
Mexico would have to follow quickly or face
large capital outflows. There is much to suggest
that political factors contributed to the day-today changes in capital outflows that ultimately
occurred but, in the end, monetary policy in
Mexico was not consistent with reversing them.

Mexico’s pre-devaluation exchange rate
and monetary policy independence
While the impossibility theorem posits that
policy authorities cannot simultaneously and
continuously follow the three objectives of free
capital mobility, fixed exchange rates, and an
independent monetary policy—the meaning of
the term continuously complicates matters for
anyone who wants to analyze Mexico. How
continuous does continuous have to be?
Before the December 1994 devaluation,
Mexico’s exchange rate was essentially pegged
to the U.S. dollar, but Mexico gave itself what
appeared to be some room to maneuver. In
pegging the peso to the dollar, Mexico was
announcing its intent to cede some control over
its monetary policy to the United States. One
advantage of taking this step and then persisting
with it is to establish credibility that, in general,
noninflationary policies would be in place.
If Mexico had fixed its exchange rate policy
hard and fast to the dollar, it would have been
fully ceding its monetary policy to the United
States. But in fact, Mexico permitted its exchange rate to fluctuate within a band whose
weak-side edge devalued at 0.0004 pesos per
dollar. With a band, Mexico’s central bank could
run expansionary or contractionary monetary
policies different from those of the U.S. central
bank—provided that the resulting movements in
the exchange rate remained within the band—
and still maintain exchange rate credibility.
An important detail of Mexico’s monetary
independence, however, involved what may be
seen as its term structure. U.S. short-term rates
appear not to have an influence on Mexican
short-term or long-term interest rates, but U.S.

Tensions within Mexico’s
exchange-rate-based stabilization plan
Incomes policies. Exchange-rate-based
stabilizations are very difficult to pursue effectively over protracted periods. In programs like
Mexico’s, devaluation is not unusual, even when
care is taken to address their typical problems
by using exchange rate pegging as only a part of
the overall program. In Mexico, pegging was an
important element of a broader program that
included reduced government spending, tax reform, deregulation, privatization, and significant
trade liberalization—including rapid reductions
in tariffs and quotas and entry into the General
Agreement on Tariffs and Trade (GATT) and
later into the North American Free Trade Agreement (NAFTA).
Fiscal stabilization preceded the exchangerate-based stabilization efforts. The history of
exchange-rate-based stabilization in the Southern Cone countries had suggested that a single
nominal anchor—such as the exchange rate—
could be inadequate to motivate quick disinflation. Policy incredibility (that firms would
not believe the exchange rate regime would
persist) as well as backward indexation and
nonsynchronized price-setting could lead to
persistent inflation (Calvo and Végh 1992).
Accordingly, an important component of
Mexico’s stabilization policy was the Pacto. Under this government-organized accord, representatives of the business community agreed to
limit price increases, the government made com-

mitments about the exchange rate and publicsector prices, and labor representatives agreed
to limit wage increases.
Although there are historical exceptions,
exchange-rate-based stabilization programs that
also include incomes policies—like the Pacto—
fairly commonly result in a specific dynamic of
consumption and investment patterns, current
account movements, and exchange rate pressures. The typical pattern (Calvo and Végh 1992
and Kiguel and Liviatin 1992) includes the following:
1. Despite reductions in inflation, the real
exchange rate rises because some inflation remains and is not offset by nominal exchange rate movements.
2. The trade and current account balances
deteriorate.
3. In the early stages of the program, capital inflows finance the excess of consumption and investment over domestic
production, allowing a boom to ensue,
but the inflows ultimately reverse.
4. With this reversal, the growing current
account deficit can no longer be financed,
and the consumption boom ends.
In recognition of this instability, a literature
has developed to suggest that exchange rate
pegging ought to be a temporary stabilization
tool, ultimately followed by a managed float
(McLeod and Welch 1991) or that, if pegging is
done at all, the exchange rate crawl should be
partially indexed to a measure of domestic prices
(Kamin 1991). Ultimately, it has been argued,
“As useful as exchange rate pegging is at the
outset, it is equally important to eliminate it as
soon as possible” (Dornbusch and Werner 1994,
281).
Trade and capital flows. Although Mexico’s
program of exchange rate stabilization cum incomes policy and trade liberalization contained
elements particular to the country, the ensuing
economic trajectory was typical of heterodox
programs. Consistent with the intentions of such
plans, inflation fell markedly—from 159.2 percent in 1987 to 8 percent in 1993. By the third
quarter of 1994, the annualized inflation rate
had declined to 7 percent.
Mexico’s trade liberalization was a part of
this disinflation effort. Oligopoly typifies the organization of domestic markets in Mexico, and
price controls could risk product shortages.
Mexico used trade liberalization to enforce price
discipline—so as to hold down inflation and to
lower the likelihood of product shortages.
Moreover, the country’s exchange rate
policy played a disinflationary role in the con-

