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Vol. 31, No. 3

ECONOMIC REVIEW
1995 Quarter 3

f

Inflation, Unemployment,
and Poverty Revisited

2
m

by Elizabeth T. Powers

Scale Economies and
Technological Change
in Federal Reserve ACH
Payment Processing
by Paul W. Bauer and Diana Hancock




FEDERAL RESERVE BANK
OF CLEVELAND

14

1

E C O N O M I C

R E V I E W

1995 Quarter 3
Vol. 31, No. 3

Inflation, Unemployment,
and Poverty Revisited

2

by Elizabeth T. Powers
Most of the research that uses income to measure economic well-being
shows that while unemployment has a strong positive effect on poverty
rates, inflation has very little effect. This paper considers the impact of
inflation and unemployment on poverty, using a poverty rate based on
goods and services actually consumed, rather than on income. The find­
ings suggest that increases in unemployment are associated with increases
in both the consumption poverty rate and the conventional income poverty
rate. However, inflation seems to have a robust and relatively large positive
influence on consumption poverty, indicating that inflation may harm the
poor more than was previously thought.

Scale Economies and
Technological Change
in Federal Reserve ACH
Payment Processing

14

by Paul W. Bauer and Diana Hancock
Since 1979, the cost to the Federal Reserve of processing an automated
clearinghouse (ACH) transaction has fallen dramatically. The authors of
this study find that three factors— scale economies, technological
change, and lower input prices— each contributed significantly to this
price decline. Their results also show that substantial scale economies
could still be achieved in ACH payments processing. This research should
be of broad interest to economists because the data provide a rare, detailed
glimpse into the workings of a service industry.




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2

Inflation, Unemployment,
and Poverty Revisited
by Elizabeth T. Powers

Elizabeth T. Powers is an econo­
mist at the Federal Reserve Bank
of Cleveland. The author thanks
David Altig, Alan Auerbach,
Louise Scheiner, Daniel Slesnick,
and the editorial board ot the Eco­
nomic Review for helpful com­
ments, and Kristin Roberts for
valuable research assistance.

Introduction
A small but influential body of literature has
attempted to estimate the effect of selected
macroeconomic variables on poverty.1 Such
exercises may serve several purposes. For
example, general knowledge of predictable
empirical relationships among these variables
might aid fiscal planning. However, most of this
work has been motivated by “the frequent out­
cries against inflation on the grounds of its
adverse effects on the distribution of income.”2
This literature consistently finds that inflation
has a relatively minor impact on the incidence
of poverty and on the well-being of poor and
near-poor households. Because most econo­
mists working in this area assume that there is a
direct trade-off between inflation and unem­
ployment, controllable by the policymaker, the
critical comparison is between the effects of the
inflation and unemployment rates on poverty.

H 1 For example, while it is not the focus here, aggregate economic
growth is a frequently used macroeconomic indicator variable in this litera­
ture. See Powers (1995) for a discussion.

 I
2 See Blinder and Esaki (1978), p. 604.


This paper considers the relationship be­
tween these macroeconomic variables and an
alternative poverty measure that is based on
consumption rather than income. Otherwise, I
follow the methodology of the existing literature
closely. My research findings suggest that
changes in the unemployment rate are impor­
tant in explaining variation in both the conven­
tional income poverty rate and a consumptionbased poverty rate (which I call the JS poverty
rate, after work by Jorgenson and Slesnick
[1987, 1990] and Slesnick [19931). However, in
sharp contrast to previous findings that inflation
has very little effect on income poverty , I find a
robust and relatively large positive relationship
between inflation and the consumption poverty
rate. Thus, my findings suggest that inflation
may have a more adverse effect on poverty than
was previously thought.
Before explaining the methodology and find­
ings, it is important to note that there are several
possible avenues for improving on the existing
literature. Perhaps most seriously, the relation­
ship between inflation and unemployment, long
a subject of intense debate, is not modeled.
Typically, aggregate indicators of poverty such
as the share of all income received by the 20

percent (quintile) of households reporting the
lowest income, or the poverty rate (the percent
of the population living in households with
income below a given level), are simply re­
gressed on measures of unemployment and
inflation. Inflation and unemployment rates are
treated as if they have no influence on each
other, or are not both partly determined by
some common factor. This is at odds with most
theoretical treatments of the macroeconomy,
and ignoring the existence of these relationships
can result in unreliable estimates.
Use of the quintile share of income as a pov­
erty indicator can also be misleading. In many
cases, this variable is not informative about
changes in the welfare of the poor. For exam­
ple, suppose that households in the top income
quintile are taxed and the proceeds destroyed.
By definition, the total income share of the bot­
tom group must rise, yet it is obvious that this
latter group is not better off in any substantive
way. For similar reasons, empirical estimates of
the influences of inflation and unemployment
on quintile shares are not easily interpreted. In­
flation or unemployment may harm low-income
groups absolutely, even while their effects on
quintile shares are positive or negative.3
Finally, except for the work of Cutler and
Katz (1991), this literature has developed under
the assumption that income poverty concepts
adequately measure economic well-being. In
the past, this has been a matter of necessity,
because income data were the most compre­
hensively and consistently collected. However,
economic theory suggests that the goods and
services actually consumed by a family or indi­
vidual are a better measure of their well-being
(the economist’s ideal measure being utility).
Poverty measures based on income and con­
sumption are expected to differ because, in
principle, money income and consumption can
differ substantially.4 This means that who is
classified as poor can vary across the two meas­
ures. Further, the predominant economic model
of consumption argues that households attempt
to protect their standard of living from short­
term income swings. This implies more yearto-year variation in household income than in
consumption. Hence, the income poverty count
should also include more families who are
transitorily poor, while consumption poverty
should include more families who view their
status as persistent. For all these reasons, rela­
tionships found to hold with respect to poverty
measured on an income basis may not be ro­
bust with respect to poverty measured on a

consumption basis.


Because of the difficulties in interpreting the
quintile-share measures of well-being, I focus
exclusively on the poverty rate.5 However, the
poverty rate has some severe limitations of its
own. After all, it is merely a head count of those
below a particular threshold, and changes in
macroeconomic conditions can dramatically
affect the well-being of the poor without chang­
ing the actual head count at all. Therefore, it is
important to remember that the poverty rate
portrays only a single (albeit important) feature
of the nation’s poverty situation.
While the modeling of the macroeconomy in
previous work is obviously open to question,
there is so little agreement on the proper model
that such an approach is unappealing. Instead,
I accept the premises on which the previous lit­
erature rests, and ask whether these findings
are robust with respect to the poverty concept
employed. Thus, this paper is best interpreted
as a sensitivity analysis of the previous findings
vis-a-vis inflation, unemployment, and the
poverty rate.
The paper’s first section discusses and inter­
prets the findings of the previous literature.
Section II traces the history of the official meas­
ure of income poverty and considers its flaws.
The development of the alternative historical
series of consumption poverty rates presented
in Slesnick (1993), and the differences between
it and the conventional poverty series, are dis­
cussed in section III. Section IV revisits the
issue of inflation, unemployment, and poverty
using alternative poverty and inflation meas­
ures. Section V concludes.

I. Unemployment,
Inflation, and the
Conventional
Poverty Rate
In this section, I discuss and update the previ­
ous literature’s findings on inflation, unemploy­
ment, and income poverty. To interpret the
■

3 In fact, it is easy to construct a model in which the impact of
inflation is consistently positive or negative on all incomes, but the rela­
tionship between inflation and any quintile’s share, and even the ratio of
low to high shares, is nonmonotonic.

■
4 In theory, low-income households could also be drawing on
savings, borrowing against future income, receiving gifts or government
transfers of goods and services, or even getting income from the under­
ground economy. Of course, whether they actually do so is an empirical
question.

■

5 While the poverty rate has its own limitations, at least its pre­
dicted relationship with the variables of interest here is unambiguous.

findings, however, it is important to consider
the microfoundations of income poverty and to
understand how changes in macroeconomic
variables are transmitted into changes in
poverty rates. How might higher overall unem­
ployment affect the number of persons living in
poverty? The majority of families rely on labormarket earnings for most of their income, so
episodes of unemployment may result in large
income declines. It is also well known that
unemployment in cyclical downturns is dispro­
portionately borne by people whose earnings
are low to begin with— those whose incomes
are most vulnerable to slipping below the
poverty level.6 These factors are expected to
produce a strongly positive relationship be­
tween unemployment and poverty rates. How­
ever, there are other, potentially mitigating, fac­
tors. Some have theorized that the pattern of
wages over the business cycle could be pro­
cyclical,7 and that dependency on government
transfer payments might also lessen the poverty
rate’s sensitivity to unemployment by reducing
the role of earned income.
Dependence on unindexed income is the
obvious channel through which inflation might
affect income poverty rates. Households that
rely on nominally fixed payments for a substan­
tial portion of their income could be driven into
poverty by inflation; this implies a positive rela­
tionship between inflation and poverty rates.
The primary sources of nominally fixed income
are means-tested transfer payments (Aid to
Families with Dependent Children [AFDC] and
states’ General Assistance programs being the
only significant unindexed cash-transfer pro­
grams) and the minimum wage.8 It is also pos­
sible that employers exercise temporary market
power in inflationary periods, allowing real
wages to fall in the short run. Finally, as the
next section discusses, the poverty line was
probably overindexed for inflation throughout
the 1970s and 1980s, implying that some por­
tion of poverty-rate increases may be explained
by increased inflation itself.
The primary studies on inflation, unemploy­
ment, and the size distribution of income in the
United States are those of Blinder and Esaki
(1978), Blank and Blinder (1986), Blank (1993),
Cutler and Katz (1991), and Mocan (1995). Ex­
cept for Blinder and Esaki (who estimate only
income shares), all of these studies estimate
straightforward empirical relationships between
poverty rates and macroeconomic variables.9
Blank and Blinder (1986) first examined the
relationship between unemployment, inflation,
 and official income poverty rates for families
and persons. Their regression findings indicate


that inflation and unemployment rates were
both positively related to the percent of all per­
sons living in poverty during the 1959-1983
period. However, while inflation was associated
with an increase in the steady-state poverty
rate, this effect was only one-seventh the mag­
nitude of the poverty-increasing effect of a rise
in the unemployment rate. This led Blank and
Blinder to conclude that while both unemploy­
ment and inflation worsen poverty, the empiri­
cal evidence supports their belief that “unem­
ployment, not inflation, is the crudest tax.”
Blank (1993) also found a significantly positive
relationship between inflation and poverty
rates. In contrast, Cutler and Katz (1991) and
Mocan (1995) reported a relatively small nega­
tive relationship between inflation and poverty.
A strong, robust, positive relationship between
poverty and unemployment has been consis­
tently observed.
Because of revisions to data series as well as
the availability of new data since the original
studies appeared, I have updated some repre­
sentative findings in the literature using the
poverty rate for persons, as computed by the
Census Bureau from 1959 to 1992 (table 1). The
specification in the first column includes an
intercept term, an inflation measure (the growth
rate of the Consumer Price Index for all urban
consumers, or CPI-U), the unemployment rate
for prime-age males, and additional explanatory
variables, including the ratio of the poverty level
for a family of four to mean household income,
and a trend for the years after 1983. In the sec­
ond column, the one-period lag of the poverty
rate is added to the specification as a crude con­
trol for any dynamic features of the evolution of
poverty.10 The unemployment rate for males

■ 6 While it is probity safe to assume that tamilies starting out
nearest the poverty line are most vulnerable to crossing it, there is also
substantial income mobility from year to year in the U.S. economy. It is
possible that some people whose incomes put them well above the poverty
line one year might find themselves below it the next.
■

7

The evidence on this matter is inconclusive.

■

8 It is doubtful that these income sources exert an important influ­
ence on the poverty rate. Very few families of any kind contain a minimum
wage earner (see, for example, Horrigan and Mincy (19931). And, while real
AFDC benefits have been declining over the past 20 years, the effect on per
capita benefits has largely been mitigated by dedintng household sizes
9 A number of studies apply this methodology to foreign
economies, a recent example being Yoshino (1993) on Japan. Minarik
(1979) used an alternative microsimulation approach to examine the effect
of inflation alone on the size distribution of income.

