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FEDERAL RESERVE BANK OF DALLAS

Issue 3 May/June 2005

Southwest Economy
. . . . . . . . . . . . . . . . . . . . .

Dallas Fed Introduces
Business-Cycle Indexes
for Texas Metros
The frequency and severity of cyclical swings
in a local economy are important to businesses
and consumers because such cycles impact production and inventory decisions, employment and
unemployment. Analyzing the overall direction of
a local economy, however, can be difficult and
confusing. Often the handful of local economic
indicators gives mixed signals. For example, if the
unemployment rate and job growth both increase,
is the local economy picking up or weakening?
Often it is not clear.
To more clearly define regional business
cycles, the Dallas Fed has developed composite
indexes that aggregate the movements of key economic indicators for nine Texas metropolitan
areas. The Metro Business-Cycle Indexes use statistically optimal weights so that movements in the
indexes best represent the underlying co-movements in the indicators and thus the underlying
(Continued on page 2)

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

INSIDE:
Texas Finding
Growth in Seeming
Disadvantage
•
Mexico Emerges from
10-Year Credit Slump

A Fitter, Trimmer Core Inflation Measure
Speaking of the challenge in interpreting monthly inflation numbers during his tenure on the Federal Reserve Board, former Vice Chairman Alan
Blinder said, “The name of the game then was distinguishing the signal from
the noise, which was often difficult. The key question on my mind was typically: What part of each monthly observation on inflation is durable and what
part is fleeting?”1
Blinder’s conception of a component of monthly inflation that is durable
as opposed to fleeting—that represents signal rather than noise—corresponds
to what most economists call core inflation. Core inflation, understood in this
way, represents the underlying trend in inflation once temporary swings have
(Continued on page 4)

Chart 2

Chart 1

Tech Centers Dallas and Austin
Hardest Hit but Bouncing Back

South Texas Border Economies
Growing Strongly

Index, January 2000 = 100
115

Index, January 2000 = 100
140
Houston

135

110

McAllen

130

San Antonio

Texas

105

125
120

100

Fort Worth
Austin

95
90

Laredo

115
110

Brownsville

105

Dallas

Texas

100
85

90

80

To more clearly
define regional business
cycles, the Dallas Fed
has developed composite
indexes that aggregate
the movements of key
economic indicators
for nine Texas
metropolitan areas.

2

El Paso

95
2000

2001

2002

2003

2004

2000

2005

state of the economy. The long-run
growth in the indexes is set equal to
growth in real personal income. The
indexes are constructed using the same
statistical techniques as the Texas Leading Index.1
In May the Dallas Fed introduced
business-cycle indexes for the metropolitan areas of Austin – Round Rock,
Brownsville – Harlingen, Dallas – Plano –
Irving, Fort Worth–Arlington, El Paso,
Houston–Sugar Land–Baytown, Laredo,
McAllen –Edinburg–Mission and San
Antonio. Movements in the indexes summarize the movements in locally measured nonagricultural employment, the
unemployment rate, inflation-adjusted
wages and inflation-adjusted retail sales.
Because the indexes are designed to
measure the economy’s overall direction
but not the magnitude of local activity
for each metro area, links are included to
the component series.
The indexes will be published
monthly on the Dallas Fed web site,
www.dallasfed.org, a couple of days
after the employment and unemployment
rate data for the state and metro areas
become available from the Texas Workforce Commission. Usually these data are
released on about the 22nd day after the
end of the reporting month.
The indexes show clear patterns of
recessions and expansions. While Texas
recessions have impacted local economies, many of the state’s metro areas
have business cycles that deviate from
those of the state, the nation and other
Texas regions. For example, the high-

FEDERAL RESERVE BANK OF DALLAS

SOUTHWEST ECONOMY

2001

2002

2003

2004

2005

tech cities of Austin and Dallas were
hit hard by the downturn that began
in early 2001 (Chart 1), but the South
Texas border cities continued to grow
(Chart 2 ). The Metro Business-Cycle
Indexes illustrate economic conditions
in other Texas metropolitan areas as
well.

Economic Conditions in
Nine Major Metros
Austin–Round Rock. After leading
Texas’ major metros in economic expansion during the 1990s, Austin was hit
hard by the high-tech bust that occurred
in 2001, as the metro’s business-cycle
index illustrates. Since mid-2003, however, Austin’s index suggests its economy has turned the corner and is once
again one of the fastest-growing in the
state.
Brownsville–Harlingen. The businesscycle index shows this metro area outperforming the state and nation since
2000. Brownsville’s economy has been
boosted by a strong peso and favorable
agricultural conditions due to adequate
rainfall and good citrus prices. Nevertheless, Brownsville–Harlingen’s economy has not performed as well as
those of some other South Texas border
areas, which is consistent with its index.
The likely cause is a sharp decline in
apparel manufacturing, which historically has been an important industry for
this metro.
Dallas – Plano – Irving. Dallas’ business-cycle index illustrates the devastating blows to the metro’s economy in

MAY/JUNE 2005

2001 — both the high-tech bust and
9/11’s negative impact on the airline
industry. Dallas’ business cycle this
decade has followed a pattern similar to
Austin’s, except that its heavier concentration of airlines and telecommunications firms likely contributed to the
larger downturn and weaker recovery.
Fort Worth – Arlington. This trade,
transportation and manufacturing center
has mimicked the business cycle of the
state overall. The area’s relatively large
manufacturing sector is not as high-tech
intensive as Dallas’ or Austin’s and thus
did not suffer as much during the sectors’
decline in 2001 and 2002.
El Paso. Since 2000 the El Paso business cycle has mimicked the Texas business cycle. While the El Paso metro area
is generally small and might be expected
to correlate less with the state and
national economies, its economic performance is closely linked to that of Texas
and the United States because of the border city’s link to the maquiladora industry. Many El Paso service and manufacturing firms provide inputs to the
maquiladoras. The El Paso economy has
been growing since mid-2003 but at a
weaker pace than Texas’ economy overall. Recent improvement in the maquiladora industry and growth in militaryrelated employment should boost the El
Paso metro index in coming months.
Houston–Sugar Land–Baytown.
Houston’s business-cycle index stagnated from mid-2001 through mid-2003.
A large health care presence and a relatively low share of high-tech industries
helped Houston avoid the downturn that
hit Dallas and Austin. Since mid-2003,
Houston’s index has risen at a moderate
pace. Expanding industries such as oil
and gas, petrochemicals and health care
are likely driving the improvement.
Laredo. According to its businesscycle index, the Laredo economy has
expanded strongly over the past four
years. This is consistent with the metro’s
solid growth in transportation, warehousing and retail sales, which have
benefited from increased international
trade and the strong peso.
McAllen–Edinburg–Pharr. McAllen’s
business-cycle index has risen robustly
over the past four years. Strength in the
metro’s economic indicators is closely
tied to the stronger peso and a relatively

healthy maquiladora sector in the border
city of Reynosa.
San Antonio. San Antonio’s economy
has expanded slightly faster than the
Texas economy over the past four years,
according to its business-cycle index.
San Antonio has a smaller share of hightech industries and a larger share of
health care—a rapidly growing sector.
Historically, the presence of stable industries such as government has allowed
San Antonio’s business cycle to swing
less than those of other metro areas. A
reduced federal government presence,
particularly military-related jobs, will
likely lead to greater business-cycle fluctuations in the future.
—Keith R. Phillips
Phillips is a senior economist at the
San Antonio Branch of the Federal
Reserve Bank of Dallas.

Note

1

The author thanks Kristen Hamden for her skillful programming and
automation of the indexes and James Hoard and Kay Champagne for
helpful suggestions and comments.
The procedure is described in more detail in “A New Monthly Index of
the Texas Business Cycle,” by Keith R. Phillips, Dallas Fed Working
Paper No. 0401, January 2004. For more detail on the local business
cycle using the new indexes, see the following Dallas Fed publications: “Composite Index: A New Measure of El Paso’s Economy,” by
Jesus Cañas, Robert W. Gilmer and Keith Phillips, Business Frontier,
Issue 1, 2003; “A New Index of Coincident Economic Activity for
Houston,” Houston Business, by Jesus Cañas, Robert W. Gilmer and
Keith Phillips, April 2003; and “Steady-as-She-Goes? An Analysis of
the San Antonio Business Cycle,” by Keith R. Phillips and Kristen
Hamden, Vista, Winter 2004. All publications are available on the Dallas Fed web site, www.dallasfed.org.

The Texas Metro
Business-Cycle Indexes
will be published
monthly on the
Dallas Fed web site,
www.dallasfed.org,
under “Economic Data.”