FEDERAL RESERVE BANK OF DALLAS

Figure 1

Real Peso– Dollar Exchange Rate
Index, 1985 = 100
150
140
130
120
110
100
90
80
70
60
’70

’72

’74

’76

’78

’80

’82

’84

’86

’88

’90

’92

’94

text of trade liberalization. The government consistently depreciated the peso more slowly than
the rate of inflation—or than the difference
between the U.S. and Mexican inflation rates.
Consequently, as is common in exchange-ratebased stabilization programs, real exchange rate
appreciation was chronic. Since real exchange
rate appreciation meant that foreign products
were increasingly cheaper than Mexican products, this exchange rate policy motivated domestic producers—at least of tradable goods—
to resist the temptation to raise prices.
Figure 1 depicts a simple real exchange
rate measure—wholesale prices in Mexico relative to those in the United States, both as measured in dollars. By the end of 1993, Mexico’s
real exchange rate exceeded the maximum rate
that preceded Mexico’s megadevaluation episodes of 1982.
Partially because of this tension between
inflation and the pace of exchange rate depreciation, the nation’s merchandise trade balance
grew increasingly negative (Figure 2 ). As may
be expected in an economy that had reoriented
itself toward a market system —and had deregulated, privatized, and generally rationalized
its policies toward the private sector —a significant portion of Mexico’s current account
deficit reflected purchases of capital goods. The
increased productivity and efficiency that these
purchases imply resulted in steady increases in
exports. But the capital imports share of total
imports was still only 16.9 percent in 1993,
versus 71.1 percent for intermediate goods and
12 percent for consumer goods.
The trade and current account deficits were
possible because the rationalization of Mexico’s
fiscal, monetary, and exchange rate policies had

ECONOMIC REVIEW FIRST QUARTER 1996

Mexico’s disinflation programs—trade liberalization, real exchange rate appreciation, and a
trade deficit financed by foreign capital inflows—collectively weakened Mexico’s financial sector.
Three factors converged to impose pressures on Mexico’s financial sector. First, differences between the pricing performance of the
nontradables and tradables sectors damaged
the latter. The increased international competition held down prices in the tradable goods
sectors. But even with the Pacto, prices of
nontradable products, including real estate and
some services, rose relative to prices of tradables. This disparity imposed profit squeezes
on tradables firms because they often used
nontradables as inputs, and because nontradables producers could bid up wages of workers for whom tradables firms had to compete.
The direct effects of trade liberalization and
real exchange rate appreciation had, of course,
also imposed cost-price-squeeze pressures on
some of these firms. These pressures were
expressed in increasing loan defaults by tradables firms.
Second, to maintain inflows of foreign
capital, real interest rates began to increase starting in 1992, even though nominal rates were
falling at this time. During the early 1990s,
Mexico’s commercial banking system did not, at
least by developed country standards, behave
very competitively.8 Spreads between cost of
funds and loan rates were large. So were return
on assets, return on equity, and other income
statement ratios (Mansell Carstens 1993; Gruben,
Welch, and Gunther 1994). Bank lending rates
were typically very high by the standards of
developed countries, in any case. But increases