■

10 The specifications reported in the first two columns are similar
to those presented in Cutler and Katz (1991).

■

5

TABLE

1

Regression Findings for
Income Poverty, 1959-1992
Dependent Variable: Income Poverty Rate for Persons
Explanatory Variables
-10.443
(1.29)b

Constant
Poverty line/
mean income

-6.65a
(1.09)

Inflation (growth in
CPI-U)

0.635a
(0.029)
-0 .1 14a
(0.043)

0.2893
(0.058)
0.065c
(0.039)

Prime-age-male
unemployment rate

0.433a
(0.068)

0.323a
(0.046)

Post-1983 trend

0.3383
(0.054)

0.1992
(0.042)

-5.10
(4.185)
0.366a
(0.111)
0.081c
(0.049)
0.584a
(0.224)

Lagged-income
0.586a
poverty rate
(0.090)
Post-1982 dummy
(1983-1992 = 1)
Government transfers
to persons/GNP
Interactions with Post-1982 Dummy

0.371a
(0.119)
-3.41
(2.39)
-0.278
(0.237)

Prime-age-male
unemployment rate

-0.748a
(0.190)

Government transfers
to persons/GNP

0.787a
(0.247)

Inflation (growth in
CPI-U)

-0.039
(0.149)

96.8%
Adjusted R2
34
Number of observations

98.5%
33

98.6%
33

a. Significantly different from zero at the 5 percent level or greater.
b. Standard errors are in parentheses.
c. Significantly different from zero at the 10 percent level or greater.
SOURCE: Author’s calculations.

aged 25-54 is used to capture unemployment
effects on poverty, since the total unemploy­
ment rate is influenced by demographic trends
that may independently affect the income
poverty rate. The ratio of the poverty line to
mean household income is intended to control
for the shape of the income distribution near
the poverty line (see Danziger and Gottschalk
[1986]). Finally, the post-1983 trend attempts to
account for that era’s unusually and persis­
tently high poverty rate (Cutler and Katz [1991],
Blank [1993]).
In the first two columns, the unemployment
rate shows a strong positive effect on the in­
 come poverty rate.11 An increase of one per­


centage point in the prime-age-male unem­
ployment rate raises the poverty rate by an
estimated 0.3 to 0.4 percentage point. Accord­
ing to the first column, periods of high inflation
are associated with poverty-rate reductions. An
increase of one percentage'point in the infla­
tion rate leads to a reduction of 0.1 percentage
point in the poverty rate. However, the findings
with respect to inflation are sensitive to specifi­
cation; the findings reported in the second col­
umn suggest that inflation has a (weakly) posi­
tive effect on the income poverty rate.
The final specification, reported in the last
column, is similar to those in Blank (1993) and
Blank and Blinder (1986). In addition to the
previous variables, Blank includes a measure of
government policy (government transfers to
persons divided by GNP) and tests for structural
change in the relationship between unemploy­
ment, policy, and poverty after 1982.1 have
added a term to test for a structural change in
the inflation effect as well. Unemployment has
the strongest effect in this specification, while
inflation has only a weakly positive impact. All
of the macroeconomic variables appear to have
perverse effects in the post-1982 world, as
noted by Blank.
Recently, Mocan (1995) has presented a
more elaborate econometric treatment of the
relationships between unemployment, inflation,
and poverty. He specifies poverty rates as a
function of unemployment, inflation, and real
wages, and uses a “flexible” model of the trend
in the poverty rate. The problem is that the
deterministic trends previously used in this liter­
ature may be inappropriate if the trend in the
poverty rate is subject to stochastic disturbances.
This issue is important because proper detrend­
ing of the data is critical for reliable parameter
estimates. Mocan also decomposes unemploy­
ment into its short- and long-run components
and inflation into its anticipated and unantici­
pated components, and analyzes black and
white poverty rates separately.12 While Mocan
finds that cyclical unemployment has almost no
effect on income poverty, long-run (structural)
unemployment has a significantly positive ef­
fect. He also finds that both expected and unex­
pected inflation significantly redu ce poverty,

■
11 It should be noted that to preserve comparability with previous
studies, I do not correct for the obvious autocorrelation in all of the specifi­
cations in table 1. However, corrected estimates (which are not reported)
are qualitatively similar.
■
12 Blank and Blinder (1986) also decomposed inflation, but found
no significant differences between unanticipated and anticipated inflation
effects.

6

with the former having the larger impact. The
negative effect of inflation on the person poverty
rates for blacks and whites is about one-third of
Mocan’s estimated poverty reductions from a
decrease in structural unemployment.
To summarize the literature’s findings, un­
employment is consistently estimated to have a
strong positive effect on the income poverty
rate, suggesting that joblessness is responsible
for pushing many households’ incomes below
the poverty level. This finding is quite robust
with respect to various empirical specifications.
While the estimated effect of inflation is very
sensitive with respect to specification, it seems
to have at most a small positive impact on the
poverty rate, and may even be associated with
poverty-rate declines.
Unfortunately, these findings are developed
in the context of a poorly specified measure of
poverty. A consumption-oriented approach to
poverty suggests that the important factors are
the total resources available to a family over
long periods, and the family’s ability to rearrange
these resources over time. If consumption and
income poverty rates turn out to be very differ­
ent, one expects that the findings vis-a-vis infla­
tion, unemployment, and poverty will also be
very different— for two reasons. First, as I dis­
cuss below, the mechanisms by which unem­
ployment and inflation may be translated into
consumption poverty are quite different from
those influencing income poverty. This suggests
that the relationship of macroeconomic vari­
ables to consumption poverty is potentially very
different from their relationship to income
poverty. Second, the income poor and the con­
sumption poor may be dissimilar groups of peo­
ple. (For example, they appear to vary in age
and racial composition, according to Slesnick
[1993].) Since the response to macroeconomic
conditions is undoubtedly heterogeneous across
the population, changing the type of house­
holds under consideration should also change
the aggregate relationships.
Of course, if income poverty is a close
approximation of the underlying “true” con­
sumption poverty rate, these issues will be sig­
nificant only in theory, not in practice. In the
next section, I review Slesnick’s (1993) calcula­
tion of consumption poverty.13

■
13 The material in section II is drawn primarily from Slesnick
 (1993) and Ruggles (1990).


II. The Mismeasurement
of Poverty
A Brief History
of the Poverty Line
The official government poverty rate is the pro­
portion of the population whose pretax income
falls below specified levels, called “poverty
thresholds” or “poverty lines.” Today’s official
poverty thresholds have their antecedents in
the poverty lines developed for the Social Secu­
rity Administration by Orshansky (1988) in the
early 1960s. Because budget studies from the
1950s found that the typical low-income family
spent about one-third of its budget on food,
Orshansky took the USDA’s Economy Food
Plan (a nutritionally adequate but inexpensive
collection of food items) and multiplied it by
three to arrive at a level of total expenditures
designated as the poverty line.
Poverty thresholds were further refined for
the heterogeneous nutritional requirements of
families with different structures. Until 1981, a
particular family’s threshold depended on family
size, sex and age of the household’s head, num­
ber of related children under 18, and farm or
nonfarm residence. Smaller families devote a rel­
atively smaller share of total expenditures to
food; women have lower caloric requirements
than men; children eat less than adults; and farm
families can consume home-grown food. All of
these considerations suggested lower multiplica­
tive factors, and hence lower poverty expendi­
ture thresholds, all else being equal.14 In 1981,
calculations of differences due to sex of the
household head and farm versus nonfarm resi­
dence were eliminated by legal challenges.
Nominal thresholds must be adjusted over
time to reflect declines in purchasing power.
Prior to 1981, the nominal poverty line was
increased by food-price inflation only. By
ignoring other prices, these adjustments some­
times overstated, sometimes understated, the
increase in total nominal expenditures required
to maintain a constant standard of living. Since
1981, the CPI-U has been used to inflate the
official poverty thresholds from their 1963 val­
ues to current dollars.

■
14 Indeed, it is possible to differentiate along many more charac­
teristics, as suggested by Slesnick (1993).

FI GURE

1

Alternative Inflation
Rates, 1951-1989
Annual percentage change

14
12

10
8
6
4
2

0
-2
1951

1955

1959

1963

1967

1971

1975

1979

1983

1987

SOURCE: Author’s calculations.

Problems with
the Official
Poverty Rate
As Slesnick (1993) points out, the conceptual
basis for the official poverty statistic, based on an
expenditure concept, is fundamentally sound.
However, several features of the poverty thresh­
olds are simplistic and may bias the measure­
ment of poverty. Foremost among these are
benchmarking against food consumption and
the inflation adjustment. Using family equiva­
lence scales based entirely on food needs will in
some cases understate, and in other cases over­
state, efficiencies in the shared consumption of
nonfood commodities. For example, a childless
couple may need almost twice as much food as
one person, but they will not need twice as
many rooms in their apartment. Thus, multiply­
ing their Economy Food Plan figure by three
may lead to a gross overstatement of their mini­
mal expenditure requirements. Because the food
equivalence scale will understate efficiencies of
shared consumption in other items, the direction
of the total bias that results from relying solely
on food shares is unpredictable a priori.
Several obvious issues are raised by adjusting
the poverty thresholds by a single inflation rate
each year, and several problems are peculiar to
the CPI-U. First, an increase in the general price
represents the combined effect of increases
(and/or decreases) over all prices, but all prices
do not necessarily rise at the same rate. When,
for example, inflation is concentrated in the
price of necessities, the poor, who devote a

greater
fraction of total expenditures to these


items, will be harmed more than others. This
suggests that poverty thresholds should be ad­
justed by price indexes that are relatively more
sensitive to rising prices of items consumed in­
tensively by the poor, rather than by the CPI-U,
which reflects inflation based on expenditure
patterns of the average family.
Another potential problem of applying a
single inflation measure to poverty thresholds
is that expenditure patterns may adjust in ways
that mitigate welfare losses from price changes.
In theory, families can accommodate fairly sig­
nificant inflationary episodes by adjusting the
types and relative quantities of goods they
consume.15 For instance, when beef prices rise
relative to chicken prices, consumers may sub­
stitute chicken for beef. These behavioral re­
sponses result in smaller declines in living
standards than if expenditures remained frozen
in their former patterns. Since the CPI-U is only
infrequently reweighted for changes in expen­
diture patterns (and not of the poor, but of the
average family), applying it to the poverty line
overstates the increase in poverty thresholds
required to approximate the same level of
well-being.
A final problem, peculiar to the CPI-U itself,
is its treatment of housing. Before 1984, the
housing component was set equal to the finan­
cial cost of housing, not the flow of housing
services. Thus, periods of high mortgage rates
are periods of overstated inflation in the CPI-U
series. Figure 1 shows both the CPI-U and the
alternative CPI-X1, which uses rental costs as a
proxy for housing service prices. The CPI-U
overstates inflation in the late 1960s and late
1970s, implying that poverty thresholds rose by
more than the amount needed to maintain a
constant standard of living, and overstating
recent poverty rates. After 1984, the two price
indexes are the same.
With the exception of the housing error, the
above factors make a relatively minor contribu­
tion to the mismeasurement of poverty
(Slesnick [19931). The most serious divergence
between theory and implementation is the use
of pretax income, rather than expenditures, as
the yardstick for poverty. This practice accounts
for most of the mismeasurement of poverty. In
the next section, I explore the construction of
alternative consumption-based poverty rates
and the biases introduced by the use of
income- rather than consumption-based rates.

■
15 That is, substitution as well as income effects are associated
with price changes.