,

FEDERAL RESERVE BANK OF DALLAS

SOUTHWEST ECONOMY

MAY/JUNE 2005

3

A Fitter, Trimmer Core Inflation Measure
(Continued from front page)

been smoothed out. Because what is
temporary and what is lasting can only
be known with the benefit of hindsight,
the true core inflation rate for any given
month cannot be known with certainty
until well after the fact. In real time—as
the data arrive and policy decisions need
to be made—the best that economists
can do is estimate the core inflation rate.
Measures of inflation that exclude
food and energy prices are probably the
best-known core inflation gauges. In fact,
the measures excluding food and
energy—which government statisticians
include in their releases of the Consumer
Price Index (CPI), Producer Price Index
(PPI) and the price index for Personal
Consumption Expenditures (PCE)— are
often spoken of as if they were synonymous with core inflation. Properly speaking, though, they represent just one of
many potential core measures. To be sure,
because of the high short-run volatility
of some food and energy prices, there is
some rationale for excluding those prices
from a measure of core inflation. But as
research over the past decade has made
clear, much better estimates can be made
by taking a more rigorous approach to
the problem of which prices to include
and which to exclude.
To date, that research has focused
primarily on developing better measures
of core inflation in the CPI.2 This article
discusses the application of some of
the insights and techniques of that
line of research to the Federal Reserve
Board of Governors’ preferred inflation
gauge, the PCE price index. (See box
titled “The Fed’s Favorite Inflation
Gauge.”) The result is a new measure of
core PCE inflation—the trimmed mean
PCE— and a somewhat different characterization of the economy’s recent inflation experience.

Food and Energy:
Signal or Noise?
Consider the following data from
March 2005. More than 200 expenditure
categories go into the PCE. Table 1
shows the 10 categories with the biggest
price increases from February to March

4

Table 1

Table 2

10 Biggest Price Increases
in March 2005

10 Biggest Price Decreases
in March 2005

Component
Gasoline and other motor fuel
Purchased fuel oil
Airline service
Hotels and motels
Medical services: labs
Farm fuel
Purchased liquid petroleum gas
Miscellaneous personal services
Watch, clock and jewelry repair
Laundry and garment repair

Change from
prior month
Component
(percent)
Eggs
– 4.4
Fresh fruit
– 2.6
Women’s luggage
– 1.8
Men’s luggage
– 1.8
Intrastate toll calls
– 1.8
Photographic equipment
– 1.8
– 1.7
Toys, dolls and games
Household operation: natural gas
– 1.7
Durable house furnishings: textiles
– 1.5
Lighting supplies
– 1.5

Change from
prior month
(percent)
8.0
5.8
4.2
4.2
3.2
2.5
2.5
2.4
2.4
2.4

2005.3 Note that the price changes are
not annualized — they are one-month
percentage changes. By way of comparison, the change in the overall PCE price
index from February to March was +0.46
percent.
Table 2 lists the 10 components that
had the largest price decreases in March
2005. While it’s true that food and energy
items show up a number of times on
both lists, there are many other items as
well. Moreover, not all food and energy
items had price changes as large as
these. Some food components in particular — such as food consumed away

from home—are notoriously stable. For
example, the price index for “other purchased meals”—which comprises meals
purchased at restaurants and bars—rose
by just 0.15 percent in March. That small
price volatility is typical for food purchased and eaten away from home—
making its exclusion from a measure of
core inflation questionable.
Clearly, in any given month, excluding only food and energy items still
leaves very volatile components in the
price index. And, excluding all food and
energy items may throw out some useful
information.

The Fed’s Favorite Inflation Gauge
Since February 2000, the Federal Reserve Board’s semiannual monetary policy reports to Congress
have described the Board’s outlook for inflation in terms of the PCE. Prior to that, the inflation outlook was
presented in terms of the CPI. In explaining its preference for the PCE, the Board stated:
The chain-type price index for PCE draws extensively on data from the consumer price index but,
while not entirely free of measurement problems, has several advantages relative to the CPI. The
PCE chain-type index is constructed from a formula that reflects the changing composition of
spending and thereby avoids some of the upward bias associated with the fixed-weight nature of
the CPI. In addition, the weights are based on a more comprehensive measure of expenditures.
Finally, historical data used in the PCE price index can be revised to account for newly available
information and for improvements in measurement techniques, including those that affect source
data from the CPI; the result is a more consistent series over time.

FEDERAL RESERVE BANK OF DALLAS

— Monetary Policy Report to the Congress,
Federal Reserve Board of Governors,
Feb. 17, 2000

SOUTHWEST ECONOMY

MAY/JUNE 2005

The Trimmed Mean Technique:
A Little Off the Top (and
Bottom)
How, then, do we decide which
items to exclude or include more rigorously? In a study focusing on the CPI and
PPI, Bryan, Cecchetti and Wiggins make
a statistical case for the use of trimmed
means as a method for estimating core
inflation.4 In spite of the arcane-sounding name, the concept of a trimmed
mean is simple. In fact, trimmed means
should be familiar to any follower of
international figure skating. In the wake
of the controversies surrounding the
judging at the 2002 Winter Olympics, the
International Skating Union adopted a
scoring system in which a skater’s highest and lowest marks are discarded
before the skater’s average score is calculated. Trimmed mean inflation rates
are derived by a similar procedure.
In any given month, the rate of inflation in a price index like the CPI or PCE
can be thought of as a weighted average,
or mean, of the rates of change in the
prices of all the goods and services that
make up the index.5 Calculating the
trimmed mean PCE inflation rate
involves looking at the price changes for
each of the individual components of
personal consumption expenditures —
the sort of data contained in Tables 1
and 2. The individual price changes are
sorted in ascending order from “fell the
most” to “rose the most,” and certain
fractions of the most extreme observa-

tions at both ends of the spectrum are—
like a skater’s best and worst marks—
thrown out, or trimmed. The inflation
rate is then calculated as a weighted
average of the remaining components.6
How many components should be
trimmed from the top and bottom of the
monthly price-change distributions?
Since our aim is to create a more accurate real-time gauge of core inflation, we
want our trimming to yield a measure
that comes as close as possible to the
core inflation we’ve observed in historical data. (See box titled “Optimal Trimming: The Nuts and Bolts” for more
detail.) Following the approach used by
Bryan, Cecchetti and Wiggins in their
CPI/PPI study, we will treat true core
inflation as a smooth underlying trend in
actual inflation (Chart 1 ).7
For data that run from 1979 through
2002, the amount of trimming that minimizes the distance between the trimmed
mean inflation rate and the proxy for the
true core inflation rate turns out to be
substantial. The optimal trim drops
roughly the top 25 percent of components (as a fraction of expenditures) and
the bottom 21 percent. That is, from each
month’s data, we discard the 25 percent
of expenditure components whose
prices rose the most and the 21 percent
whose prices fell the most (or rose the
least). The trimmed mean inflation rate is
then calculated as the weighted average
of the remaining expenditure components, the middle 54 percent. Note that

In spite of the
arcane-sounding
name, the concept of
a trimmed mean is
simple. In fact, trimmed
means should be
familiar to any follower
of international
figure skating.

Chart 1

Actual PCE Inflation and Proxy for True Core Inflation
Percent, annualized
20

15

Proxy for true core inflation
Actual monthly PCE inflation

10

5

0

–5
’80

’82

’84

’86

’88

’90

’92

’94

’96

’98

’00

’02

SOURCES: Bureau of Economic Analysis; author’s calculations.

FEDERAL RESERVE BANK OF DALLAS

SOUTHWEST ECONOMY

MAY/JUNE 2005

5

the set of goods and services discarded
each month — items adding up to
roughly 46 percent of expenditures—
must include a good deal more than just
food and energy, which account for only
about 20 percent of total PCE.

So Which Goods Get Trimmed?

The optimally trimmed
mean performs much
better as an estimator
of core PCE inflation
than the usual
measure excluding
food and energy.

As suggested above, some food
components, like food purchased and
consumed away from home, are rarely
excluded when one approaches the trimming problem rigorously. This is a feature of the inflation data that Bryan and
Cecchetti (1994) highlighted in their
study of the CPI, and it is true of the PCE
as well. Chart 2 shows the monthly inflation rate for the PCE component “other
purchased meals,” together with the upper
and lower trim points for the optimally
trimmed mean, from 1990 through 2004.
The trim points have the following
interpretation. In each month, items
whose prices rose by more than the
upper trim points in the chart are
excluded from the optimally trimmed
mean that month, as are items whose
prices fell by more (or rose by less) than
the lower trim points. There is only a
handful of months during this 14-year
period in which the purchased meals
component was excluded from the optimally trimmed mean.
Food items of this sort are well represented among the components least
often excluded from the optimally trimmed mean. Table 3 lists the top 20 leastoften-excluded components for the sam-

ple period 1977–2004. Food items actually occupy five of the top 10 spots, with
“other purchased meals” coming in first.
Out of a sample of 335 months, it’s
excluded only 13 times. The other dominant category in the least-often-excluded list is housing, which shows up
in various forms.
Table 4 gives a corresponding list of
the top 20 most-often-excluded items.
Food items figure prominently here, too,
with “fresh vegetables” topping the list.
Fuels, financial services and electronics
items are also prominent.