Figure 2

Merchandise Trade Balance
Billions of dollars
0
–1
–2
–3
–4
–5
–6
–7
1990

1991

1992

1993

1994

helped stimulate large inflows of foreign investment funds through early 1994. These flows
also gave Mexico the reserves it would need to
defend the peso later, if exchange rate pressures
required it.
Increased capital inflows are common to
chronic inflation countries that introduce exchange-rate-based stabilization programs. Most
of these flows (Figure 3) into Mexico involved
portfolio investment—inflows typically for the
purchase of bonds and stocks—rather than foreign direct investment. While portfolio investment permitted Mexican enterprises to fund
privately owned toll roads, the recently privatized telephone company, and some manufacturing operations, the focus of this investment
on the production of nontradables made inflows
and outflows susceptible to concerns of devaluation risk.
But to the extent that capital is not perfectly mobile, Mexico’s chronically low and, in
the 1990s, falling saving rate meant that the
country’s investment and growth were more
susceptible to external financial events. There is
much to suggest that capital flows into Mexico
did not occur solely because of Mexico’s policies. During the early 1990s, foreign capital
began to flow into Latin America generally,
despite wide differences in macroeconomic
policies and economic performance among
countries there. An important reason appears
to be low U.S. interest rates, suggesting that
increases in U.S. interest rates might have the
opposite effect.7
Central bank policy and the financial
sector. One reason tensions surfaced between Mexico’s exchange rate regime and other
policies is that international elements of

Figure 3

Capital Account
Billions of dollars
10
8

Portfolio investment

6
4
2

Direct investment

0
–2
–4
–6
1990

1991

1992

1993

1994

The currency configuration of
Mexican short-term debt

in real rates made it particularly difficult for
some firms to compete with foreign producers
from countries where financial costs happened
to be lower.
Third, to take advantage of the consumption boom of the early 1990s, Mexico’s financial institutions had issued many more credit
cards—to the wrong borrowers. By the standards of developing companies, the reporting of
consumer credit histories was relatively sketchy
and unorganized in Mexico. Defaults became
common.
These factors converged to pressure
Mexico’s banking system.9 Just between the
fourth quarter of 1992 and the third quarter of
1994, the percentage of nonperforming loans
rose from 5.6 percent to 8.3 percent.10 Moreover,
between December 1991 and September 1994,
the ratio of high-risk assets to bank net worth
rose from 51 percent to 70 percent.
Banking system problems like these take
on special significance anywhere a central bank
is both monetary authority and, as in Mexico,
administrator of the deposit insurance system.
As Heller (1991) argues, to the extent that a
central bank is not only the nation’s monetary
authority but also is responsible for the health of
the banking system, policy tensions may exist.
Even though central banks are typically committed to the restraint of monetary expansion,
and Mexico’s is, an incipient banking crisis may
create incentives to expand credit to the banking system.
It is here that the tensions expressed in the
impossibility theorem appear, since it holds that
free movement of capital, independent monetary policy, and a pegged exchange rate are
sooner or later incompatible. Mexico followed a
sterilization rule for its inflows of foreign reserves. To impose a monetary stabilization rule
atop the exchange rate based stabilization, accumulations of foreign currency reserves were sterilized via offsetting reductions in domestic credit
the central bank created for the financial system.
Conversely, outflows of foreign currency reserves
were sterilized through offsetting increases in
domestic credit.11,12
Recall that a central bank can always maintain a pegged exchange rate, but sometimes the
price is otherwise undesirably tight monetary
policy. Outflows of foreign currency reserves,
even if for purely political reasons, can signal
that a monetary contraction or interest-rate increase is in order.13 Such policies can be inconsistent with the expansion of domestic credit as
an offset to capital outflows, even if the policy is
purely an act of sterilization.14