8

III. A ConsumptionBased Poverty Rate
The accurate estimation of consumption-based
poverty rates is a daunting task. Slesnick (1993)
overcomes several obstacles to arrive at a series
that addresses the many problems discussed in
the previous section. His estimates are devel­
oped under the assumption that families act as
life-cycle consumer units, saving and dissaving
to smooth consumption over time.16 An im­
plicit assumption is that the fraction of “misers”
in the population is small. Presumably, for most
families, a consumption poverty classification
reflects low resources rather than a preference
for low consumption.
Slesnick’s basic consumer data are from the
Consumer Expenditure Surveys (CES) for 19601961, 1972, 1973, and 1980-1989.17 Measuring
poverty on the basis of consumption, rather
than income, is not a simple matter of compar­
ing CES expenditure data to the standard
poverty thresholds. First of all, expenditure and
consumption are not equivalent concepts. For
example, contributions to retirement funds
(including Social Security taxes), which the CES
records as expenditures, are really savings,
since they contribute directly to future living
standards. Contributions or gifts to other house­
holds, while available, are not used by Slesnick,
since a consistent treatment would greatly com­
plicate the modeling of consumption. As in
computing official poverty status, Slesnick
excludes in-kind transfers of housing subsidies
and health care from his measure of consump­
tion, although conceptually they should be
included. Finally, many goods are consumed
over long periods and not immediately upon
purchase. Expenditures for these “durable”
goods may occur all at once or over a period of
years (homes and cars are frequently paid for
in this way). There is no reason to expect pay­
ment schemes to exactly match the flow of
value from the consumption of these services.
Instead, Slesnick imputes the rental equivalent
(what one would be willing to pay to rent the
identical item) for durables in each survey year.
The JS equivalence scales used to adjust for
differences across family types are more
detailed than the official equivalence scales.
They measure how expenditure patterns for all
items (not just food) change when household
composition changes. In contrast to the official
rates, which are based solely on nutritional
requirements, and which vary only according
to size and age characteristics of families and
individuals, the “JS equivalence scales ... vary

over
any set of demographic attributes that


influence household expenditure patterns”
(Slesnick [1993DSlesnick addresses many of the indexing
problems associated with the conventional
poverty rate. For any combination of price
changes, he estimates the minimum nominal
change in total expenditures necessary to main­
tain a constant standard of living, which
amounts to a specific cost-of-living index for
each household. The index is applied to the
base-year poverty threshold (which has been
converted to a consumption-equivalent basis).
This general deflator accounts for several factors
excluded by the CPI-U, including the fact that
price changes affect families with different con­
sumption patterns differently and lead to substi­
tution of less expensive for more expensive
commodities. However, Slesnick shows that
these adjustments’ effects on measured poverty
are quite small. The primary impact on poverty
rates comes through the correction for the over­
statement of inflation in the CPI-U due to the
mistreatment of owner-occupied housing costs.
Figure 2 shows the official income poverty
rate and the JS consumption poverty rate for
1959-1989. Both the levels and trends of the
two rates are quite different. Except for a period
in the late 1960s, the official poverty rate is
higher than the JS rate. Both rates decline from
1961 to the beginning of the 1970s. However,
they paint dramatically different pictures of
recent poverty trends. Because the JS family
equivalence scales set a relatively lower poverty
threshold for female heads than do the official
equivalence scales, JS poverty continues to
trend downward over the 1970s, when the pro­
portion of female heads in the general popula­
tion was rising dramatically and pushing up the
conventional poverty rate.18 While the official
rate indicates a strong resurgence in poverty
throughout the early 1980s and persistently high
rates thereafter, the JS rate, after a sharp increase
around the time of a recessionary trough in

■ 16 Since a brief discussion can convey only the major contribu­
tions, readers with a deeper interest in the methodology and implementa­
tion should consult Slesnick (1993) and the references therein It should
also be noted that the application of the life-cycle model to low-resource
households is controversial.
■ 17 Imputation methods involving auxiliary information from the
Current Population Surveys are used to derive poverty rates for the years
not covered by the CES. Given the available dafla, this is the best one can
do. The imputation process probably errs on the side of making the con­
sumption poverty measure aid the official poverty measure more similar.
18 The primary reason for the lower JS thresholds for female­
headed households is that children in these families are on average
younger than children in two-parent families and so consume less.
■

g

FI GURE

2

Alternative Poverty
Rates, 1959-1989
Percent ol persons in poverty

SOURCES: Author’s calculations; and Slesnick (1993).

1980, shows continued progress in the war on
poverty throughout the 1980s.
Although he does not compute the degree
of overlap between the income-poor and
consumption-poor groups, evidence provided
by Slesnick from the CES supports the notion
that the officially poor group is dominated by
those with only temporarily low incomes and
fairly high consumption. For example, in a typi­
cal year, 40 percent of the income poor are
homeowners (as are 60 percent of the general
population); in contrast, only 17 percent of the
consumption poor own their homes. Thus, a
significant minority of the income poor receive
substantial service flows from housing, while
most of the consumption poor do not. The
consumption poor also devote a larger share of
total expenditures to necessities such as food
(ranging from 31.6 percent to 37.3 percent over
the 1961-1989 period) than do the income
poor (22.2 percent to 28.1 percent). The life­
cycle model implies that dissavers view their
low income as a transitory circumstance; in­
deed, Slesnick finds substantial dissaving occur­
ring among the income poor. While 59 percent
to 76 percent of the income poor dissave over
the CES surveys, only 21.7 percent to 36.4 per­
cent of the consumption poor dissave, suggest­
ing that the consumption poor view their lack
of resources as a permanent condition.
The divergence between the two poverty
measures is expected to grow over time, since
the poverty line is an absolute— not a rela­
tive— notion of well-being. When average
income is fairly low, there are relatively more
 people whose “typical” annual income is near
http://fraser.stlouisfed.org/
or below the poverty line. As average real
Federal Reserve Bank of St. Louis

income grows, as it has since I960, there are
relatively fewer people whose typical income is
below the poverty line. Thus, the income-poor
population is increasingly dominated by people
with extraordinarily bad income realizations—
just the people for whom consumption does not
equal income.

IV. Inflation, Unemployment,
and Poverty Revisited
Before discussing the empirical approach and
findings, it is useful to describe the ways in
which inflation and unemployment might be
expected to influence consumption poverty.
Unemployment may affect consumption
poverty rates in several ways. If the household
is liquidity constrained (so that consumption is
limited to current income), then a spell of un­
employment may result in both income and
consumption poverty. When the household is
not liquidity constrained, a spell of unemploy­
ment should be harmful to the extent that it
decreases permanent, but not transitory, in­
come. For example, if earnings losses in reces­
sions are offset by increased opportunities in
expansions, cyclical unemployment should not
affect permanent income or consumption pov­
erty rates. However, if the labor market rewards
continuity in employment, time out of the labor
force may permanently reduce income, and
hence consumption. Finally, periods of high
unemployment may be periods of heightened
uncertainty about the future, which may lead to
reduced consumption and a higher incidence
of consumption poverty.

10

TABLE

2

Estimated Effects of Unemployment
and Inflation on Income Poverty,
1959-1992
Dependent Variable: Income Poverty Rate—Persons8
Explanatory Variables

Constant
Inflation (CPI-U)

-0.26
-0.179
(0.192)c (0.146)
-0.052
-0.015
(0.054)
(0.054)

Inflation (CPI-X1)
Prime-age-male
0.4l7b
unemployment rate (0.099)
Demographic controls'1
Real hourly earnings

0.373b
(0.104)

Autocorrelation
coefficient

0.50b
(0.151)

-0.81 l b
(0.327)
0.335b
(0.169)

Adjusted R2
Number of
observations

43.5%

57.7%

33

32

-1.62b
(0.279)
-0.036
(0.061)

-1 .6 lb
(0.275)

-0.041
(0.056)
0.396b
0.390b
(0.095) (0.095)
yes
yes

n.a.

n.a.

67.6%

67.9%
30

30

Dependent Variable: Consumption Poverty Rate—Persons*
Explanatory Variables

-0.389b
(0.147)
0.207b
(0.076)

-0.262e
(0.155)
0.180
(0.073)

0.453b
unemployment rate (0.153)
Demographic controls
Real hourly earnings

0.317b
(0.158)

Constant
Inflation (CPI-U)
Inflation (CPI-X1)
Prime-age-male

Adjusted R2
Number of
observations

-1.349b -1.375b
(0.414) (0.433)
0.204b
(0.068)
0.21915
(0.088)
0.347b
0.322b
(0.144) (0.150)
yes
yes

-0.106b
(0.485)

26.9%

35.7%

30

29

41.9%

36.4%

30

30

a. All data are first-differenced.
b. Significandy different from zero at the 5 percent level or greater.
c. Standard errors are in parentheses.
d. Demographic controls include percent of persons over age 65, percent of
white persons in population, and percent of families headed by a woman.
e. Significandy different from zero at the 10 percent level or greater.
SOURCE: Author’s calculations.

Inflation may also be associated with height­
ened uncertainty and increased consumption
poverty. Inflation can reduce permanent income
(and hence consumption) by increasing the dis­
count rate applied to future income flows; this

would also tend to increase the consumption
http://fraser.stlouisfed.org/poverty rate. There are at least two other ways
Federal Reserve Bank of St. Louis

in which higher inflation might be associated
with higher consumption poverty rates. First,
inflation tends to benefit debtors at the expense
of creditors, thus eroding asset values. Both liq­
uidity constraints and imperfect access to useful
financial instruments may cause the net wealth
of the consumption poor to be weakly hedged
against inflation. Second, it is possible that
households are slow to adjust their consump­
tion patterns to rapidly rising prices. This, too,
might contribute to a higher rate of consump­
tion poverty.
Rather than simply recomputing the regres­
sions reported in table 1 using the JS poverty
rate in place of the conventional income
poverty rate, all the data are first-differenced
beforehand.19 This simple but effective method
of detrending the variables is a special case of
the flexible trend model employed in Mocan
(1995). The top panel of table 2 presents the
findings for the conventional poverty rate, and
the bottom panel for the JS poverty rate.20
In the first column are the findings for the
regression of the poverty rate on an intercept,
the prime-age-male unemployment rate, and
the growth of the CPI-U (with all variables firstdifferenced). For the conventional poverty rate,
the findings remain qualitatively similar to those
in the first column of table 1. The unemploy­
ment rate has a strong positive effect on pov­
erty, while the inflation rate has a negative, but
statistically insignificant, effect.21 Both inflation
and unemployment significantly increase the JS
poverty rate. In contrast to the findings of
Blank and Blinder (1986) and Blank (1993) that
inflation’s effect is quite small relative to that of
unemployment, the magnitude of the inflation
effect on the JS poverty rate is nearly half that
of the unemployment rate.
The second column includes real wages, as
suggested by Mocan (1995), who argues that if
wage gains cause inflation, the effect of inflation
on poverty may be biased downward when this
variable is excluded. However, the findings indi­
cate that the estimated effect of inflation is
robust with respect to the inclusion of real earn­
ings. The third column includes demographic
variables (for age, race, and family type) that

I
19 The model employs the same variables as Mocan (1995). It is
noted below when the omission of variables from the models presented in
table 1 affects the findings.
■
20 The income-poverty-rate errors appear to follow an autoregres­
sive process of order one.
I
21 Trend variables for the post-1962 and post-1983 periods were
insignificant in the differenced specification and were dropped.

11

may have affected the overall incidence of
poverty. The demographic variables are jointly
significant. In both the conventional and JS
poverty-rate specifications, the estimated coeffi­
cients are robust with respect to the inclusion of
demographic variables, although the importance
of inflation relative to unemployment in ex­
plaining the JS poverty rate grows even more
pronounced.22 Due to the overstatement of
inflation by the CPI-U and its possible contribu­
tion to overstating the conventional poverty
rate, the alternative inflation rate based on the
CPI-X1 was included, but the findings were not
much affected.
Overall, unemployment seems to have a
strong positive influence on both poverty rates,
while inflation is only influential for the JS
poverty rate. The finding that unemployment
increases the JS poverty rate suggests that either
structural (long-run) unemployment is affecting
the lifetime incomes of the poor, or that cyclical
unemployment imposes permanent income
losses. While Mocan (1995) presents evidence
that the influence of unemployment on con­
ventional poverty rates is due to the adverse
effects of long-run, not cyclical, unemployment,
his findings are difficult to interpret, since the
composition of the income poor is no doubt
somewhat cyclical itself. In contrast, the esti­
mated effects of inflation on the two poverty
rates are dramatically different. Inflation has a
marginally negative effect on the conventional
poverty rate, but a fairly large positive effect on
consumption poverty.