How Well Does the
Trimmed Mean Perform?
Just as Bryan, Cecchetti and various
co-authors found regarding the CPI, the
optimally trimmed mean performs much
better as an estimator of core PCE inflation than the usual measure excluding
food and energy.
In data running from 1979 through
2002, the gain in accuracy from using the
optimally trimmed mean rather than the
measure excluding food and energy is
about 0.77 percentage point annually.
That is, compared with the usual measure excluding food and energy, on
average the monthly trimmed mean
measure would be expected to come
closer to true monthly core inflation by
just over three-fourths of a percentage
point when the inflation rates are
expressed in annual terms.
These results compare the performance of one-month inflation rates,

Chart 2

Food Away from Home Is Generally Not Trimmed
Percent, annualized
14
12
Upper trim points

10

Inflation rate for "other purchased meals"

8
6
4
2
0
–2
–4
Lower trim points

–6
–8
’90

’92

’94

’96

’98

’00

SOURCES: Bureau of Economic Analysis; author’s calculations.

6

FEDERAL RESERVE BANK OF DALLAS

SOUTHWEST ECONOMY

MAY/JUNE 2005

’02

’04

Table 3

Table 4

20 Least-Often-Excluded Components,
1977– 2004
Component
Other purchased meals
Owner-occupied stationary homes
Casino gambling
Tenant-occupied stationary homes
Tenant-occupied mobile homes
Purchased meals: elementary and secondary schools
Purchased meals: higher education
Food furnished to employees: military
Food furnished to employees: civilian
Club and fraternity housing
Tenant group room and board
Tenant group employee lodgings
Auto repair
Owner-occupied mobile homes
Military clothing
Domestic service paid in cash
Household operation, not elsewhere classified
Social welfare including child care
Medical care: other professional services
Dry cleaning

20 Most-Often-Excluded Components,
1977– 2004

Number of months
excluded (out of 335)
13
16
34
35
40
41
41
41
42
50
52
53
54
57
87
88
91
94
95
96

Component
Fresh vegetables
Eggs
Computers and peripherals
Food produced and eaten on farms
Airline services
Brokerage charges and investment counseling
Software
Fresh fruit
Purchased fuel oil
Gasoline and other motor fuel
Farm fuel
Poultry
Video equipment, excluding TVs
Auto insurance net premiums
Purchased liquid petroleum gas and other fuel
TVs
Durable house furnishings: textiles
Semidurable house furnishings
Commercial bank imputed interest
Infants’ clothing

Number of months
excluded (out of 335)
314
314
311
304
299
298
297
296
294
286
285
285
285
284
279
278
275
274
273
273

Optimal Trimming: The Nuts and Bolts
As discussed in the text, we want our trimming to yield a measure that
comes as close as possible to a specific proxy for true core inflation, in this
case a centered, 36-month moving average of actual monthly PCE inflation.
What do we mean by “as close as possible”?
The numbers reported in the article are for the case where the closeness
is measured with a root-mean-square-error criterion — that is, the trimmed
mean’s distance from the proxy for true core inflation is measured by the
square root of the average squared monthly deviation between the two series.
Each possible amount of trimming — 5 percent off the top, 10 percent off the
bottom, or 20 percent off the top, nothing off the bottom, and so forth —
results in a trimmed mean inflation rate that is some calculable distance from
the proxy for true core inflation. The optimal trim is the one that minimizes the
distance between the trimmed mean and core proxy over our sample period,
1979 – 2002. This turns out to be the trimming: 25.3 percent off the top, 20.6
percent off the bottom.
Table A shows the value of our measure of fit — the root-mean-square
error, or RMSE — for inflation horizons of one, three, six and 12 months, for
both the optimally trimmed mean and the measure excluding food and energy.
The three-, six- and 12-month inflation rates for the trimmed mean are
obtained by cumulating the optimally trimmed series of one-month rates to
obtain a price index, then taking three-, six- and 12-month annualized percent-

age changes of that price index. Smaller numbers are better than larger ones
in both Tables A and B.
The optimally trimmed mean also performs better than the measure
excluding food and energy in terms of its average error, as can be seen in
Table B. The average, or mean, error of an inflation measure is simply the sum
of its monthly deviations from the true core proxy divided by the number of
months in the sample.
To see the relevance of this last point, suppose that true core inflation is
zero in two consecutive months. Imagine that one measure (call it X) estimates core inflation as being + 0.25 percent in each of the two months, while a
second (Y) estimates it at + 1 percent in the first month and – 1 percent in the
second month. Then Y would have a higher RMSE than X — on average, Y is 1
percentage point away from the truth, versus 0.25 percentage point for X —
but it would have a smaller average error than X. Y’s average error is zero (the
+ 1 and – 1 cancel out) compared with X’s average error, which, like X’s RMSE,
is 0.25 percentage point. If the trimmed mean and excluding food and energy
measures followed this pattern — one better in terms of RMSE, the other
better in terms of average error — we might be hard-pressed to say which was
the better measure. Fortunately, Tables A and B show the trimmed mean is
better on both dimensions.

Table A

Table B

Root-Mean-Square Errors for Various Inflation
Horizons (in percentage points)

Average Errors for Various Inflation Horizons
(in percentage points)

Trimmed mean
Excluding food and energy

1-month
.87
1.63

3-month
.58
.94

6-month
.49
.72

12-month
.51
.76

FEDERAL RESERVE BANK OF DALLAS

Trimmed mean
Excluding food and energy

SOUTHWEST ECONOMY

1-month
.04
.11

MAY/JUNE 2005

3-month
.06
.11

6-month
.09
.14

12-month
.15
.19

7

For the average person,
however, transitory
surges in overall
inflation are no
less inconvenient
simply because they
are transitory.

which are quite volatile relative to the
slower-moving core series. This is true
for both the optimally trimmed mean
and the measure excluding food and
energy, though less so for the trimmed
mean. Looking at the CPI, Cecchetti
(1997) emphasized the additional noise
reduction that can be achieved by examining longer-horizon inflation rates.8 Cecchetti’s point is equally valid with regard
to the PCE. Looking at three-, six- or 12month inflation rates improves the accuracy of both the trimmed mean and the
measure excluding food and energy as
gauges of core inflation.
For both measures, six-month changes
give the highest accuracy in gauging core
inflation. While the longer horizons benefit the measure excluding food and
energy more than the trimmed mean, the
latter is still the more accurate core inflation gauge. For the three-month inflation
horizon, the relative gain in accuracy from
using the trimmed mean is almost 0.4 percentage point. For the six- or 12-month
horizons, the gain in accuracy is 0.23–
0.25 percentage point, a not-insignificant
difference. (See Table A in box titled “Optimal Trimming: The Nuts and Bolts.”)
Chart 3 gives a visual sense of how
the trimmed mean performs relative to
the measure excluding food and energy.
The chart shows the annualized sixmonth inflation rates in the two measures, together with the proxy for
true core inflation. The series are shown

for the full sample period used in the
optimal trim calculations, 1979–2002.

What Has the Trimmed Mean
PCE Inflation Rate Been Telling
Us Lately?
Chart 4 shows the recent behavior of
the trimmed mean PCE inflation rate,
together with the more common excluding-food-and-energy inflation rate for the
three different time intervals. Here are
the salient points:
• While both the trimmed mean and
excluding-food-and-energy inflation rates
decline in 2003, the lows hit by the trimmed mean measure are not nearly as low
as those reached by the measure excluding food and energy. For example, the
three-month trimmed mean inflation rate
falls below 1 percent in only one month
of 2003, versus five such months for the
inflation rate excluding food and energy.
The lows for the six- and 12-month
trimmed mean rates are nearer 1.5 percent.
• Both inflation rates began to climb
in early 2004. The highs reached in mid2004, however, are both higher and
more sustained in the trimmed mean
measure than in the measure excluding
food and energy. The three- and sixmonth trimmed mean inflation rates both
spent time in the neighborhood of 2.5
percent.
• Inflation decelerated in the second
half of 2004, according to both inflation

Chart 3

Comparison of Core Inflation Measures
Percent, annualized
12

10
Proxy for true core inflation
Excluding food and energy, six-month change
Trimmed mean, six-month change

8

6

4

2

0
’80

’82

’84

’86

’88

’90

’92

’94

SOURCES: Bureau of Economic Analysis; authors’ calculations.