FEDERAL RESERVE BANK OF DALLAS

Mexico has simultaneously issued shortterm, peso-denominated debt (cetes) and shortterm dollar-indexed debt (tesobonos), but as
1994 progressed, Mexico radically altered the
currency configuration of its short-term debt so
as to strengthen the peso. In January 1994, the
dollar value of cetes outstanding was $12.9
billion, compared with $302 million in tesobonos. By November, cetes outstanding had
fallen to $7.27 billion, while tesobonos had
risen to $12.9 billion.
An interesting characteristic of these debt
issues, as demonstrated econometrically (Dornbusch and Werner 1994), is that the changes
in spreads between their interest rates are not
affected by changes in factors normally associated with exchange rate expectations. Dornbusch
and Werner (1994) argue that changes in spreads
between the interest rates of cetes and tesobonos are not explained by changes in the real
exchange rate or in Mexico’s trade balance because the government managed the composition of its domestic debt so as to respond to
cost differentials. That is, as exchange rate risk
rose, Mexico shifted its composition of shortterm debt toward tesobonos and away from
cetes. The authors argue that this shift reflects
government responses to cost differentials. As
rates on tesobonos fell relative to rates on cetes,
the government replaced cete issues with tesobono issues.
If one advantage of a shift toward tesobonos was to save on interest expenses while
gaining foreign exchange by selling debt to foreigners, it was not the only advantage. Mexico’s
increased issuance of tesobonos may also be
seen as making its exchange rate regime more
credible by imposing a clear and obvious fiscal
penalty for devaluation.
Ize and Ortiz (1987) note that devaluation
is tantamount to a default on domestic debt
because, by raising the price level, the government erodes the debt’s real value. Accordingly, a
large overhang of domestic debt may be seen as
a motivation to devalue, particularly when the
debt is held by foreigners.
While this motivation may exist when a
nation’s domestic debt is denominated in the
home currency, the motivation erodes if, as with
the tesobonos, the debt instrument is indexed
to the dollar. A shift out of cetes and into
tesobonos is a shift out of an instrument for
which outstanding real debt falls with devaluation and into an instrument for which devaluation means a real debt increase. This statement

ECONOMIC REVIEW FIRST QUARTER 1996

holds whenever the subsequent rate of inflation
does not match or exceed the rate of devaluation by the time the debt matures.
The tesobono shift’s role in enhancing credibility that the exchange rate regime will
persist may be indirectly measurable. Insofar as
agents recontract for higher wages or higher
purchase or selling prices now in anticipation of
a generalized bout of price increases—so that
present prices reflect expectations of future
price increases—and insofar as a devaluation
may be seen as triggering a future generalized
bout of price increases, the implications of a
shift into tesobonos as a commitment technology for the exchange rate regime may be
expressed in current price increases.15
Preliminary econometric research by David
Gould shows that, even when monetary base
growth and other factors linked to inflation are
included in a model of Mexican consumer price
changes, a negative and significant relation exists between consumer inflation and the share of
Mexican short-term debt that is indexed to the
dollar. That is, with this credibility enhancement
in place, the market seems to reduce its expectation of the devaluation and so of the inflation
that typically follows devaluation.16 It does not
seem unreasonable to conjecture that this credibility enhancement could also have been seen
as a potential enhancement for transitory monetary independence.