V. Conclusion
This paper has reexamined the empirical rela­
tionships between inflation, unemployment,
and poverty, using a methodology similar to
that of previous work that apparently had
shown the importance of unemployment and
unimportance of inflation in influencing
poverty rates. I have demonstrated that these
previous findings are sensitive to seemingly
reasonable alternative poverty measures. The
findings presented here suggest that although
unemployment’s effect on poverty rates is rela­
tively robust with respect to the poverty con­
cept, the effect of inflation on poverty may be
more serious than previously thought.
How should these new findings influence
thought about the role of monetary policy? For
those who subscribe to the view that the mone­
tary authority can lower or raise unemployment
by enlarging or shrinking the money supply,

the previous literature appeared to provide


some evidence that expansionary monetary
policy could make the average person better
off by reducing unemployment, without the
unpleasant side effect of making people worse
off through inflation. The work presented here
suggests that even if one accepts the existence
of a trade-off between inflation and unemploy­
ment, one cannot be sanguine about the poten­
tial distributional costs of short-run stabilization
policies, since the estimates are not robust with
respect to alternative definitions of poverty.
In further research, it would be interesting to
decompose inflation into its anticipated and
unanticipated components, and unemployment
into its cyclical and long-run components.
Unanticipated inflation might have the most
adverse effects on consumption poverty if peo­
ple incorporate inflation expectations into their
decisionmaking. It is also important to discover
to what extent losses from transitory periods of
high unemployment are made up in boom
periods. Blank (1993) suggests that before the
1980s, low-income workers could make large
real income gains during recoveries by increas­
ing their hours of work. In a consumption
poverty framework, one would expect the
cyclical effects of unemployment to be miti­
gated to the extent that these earnings gains are
anticipated. However, there may be penalties
for discontinuity in labor-force participation,
implying that even cyclical unemployment
could affect permanent income.
It would be desirable to extend the data and
analysis to examine the relationship between
unemployment and inflation and the incidence
of poverty within specific population sub­
groups. While the harmful impact of unemploy­
ment is still found to be larger than that of infla­
tion when consumption-based poverty
measures are used for the entire population, it
would be interesting to discover whether this
qualitative finding is uniform across households,
or whether a very strong effect of inflation on
some, but not all, groups is driving the findings.
Finally, the measurement of consumption
poverty is a new and still controversial area.
Based on their examinations of the CES sam­
ples (also used by Slesnick [1993D, Cutler and
Katz (1991) conclude that “trends in the distri­
bution of consumption closely mirror those in

■
22 The estimated coefficient of unemployment is highly sensitive
to the inclusion of a government transfer variable in both the JS and con­
ventional poverty specifications, suggesting that innovations in govern­
ment policy and the prime-age-male unemployment rate are related.

12

the distribution of income” and that “while con­
sumption poverty rates are below income pov­
erty rates in every year, the time-series patterns
for the two measures are quite similar.” Apply­
ing the Census equivalence scales and conven­
tional indexing to expenditure rather than
income data, Slesnick finds the same pattern. It
is his adjustment for the overstatement of infla­
tion and his and Jorgenson’s alternative equiva­
lence scales that generate the very different
findings.23 Consequently, it is important to fur­
ther explore the extent to which the findings
presented here are driven by specific assump­
tions employed in the construction of Slesnick’s
consumption poverty rates.
Nevertheless, the findings of this new
research into the relationship between inflation,
unemployment, and poverty have called the
robustness of the earlier findings into question.
More research is needed before we can confi­
dently say how macroeconomic developments
affect poverty.

References
Blank, R.M. “Why Were Poverty Rates So High
in the 1980s?” in Dimitri B. Papadimitrou and
Edward N. Wolff, eds., Poverty a n d Prosper­
ity in the USA in the Late Twentieth Century.
New York: St. Martin’s Press, Inc., 1993,
pp. 21-55.
________ , and A.S. Blinder. “Macroeconomics,
Income Distribution, and Poverty,” in Shel­
don H. Danziger and Daniel H. Weinberg,
eds., Fighting Poverty: What Works an d
What D oesn’t. Cambridge, Mass.: Harvard
University Press, 1986, pp. 180-208.
Blinder, A.S., and H.Y. Esaki. “Macroeconomic
Activity and Income Distribution in the Post­
war United States,” Review o f Econom ics an d
Statistics, vol. 6, no. 4 (November 1978),
pp. 604-09.
Cutler, D.M., and L.E Katz. “Macroeconomic
Performance and the Disadvantaged,”
Brookings Papers on Econom ic Activity,
vol. 2 (1991), pp. 1-61.
Danziger, S.H., and P. Gottschalk. “Do Rising
Tides Lift All Boats? The Impact of Secular
and Cyclical Changes on Poverty,” A m erican
E conom ic Review, vol. 76, no. 2 (May 1986),
pp. 405-10.
Horrigan, M.W., and R.B. Mincy. “The Mini­
mum Wage and Earnings and Income In­
equality,” in Sheldon H. Danziger and Peter
Gottschalk, eds., Uneven Tides: Rising
Inequality in A m erica. New York: Russell
Sage Foundation, 1993, pp. 251-75.
Jorgenson, D.W., and D.T. Slesnick. “Aggregate
Consumer Behavior and Household Equiva­
lence Scales,”Jo u rn al o f Business an d Eco­
nom ic Statistics, vol. 5 no. 2 (April 1987),
pp. 219-32.
_______ , and________. “Individual and Social
Cost-of-Living Indexes,” in W. Erwin Diewert,
ed., P rice Level M easurement. Amsterdam:
North-Holland, 1990, pp. 155-234.

I
23 Interestingly, the Jorgenson-Slesnick equivalence scales
assume smaller efficiency gains in consumption as family size increases
than do the Census scales. Thus, Slesnick argues that the Census meas­
ure actually understates poverty in the 1960s and earty 1970s.




Minarik, J.J. “The Size Distribution of Income
during Inflation,” Review o f Incom e an d
Wealth, vol. 25, no. 4 (December 1979),
pp. 377-92.

13

Mocan, H.N. “Income Inequality, Poverty, and
Macroeconomic Conditions,” paper pre­
sented at the American Economic Association
Meetings, Washington, D.C., January 7, 1995.
Orshansky, M. “Counting the Poor: Another
Look at the Poverty Profile,” Social Security
Bulletin, vol. 51, no. 10 (October 1988),
pp. 25-51.
Powers, E.T. “Growth and Poverty Revisited,”
Federal Reserve Bank of Cleveland, Eco­
nom ic Com m entary, April 15, 1995.
Ruggles, P D raiving the Line: Alternative
Poverty M easures an d Their Im plications fo r
Public Policy. Washington, D.C.: The Urban
Institute Press, 1990.
Slesnick, D.T. “Gaining Ground: Poverty in the
Postwar United States "Jou rn al o f P olitical
Economy, vol. 101, no. 1 (February 1993),
pp. 1-38.
Yoshino, O. “Size Distribution of Workers’
Household Income and Macroeconomic
Activities in Japan: 1963-88,” Review o f
Incom e an d Wealth, vol. 39, no. 4 (Decem­
ber 1993), pp. 387-402.




Scale Economies and
Technological Change
in Federal Reserve ACH
Payment Processing
by Paul W. Bauer and Diana Hancock

Introduction
Technological advances — accompanied by
corresponding cultural changes and behavior
adjustments — have had a tremendous influ­
ence on the array of payment instruments
offered in the United States, on the diverse sys­
tems for processing them, and on their relative
costs. Starting with the development of Mag­
netic Ink Character Recognition (MICR) in the
1950s, which facilitated the automation of check
processing, the use of computers has trans­
formed virtually every aspect of banking and
the payments system.1 For example, many new
products, such as automated teller machines,
point-of-sale terminals, touch-tone bill paying,
and customer-initiated cash management serv­
ices, are now widely available. Advances in
computer technology— speed, storage, commu­
nications, and encryption capabilities— have
meant faster, more accurate, more secure, and
less costly back-office processing.
Since 1973, the use of electronic funds trans­
fers has been accelerated by development of
the automated clearinghouse (ACH). The ACH
system, a nationwide, value-dated electronic
funds transfer system typically used for recur­

http://fraser.stlouisfed.org/ ring consumer and commercial payments,
Federal Reserve Bank of St. Louis

Paul w. Bauer is an economic ad­
visor in the Financial Services
Research Group at the Federal
Reserve Bank of Cleveland, and
Diana Hancock is an economist in
the Division of Reserve Bank Oper­
ations and Payment Systems at the
Federal Reserve Board of Gover­
nors. For helpful comments, the
authors thank Allen Berger, David
Humphrey, Jeffrey Marquardt, and
Florence Young.

accommodates many types of transfers. The
most common uses are to make utility, payroll,
Social Security, tax, insurance premium, school
tuition, mortgage, monthly investment, and divi­
dend payments, and to manage business’ cash
concentration and disbursement activities.2
In the early 1980s, many observers argued
that it would eventually become less expensive
to transfer funds and settle most accounts elec­
tronically than to use traditional paper-based
methods.3 Consistent with those expectations,
the Federal Reserve’s direct and support costs
for processing ACH payments today (approxi­
mately 1.4 cents per transaction) are less than

1 Payment data encoded at the bottom of checks have allowed
high-speed check-sorting machines to process 80,000 to 100,000 checks
per hour.

■

2 The National Automated Clearing House Association (1995) esti­
mates that 42 percent of the private-sector workforce and 84 percent of
government employees are paid using direct deposit. Also, more than
50 percent of Social Security recipients currently receive their benefits
through direct deposit.

■

■

3

See, for example, Humphrey (1982,1984,1985).

for paper checks (about 2.5 cents per check).4
Based on Federal Reserve data, the real unit
cost (in 1994 dollars) of processing an ACH pay­
ment fe ll from 9.1 cents in 1979 to 1.4 cents in
1994. In contrast, the real unit cost of processing
paper checks rose from 2.0 cents to 2.5 cents
over the same period.5
Several hypotheses could account for the
dramatic decline in both the absolute and the
relative real costs of ACH processing. First, as
the volume of ACH payments grew at double­
digit rates, per-item costs may have dropped
because processing sites were able to achieve
greater scale efficiency. By their basic nature,
telecommunication systems, which consist of
communication equipment and circuits, offer
significant economies of scale over wide
ranges of output.6 Such systems are one of the
major inputs used in ACH payment processing.
Early studies by Humphrey (1982, 1984, 1985),
which used cross-sectional data from Federal
Reserve ACH operations over the 1977-1982
period, verified that average ACH production
costs fell as volume expanded. During that
time, for each 1 percent rise in ACH processing
volume, total production costs increased only
0.6 to 0.7 percent.
Second, technological change may have
made it cheaper to provide ACH services. With
the same quantities of inputs, more funds trans­
fers could be processed. Software improve­
ments, for example, could have resulted in
fewer computing resources being used to proc­
ess the same number of electronic payments.
Third, some of the major inputs used for
electronic payment processing, including com­
puters, experienced large quality-adjusted price
declines during the 1980s. For the same cost,
newer machines could process payments faster
than their predecessors and could perform
sophisticated tasks that were not previously
feasible. Falling input prices would help to
explain the absolute decline in real processing
costs. At the same time, employee wages,
paper costs, and other expenses associated
with processing paper checks were generally
rising.7 The change in relative input prices
would help to explain the decline in the rela­
tive real unit costs of ACH processing.
This study estimates the contribution of each
of these factors— scale economies, technologi­
cal change, and falling input prices— to the
absolute reduction in the real processing cost
of an ACH transfer. We use Federal Reserve
data over the 1979-1994 period and various
specifications for ACH cost functions.8 Not sur­
prisingly, we find that all three factors played a
significant role. The split between cost savings


attributed to scale economies (through volume
growth) versus technological change depends
on the specification chosen for the cost func­
tion. While scale economies accounted for a
decline in unit costs on the order of 20 to 40
percent, technological change explained more
than 30 percent. Cost savings attributed to input
price reductions generally accounted for less
than 10 percent of the real per-unit decline in
ACH payment processing costs.
Our findings suggest that consolidating the
Federal Reserve ACH processing sites will im­
prove scale efficiency, further reducing pro­
cessing costs. If recent experience is any
guide, technological change will also present
opportunities for further unit-cost declines. In
addition, the marginal cost estimates presented
in this study suggest that replacing paper
checks with ACH transfers could enhance eco­
nomic efficiency.