8

FEDERAL RESERVE BANK OF DALLAS

SOUTHWEST ECONOMY

MAY/JUNE 2005

’96

’98

’00

’02

Chart 4

Trimmed Mean and Excluding-Food-and-Energy PCE Inflation
6-Month Changes

3-Month Changes

12-Month Changes
Percent

Percent, annualized

Percent, annualized

3

3

3
Trimmed mean

2.5

2.5

2.5
Trimmed mean

2

2

2

1.5

1.5

1.5

1

1

Trimmed mean

1
Excluding food & energy

Excluding food & energy
.5

.5

.5
Excluding food & energy

0

0

0
Jan.
’02

Jan.
’03

Jan.
’04

Jan.
’05

Jan.
’02

Jan.
’03

Jan.
’04

Jan.
’02

Jan.
’05

Jan.
’03

Jan.
’04

Jan.
’05

SOURCES: Bureau of Economic Analysis; author’s calculations.

measures. This shows up as a decline in
the three- and six-month inflation rates
and a stabilization in the 12-month rates.
The three- and six-month trimmed mean
rates bottom out around 1.5 percent,
compared with around 1 percent for the
three- and six-month troughs in the rate
excluding food and energy. Similarly, the
12-month trimmed mean rate stabilizes
at about 2 percent, or half a percentage
point higher than the 12-month rate
excluding food and energy.
• While the 12-month inflation rates
in both measures look stable, the threeand six-month rates show that inflation
has accelerated since late 2004. Both
rates suggest core PCE inflation is currently running above 2 percent.

due to a temporary jump in the prices of
food, energy or other items, this does
not change the fact that a household’s
dollars couldn’t buy as much food,
energy or other items as they otherwise
could have.
So why should anyone outside of a
central bank care about the latest
trimmed mean PCE inflation rate (or any
other core measure)? Individuals routinely make decisions that rely, at least
implicitly, on forecasts of future inflation— for instance, whether to invest in
fixed-income securities or to take on
fixed-income obligations. For decisions
of this sort, knowledge of whether
recent changes in inflation are durable or
transitory— signal rather than noise— is
likely to be of value.

3

4

5

6

Why Should We Care?
This article began with a quote from
former Fed Vice Chairman Blinder
describing a policymaker’s difficulties in
interpreting monthly movements in the
inflation rate. Why the individuals setting
monetary policy would care about core
inflation—and why, as a result, they
continually seek improved estimates of
core inflation— is fairly clear. Changes in
inflation that are known to be transitory
and, thus, soon to be reversed pose less
threat to the goal of long-run price stability than more lasting changes.
For the average person, however,
transitory surges in overall inflation are
no less inconvenient simply because
they are transitory. If last month’s consumer price inflation was high mainly

—Jim Dolmas
7

Dolmas is a senior economist and policy
advisor in the Research Department of
the Federal Reserve Bank of Dallas.

Notes

1

2

8

I thank Mark Wynne and Evan Koenig, who provided numerous helpful comments at various stages of this research, and Jennifer Afflerbach, who suggested many improvements in exposition.
“Commentary on ‘Measuring Short-Run Inflation for Central Bankers,’”
by Alan Blinder, Federal Reserve Bank of St. Louis Review, May/June
1997.
That more rigorous approach was pioneered by Michael Bryan and
Stephen Cecchetti. See their article “Measuring Core Inflation,” in N.
Gregory Mankiw, ed., Monetary Policy, Chicago: University of
Chicago Press, 1994. For a good survey of these methods, see “Core
Inflation: A Review of Some Conceptual Issues,” by Mark A. Wynne,
European Central Bank Working Papers Series, No. 5, 1999.

FEDERAL RESERVE BANK OF DALLAS

SOUTHWEST ECONOMY

All data used in this article are from the Bureau of Economic Analysis
via Haver Analytics. The data on the detailed components of the PCE
index are as reported in Tables 2.4.4U and 2.4.6U in the “Underlying
Detail Tables” section of the Bureau of Economic Analysis web site:
www.bea.doc.gov/bea/dn/nipaweb/nipa_underlying/Index.asp.
“Efficient Inflation Estimation,” by Michael Bryan, Stephen Cecchetti
and Rodney Wiggins, National Bureau of Economic Research Working
Paper Series No. 6183, September 1997.
In the CPI, the weight an individual component receives corresponds
to its share in consumer spending, on average, over a two-year reference
period. CPI weights are thus fixed for two years at a time. Weights in
the PCE are slightly more complicated and change from month to
month. To a first approximation, the weight a component receives this
month is an average of (1) its expenditure share last month and (2)
what its expenditure share would be if consumers bought this month’s
quantities at last month’s prices.
The weighted median CPI, which is produced by the Federal Reserve
Bank of Cleveland and is perhaps familiar to some readers, is an
extreme form of trimmed mean. It corresponds to the limiting case
where nearly all the price changes in the upper and lower halves of the
distribution are trimmed, leaving only the price change of the single
component exactly in the middle. Pursuing the skating analogy from the
text, imagine that judging panels consist of seven members. The
median inflation rate is analogous to a scoring formula that discards a
skater’s three highest and three lowest marks.
In particular, the calculations in this article use a centered, 36-month
moving average of monthly inflation rates to proxy for true core inflation—that is, the true core inflation rate in any given month is assumed to be the average of that month’s inflation rate together with the
inflation rates of the prior 18 months and those of the subsequent 18
months. In a more technical version of this article (forthcoming), I consider other proxies for true core inflation.
“Measuring Short-Run Inflation for Central Bankers,” by Stephen Cecchetti, Federal Reserve Bank of St. Louis Review, May/June 1997.

MAY/JUNE 2005

9

Texas Finding Growth in Seeming Disadvantage

O

il booms in the 1970s and
early ’80s. A high-tech explosion in the 1990s. For more
than three decades, Texas led the nation
in employment growth by over 1 percentage point annually. The United
States faced six recessions over this
period, while Texas saw only three.
In the post-1991 recovery and
expansion, Texas consistently outperformed the nation, posting a 2.7 percent
annualized employment gain to the
country’s 1.8 percent (Chart 1 ). Overall,
Texas employment grew a whopping 32
percent against the nation’s 21 percent
over the 127-month expansion that ran
from March 1991 to November 2001. So
in an economic contest with the nation,
the maverick state dominated by multiple measures. Game, set, match: Texas?
Not quite. Economic progress
ground to a halt with the 2001 recession,
when both the Texas and U.S.
economies lost thousands of jobs. But
while the country hit bottom within eight
months, the Texas recession dragged on
until August 2003.
It seemed as though the rapid
growth of the 1990s had set up the
weakness that followed in the new

Chart 2

Texas and Nation Mostly in
Tandem After 2001 Recession
(Nonfarm employment)
Index, November 2001 = 100
102

101
United States
100

99

Texas

98
0

6
12
18
24
30
Months since 2001 U.S. economic trough

36

NOTE: The data cover November 2001–December 2004.
SOURCES: Bureau of Labor Statistics; Federal Reserve Bank
of Dallas.

10

Chart 1

Texas Outpaces the Nation in 1990s Expansion
(Nonfarm employment)
Index, March 1991 = 100

135
130
Texas
125
120
1991 U.S. economic trough

115

October 2001, last month
before 2001 trough

United States
110
105
100
95
0

12

24

36

48

60

72

84

96

108

120

Months since 1991 U.S. economic trough
SOURCES: Bureau of Labor Statistics; Federal Reserve Bank of Dallas.

decade. The cornerstone of the recession
was the dot-com bust, wherein overinvestment in and overexpectations from
the nascent high-tech industry led to its
downfall. Texas had seen some of the
most rapid growth in high tech and then
saw a steep plunge.
After the November 2001 U.S. economic trough, both the Texas and U.S.
economies were lackluster in generating
employment (Chart 2 ). But after keeping up with the nation on this front,
Texas slipped behind.
So what is holding Texas back and
preventing a ’90s-style recovery today?
Historically, Texas has found reliable drivers of economic growth to propel it
above the national average. In the post2001 recovery, however, some of these
drivers seem to have been pulled over
for speeding. A comparison of the propellers of economic growth enables a
more thorough consideration of how the
Texas economy performed during the
1990s expansion and the post-2001
recovery. Merely identifying the highgrowth industries is not enough; it is
important to understand how key industries combine as a central driving force

FEDERAL RESERVE BANK OF DALLAS

SOUTHWEST ECONOMY

for the Texas economy and act as the
economic base for its business cycles.