prior claims that “the Mexican government would
not lose credibility from a devaluation, because
it would be recognized as a constructive response to a crisis.” 17
I noted earlier that one reason Mexican
bonds and stocks attracted U.S. and other foreign investors was low U.S. interest rates. During first-quarter 1994, U.S. monetary policy began
to tighten, raising U.S. interest rates and attracting capital back to the United States. While the
increase in U.S. rates signified that factors pushing capital toward Mexico were diminishing,
political events in Mexico weakened the country’s
pull effects for capital.
Chiapas rebels may not have threatened
the nation’s stability, but the assassination of
Mexican presidential candidate Colosio in March
1994 was another matter to investors. After rising earlier in the year, reserves fell profoundly
just after the assassination but stabilized in April.
To hold foreign capital in the country, Mexico
raised interest rates significantly, signaling that
exchange-rate preservation remained important.
But U.S. interest rates were also rising, and they
continued to do so throughout the year. The exchange rate moved toward the weak edge of the
band but remained within it.
It is in this context of rising U.S. rates at a
time when increasing financial problems offered
motivations to lower or at least hold Mexican
rates that the value of the tesobonos as a commitment technology can be appreciated. Instead
of offering a commitment technology based on
the accumulation of larger foreign currency reserves to defend the peso, when real rates were
already at high levels, the issuance of tesobonos
might be thought a reasonable substitute, at
least temporarily.

Putting the pieces together
The implications of the general dynamics
of heterodox exchange-rate-based stabilization
programs, of the role of domestic credit expansion in addressing systemic bank crises and in
triggering currency collapses, and of the use of
tesobonos as a commitment technology become
more dramatic when we consider the roles they
played in Mexico in 1994.
Recall that typical patterns of exchangerate-based stabilization programs include falling
inflation, rising real exchange rates, consumption booms, capital inflows in the early stages
that fund increasingly negative balances of trade
and current account and, finally, capital outflows that ultimately induce currency collapses.
Recall also that a typical characteristic of a currency collapse is not the impossibility of maintaining a pegged exchange rate, but a policy
priority rearrangement in which the exchange
rate is subordinated.
Finally, note that the intention of this article is not only to explain why the choices were
made that triggered the devaluation, but to explain why its aftermath was explosive despite

Figure 4

Real Central Bank Domestic Credit
To Commercial Banks
Millions of 1990 new pesos
10,000

8,000

6,000

4,000

2,000

0
1990

1991

1992

1993

1994

Figure 5

Figure 6

Tesobonos and Cetes Held by the Public

Real Interest Rate in Mexico

Billions of dollars

Percent

27.5

30
22.5
Tesobonos

17.5

15
12.5

7.5
Cetes
5

2.5
0

0
1990

Jan. Mar. May Jul. Sep. Nov. Jan. Mar. May Jul. Sep. Nov.

1993

1991

1992

1993

1994

When the Colosio assassination triggered a
capital outflow, Mexico sterilized by raising domestic credit (Figure 4 ). At the same time, Mexico
stepped up its substitution of dollar-indexed
tesobonos outstanding for peso-indexed cetes
outstanding (Figure 5 ). By midyear, tesobonos
outstanding began to exceed cetes. The substitution increased through the rest of the year.
In the third quarter, Mexico began to relax
its interest rate pressures, as can be seen from
Figure 6. Interest rates remained considerably
higher than they had been at the beginning
of the year. But they were not high enough
to restore reserves to the levels of the first
quarter—not, at least, when U.S. rates were
also rising.
Nevertheless, reserves remained relatively
stable during the third quarter. One reason may
be that, as the summer ensued, it became more
obvious that substitute Institutional Revolutionary Party presidential candidate Ernesto Zedillo
was likely to defeat the other candidates, whose
abilities or policies may have inspired more
investor uncertainty about future growth. Then,
in August, he did win. But Gould’s (1994) results
on the negative influence of tesobonos’ share
of total short-term debt on inflation rates suggest than an exchange rate commitment technology also helped stabilize foreign currency
reserves. The third quarter ended with foreign
currency reserves as high as those with which
it had begun.
As 1994 ensued, the differential between
Mexican and U.S. interest rates began to fall,
much as one might expect, other things being
equal, as a reasonable policy response in the
face of mounting problems in the Mexican
financial system (Figure 7 ).18 Nominal cetes