■

4 Direct and support costs cover all expenses specifically attribut­
able to providing Federal Reserve priced services, including labor, build­
ing, data processing, and data communication costs. They do not include
allocations of overhead expenses, such as legal, accounting, and personal
services, nor the Private-Sector Adjustment Factor (PSAF), which takes
into account the taxes that would have been paid and the return on capital
that would have been provided had the services been performed by a pri­
vate firm. Further, this definition of direct and support costs does not
include the costs to payors and payees of processing payments. Thus, the
Federal Reserve's costs are only a portion of the social costs of providing
payment services.
■

5 The real unit costs of processing ACH transfers and checks are
calculated using the implicit GDP price deflator for 1979 and 1994.

■

6 Scale economies were first studied in industries employing
pipelines and boilers. There is a clear mathematical reason for this.
Expanding the diameter of a pipe increases the amount of material required
to manufacture it by only two-thirds as much as its capacity. (See, for
example, Berndt [1991].) Similarly, in the context of communication sys­
tems, laying a fiber-optic line is not much costlier than laying a copper
wire, but the former has many times the carrying capacity.

■

7

Per-item wages have fallen over time because of capital

improvements.

■

8 The cost function is the minimum cost of producing any speci­
fied level of output given technological constraints and input prices.

16

I. What Is an
ACH Transfer?

Volume per quarter (millions of items)

1979

1981

1983

Volume per quarter (millions of items)

1985

1987

1989

1991

1993

The ACH system is a value-dated electronic
funds transfer system. The principal participants
in an ACH transaction are the payor, the payee,
the payor’s bank, the payee’s bank, and the ACH
operator.9 Either credit transfers or debit trans­
fers may be made using an ACH system. With
credit transfers, such as direct payroll deposits,
the payor’s bank typically initiates the transfer,
and funds flow from the payor’s bank to the
payee’s bank. With debit transfers, such as
mortgage payments, the payee’s bank initiates
the transfer and receives funds from the
payor’s bank.
ACH transactions offer several key advan­
tages over paper instruments. First, in most
cases, payors know exactly when the funds
will be removed from their accounts, and pay­
ees know exactly when the funds will be
deposited to their accounts. Second, particu­
larly for consumer bill payments, ACH transac­
tions may be convenient because the payor
does not have to remember to write and
deliver a paper check, and the payee does not
have to cash or deposit it. Third, the total costs
to all parties are much lower for ACH transac­
tions than for paper checks.10 Finally, account­
ing efficiencies may exist for business payors
and payees who have implemented electronic
data interchange to facilitate communications
with trading partners.11

1995

II. A Look at
the Raw Data
Before presenting statistical measures of scale
economies and technological change, it is
instructive to look at the raw data to determine
how Federal Reserve ACH processing costs
have varied over time and with different vol­
ume levels. Figure 1 presents unit costs (in
1994 dollars) over the 1979-1994 period, using

■

9

We use the term "bank" to refer to all depository institutions.

■ 10 The full social cost of processing an ACH item is only about a
third to a half as much as for a check (see Humphrey and Berger [1990]
and Wells [1994]).
0

10

20

30

4

0

5

0

6

0

Volume per quarter (millions of items)

SOURCE: Authors’ calculations.




7

0

8

0

9

0

■ 11 See Knudson. Walton, and Young (1994) for a discussion of
the potential benefits of financial electronic data interchange (a combina­
tion of electronic remittance data and electronic funds transfers) for busi­
ness payments.

processing volumes as the measure of output.12
Despite improvements in the ACH service—
including the introduction of encryption, in­
creased use of backup facilities, more deliveries
per day, a wider variety of formats, provisions
allowing more information to be supplied with
the payment, and conversion to an all-electronic
ACH environment— Federal Reserve per-unit
costs have fallen steadily. Similar declines are
observed at each processing site. For example,
the Cleveland District’s unit-cost decline paral­
leled that of the System as a whole. This sug­
gests that technological change could have been
the dominant factor driving down ACH process­
ing costs. However, output volume and input
prices did not remain constant.
Between 1979 and 1994, total ACH proc­
essing volume at the Federal Reserve grew at
an average annual rate of more than 22 per­
cent (see figure 2), reaching 2.4 billion pay­
ments valued at $8.4 trillion by the end of
1994.13 If scale economies exist, then volume
growth of this magnitude could account for a
large share of the decline in unit costs.
Figure 3 plots the unit cost per ACH transfer
processed in the Cleveland Federal Reserve
District against the number of quarterly trans­
fers processed at that site over the 1979-1994
period. Note that unit costs fell fairly steadily as
volume increased. The experience at other Fed­
eral Reserve ACH processing sites was similar.
Figure 3 suggests that scale economies (result­
ing from increased volume) were the dominant
factor pushing down ACH processing costs. In
general, however, output, technology, and input
prices were all fluctuating (in some cases dra­
matically) over this period, necessitating a multi­
variate approach to data analysis to investigate
changes in ACH costs. Both the formulation of
public policies for electronic payments and the
appropriate pricing framework for such pay­
ments hinge on an accurate understanding of
the different sources of real unit-cost reductions.
In general, the cost-function approach we
employ in this paper is well suited to handling
the contemporaneous effects of scale econo­
mies, as well as technological change and other
factors. Unfortunately, the Federal Reserve s
ACH data for each processing site show a strong
correlation (greater than 99 percent) between
the number of items processed (output) and a
time trend (a technology index that is commonly
used when a better measure is lacking). With
such a high degree of correlation, it is difficult
to disentangle the effects of technological
change (the time trend) from those of scale
economies (volume growth).




From a technical standpoint, econometric
models that include two highly correlated vari­
ables have upwardly biased standard errors,
making it difficult to obtain precise estimates of
the model’s parameters. Also, the cost-function
coefficient estimates could be sensitive to small
changes in the model’s specification.
Since there is reason to believe that both
scale economies and technological change are
important factors in the real unit-cost decline for
ACH processing, we choose to test for model
robustness by trying alternative specifications
for the cost function (for example, employing
yearly indicators instead of a time trend to allow
for technological change). We also use different
sample periods within our pooled cross-section
and time-series samples.

III. Estimation
To determine the effects of scale economies,
technological change, and falling input prices
on ACH processing costs, we estimate a cost
function using quarterly cost data for Federal
Reserve processing sites. This function maps
the best (least-cost) method of processing each
level of transfers when inputs, such as labor
and computers, can be varied freely. In general,
the least costly production method depends on
the scale of operations. The cost function is a
useful concept for our purposes, because many
characteristics of technology can be derived
from it, such as estimates of scale economies,
marginal costs, and technological change (as
will be explained more fully below).
We employ the translog cost function be­
cause it provides a good local approximation of
any arbitrary twice-differentiable cost function.
Thus, the translog function can model many

■
12 Throughout this paper, payments initiated and received at a
processing site are counted once. Payments received and partially
processed at one site and then transmitted and processed again at another
are counted at both the sending and receiving sites. Theretore, processing
volumes exceed the number of ACH payments made.
■
13 A National Automated Clearing House Association press
release dated March 27,1995 (“ACH Statistics Fact Sheet”) estimates that
the total volume of payments handled by ACH processors (including the
Federal Reserve) was 2.5 billion, valued at $10.1 trillion, in 1994. These
statistics exclude estimated “on-us" items (wherein the payor and payee
accounts are held at the same bank and consequently do not require exter­
nal processing). Although the growth rate and volume of ACH payments
may seem impressive, these payments accounted for fewer than 4 percent
of all noncash transactions processed domestically and only about 1 per­
cent of the dollars exchanged in 1994.

18

different possible relationships among the num­
ber of transfers processed (outputs), inputs, and
environmental factors, depending on its para­
meter values. Our general translog cost func­
tion can be written as
(1)

InCtt =

f io

+ j8yln> + 1/2 j8,.,(lny,,)2

K
+ X yMwki,
k =i
K
K

+ 1/2 X

k=i

j

X

8k] \nWkit\nWju

=i

K

“I- X

k =1

M
Syk lny„ \viiVku

1994

+ X
; = 1980

X

\ mZ mn

m= 1

(2)

d2..— _ J? C _
dwkdw,
dwjdwk

12

<bjYRj

+ X
j=

Zj Dj

+

Vu

,

2

where
is the number of ACH items proc­
essed at site i in period t, w„ is a vector of K
input prices for site i in period t, Z„ is a vector
of M environmental variables for site i in
period t, Dt ( j =2,..., 12) is a set of site indicator
variables (one for every processing site),14 YRj
( y =1980,...,1994) is a set of T-\ yearly indica­
tor variables (one for every year except the
first), and v represents the error term.15 In
some specifications, we use the time-trend term
T=
and its squared term, T 2, instead of
the yearly indicator variables, YRj, to represent
technological change.
Depending on the model specification and
the sample period selected, we set some of the
coefficients of the translog cost function equal
to zero. Several specifications of the cost func­
tion are estimated using ordinary least squares
(OLS), and we denote these models as OLS
Models 1 and 2. These elementary cost func­
tions include only an intercept, the log of the
•number of items processed at each site, yearly
indicators or a time-trend variable, and, in the
case of OLS Model 2, some environmental vari­
ables. OLS Model 2 is similar to the cost func­
tion estimated by Humphrey (1982, 1984, 1985)
for the ACH service.
In our most sophisticated specification, we
estimate the translog specification of the cost
function jointly with the input share equations
derived using Shephard’s Lemma.16 Estimation
of both the cost function and the input share
equations provides additional degrees of free­
dom and statistical precision. The system of
cost and share equations is estimated using the
iterative seemingly unrelated regression
(ITSUR) technique.17 We denote these models
as ITSUR Models 1 and 2. ITSUR Model 1 does
not include site indicator variables (D,), in




effect forcing the coefficients £,•(/= 2,..., 13) to
equal zero. For both ITSUR models, we esti­
mate equation (1), along with the correspond­
ing equations for input shares, imposing the
usual mathematical restrictions of symmetry
and linear homogeneity in input prices. These
restrictions, derived from economic theory,
reduce the number of cost-function parameters
that need to be estimated and thereby increase
the number of degrees of freedom available.
Symmetry restrictions follow from assuming
that the cost function is twice differentiable in
input prices, or

This forces 8kl = 8,k for every k and j. Linear
homogeneity in input prices means that only
relative input prices matter. That is, propor­
tional changes in input prices affect only the
level of cost, not the cost-minimizing set of
inputs.18 Linear homogeneity restrictions result
from defining the cost function as yielding the
minimum cost of producing a given output
level when faced with a particular set of input
prices. In order to impose linear homogeneity,
the following parameters related to the ln^*„’s
are restricted such that

Xk 7 * =

(3)

1

and

Xk 0 * = Xk 8 */ = 0.

IV. Decomposition
of Cost Savings
over Time
For a particular site, one could examine the
ratio of unit costs in two periods. Although this
ratio would show whether unit costs had risen
or fallen, it would not indicate whether the shifi

14 The first processing site is the base against which the others
are measured. Consequently, it does not have a site indicator variable. The
choice of the base site does not affect our final results.