The Economic Base
Economists take the perspective that
a region cannot sustain strong growth by
providing its own subsistence. Although
a region may be able to survive as an
entity solely on local production and
consumption, strong growth is driven by
outside income generated through the
export of goods and services. The industries generating this export income form
the region’s economic base.
The Texas base consists of industries
that meet two criteria: They produce
tradable goods or services, and they
command a higher share of Texas
employment than their U.S. counterparts
do of national employment.1 For some
sectors, such as manufacturing, tradability is fairly easily determined. For others,
high geographic concentration (as measured by a Gini coefficient) indicates
tradability. If it’s also assumed that the
productivity of Texas and U.S. workers is
similar (and Texans and other Americans
have similar tastes), a Texas industry
producing tradables and with above-

MAY/JUNE 2005

The state’s strong growth was the
result of Texas industries outperforming
their national counterparts. Texas base industries grew so fast in the 1990s that they
more than made up for the state’s compositional handicaps relative to the United
States. Even with those disadvantages,
Texas base industries gradually edged the
growth rates of U.S. tradables and propelled the state to a strong performance.

Chart 3

Differences Between Texas and U.S. Job Growth Rates,
1990s Expansion
(Economic base industries)
Texas faster
(percent)
5
Includes:
Oil and gas extraction
Mining support activities
Air transportation
Basic chemical manufacturing
Semiconductor manufacturing

4
3
2

The Post-2001 Recovery

1
0

Texas heavy

–1
Includes:
Aerospace products and parts manufacturing
Specialized freight trucking
ISPs, search portals, data processing services

–2
–3
–4
–5
1

2

3

4

5

6

7

8

U.S. faster
NOTES: The vertical axis measures the difference in Texas and U.S. annualized growth, March 1991–October 2001. The horizontal axis
measures the Texas–U.S. ratio of each industry’s employment share in November 2001.The blue dot represents the economic
base’s average composition and growth.
SOURCES: Bureau of Labor Statistics; Federal Reserve Bank of Dallas; authors’ calculations.

average employment must be producing
for export to other states.2 Combined,
these two criteria yield the 35 industries
that make up the Texas economic base.
(See Table 1 on page 13.)
On average, the industries in the
Texas base have a 45 percent higher concentration of workers in the state than in
the nation. The Texas base accounts for
about 18 percent of private employment,
or nearly 15 percent of the state’s total
nonfarm employment. These industries
account for about 12 percent of national
private employment, or only 10 percent
of total U.S. nonfarm employment.

tries in the base fared worse than total
U.S. tradables (Chart 4 ). Had Texas base
industries grown at national rates, their
combined growth would have fallen from
an annualized rate of 2.1 percent to 0.6
percent. In contrast, their national counterparts grew at a 1.1 percent annual rate.

After the 2001 recession, the picture
was very different. Chart 5 shows a neareven split between industries in the
Texas economic base growing faster in
the state and those growing faster in the
nation from November 2001 to December 2004. Taking into account the size
and growth rates of the individual industries, however, employment in the Texas
economic base fell by about 15 percent
more than for its U.S. counterpart.
Once again, its economic base had
put the state at a disadvantage. The
national counterparts of Texas base
industries did not generate employment
as rapidly as total U.S. tradables (Chart
6 ). Moreover, had the industries in the
Texas base grown at national rates, average annual growth would have been
–1.9 percent, somewhat better than the
actual Texas rate of –2.1 percent but
only slightly worse than the –1.8 percent
national average for these industries.

Chart 4

The Texas – U.S. Gap, 1990s Expansion
Index, March 1991 = 100
U.S. tradables
at Texas growth rates

135
130

The 1990s Expansion

U.S. tradables
125

As Chart 3 shows, all but a handful
of Texas base industries grew faster than
their U.S. counterparts during the 1990s
expansion. On average, the Texas industries grew faster by more than 1 percentage point a year—a 90 percent faster
growth rate. The base set the pace and
pulled Texas private-sector employment
to a growth rate more than 45 percent
stronger than the nation’s.
Nonetheless, the composition of the
Texas base was not particularly favorable
in the 1990s. At the national level, indus-

U.S. counterparts of
Texas base industries

120
115
110
Texas economic base
105
100
Texas base
at U.S. growth rates

95
90
0

12

24

36

48
60
72
84
Months since U.S economic trough

96

108

120

SOURCES: Bureau of Labor Statistics; Federal Reserve Bank of Dallas; authors’ calculations.

FEDERAL RESERVE BANK OF DALLAS

SOUTHWEST ECONOMY

MAY/JUNE 2005

11

economy, plentiful real estate, a large
labor pool and generally businessfriendly policies can accommodate a
transition to a more vital economic base
or another great driver of economic
growth.5 The Lone Star State seems to
have all the elements needed for an economic resurgence.

Chart 5

Differences Between Texas and U.S. Job Growth Rates,
Current Recovery
(Economic base industries)
Texas faster
(percent)
7

Includes:
Aerospace products and parts manufacturing
Petroleum and coal product manufacturing
Semiconductor manufacturing
Computer and peripheral product manufacturing
Air transportation

5
3

— Raghav Virmani
Stephen P. A. Brown

1
0
–1

Texas heavy

Virmani is an economic research assistant and Brown is director of energy economics and microeconomic policy
analysis in the Research Department of
the Federal Reserve Bank of Dallas.

Includes:
Oil and gas extraction
Communications equipment manufacturing
Architectural and structural metals manufacturing
Basic chemical manufacturing

–3
–5
–7
1
U.S. faster

2

3

4

5

6

7

8

Notes
1

NOTES: The vertical axis measures the difference in Texas and U.S. annualized growth, November 2001 – December 2004. The
horizontal axis measures the Texas–U.S. ratio of each industry’s employment share in November 2001. The blue dot
represents the economic base’s average composition and growth.
SOURCES: Bureau of Labor Statistics; Federal Reserve Bank of Dallas; authors’ calculations.

2

With the economic base performing
poorly, what has kept Texas from slipping further behind is stronger growth in
the industries that produce tradables but
are not part of the base. The tradable
industries that are performing badly
nationally are doing worse in Texas, but
those doing well nationally are doing
better in Texas. In magnifying these
national trends, Texas is adapting to
changing market conditions. Such adjustments take time, but adaptability is important for long-term economic resilience.

Advantage Texas
The Texas economy grew at lightning speed in the 1990s, but such a pace
is often not sustainable for that long.3
Although Texas may not resume that
kind of pace in the near future, for now
it seems set for growth rates similar to
the nation’s. The 1990s, however, provide evidence that Texas can generate
superlative economic growth from a
seeming disadvantage.
During the current recovery, the
composition of its economic base has
accounted for most of the state’s weak
performance. Texas has a large share of
slow-growing industries in its economic
base. In addition, most of those industries are not performing as well in Texas
as they are in the nation. Like in the early

12

1990s, however, Texas is generating good
growth from a weak mix of industries.
Texas industries that produce tradables
but are not in the economic base are outperforming their national counterparts.
What is growing is coming to Texas.
To its advantage, Texas has a mix of
amenities, property values and wages
that attracts workers.4 While the education system is a potential drag on the

Chart 6

3

4

5

In this analysis, the U.S. economic trough of November 2001 is used
as a fulcrum on which hinge two periods of growth: March 1991
(trough)–October 2001 (the last month before the next trough) and
November 2001–December 2004 (the cutoff for data). Throughout this
analysis, the Texas economic base is essentially chained to its November 2001 composition.
Because this regional methodology ignores exports outside the United
States, it actually underestimates the economic base.
For a more detailed account of the reasons for the state’s sluggish
growth after the 2001 recession, see “A Texas Revival,” by Fiona
Sigalla, Federal Reserve Bank of Dallas Southwest Economy,
July/August 2004.
See “What Wages and Property Values Say About Texas Cities,” by
Stephen P. A. Brown and Lori L. Taylor, Federal Reserve Bank of Dallas Southwest Economy, March/April 2003.
For more on education and the Texas economy, see “Don’t Mess with
Texas,” by Fiona Sigalla, Federal Reserve Bank of Dallas Southwest
Economy, January/February 2005.

‘

The Texas – U.S. Gap, November 2001 – December 2004
Index, November 2001 = 100

U.S. tradables
at Texas growth rates

102
101
100

U.S. tradables
99
98
97
96
Texas economic base
95
U.S. counterparts of
Texas base industries

94
93

Texas base
at U.S. growth rates

92
0

6

12

18
24
Months since U.S. economic trough

SOURCES: Bureau of Labor Statistics; Federal Reserve Bank of Dallas; authors’ calculations.

FEDERAL RESERVE BANK OF DALLAS

SOUTHWEST ECONOMY

MAY/JUNE 2005

30

36

Table 1

Components of the Texas Economic Base

Industry
Oil and gas extraction
Support activities for mining
Pipeline transportation
Basic chemical mfg.
Petroleum and coal products mfg.
Agriculture, construction and mining machinery mfg.
Communications equipment mfg.
Air transportation
Architectural and structural metals mfg.
Semiconductor and other electronic component mfg.
Wholesale chemical and allied products
Cement and concrete product mfg.
Wholesale machinery, equipment and supplies
Wholesale professional and commercial
equipment and supplies
Computer and peripheral equipment mfg.
Telecommunications
Synthetic rubber/fibers and filaments mfg.
Funds, trusts and other financial vehicles
Nondepository credit intermediation
Wholesale electrical and electronic goods
ISPs, search portals and data processing services
HVAC and commercial refrigeration equipment mfg.
Other wholesale durable goods
Wholesale hardware, plumbing and heating equipment

November
2001
Employment Shares
(percent)

Annualized Growth Rates
(percent)
1990s Expansion

Current Recovery

Texas

U.S.