FEDERAL RESERVE BANK OF DALLAS

rates fell absolutely in August and remained
below their spring and summer highs until the
devaluation.
In the fourth quarter, another political
event preceded capital outflows from Mexico,
but a concurrent economic event makes interpretation difficult. After the assassination
of Institutional Revolutionary Party official
Carlos Francisco Ruiz Massieu, his brother was
appointed to investigate the case; in November
he resigned, alleging that powerful officials
were stymieing his investigation. Meanwhile, on
November 15 the Federal Open Market Committee of the U.S. Federal Reserve System met and
decided on policies that would lead to a
75-basis-point increase in the federal funds
rate, its most restrictive monetary policy action
since 1990.

Figure 7

Past-Due Loan Ratio
Percent
12

0
1992

1993

NOTE: Data are quarterly.

ECONOMIC REVIEW FIRST QUARTER 1996

1994

Conclusion
The chief problems Mexico faced in 1994
were that the controlled rate of depreciation of
the peso was inconsistent with the persistent
inflation rate differential between the United
States and Mexico, that capital outflows drew
down foreign exchange reserves that Mexico
was using to defend the peso, and that, in the
conflict between greater monetary tightness to
support the exchange rate and less tightness to
avoid further financial-sector problems and a
downturn in the economy, the latter won out.
While Mexico wished growth, it was caught in
an episode of U.S. monetary tightening that
only de facto monetary independence would
have permitted it to avoid following with a
vengeance—and in financially destabilizing episodes of political unrest.
Despite evidence that some monetary independence was available transitorily, as the
short run grew into a longer run, independence
and dependence collided with a result long
since posited as the impossibility theorem. But
while these factors are consistent with an ensuing devaluation, they alone are not consistent
with the explosive nature of Mexico’s financial
crisis in the wake of the initial devaluation.
The explosive nature of the crisis seems
to have been linked to reactions to the term
structure and volume of Mexico’s short-term
dollar-indexed debt, even though there is little
evidence to suggest that the tesobono debt
was seen as problematic before the devaluation and that it had served as a positive commitment technology.21 That the tesobono maturity
schedule signified obligations in early 1995 that
were considerably in excess of Mexican dollar
reserves to cover them may have triggered the
anticipation of a financial musical chairs game
in which each investor began to fear that her
or his tesobono would be the one left out of
convertibility.

Table 1

1995 Tesobonos Maturity Schedule
(Billions of U.S. dollars)
January
February
March
April
May
June

$ 3.626
3.487
3.159
1.858
2.723
1.907

Total

$16.760

In sterilizing the subsequent outflow of
foreign capital, Mexico’s central bank again increased domestic credit to the banking system.19
Mexican interest rates were not pushed up sufficiently to maintain reserves.
Perhaps as a result of the fiscal implications that the large overhang of tesobonos
offered in the event of a devaluation, the exchange rate continued to show signs of credibility.20 But this tesobono commitment technology
had been imposed in a period of increasing risk
to the financial system and of the additional
trade balance pressures partially induced by
the commencement of NAFTA in January 1994.
Taken collectively, these factors meant that
Mexico could be risking a financial crisis if it
devalued the currency and allowed interest rates
to go where they would, or if it defended the
currency by raising interest rates.
On December 20, the tesobono overhang
that had suggested exchange rate credibility now
signified financial market as well as currency
collapse. When the Mexican government announced that the peso would move from 3.47
pesos per dollar to 3.99, it also announced that
the exchange rate pegging regime, in which the
peso would devalue against the dollar at a rate
of 0.0004 pesos per day, would persist. The
band would simply be lowered.
But instead of settling markets, the announcement incited massive capital flight. Large
increases in interest rates ensued. Perhaps the
fiscal implications of the tesobono overhang,
with a maturity schedule in which the value of
tesobonos falling due within the first six months
of 1995 exceeded the value of Mexico’s foreign
exchange reserves, were calculated by financial
markets (Table 1 ). But given the moderate
magnitude of the initial announced devaluation,
the pure act of abridgment of such a commitment seems to have played an important role in
and of itself.