■

15 The number of yearly indicator variables, YRt, depends on how
many years of data are included in the sample.

■

■

16

See Diewert (1982) for a discussion of Shephard’s Lemma.

17 See Bauer and Hancock (1993) for a look at the various econo­
metric techniques that can be used to estimate a system of cost and share
equations.

■

■

18

Mathematically, linear homogeneity can be expressed as

\C(y, w) =C(y, X w), where X is greater than zero (x =2 if input prices
double).

19

stemmed from scale economies, input price dif­
ferences, environmental differences, or techno­
logical change. To decompose the movements
in unit costs attributable to various factors
across time using cost-function (1), we can
rewrite the ratio of a site’s current unit costs
(with the period denoted by subscript S) to that
of the first period (with the period denoted by
subscript 0 ) as follows:19

C

= In

(Ks,

We,

ztf)exp(€g)
Jfc

C (y ,o ,

In

w,o, Zto) exp (e,0)
y-o

Using the cost function defined in equation
(1) and recalling that the log of a ratio is equal
to the difference of the log of the numerator
minus the log of the denominator, the percent­
age change in unit costs between periods, S
and 0, or equation (4), can be rearranged into
the following expression:
(5)

In

(C,
(C

S S l'M

)3y (lny,* - lny.o)
+ 1/2 P y y Q n t f s - In y*i0)

- (InjVs - lny,(,)
K
I
7 * (In W ks- InWko)
+
k= i
AC

K

+ 1/2 I
X 8k)(\nWkis\nWk,s
k=\ j= i

where the bracketed terms are defined as the
technological change effects, scale effects (dif­
ferent processing volumes), input price effects
(different input prices), interaction effects be­
tween processing volumes and input prices,
environmental effects, and a random effect.20,21
Although these terms are in logarithmic differ­
ences, they can be roughly interpreted as the
percentage difference in costs stemming from
the various effects.22 Equation (5) provides a
convenient framework for quantifying the
source of cost savings over time.

V. Data Construction
We collected quarterly data from 1979 to 1994
on total costs, ACH processing volumes, input
prices, and environmental variables for Federal
Reserve ACH processing sites. During the
1979-1989 period, the number of these sites
fell from 38 to 21. By an overwhelming mar­
gin, the largest volumes were handled by the
12 Reserve Banks and the Los Angeles branch
of the San Francisco Fed. By 1993, only the 12
main Reserve Bank offices were still process­
ing ACH items. Consequently, we aggregated
the data at the District level, with the exception
of the Los Angeles facility, which we treated as
a separate site. The New York Fed was omit­
ted from the estimations because most of the
commercial ACH volume in its region was
processed by the New York Automated Clear­
ing House.
Our primary data source is quarterly cost
accounting reports prepared by the Federal
Reserve in its Planning and Control System
(PACS). This information is supplemented by
other cost and revenue data, results from occa­
sional Federal Reserve surveys, and price index
figures from the Bureau of Economic Analysis
(BEA) and the Bureau of Labor Statistics.
Production costs for processed ACH transac­
tions are included in our calculations, but

■
19 Any two periods could be chosen to compare unit costs, but
comparing the first to the last is likely to be the most informative.

- \nWk,o \nwkJo)

■

+

^dkdnyjnui.s - lny,olnMw)j

k=

■

21 The interaction effect is a collection of terms that cannot be
classified cleanly into any of the other categories. Fortunately, the magni­
tude of this effect tends to be small.

M

+

—

m= 1

20 This decomposition uses the same methodology employed in
Bauer (1993) to study differences in unit costs across Federal Reserve
check-processing sites.

Am

(lnZm,s


€,s — €,0


\nZmto)

■

22 For the exact percentage difference, the antilog of each expres­
sion minus one should be used. We report the exact percentage differences
of our results in table 5.

20

TABLE

1

Average Input Cost Shares,
1989-1994 (percent)
Input Classification

Cost Shares

Labor
Materials
Communications
Building

21.3
40.6
35.6
2.5

SOURCE: Authors’ calculations.

imputed costs and certain overhead expenses,
such as accounting costs and special District
projects, are not. For the output measure, we
use site-specific figures that focus on transac­
tions processed at a site, rather than the num­
ber of payments (see footnote 12).
Labor, material, communication, and build­
ing costs are inputs for ACH processing. The
shares of direct and support costs for each of
these factors over the 1989-1994 period are
reported in table l.23 Labor expenditures
include salaries, retirement, and other benefits.
The price of labor is total labor expenditures
divided by the number of employee hours
spent processing ACH transactions.
While buildings’ share of costs is small, the
interest expenses associated with the acquisi­
tion of fixed assets are not represented in the
cost-accounting framework (these are included
in the inputed costs [PSAF] rather than in direct
and support costs). Cost accounting informa­
tion is supplemented by annual replacementcost indexes for each site, available from the
R.S. Means Company.24 Square-foot replace­
ment costs, adjusted by the depreciation rate,
are used to calculate maintenance and building
prices for each site.
Expenditures for materials are composed of
outlays for office equipment and supplies, print­
ing and duplicating, and data processing. The
service price for materials is constructed by sup­
plementing cost-accounting expenditure data
with indexes for information and processing
equipment.25 For computer hardware, an esti­
mate of the service value, or price, of machines
is constructed using formulas that employ a per­
petual inventory model.26 For data system sup­
port services, which are primarily used for inhouse, product-specific software development,
we construct a price by utilizing expenditures
for labor and hours worked in that area of each



Reserve Bank. For the service price of supplies
(printing and duplicating, office supplies, and
office equipment), we use the GDP implicit
price deflator. We apply index number theory to
construct a price index for materials that uses
expenditures and prices for the components of
materials— data processing, data systems sup­
port, and office supplies and equipment.
Communications expenditures comprise the
expenses associated with data and other com­
munications, shipping, and travel. The implicit
price deflator for communications equipment
purchases by nonresidential producers is used
for data and other communications. The fixedweight aircraft price index for private purchases
of producers’ durable equipment is employed
for shipping and travel expenditures. Using
index number theory, we calculate an overall
price index for communications using the
expenditure shares of two categories of com­
munications (communications and shipping)
and their individual price indexes.
Environmental variables that may affect ACH
processing costs are the proportion of federal
government items in the processing stream, the
number of banks served by a processing site,
and the proportion of banks receiving electronic
payment information. On one hand, govern­
ment items may be less expensive to process
because the Federal Reserve has more discre­
tion over file-processing times for these items
than for commercial items. On the other hand,
government items could be more expensive to
process than commercial items because they are
concentrated over short periods during the
month and thus may drive processing capacity
needs. The number of endpoints is the number
of banks or processors to which ACH payments

■ 23 We focus on this period for several reasons. First, all of the data
series are complete. Second, in the early period, full-cost pricing (required
by the Monetary Control Act of 1980) was gradually introduced. Third, con­
solidation of processing sites could cloud the effects of scale economies in
the early period. Consolidation effects are likely to be of minor significance,
however, because of the low processing volumes and costs incurred at the
additional sites. Finally, such dramatic technological changes occurred that
a single cost function may be unable to fit the entire sample period ade­
quately. Consequently, by concentrating on the most recent data, we should
get the best estimates of the current cost function for ACH processing.
■

24

Data on replacement costs for buildings are taken from Means

(1994).
■ 25 The BEAs implicit price deftator for information processing and
related equipment is used for data processing and computer hardware.
■

26

These formulas were derived by Hall and Jorgenson (1967).

21

t a b

L E 2

Technological Change Indexes
(1989 s 1.000)
Year

ITSUR Model 1

1989
1990
1991
1992
1993
1994

ITSUR Model 2

1.000

1.000

0.889
0.739
0.716
0.691
0.568

0.973
0.876
0.847
0.818
0.676

SOURCE: Authors’ calculations.

D

Q

U R E 4

Technological Change Indexes
Index, 1989 =1.00

SOURCE. Authors’ calculations.

information is delivered. Nonelectronic deliver­
ies by computer tapes, diskettes, and paper
methods increase transportation costs.27 In con­
trast, using electronic networks for deliveries
may create greater scale efficiencies.

VI. Empirical Results
We estimated cost functions with and without
the data for the early (1979-1988) period both
to provide a historical perspective and to ease
comparison with previous studies. The empiri­
cal results for the OLS cost-function models are
reported in the appendix. Estimates from these
models demonstrate that our qualitative findings
are robust to changes in the assumptions
employed in the estimation and in the sample
period selected. In the body of the paper, we
 focus on the two ITSUR models estimated using
http://fraser.stlouisfed.org/
data from 1989 to 1994. It is only during this
Federal Reserve Bank of St. Louis

period that data on the number of endpoints
with electronic connections are available.
Another reason we concentrate on the more
recent period is that the methods used for ACH
processing have changed dramatically over
time. In the earlier period, ACH transaction data
were delivered to the Federal Reserve Banks on
computer tapes, and the Fed delivered data to
receiving institutions on both computer tapes
and paper listings. In the more recent period,
however, ACH processing has essentially be­
come a computer network-based system. We
are interested in whether different technologies
for transmitting ACH transfers yield strikingly
different estimates for scale economies and for
technological change. Therefore, we estimate
the cost function for the latest period possible
— subject to the constraint of having sufficient
degrees of freedom to estimate the model with
statistical precision.
ITSUR Models 1 and 2 estimate the cost func­
tion jointly with three of the four input share
equations using the ITSUR technique. These
models are preferred because they allow for a
fuller complement of regressors and because
including the cost-share equations increases sta­
tistical precision. ITSUR Model 2 differs from
ITSUR Model 1 in that it includes processing-site
indicator variables that allow for site-specific
conditions not otherwise controlled for.

Technological
Change
Table 2 presents estimates of technological
change obtained from the two models above.
The technological change index is set equal to
one in 1989, with numbers below that indicat­
ing technological advance over the base year.
For example, ITSUR Model l ’s 1994 index indi­
cates that unit costs are only 56.8 percent of
costs in 1989, other things held constant. ITSUR
Model 2 finds somewhat less technological
change, with 1994 costs only 67.6 percent of
those incurred in 1989. For ITSUR Model 1, this
works out to a technological change estimate of
more than 10 percent per year from 1989 to
1994. Inclusion of processing-site-specific inter­
cepts (ITSUR Model 2) lowers the estimate to
just over 7.5 percent per year— still a rather
hefty reduction. While the estimates of techno­
logical change differ, both models find the same
pattern of unit-cost declines (see figure 4).

■

27

All ACH transactions were delivered electronically as of July 1,

1993 for the commercial (non-federal government) sector and as of July 1,
1994 for the federal government sector.

22

TABLE

3

Cost Elasticity Estimates
Federal
Reserve ACH

ITSUR Model 1

cessing Site

1994
0.764
0.776
0.761
0.771
0.766
0.763
0.760
0.768
0.765
0.761
0.772

1989
0.756
0.766
0.754
0.760
0.760
0.756
0.758
0.761
0.758
0.761
0.763

1
2
3
4
5
6
7
8
9
10
11

ITSUR Model 2

1989

1994

0.413
0.280
0.449
0.328
0.301
0.390
0.256
0.192
0.381
0.349
0.212

0.587
0.444
0.661
0.550
0.486
0.566
0.487
0.442
0.583
0.624
0.419

SOURCE: Authors’ calculations.

F I GURE

5

Estimated Average
Cost Functions
Average cost per item (dollars)

Volume per quarter (millions of items)

SOURCE: Authors’ calculations.