Texas

U.S.

Texas

U.S.

.68
.82
.19
.41
.26
.39
.31
.76
.50
.69
.15
.25
.73

.09
.14
.03
.13
.09
.16
.16
.44
.31
.45
.10
.18
.53

7.85
4.16
3.20
6.92
6.64
1.66
3.87
2.44
1.86
2.38
2.94
3.59
3.44

8.98
.61
1.55
4.95
5.81
2.21
1.98
1.05
.91
.32
1.28
1.03
1.20

– .66
.05
– 12.48
– 5.27
– .94
– 3.06
– 15.20
– 3.03
– 3.48
– 7.40
1.35
– 1.65
– 1.82

.03
2.09
– 5.25
– 4.47
– 1.89
– 1.60
– 10.04
– 3.42
– 1.11
– 8.28
– .53
.74
– 1.52

.71
.28
1.29
.12
.09
.67
.37
.44
.16
.43
.20

.52
.20
.97
.09
.07
.51
.30
.36
.13
.39
.18

5.35
2.99
– 4.66
4.39
1.40
– .11
1.24
.85
3.10
.46
4.67

2.54
1.25
– 4.20
.61
– .26
– 2.54
1.02
– .44
1.76
– 1.69
2.41

0
– 5.98
– 8.50
– 5.06
– .78
5.81
– 4.91
– 5.29
– 1.52
1.33
1.65

– 1.62
– 7.41
– 6.54
– 3.57
– 1.67
5.24
– 4.17
– 5.63
– 4.08
– .43
– .27

Aerospace product and parts mfg.

.43

.39

– 1.49

– 2.20

3.39

– 3.38

Wholesale lumber and other construction materials
Cable and other subscription programming
Specialized freight trucking
Software publishing
Wholesale grocery and related products
Agencies, brokerages and other insurance-related activities

.19
.08

.17
.07

– .57
1.61

– 2.87
1.73

1.80
.42

3.30
– 3.14

.33
.22
.56
.66

.30
.20
.52
.62

5.95
3.72
5.34
– 1.29

4.18
6.29
2.56
– 2.66

– .74
– 6.07
– .99
2.47

1.01
– 2.40
.21
2.20

General freight trucking
Wholesale motor vehicles, parts and supplies
Animal slaughtering and processing
Alumina and aluminum production and processing

.79
.27
.40
.07

.74
.26
.40
.07

2.55
– 1.48
1.32
– 2.99

1.22
– 2.17
– .49
– 4.04

– 1.09
– 2.57
.68
– 1.06

– .26
– .49
– .99
– 5.71

10.28
83.70
—

2.13
2.76
2.66

1.11
1.90
1.81

– 2.11
.04
.24

– 1.82
.35
.39

Total Texas economic base
Total private employment
Total nonfarm employment

14.90
83.01
—

NOTES: The 1990s expansion covers March 1991– October 2001. Data for the current recovery are for November 2001– December 2004. Other wholesale durable goods includes furniture,
furnishings, metals, minerals and miscellaneous durable goods.
SOURCES: Bureau of Labor Statistics; Federal Reserve Bank of Dallas; authors’ calculations.

FEDERAL RESERVE BANK OF DALLAS

SOUTHWEST ECONOMY

MAY/JUNE 2005

13

Beyond the Border

Mexico Emerges from 10-Year Credit Slump

S

ince the Tequila Crisis of
1994–95, one of Mexico’s most
persistent and striking economic
contradictions has been a recovery in
economic growth coupled with stagnation in bank lending. This contradiction
has fueled increasing concerns about
bottlenecks within Mexico’s production
chains and about what some analysts
view as expansion rates below potential.
Lending typically declines in the
wake of a financial shock, and Mexico’s
Tequila Crisis was no exception. Mexico’s currency lost half its value in just a
few months. The interbank interest rate
rose some 60 percentage points, to over
90 percent, and remained above 20 percent until late 1999. Mexican banks
needed most of their resources to
resolve problem assets, leaving little
room for new lending.
Even worse, credit extended by
Mexico’s banks continued to fall long
after the national economy had recovered. Compared with other countries in
similar circumstances, Mexico’s stagnation in lending has been unusually
severe and long-lasting.1
However, Mexican banks report that
business loans began to grow substantially
in fourth quarter 2004 and that healthy
growth rates continued through the first
quarter of this year, signaling a possible
reversal of the credit slump of the past
10 years. We address the credit slump’s
possible causes, its implications and what
the nascent loan upturn seems to be
telling us.

can help individuals tap future income
for present critical needs, such as housing and education.
Three years ago, in this same publication, we advocated financial globalization, using Mexico’s banks as a case
study.3 We concluded that the growing
prominence of foreign firms in the Mexican banking system (Chart 1 ) was not
cause for alarm, but would promote
world-class banking practices, enhance
financial competition and result in
greater financial stability. This was not to
say Mexico’s banking system was in particular need of foreign involvement, but
rather represented our view that international competition can promote economic and financial rigor in any country.
Our analysis contrasted sharply with
globalization’s detractors, who broadly
claim foreign influences and international linkages are harmful.
Today, many of the benefits we
claimed would result from the international openness of Mexico’s banking system have been realized, but business
lending has been slow to resume. In particular, evidence suggests that certain

14

Crisis and the Beginning
of Reform
A few years prior to the Tequila Crisis,
Mexico privatized its commercial banks
after a decade of government ownership. During that decade, the banks had
channeled most lending to the federal
government. As a result, credit and market risk assessment were minimal.
Once privatized, the banks took

Chart 1

Globalization of Mexican Banking
1994

2004

Foreign banks (2)
1.3%

Other domestic
banks (10)
4.6%

Inbursa
4.2%

Globalization and Bank Credit
A vibrant banking system that growing businesses can turn to for credit facilitates firms’ entry into previously segmented markets, enhancing competition.
The availability of finance promotes economic freedom by enabling entrepreneurs to leverage resources in pursuit of
business opportunities.2 Similar considerations apply to consumer credit, which

small- and mid-sized Mexican businesses
have lacked adequate financing, resulting in bottlenecks in the production of
key goods and services and holding
Mexico’s economic competitiveness
below its potential.4
It is in this context that the recent
upsurge in business lending takes on
particular importance. Consumer lending
has been growing rapidly for many years
now, but business lending was relatively
restrained before the fourth quarter of
last year, when real year-over-year
growth reached 15 percent (Chart 2 ).
Through the first quarter of 2005, aggregate business loans continued to grow
strongly at a rate of 17 percent.

Inbursa
.8%
Mercantil del Norte
2.4%
Inverlat
6.3%

Other domestic
banks (22)
25.3%

Mercantil del Norte
8%

Bancomer
18%

Banamex
21.4%

Santander Serfin
6.5%
Serfin
12.5%
Mexicano
7%

Bital
5%

Mexican banks

Santander Mexicano
8.3%

Foreign banks

SOURCE: Comisión Nacional Bancaria y de Valores.

FEDERAL RESERVE BANK OF DALLAS

BBVA Bancomer
26.2%

Other foreign banks (12)
5.1%
ScotiaBank Inverlat
5%

SOUTHWEST ECONOMY

MAY/JUNE 2005

HSBC
Bital
9.7%

Banamex
(Citigroup)
22.4%

Chart 2

Loan Growth in the Mexican Banking System
Real year-over-year growth (percent)

50
Consumer

Business

40

30

20

10

0

–10
Dec.
’01

Mar.
’02

June
’02

Sep.
’02

Dec.
’02

Mar.
’03

June
’03

Sep.
’03

Dec.
’03

Mar.
’04

June
’04

Sep.
’04

Dec.
’04

Mar.
’05

NOTES: Government interventions and loan sales preclude accurate and consistent calculations of year-over-year loan growth prior to 2001.
Consumer loans do not include mortgages. As of year-end 2004, real year-over-year growth in mortgage loans was 2.4 percent, the
first real increase in six years. As of March 31, 2005, mortgage loan growth was sustained, with a portfolio increase of 5.3 percent
year-over-year in real terms.
SOURCE: Comisión Nacional de Bancaria y de Valores.