Notes
1
2
3

4
5

Wall Street Journal (1994).
Fidler and Bardacke (1994).
See, for example, Torres and Campbell (1994), Fidler
and Bardacke (1994), and Tricks (1994).
For a discussion of this last, see Lustig (1995).
How does raising interest rates affect the exchange
rate? Rising Mexican interest rates inspire foreigners
to buy Mexican financial assets—triggering capital
inflows, increasing the demand for pesos, and so
bidding up the exchange rate. As foreigners trade
dollars for pesos to buy peso-denominated assets or
simply buy dollar-denominated assets from the
Mexican financial sector, Banco de México accumu-

lates dollar reserves. If pressures to devalue arise,
Banco de México can use its dollar reserves to buy up
pesos—raising their dollar price. Also, squeezing
monetary growth and raising interest rates lower
Mexican inflation. Insofar as dollar prices of Mexican
goods rise faster than dollar prices of U.S. goods, both
Mexican and U.S. buyers are discouraged from buying
Mexican products and encouraged to buy U.S. products. The resulting trade deficit increase means declining demand for pesos—as fewer Mexican products
are bought—and rising demand for dollars—as more
U.S. products are bought. Pressure arises to devalue
the peso—which lowers the dollar price of Mexican
products and raises the peso price of Mexican products, erasing the deficit. A tight monetary policy that
includes raising interest rates dampens Mexican
inflation, squeezes the differential between Mexican
and U.S. inflation, and lowers pressure to devalue.
6

Gruben, McLeod, and Welch (1995) show that threemonth U.S. Treasury bill rates do not Granger-cause
and are not Granger-caused by three-month Mexican
Treasury bill (cetes ) rates and that three-month U.S.
Treasury bill rates do not Granger-cause and are not
Granger-caused by Mexican Brady par bonds. However, thirty-year U.S. Treasury bonds do Grangercause Mexican Brady par bonds, which, it should be
noted, may be seen as long-term bonds. These results
suggest that financial integration between Mexico and
the United States can be significantly abridged in the
short term but not in the long run.
For a more complete discussion of external factors
leading to such flows, see Calvo, Leiderman, and
Reinhart (1993); Chuhan, Claessens, and Mamingi
(1994); and Dooley, Fernandez-Arias, and Kletzer
(1994). Most foreign capital flowing into Latin America
did go to Mexico, however.
With the exception of union-owned Banco Obrero and
U.S.-owned Citibank, the entire Mexican commercial
banking system was nationalized in 1982. With a series
of consolidations, the original fifty-three nationalized
banks were pared to eighteen. These eighteen institutions were privatized, one by one, in 1991 and 1992.
For a fuller development of the links between Mexico’s
banking problems and the subsequent exchange rate
crisis, see Calvo and Mendoza (1995).
In the United States, once a loan goes into arrears, the
entire remaining loan balance is considered in arrears.
In contrast, Mexico’s calculation procedure does not
consider the entire remaining loan balance to be in
arrears. For example, in Mexico, if a loan is three
months in arrears, only the balance that had been
contracted to be paid during those three months is
calculated as in arrears. Any loan balance scheduled
to be paid thereafter is not yet calculated as in arrears.
Other things being equal, U.S. protocols would sometimes result in higher past-due loan ratios than Mexican protocols.