Scale Economies
Cost elasticities measure the effect of a onepercentage-point increase in output on total cost.
For example, a cost elasticity of 0.75 means that
if output increases 1 percent, costs would rise
only 0.75 percent. A cost elasticity of less than
one indicates the existence of scale economies

http://fraser.stlouisfed.org/ (that is, average cost falls as output increases).
Federal Reserve Bank of St. Louis

Alternatively, a cost elasticity greater than one
indicates the existence of scale diseconomies
(average cost rises as output increases).
Although still finding significant scale econo­
mies during the 1989-1994 period, ITSUR Mod­
els 1 and 2 provide estimates of cost elasticities
of widely different magnitudes. Table 3 presents
cost elasticity estimates for each of the process­
ing sites that remained in operation for the entire
sample period, using their mean processing vol­
ume levels for 1989 and 1994. ITSUR Model 2
provides greater estimates of scale economies
for all sites than does ITSUR Model 1. To under­
stand why, consider figure 5, which plots the
estimated average cost curves using both mod­
els. To the naked eye, these curves appear to be
reasonably similar. In ITSUR Model 1, however,
the coefficient of the squared term for the num­
ber of items processed is close to zero and is sta­
tistically insignificant. Based on this model, the
cost elasticity is essentially constant at around
0.75 throughout the full range of observed out­
put, implying that scale economies are never
exhausted. In contrast, for ITSUR Model 2, the
squared term for the number of items processed
is positive and statistically significant. This means
that the cost elasticity varies along with the num­
ber of items processed. Consequently, ITSUR
Model 2 suggests that scale economies will
eventually be exhausted (that is, the average
cost curve will eventually begin to rise).
The volume level at which scale economies
are exhausted is important, because it helps to
determine whether consolidating the Federal
Reserve processing sites could lower unit costs.
Scale economies are exhausted when the cost
elasticity equals one. By setting the cost elastic­
ity equal to one, we can solve for the implied
number of items processed by a site operating
at an efficient scale. Using this procedure and
ITSUR Model 2, an estimate of about 800 mil­
lion items processed per quarter for a scaleefficient site is implied. This is more than five
times the quarterly processing volume of the
largest Federal Reserve site observed in our
sample (144 million items per quarter).
For both ITSUR models, estimates of the vol­
ume level at which scale economies are ex­
hausted need to be viewed with a fair degree
of skepticism. Recall that the translog cost func­
tion is a good local approximation of the cost
function and is therefore quite reliable in study­
ing output ranges actually observed in the data.
While both models find significant scale econo­
mies in the current range of output, going be­
yond this range is highly speculative.

23

TABLE

4

Marginal Cost Estimates
(dollars per item)
Federal
Reserve ACH
Processing Site

ITSUR Model 1
1994
1989

0.0204

0.0095

2

0.0215

3
4

0.0233
0.0182

0.0095
0.0087
0.0068

5
6

0.0269
0.0204

0.0135
0.0056

7
8

0.0321

0.0085

0.0215
0.0205
0.0262
0.0361

0.0069
0.0064

1

9
10
11

Volume-weighted
System average 0.0234

ITSUR Model 2
1994

1989

0.0112
0.0079
0.0139
0.0078
0.0107
0.0105
0.0109
0.0054

0.0073
0.0054
0.0076
0.0049
0.0086
0.0042
0.0054
0.0040

0.0110

0.0103
0.0120
0.0101

0.0048
0.0062

0.0083

0.0106

0.0060

0.0076

0.0060

SOURCE: Authors’ calculations.

The presence of scale economies implies
that scale efficiency could be improved by con­
solidating the Federal Reserve ACH processing
sites. Indeed, the Fed is currently consolidating
its ACH operations at one computing site with
backup facilities at another. Our empirical re­
sults suggest that these efforts will reduce aver­
age processing costs significantly. Comparing
the average ACH processing cost at the current
largest site with a forecasted average cost for a
consolidated site handling all currently proc­
essed ACH items, the predicted average decline
is 30 percent and 25 percent for ITSUR Models
1 and 2, respectively.28 Neither model predicts
that scale economies would be exhausted with
one processing site, but ITSUR Model 2 predicts
that further scale efficiencies from additional
volume growth could be quite small.

Pricing
The Monetary Control Act of 1980 directs the
Federal Reserve to establish fees on the basis
of all direct and indirect costs incurred in pro­
viding payment services, including “interest on
items credited prior to actual collection, over­
head, and an allocation of imputed costs
 which takes into account the taxes that would
http://fraser.stlouisfed.org/
have been paid and the return on capital that
Federal Reserve Bank of St. Louis

would have been provided had the services
been provided by a private business firm.”
Thus, the total revenues raised from providing
payment services must match the total costs
incurred in production.
Generally, allocations of goods and services
are most efficient when prices (the amount a
consumer must pay to receive one unit of the
good) are set equal to marginal costs (the addi­
tional cost of producing one more unit of out­
put).29 With scale economies of the magnitude
we have found for ACH transactions, marginal
cost pricing alone would not generate sufficient
revenue to cover costs. The reason is that the
presence of scale economies means unit costs
fall as additional units are produced, and this
can occur only if marginal costs are lower than
average costs. The current Federal Reserve fee
structure for the ACH service solves this prob­
lem by employing a multipart structure with
both fixed and variable components.30 Ideally,
to encourage greater use of electronic pay­
ments, the variable fee should be set equal to
marginal costs and the fixed fees set to make
up the shortfall.
Our estimates of marginal costs, calculated
using the two ITSUR models, are presented in
table 4. Consistent with its finding of larger scale
economies, ITSUR Model 2 generates lower
marginal cost estimates than does ITSUR Model
1. Marginal costs for ITSUR Model 1 range from
$0.0056 to $0.0135 per item in 1994, with a
volume-weighted System average of $0.0083.
ITSUR Model 2’s marginal costs are all estimated
to be under $0.01 per item in 1994, with a
volume-weighted System average of $0,006.

Sources of
Cost Savings
In table 5, we use equation (5) to decompose
unit-cost declines over the 1989-1994 period
into technological change effects, scale econ­
omy effects, input price effects, environmental

■ 28 Note that with full consolidation, the number of items
processed will equal the number of payments processed, approximately
600 million items per quarter.
■ 29 Mathematically, marginal cost (MC), the change in costs
resulting from a unit increase in output, is defined as M C =dC /dy.
■ 30 See Baumol and Bradford (1970), Oi (1971), Roberts (1979),
Humphrey (1984), Sheshinski (1986), Brown and Sibley (1986), Hirshleifer
and Glazer (1992), and Tirole (1994) for discussions about efficient pricing
methods when there are positive scale economies for an industry’s output
level.

24

TABLE

5

Sources of Cost Savings
ITSUR Model 1
Federal
Reserve ACH
Processing Site

1
2
3
4
5
6
7
8
9
10
11

Unit Cost51

1989

1994

0.027
0.028

0.012
0.012
0.011
0.009
0.018
0.007
0.011
0.009
0.008
0.010
0.014

0.031
0.024
0.035
0.027
0.042
0.028
0.027
0.034
0.047

Overall Technological Scale
Economy
Change
Percentage
Effects
Effects
Change

Input
Price
Effects

-43.2
-43.2
-43.2
-43.2
-43.2
-43.2
-43.2
-43.2
-43.2
-43.2
-43.2

-5 .8
-8 .3
-6 .0
-14.1
-9 .8
-9 .2
-6 .5
-8.1
-8 .8
-4 .0
-5 .3

-53.9
-56.5
-63.0
-62.9
-50.1
-72.7
-73.7
-68.1
-69.3
-71.0
-69.8

-16.9
- 1 6 .2
-20.2
-21.2
-18.4
-17.2
-23.0
-24.6
-19.6
-26.5
-20.4

Environmental
Effects

0.1
-22.0
-5 .3
-5 .7
-5 .8
8.1
2.5
-28.3
-0 .8
1.3
-0 .5

Interaction
Effects

0.5
-0 .3
1.0
0.3
-0 .4
0.2
-1 .0
-1 .4
0.5
0.5
-1.1

ITSUR Model 2
Federal
Reserve ACH
Processing Site

1
2
3
4
5
6
7
8
9
10
11

Unit Cost3
1989
1994

0.027
0.028
0.031
0.024
0.035
0.027
0.042
0.028
0.027
0.034
0.047

0.012
0.012
0.011
0.009
0.018
0.007
0.011
0.009
0.008
0.010
0.014

Overall Technological Scale
Economy
Percentage
Change
Effects
Effects
Change

-53.9
-56.5
-63.0
-62.9
-50.1
-72.7
-73.7
-68.1
-69.3
-71.0
-69.8

-32.4
-32.4
-32.4
-32.4
-32.4
-32.4
-32.4
-32.4
-32.4
-32.4
-32.4

-37.5
-28.3
-46.8
-41.4
-34.2
-36.9
-40.5
-38.5
-41.3
-53.3
-32.2

Input
Price Environmental
Effects
Effects

-5 .9
-8 .3
-6 .0
-14.1
-9 .8
-9 .2
-6 .4
-8 .0
-8 .9
-4 .0
-5 .2

0.8
13.7
0.8
4.2
1.8
-1.1
1.8
18.9
2.3
0.1
2.5

Interaction
Effects

0.6
-0 .4
1.1
0.3
-0 .4
0.3
-1 .0
-1 .5
0.6
0.5
-1.1

a. In dollars.
SOURCE: Authors’ calculations.

effects, and interaction effects. For each of the
processing sites, unit costs fell precipitously.
ITSUR Model 1 attributes the bulk of the decline,
43.2 percent, to technological change, versus
only 32.4 percent for ITSUR Model 2. In contrast,
ITSUR Model 2 finds larger cost savings due to
scale economies than does ITSUR Model 1.
Falling input prices— mainly for data com­
munications and data processing— generally
account for less than 10 percent of the savings.
As described in section V, we rely on the BEA’s
price indexes to help construct our measure of
materials, which includes information-processing
and related equipment. The quality of such
equipment changed rapidly during the 1980s
and
1990s. Thus, to the extent that the price

http://fraser.stlouisfed.org/ series for materials do not adequately control for
Federal Reserve Bank of St. Louis

the qualitative changes in these inputs, our
decomposition of cost savings resulting from
technological change may be overstated, while
cost savings resulting from input price reduc­
tions may be understated. To some degree, the
distinction is arbitrary. The decline in ACH costs
may stem from technological change within
ACH payments processing itself or from techno­
logical change in the computer industry that has
lowered input prices. In either case, reduced
costs from technological change are not misattributed to scale economies.
Environmental and interaction-term effects
tend to be relatively small, except for two sites,
and these sites have by far the fewest number
of endpoints. ITSUR Model 1 attributes their
lower costs (other things held constant) only to

25

this factor. ITSUR Model 2, however, also allows
for a different intercept term for these sites and
finds smaller District indicator variable coeffi­
cients, suggesting that some other site-related
factor is at work.

with processing payments. Finally, in order to
construct a pricing mechanism that encourages
efficiency in the payments system and yet still
recovers costs, the demand side of payment
service markets— including cross-elasticities
between payments instruments— needs to be
more fully understood.