steps to generate high returns and justify
the steep auction prices at which they
had been bought. The result was highrisk lending to the private sector. Incomplete legal enforcement of financial contracts, an underdeveloped system of
supervision and regulation, an implied
unlimited government guarantee of bank
liabilities and the banks’ own inexperience in assessing the risks associated
with lending to the private sector aggravated the problem. Bank lending
expanded at an average annual rate of
25 percent from 1989 through 1994,
resulting in a quadrupling of bank credit
as a percent of GDP.
Bank credit to the private sector
serves a vital economic role when properly extended, but this undisciplined
explosion in lending gave rise to imbalances. At the end of 1994, Mexican
banks’ risky loans became more precarious with the collapse of the peso and
subsequent jumps in inflation and interest rates. The Tequila Crisis devastated
the ability, and in some cases the willingness, of borrowers to repay their
debt. The banks’ financial condition
deteriorated severely.
Government programs to support
the banking system took on a variety of
forms. The government initiated pro-

grams to improve bank balance sheets
by easing debtor burden. Discounts on
loan balances and future payments were
offered. Their cost was shared by the
government and the banks. For the most
part, the general public regarded these
programs with indifference.
In contrast, the government’s forbearance policy for the banks themselves was wildly unpopular. The public
viewed it as a taxpayer bailout of bank
shareholders. Under the Loan Purchase
and Recapitalization Program, the government gave the banks good bonds in
exchange for bad loans. Suspicions were
widespread that many of the loans were
granted or defaulted upon fraudulently
or had been extended to insiders. These
bonds helped prevent failure, but their
high volume and nonnegotiable nature
constrained liquidity (Chart 3 ). At the
hardest hit banks, shareholder value was
substantially reduced or even eliminated.
The crisis’ effect on the banks led
many to question their privatization.
There is much evidence that the source
of bank problems was not privatization
itself, but the lack of regulatory, risk
management and legal infrastructure.
Whatever its liabilities, the Tequila Crisis
highlighted these problems and motivated change.

FEDERAL RESERVE BANK OF DALLAS

SOUTHWEST ECONOMY

The Tequila Crisis
devastated the ability,
and in some cases
the willingness, of
borrowers to repay
their debt. The banks’
financial condition
deteriorated severely.

MAY/JUNE 2005

15

Chart 3

Bank Loan Portfolios in Mexico
1994

Consumer
8%

2004

Government
4%

Business
18%

Mortgage
18%

Government
60%

Mortgage
12%

Consumer
10%

Business
70%

For most of the
largest banks, full
balance sheet recovery
did not occur until
foreign banks began
to purchase them.
These purchases,
which commenced in
2000, often involved
infusions of capital.

16

NOTE: The government category includes the government notes issued in resolving the banking difficulties of the mid-1990s.
SOURCE: Bank of Mexico.

The Promise of Sustained
Loan Growth
An examination of the primary problems inhibiting growth in lending activity
over the past 10 years reveals substantial
progress toward resolution, suggesting
the recent widespread growth in lending
will continue.
Reparation. The fallout from the
Tequila Crisis explains banks’ initial reluctance to lend. Banks had to work out
problem loans, raise their low capital
levels and engineer a quality-led escape
from high funding costs. Other problems
included generally inefficient operations
and inadequate information technology.
In response, the banks streamlined their
operations, rationalized costs and generated increased revenue. For most of the
largest banks, however, full balance
sheet recovery did not occur until foreign banks began to purchase them.
These purchases, which commenced in
2000, often involved infusions of capital.
One reason for the delayed business
credit recovery involves the nonnegotiable notes the government gave banks
in trade for their bad loans. Banks could
not sell these bonds and use the proceeds to lend to businesses. Beginning
in the fourth quarter of this year, the
nonnegotiable notes will begin to mature
and will likely be rolled over into negotiable notes. These new notes will provide banks with a fresh source of liquidity, as the notes will no longer tie down

FEDERAL RESERVE BANK OF DALLAS

SOUTHWEST ECONOMY

bank funds that otherwise could be
diverted to support loan growth.
Regulatory and Risk Management
Infrastructure. The years following the
Tequila Crisis have been a time of profound regulatory change. Mexican regulations now generally conform to international standards — or are even more
demanding—in risk management, internal control policies and loan provisioning.
At the time of the Tequila Crisis, and
for many years thereafter, credit bureaus
were not fully developed and banks did
not use them. However, a subsequent
regulatory change requires banks to
obtain, review and document a borrower’s past repayment performance and
current financial situation before making
a loan. Consumer and mortgage loans
extended without following these procedures are subject to a specific reserve
requirement equal to 100 percent of the
loan balance.
Though reluctant at first, bankers
now embrace these procedures. Credit
bureaus have grown in importance, and
the public now values a good credit rating, helping to establish a positive repayment culture. With the new credit rating
infrastructure, consumer lending has
experienced strong, sustained growth.
Spillovers of these methods and technologies, together with increased regulatory attention on all types of lending,
suggest business credit is poised to
expand.

MAY/JUNE 2005

Legal Infrastructure. Another impediment to loan growth, and secured lending in particular, has been the legal environment. Understaffing and overwork
have plagued the Mexican courts. Court
personnel, especially judges, tend to be
poorly paid. These problems have been
particularly acute at the local level. Many
bankers and industry analysts feel the
local courts are corrupt and susceptible
to political meddling. And, until relatively recently, Mexican bankruptcy and
collateral repossession laws were vague
and heavily tilted in favor of the borrower. As a result, banks turning to the
judicial system to collect delinquent
loans often found the proceedings
lengthy and unfruitful. Before the recent
reforms, observers indicated court decisions on foreclosure and repossession
required at least five years.
Recent years, however, have ushered in significant improvements in Mexico’s legal infrastructure. In 2000, the
Mexican Congress passed a law implementing new processes governing bankruptcy and the repossession of collateral.
A subsequent reform in 2003 further clarified the resolution process behind bankruptcy and loan default.
Anecdotal reports suggest the laws
overhauling bankruptcy proceedings
and detailing collateral repossession
have proven generally effective and
have greatly shortened the time for a
decision. Moreover, most such cases
now can be resolved outside the court
system. These options have also permitted financial institutions to become more
adept at working directly with customers
in encouraging payment.
Still, in some cases, contract enforcement may be difficult. Property rights
systems involve numerous mutually reinforcing institutions. Some local authorities responsible for enforcing property
rights in Mexico are still weak, reflecting
the country’s not too distant history of
authoritarian rule. These circumstances
may prove difficult to remedy, as they
can involve political institutions or informal customs.5
Even so, positive financial system
developments associated with improvements in the legal infrastructure are not
hard to find. Mexico’s burgeoning assetbacked securities market is testament to
a growing faith in the enforceability of

secured lending contracts. Despite a
slight rise in interest rates over the second half of the year, Mexico’s securitization market almost quadrupled in 2004,
making it the top such market in Latin
America. Some examples of new, structured financial transactions include securitizations of truck, auto and credit card
loans, as well as municipal and state
government debt. Mexico’s first mortgage-backed security (MBS) issuance occurred in December 2003. The MBS market increased from a single $53 million
issuance in that year to six issuances
totaling $477 million in 2004. Continued
economic and political stability, emergence of new securitization products for a
broader group of assets and liberalization of regulations have all worked to
increase institutional demand for securitized assets. All this bodes well for continued expansion in bank lending activity.
Bank Competition. In their continuing struggle to regain adequate financial
footing in the wake of the Tequila Crisis,
banks invested in government securities,
replaced high-cost time deposits and
borrowings with low-cost demand
deposits, cut overhead expenses through
layoffs, shed unprofitable operations,
and pushed up transaction volume and
service fee income. Opportunities for
further advances along these lines
appear rather limited. Net interest margins have thinned and stabilized. With
increased accuracy in credit scoring,
monitoring and contract enforcement, a
return to loan markets seems to be the
next step in increasing profitability.
Economic Conditions and Loan Demand. In spite of strong economic
growth, high real interest rates and price
fluctuations did not moderate in Mexico
until 1999–2000. By then, business lending seemed ready to grow, but the subsequent economic slowdown in the
United States stalled economic growth in
Mexico and ended the momentum behind the initial signs of credit expansion.
Fortunately, economic growth has
resumed in both the United States and
Mexico. The comovement of these two
economies partly reflects the unifying effects of 1995’s North American Free Trade
Agreement in promoting further integration of their business and economic cycles
(Chart 4 ). The increase in loan demand

FEDERAL RESERVE BANK OF DALLAS

SOUTHWEST ECONOMY

Recent years
have ushered in
significant
improvements
in Mexico’s legal
infrastructure.

MAY/JUNE 2005

17

Chart 4

Economic Conditions in Mexico Compared with the United States
Growth in real GDP (percent)

7

5
United States
3

1

Mexico

–1

–3

By rebuilding capital
and improving risk
management systems,
Mexico’s banks have
positioned themselves
to take advantage of the
positive trends shaping
business loan demand.