FEDERAL RESERVE BANK OF DALLAS

17
18

Banco de México (1995, 64) notes that “the increase in
domestic credit in 1994 occurred in response to reserve declines” and that reserves “did not fall because
domestic credit was expanded” [author’s translation].
While I am presenting a case for the possibility of
separation between reserve outflows and the domestic
credit creation that is implied by sterilization, it is true
that some argue that when central banks sell reserves,
they must sterilize automatically.
Kamin and Rogers (1995) offer econometric evidence
to suggest that when interest rates did rise, they rose
only moderately less than could be predicted by the
authorities’ standard reaction function. Kamin and
Rogers argue that, to have maintained the peg, the
authorities would have had to intensify their responses
to exchange market developments. That is, policymakers would have had to alter their reaction regime,
and they would have had to at a time when concerns
for the health of the banking system would have
suggested a relaxation of monetary policy.
While the merits and liabilities of currency boards are
a subject beyond the scope of this article, one discipline they impose is that when foreign exchange
reserves flow out, the resulting reduction in the stock
of money is not offset. Although such boards may be
seen as having significant liabilities, Argentina’s peso
(which is disciplined by a currency board) has maintained its nominal value over the past two years while
Mexico’s has not.
Brown and Whealan (1993) offer econometric evidence to suggest, for example, that present oil prices
reflect agent expectations of futures prices.
Lustig (1995, 379) notes that “this dollarization of
internal debt probably explains the surprising stability
of international reserves before such adverse events
as the increase of foreign interest rates and domestic
political unrest” [author’s translation]. Moreover,
Banco de México (1995, 69) states that “the issuance
of tesobonos was carried out in order to reduce
exchange market pressures” [author’s translation].
Werner (1994, 310).
Recall that inasmuch as a central bank can always
preserve a pegged exchange rate through a sustained
high interest rate or a contraction in monetary base,
interest rates insufficient to prevent declining reserves
suggest that other policies must dominate a commitment to a pegged exchange rate. Garber and
Svensson (1994, 29) note that one of these policies
may be “the preservation of solvency of a banking
system.”
The capital outflows were not well-known, however,
and a number of analysts have complained that
something kept Mexico during the latter portions of
1994 from releasing data on central bank holdings of
foreign reserves.
Interest rates typically reflect nervousness about
devaluations. Consider, for example, the movement of

ECONOMIC REVIEW FIRST QUARTER 1996

yields on the twenty-eight day cetes auction for the
following dates: November 9 —13.49 percent, November 16 —13.45 percent, November 23 —13.95 percent,
November 29 —13.85 percent, December 7—13.30
percent, and December 14 —13.75 percent.
Calvo and Mendoza (1995) and Sachs, Tornell, and
Velasco (1995) address other aspects of the sudden
and explosive nature of Mexico’s financial crisis that
clearly deserve attention. Calvo and Mendoza argue
that this phenomenon reflects, among other things, a
trade-off between diversification and information that
investors face when information is costly to acquire.
As investment opportunities expand across countries,
the payoff to purchasing information about a particular
country declines. It becomes rational for investors to
become sensitive to even “small” bad news, especially
when it follows previous bad news, even if none of the
news is related to fundamentals. In sum, the reduced
incentives to acquire much information about Mexico
in particular and Latin America in general motivated a
herd behavior that triggered the tequila effect.
Sachs, Tornell, and Velasco (1995) argue that,
while real disequilibria and reserve erosion lay the
groundwork for the crisis, the timing and magnitude
of the crisis came from a self-fulfilling panic after the
government ran up its short-term tesobono debt and
ran down gross reserves. That is, like Calvo and
Mendoza (1995) and those cited at the beginning of
this article, Sachs, Tornell, and Velasco (1995, 7) do
not believe that the crisis was fully consistent with
fundamentals. Instead, they conclude, “the panic
was self-fulfilling in that expectations of a run on both
pesos and teso-bonos by other agents led each
individual investor to engage in the same kind of
speculative behavior.”

Chuhan, Punam, Stijn Claessens, Nlandu Mamingi (1994),
“Equity and Bond Flows to Asia and Latin America: The
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Deficit and Dissidence: Stephen Fidler and Ted Bardacke
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FEDERAL RESERVE BANK OF DALLAS

ECONOMIC REVIEW FIRST QUARTER 1996

Full text of Review (Federal Reserve Bank of Dallas) : First Quarter 1996

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