VII. Conclusion
We employ a cost-function model of ACH
processing to derive estimates of both scale
economies and technological change from
1979 to 1994. Substantial and statistically signif­
icant scale economies are found to exist at all
Federal Reserve processing sites. For example,
using cost system models, we estimate that for
each 10 percent increase in ACH processing
volume, total production costs rose by less
than 8 percent, indicating that average costs
fall as volume rises. Therefore, consolidating
the System’s processing sites should reduce
ACH processing costs substantially in the long
run. In addition, given the potential scale
economies for electronic payments processing
and the low marginal costs, more attention is
warranted for demand-side issues that would
encourage payors to shift from paper checks
to ACH transactions.
More than 30 percent of the decline in real
unit costs between 1989 and 1994 can be attrib­
uted to technological change, with an annual
rate of change of at least 7.5 percent. Scale econ­
omies led to a further 20 to 40 percent reduc­
tion. Another significant contributing factor to
the decline in unit costs was lower input prices
(primarily for communications and computing
technology), which translated into a cost savings
of about 8 percent between 1989 and 1994.
In the 1980s, some observers believed that
scale economies would eventually push the
interbank unit costs of processing ACH trans­
actions below those of processing paper
checks.31 Our findings suggest that their expec­
tations were correct. In addition, technological
change and lower prices for communications
and computing technology have also played a
major role.
Clearly, more empirical research is needed
on how new technologies affect the efficiency
of the payments system. For example, scope
economies between ACH payment processing
and other payment processing, such as Fedwire
and paper-based checks, could also be impor­
tant in determining the scale efficiency and
optimal product mix for payment service
providers. Such scope economies could enable
many more suppliers to operate efficiently and

to reduce the real resource costs associated


Appendix:
Sensitivity
Analysis
The strong correlation between the number of
ACH items processed and the time-trend vari­
able makes it difficult to separate the effects of
scale economies and technological change.
Thus, we estimate equation (1) in several differ­
ent ways in order to determine the robustness
of our qualitative findings of significant scale
economies and technological change. First, we
measure technological change either with a
smooth time trend or with discrete yearly indi­
cator variables. Second, we estimate the model
using data from the entire sample period, from
each year separately, and from the 1989-1994
period only.
OLS Model 1 is the most basic cost-function
specification. Costs are regressed against the
output measure (number of items processed)
and against quarterly indicator variables. Models
indicated by an a use yearly indicator variables
to measure technological change, while models
indicated by a b use a time trend and its
squared term. OLS Model 2 is similar to OLS
Model 1, except that control variables for the
price of labor, the number of endpoints, and the
percentage of government items are also in­
cluded. Model 2 is also similar to those esti­
mated by Humphrey (1982, 1984, 1985). A
squared term for output is not included because
the high correlation between the time-trend
variable and output implies that the square of
the latter could not be adequately handled in a
single-equation setting. Below, we briefly sum­
marize the findings of these OLS estimations
and compare them to the two ITSUR models
presented in the body of the paper.

■

31

For example, see Humphrey (1982,1984,1985) and

Zimmerman (1981).

26

T A B L E

A- 1

Technological Change Indexes
(1989 = 1.000)
OLS
Model
lb

OLS
Model
2a

OLS
Model
2b

OLS
Model
la

OLS
Model
lb

OLS
Model
2a

OLS
Model
2b

ITSUR
Model

Year

OLS
Model
la

1

ITSUR
Model
2

1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994

1.097
1.248
1.266
1.272
1.287
1.154
1.271
1.156
1.105
1.127
1.000
0.869
0.753
0.645
0.581
0.428

1.139
1.217
1.277
1.317
1.335
1.329
1.300
1.250
1.181
1.096
1.000
0.896
0.789
0.683
0.581
0.485

0.764
0.914
0.991
1.034
1.090
1.027
1.179
1.079
1.045
1.083
1.000
0.905
0.829
0.815
0.744
0.579

0.809
0.888
0.960
1.020
1.067
1.099
1.114
1.110
1.090
1.052
1.000
0.935
0.861
0.780
0.695
0.610

1.000
0.875
0.763
0.658
0.596
0.441

1.000
0.900
0.791
0.679
0.569
0.465

1.000
0.890
0.777
0.732
0.638
0.482

1.000
0.919
0.822
0.717
0.609
0.503

1.000
0.889
0.739
0.716
0.691
0.568

1.000
0.973
0.876
0.847
0.818
0.676

SOURCE: Authors’ calculations.

F I G U R E

A-1

Technological Change Indexes
Index, 1989 =1.00

SOURCE: Authors’ calculations.

Technological
Change
Estimates of technological change for all mod­
els are reported in table A-1 and plotted in
figure A-1. To ease comparison, all of the
technological change indexes are normalized

http://fraser.stlouisfed.org/ to equal one in 1989. Whether yearly indicator
Federal Reserve Bank of St. Louis

variables or a time trend and its squared term
are used to measure technological change, the
technological indexes are similar in magnitude
(see figure A-1 and compare Models a and b).
This finding holds up only when data from the
more recent period, 1989 to 1994, are em­
ployed. Basically, the time-trend approach
reports a smoothed version of the yearly indi­
cator approach.
The rise in the technological change indexes
during the early 1980s could suggest technologi­
cal regress, defined as an upward shift in the
cost function due to technological change. The
technological change estimates from the early
period (essentially, the start-up phase for ACH
payments) are difficult to interpret, however,
because it is plausible that the ACH cost func­
tion may have shifted substantially across time
not only because of technological change, but
also because of leaming-by-doing economies.32
■
32 Leaming-by-doing economies may have resulted from several
factors. Workers performing repetitive tasks may have learned from cumu­
lative experience to perform these jobs more quickly and efficiently. Opera­
tions management at a processing site may have been able to call on its
experience to modify job assignments, rearrange the layout of facilities, or
devise ways to reduce paper or other material wastes In addition, software
engineering may have improved computers' efficiency in processing
batches of payments, so that the same amount of computer technology
could process more ACH payments faster, or with greater security
enhancement, and at lower cost.

27

T A B L E

A- 2

Estimated Cost Elasticities
[at sample means)
Cost-Function Model
Sample
Period

1979-1994

OLS Model 1
Model a
Model b

1989
1990
1991
1992
1993
1994

0.638a

0.7623
0.8523
0.897
0.968
0.8283
0.8493

0.6403
0.424a
0.523a
0.6713
0.6783
0.6483
0.748

o
00
o

J9 8 9 -1 9 9 4

0.881a

0.8853

OLS Model 2
Model a
Model b

0.851a

0.6343

0.6483

ITSUR
Model 1

ITSUR
Model 2

—

—

—

—

—

—

—

—

—

—

—

—

0.7613

0.4483

a- Cost elasticity estimate is statistically different from one.
SOURCE: Authors’ calculations.

These cost-function shifts are difficult to
model separately, particulady when a high cor­
relation exists between output and a time trend.
In addition, we use year-specific indicator vari­
ables or a time trend and its squared term to
derive estimates of technological change. Thus,
it need not be the case that technological
regress occurred: Other time-specific factors
could also have increased ACH processing
costs in the early years. Plausible candidates
include the one-time transition costs to newer
technologies, shifts to higher-quality (highercost) services with more bells and whistles, and
various changes in cost-accounting procedures.
Unfortunately, adequate control variables for
such factors are unavailable, so we could not
further decompose these time-specific effects.
Estimates of technological change derived
using models with more control variables tend
to be larger, possibly because the models incor­
porate a greater number of environmental vari­
ables that control for site-specific characteristics.




Scale
Economies
Estimates of cost elasticities at the sample
means for the OLS models are reported in table
A-2 for several different periods. Inclusion of
additional site-specific regressors affects the
estimates of scale economies, with OLS Model
2 yielding larger estimates (smaller cost elastici­
ties) than OLS Model 1. All of these cost elastic­
ities are statistically different from one at the 95
percent confidence level, confirming the pres­
ence of scale economies. Essentially, OLS
Model 1 assigns more of the cost savings to
technological change (and consequently, less to
scale economies) than does OLS Model 2. Our
estimates of cost elasticities are fairly close to
the 0.70 to 0.75 figures reported in Humphrey
(1982, 1984, 1985). Given the high degree of
multicollinearity present in the data (particularly
between output and a time trend), which over­
states standard errors and implies a bias toward
rejecting the hypothesis of scale economies, a
finding of statistically significant scale econo­
mies shows strong support for this hypothesis.
As a further test of robustness, we also esti­
mate the two OLS models using quarterly crosssectional data for each year. Again, the scale
economy estimates are larger when site-specific
characteristics are included. Generally, the
yearly cost elasticity estimates are statistically
different from one at the 95 percent confidence
level. Using OLS Model 1 in 1991 and 1992,

28

however, we do not find statistically significant
scale economies. These estimates are bound to
be less precise than those generated by our
other models because they are based on very
few observations.
In summary, we subjected our cost-function
model to a number of tests for robustness, pri­
marily by varying the sample period and the
regressors. While the magnitude of some of the
results varies significantly, our qualitative find­
ings across models are consistent. The sharp
declines in unit cost appear to stem primarily
from technological change and scale economies.
Our finding of significant scale economies is ro­
bust to the model specification and selection of
sample period.




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30

Economic Commentary

Banking and the Flow of Funds:
Are Banks Losing Market Share?
by Katherine A. Samolyk
September 1, 1994

Is Public Capital Productive?
A Review of the Evidence
by Kevin J. Lansing
March 1, 1995

The Economics of Health Care Reform
by Charles T. Carlstrom
September 15, 1994

A Mexican Currency Board?
by Owen F. Humpage
March 15, 1995

Bank Receivership and Conservatorship
by Walker F. Todd
October 1, 1994

Are Wages Inflexible?
by Ben Craig
April 1, 1995

Specialization in Risk Management
by Jerry L. Jordan
October 15, 1994

Health Care Reform from
a Generational Perspective
by David Altig and Jagadeesh Gokhale
April 15, 1995

Fear and Loathing in Executive Pay
by Joseph G. Haubrich
November 1, 1994
Understanding Differences in
Regional Poverty Rates
by Elizabeth T. Powers and Max Dupuy
November 15, 1994

Can Foreign Exchange Intervention Signal
Monetary Policy Changes?
by William P. Osterberg
May 1, 1995
How Much Is Daylight Credit Worth?
by E.J. Stevens
May 15, 1995

How Important Are U.S. Capital Flows
into Mexico?
by William P. Osterberg
December 1, 1994

SAIF Policy Options
by William P. Osterberg and James B. Thomson
June 1995 (combines June 1 and June 15 issues)

Year-End Report of the Fourth District
Economists’ Roundtable
by Michael F. Bryan and John B. Martin
December 15, 1994

Monetary Policy and the Federal
Funds Futures Market
by John B. Carlson and Jean M. Mclntire
July 1995 (combines July 1 and July 15 issues)

Allocating Publicly Owned Assets:
The Case of Personal Communications Services
by Ian Gale
January 1, 1995

Regulation and the Future of Banking
by Jerry L. Jordan
August 1, 1995

The Myth of the Overworked American
by Kristin Roberts and Peter Rupert
January 15, 1995
Another Look at Part-time Employment
by Max Dupuy and Mark E. Schweitzer
February 1, 1995
Monetary Policy: An Interpretation of 1994,
a Challenge for 1995
by David Altig
February 15, 1995




A Monetary Policy Paradox
by Charles T. Carlstrom
August 15, 1995
Derivative Mechanics: The CMO
by Joseph G. Haubrich
September 1, 1995
Should Social Security Be Privatized?
by Jagadeesh Gokhale
September 15, 1995

31

Economic Review

■

1994 Quarter 3

I

Restoring Generational Balance
in U.S. Fiscal Policy: What Will It Take?
by Alan J. Auerbach,
Jagadeesh Gokhale, and
Laurence J. Kotlikoff

A Conference on Federal Credit Allocation
by Joseph G. Haubrich and
James B. Thomson
Employment Creation and Destruction:
An Analytical Review
by Randall W. Eberts and
Edward B. Montgomery

Vagueness, Credibility,
and Government Policy
by Joseph G. Haubrich

A Monte Carlo Examination of Bias
Tests in Mortgage Lending
by Paul W. Bauer and
Brian A. Cromwell
■

Federal Funds Futures as an Indicator
of Future Monetary Policy: A Primer
by John B. Carlson,
Jean M. Mclntire, and
James B. Thomson

1994 Quarter 4
Tax Structure, Optimal Fiscal Policy,
and the Business Cycle
by Jang-Ting Guo and
Kevin J. Lansing
Cross-Lender Variation in Home
Mortgage Lending
by Robert B. Avery,
Patricia E. Beeson, and
Mark S. Sniderman
The Efficiency and Welfare Effects
of Tax Reform: Are Fewer Tax
Brackets Better than More?
by David Altig and
Charles T. Carlstrom




1995 Quarter 1

I

1995 Quarter 2
An Introduction to Currency Boards
by Owen F. Humpage and
Jean M. Mclntire
The Seasonality of Consumer Prices
by Michael F. Bryan and
Stephen G. Cecchetti

32

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