18

–5

–7
1995

1996

1997

1998

1999

2000

associated with stronger economic activity should work along with the other factors discussed to generate lasting growth
in business loans at Mexico’s banks.

2001

2002

2003

2004

est step in the monumental restoration of
Mexico’s banking system, characterized
by sound loan growth and the types of
achievements present in the most
advanced banking systems.

Outlook
Mexico represents a unique banking
opportunity. Macroeconomic conditions
are stable and improving, the country’s financial infrastructure continues to develop and modernize, and business cycle
convergence with the United States should
help spur future growth. Slowly but surely, various impediments to the supply of,
and demand for, business loans have been
resolved. By rebuilding capital and improving risk management systems, Mexico’s banks have positioned themselves
to take advantage of the positive trends
shaping business loan demand. Stable
net interest margins and limited ability to
raise fees and cut costs will help propel
the supply of loans as banks pursue profits to boost shareholder value.
These considerations suggest Mexico’s 10-year slump in business lending
is over. Lending’s rejuvenation is the lat-

FEDERAL RESERVE BANK OF DALLAS

SOUTHWEST ECONOMY

—Robert V. Bubel
Edward C. Skelton
Bubel and Skelton are international
financial analysts in the Financial
Industry Studies Department of the Federal Reserve Bank of Dallas.

Notes
1

2

3

4
5

“NAFTA and Mexico’s Less-Than-Stellar Performance,” by Aaron Tornell, Frank Westermann and Lorenza Martinez, National Bureau of Economic Research, Working Paper No. 10289, February 2004.
“Financial Markets and Economic Freedom,” by Luigi Zingales, in The
Legacy of Milton and Rose Friedman’s Free to Choose, Federal
Reserve Bank of Dallas, 2004, pp. 175–89.
“Financial Globalization: Manna or Menace? The Case of Mexican
Banking,” by Robert Bubel and Edward Skelton, Federal Reserve Bank
of Dallas Southwest Economy, January/February 2002.
Tornell, Westermann and Martinez (2004).
“Why Institutions Matter: Banking and Economic Growth in Mexico,”
by Stephen Haber, Stanford Center for International Development,
Working Paper No. 234, November 2004.

MAY/JUNE 2005

Regional Update

T

exas continued its steady rebound in the first four
months of the year, posting a 1.3 percent employment gain to match the state’s 2004 rate. The Texas
Coincident Index, an aggregate measure of statewide economic activity, rose at an annualized 2.1 percent, compared
with 1.9 percent in 2004.
Although still lagging the nation, Texas reported broadbased employment increases. All major sectors posted gains
for January–April, although manufacturing and government
reported the weakest growth, at 0.1 percent each. Professional
and business services jobs sharply increased, growing 4.6 percent for the period, the highest rate for any sector. Other areas
showing strong gains were other services, leisure and hospitality services, and information.
Rising crude oil prices may have pushed some prices

higher, but the Texas consumer price index remained reasonably low, up 2.6 percent year-over-year in January. The core
CPI—which excludes gasoline and food—was up 1.8 percent.
Higher energy prices are apparently not impacting pricing in
other industries significantly.
The Texas Leading Index showed continued positive
growth of 1 percent in the first quarter. Real oil prices and
average weekly hours posted sizable gains. Based on the
index, Texas can expect job growth of 2 percent in 2005. Temporary-employee hiring, a leading indicator, also picked up
sharply, growing at an annualized 9 percent in the first four
months. Overall, Texas appears poised for moderate, broadbased growth this year.
—Kristen Hamden

Texas Coincident Index Shows Steady Rebound

Texas Job Growth Lags Nation's Slightly

Percent*
5

Percent*
5

4

4

3

3

2

2

1

1

0

0

–1

–1

–2

–2

–3

–3

1999
2000
2001
2002
*Month-over-month, seasonally adjusted, annualized rate.

2003

2004

.1%
–.1%

1.7%
1.3%

1.9%
1.3%

United States

2005

2003

Texas

2004

2005

*Month-over-month, seasonally adjusted, annualized rate.

Texas Inflation Remains Reasonably Low

Net Contributions to the Texas Leading Index

Percent*

January – March 2005

4.5
4
3.5
3
2.5
2
1.5
1
.5
0

Net change in leading index

.88

–.02

Texas CPI

Texas value of the dollar
U.S. leading index

–.3
Real oil price

Texas Core CPI

2001
*Year-over-year change.

2002

2003

2004

.55

Well permits
–.06
New unemployment claims
.19
Texas Stock Index
–.08
Help-wanted index
–.21
Average weekly hours
–.4

2005

–.2

0

.2
Percent

.81
.4

Regional Economic Indicators
TEXAS EMPLOYMENT*

3/05
2/05
1/05
12/04
11/04
10/04
9/04
8/04
7/04
6/04
5/04
4/04

Texas
Leading Index

TIPI † total

Mining

Construction

122.0
121.4
120.2
121.0
119.4
118.6
118.1
117.7
117.2
117.2
117.9
118.1

129.8
130.1
129.2
129.2
129.1
128.8
129.8
129.4
129.3
128.6
128.7
128.4

153.2
153.0
152.8
152.5
152.1
151.7
151.9
151.4
151.3
151.0
150.6
150.4

542.6
541.3
541.8
541.0
540.7
540.3
539.7
539.3
541.4
540.8
541.1
544.5

Manufacturing
883.7
884.1
883.8
885.5
887.2
888.1
888.4
890.2
891.6
888.7
890.0
889.7

TOTAL NONFARM EMPLOYMENT*

Government

Private
service-producing

Texas

Louisiana

New
Mexico

1,665.6
1,664.7
1,666.6
1,665.6
1,663.7
1,662.5
1,659.1
1,658.6
1,662.9
1,655.0
1,652.1
1,650.1

6,305.5
6,298.2
6,291.0
6,275.3
6,268.1
6,259.5
6,249.7
6,250.0
6,252.8
6,235.8
6,227.4
6,229.9

9,552.5
9,543.1
9,537.9
9,521.6
9,513.5
9,503.6
9,490.8
9,491.4
9,501.9
9,473.1
9,463.1
9,466.4

1,929.5
1,924.8
1,926.5
1,916.9
1,920.3
1,919.3
1,913.5
1,921.3
1,921.3
1,919.5
1,917.9
1,922.8

801.4
799.4
799.7
799.1
796.9
795.1
792.6
791.2
791.5
789.4
789.3
789.2

* In thousands. † Texas Industrial Production Index.

FEDERAL RESERVE BANK OF DALLAS

SOUTHWEST ECONOMY

MAY/JUNE 2005

.6

.8

1

For more information on
employment data, see “Reassessing
Texas Employment Growth” (Southwest
Economy, July/August 1993). For TIPI,
see “The Texas Industrial Production
Index” (Dallas Fed Economic Review,
November 1989). For the Texas Leading
Index and its components, see “The
Texas Index of Leading Indicators:
A Revision and Further Evaluation”
(Dallas Fed Economic Review, July
1990). Online economic data and
articles are available on the Dallas Fed’s
web site, www.dallasfed.org.

19

FEDERAL RESERVE BANK OF DALLAS

Southwest
Economy
Coming Soon!
Dallas Fed
Texas Manufacturing
Outlook Survey

Introducing a new tool to monitor manufacturing activity in Texas
Texas has the second-largest manufacturing sector in the country. Starting
soon, the Dallas Fed will be releasing the results of a new monthly survey of
manufacturing activity in the state. The survey will give insights into the current
pace of manufacturing activity and future expectations for growth in Texas.

Southwest Economy is
published six times annually
by the Federal Reserve Bank of
Dallas. The views expressed
are those of the authors and
should not be attributed to the
Federal Reserve Bank of Dallas
or the Federal Reserve System.
Articles may be reprinted
on the condition that the
source is credited and a copy
is provided to the Research
Department of the Federal
Reserve Bank of Dallas.
Southwest Economy is
available free of charge by
writing the Public Affairs
Department, Federal Reserve
Bank of Dallas, P.O. Box 655906,
Dallas, TX 75265-5906, or by
telephoning (214) 922-5254.
This publication is available
on the Internet at
www.dallasfed.org.

The results will be posted each month on the Dallas Fed web site. You can
receive the results automatically through an electronic mailing list.

Richard W. Fisher
President and Chief Executive Officer

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First Vice President and
Chief Operating Officer

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Director of Research

W. Michael Cox
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Chief Economist

Robert D. Hankins
Senior Vice President,
Banking Supervision

Executive Editor
Harvey Rosenblum

Editors
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Associate Editors
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To subscribe, go to the Dallas Fed web site at www.dallasfed.org and click
on “E-mail Alerts” under “Tools.”

Texas Manufacturing Outlook Survey
www.dallasfed.org

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