Full text of Review (Federal Reserve Bank of Dallas) : Second Quarter 1997

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FEDERAL RESERVE BANK OF DALLAS
SECOND QUARTER 1997

Welfare and the Locational
Choices of New Immigrants
Madeline zavodny

Output, Growth, Welfare, and
Inflation: ASurvey
joseph H. Haslag

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

Economic Review
Federal Reserve Bank of Dallas

Robert D. McTeer, Jr.
President and Chiel Executive OffICer

Helen E. Holcomb
First VICe President and Chiet Operating Olticer

Harvey Rosenblum
Senior Vice President and Director of Research

W. Michael Cox
Vice President and Economic Advisor

Senior Economists and
Assistant Vice Presidents
Stephen P. A. Brown
John Duca
Robert W. Gilmer
Evan F. Koenig
Director, Center for latin American
Economics, and Assistant Vice President
William C Gruben
Research Officer
Mine K YOcel
Economists
Kenneth M Emery
Robert Formaini
David M Gould
Joseph H Haslag
D'Ann M. Petersen
Keith R. Phillips
Stephen D Prowse
Marci Rossell
Jason L Saving
Fiona 0 Sigalla
Lori L. Taylor
Lucinda Vargas
Mark A. Wynne
Carlos E. Zarazaga
Madel ine Zavodny
Research Associates
Professor Nathan S. Balke
Southern Methooist University

Professor Thomas B Fomby
Southern Methooist University

Professor Kathy J. Hayes
Southern MethQdist University

Professor Gregory W. Huffman
Southern Me1hodist University

Professor Finn E. Kydland
Carnegie Mellon University

Professor Roy J Ruffin
University 01 Houston

Editors
Stephen P. A Brown
Evan F. Koenig
Copy Editors
Anne Coursey
Rhonda Harris
Monica Reeves
Graphic Design
Gene Autry
Laura J. Bell
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Contents

Welfare and the
Locational Choices of
New Immigrants
Madeline Zavodny
Page 2

Output, Growth,
Welfare, and Inflation:
ASurvey
Joseph H. Haslag
Page 11

The 1996 welfare law ends most noncitizens' eligibility for
federally funded public assistance programs and allows states to
cut off payments under other welfare programs to noncitizens. If
some states choose to continue extending benefits while others
terminate payments to immigrants, interstate differentials in welfare
generosity will widen. Potential policy differences create concern
that states that continue to offer benefits to immigrants will become
welfare magnets.
In this article, Madeline Zavodny examines whether welfare
generosity is correlated with the number of new immigrants arriving in a state in 1982 and 1992. The data indicate that welfare payments are not correlated with immigration levels; rather, the
presence of earlier immigrants is the primary determinant of the
locational choices of new immigrants.

In this article, Joseph Haslag surveys both the theoretical
results and the empirical evidence relating inflation to per capita
real GDP growth. Theory yields mixed results: a permanent change
in inflation can raise, lower, or have no impact on per capita output or its rate of growth. The crucial factor seems to be the role
money plays in the model economy. However, in most cases, a permanent increase in inflation lowers the average person's welfare.
The empirical evidence is similarly inconclusive. A body of evidence suggests that high-inflation countries do grow more slowly
than low-inflation countries. However, the systematic relationship
between inflation and output growth does not survive when
researchers include other potential determinants of growth or adopt
an alternative definition of trend.

Welfare and the
Locational Choices
of New Immigrants

New restrictions on immigrants’ eligibility
for welfare benefits will account for 44 percent
of the expected $55 billion reduction in federal
welfare expenditures over the next few years.
Most noncitizens are now ineligible for federally
funded programs, and states may restrict legal
immigrants’ access to other public assistance programs. States have the option to continue extending benefits to immigrants at state expense.
These changes have raised concerns that states
that continue to provide benefits to immigrants
risk becoming “welfare magnets.” In this article,
I examine whether differences in benefit levels
across states affect the locational choices of new
immigrants in an effort to determine whether
states that continue to offer benefits to immigrants will face an influx of immigrants.
The strictest restrictions in the 1996 welfare law, which ends the guarantee of public
assistance to impoverished persons, are imposed
on immigrants. Most legal immigrants are automatically barred from receiving food stamps and
Supplemental Security Income (SSI), which provides cash benefits to the disabled and elderly
poor. Future legal immigrants will also be ineligible for Aid for Families with Dependent
Children (AFDC) and Medicaid for the first five
years they are in the United States.1 States have
discretion to determine noncitizens’ eligibility
for AFDC, Medicaid, and other public assistance
programs previously jointly funded by federal
and state governments.2 Since the law was
signed in August 1996, thirty-six states have
promised to maintain AFDC benefits to immigrants already in the country, and four states
have said they will not do so.
The new welfare law affects a potentially
large number of immigrants. In 1996, 2 million
noncitizens received food stamps, 800,000
received SSI, and 640,000 received AFDC.3 An
estimated 9.5 million legal, permanent-resident
noncitizens live in the United States, about 40
percent of whom are in California.
Some officials who believe that many immigrants came to the United States to take advantage of public assistance are now concerned
that low-income households will migrate to the
states offering the highest benefits. Differences
in maximum AFDC payments across states are
already large—ranging from $923 a month in
Alaska to $120 in Mississippi in 1996 for a
family of three—and will become larger when
immigrant eligibility rules vary across states. As
one policy analyst claims, immigrants are “quite
prepared to move. They already have.” 4
Beliefs that immigrants have disproportionately high rates of welfare recipiency moti-

Madeline Zavodny
Economist
Federal Reserve Bank of Dallas

I

find little evidence to

support the contention that new
immigrants will choose their
destinations based on welfare
generosity. New immigrants are
attracted to areas with large
immigrant populations.

2

vate much of the concern that immigrants move
to states with high welfare benefits. Economists
disagree about whether immigrants are more
likely than natives to receive welfare. Borjas
(1994) summarizes the literature. In 1970, immigrants were less likely than natives to receive
welfare, but by 1990 they were slightly more
likely than natives to receive welfare. Differences in socioeconomic characteristics, such
as household composition and educational
attainment, account for the disparity between
immigrant and native welfare recipiency rates;
recent immigrants are less skilled than both
natives and previous immigrants. Immigrants
as a whole are therefore more likely than
natives to receive welfare benefits, but immigrants’ recipiency rate is the same as that of
similar natives.
Previous research suggests that differences
in welfare benefits across areas affect the locational decision of low-income households.
Blank (1988) finds that low-income femaleheaded households are 12 percentage points
more likely to leave areas with low welfare payments and low wages than areas with high payments and high wages. Gramlich and Laren
(1984) find that households receiving AFDC
that move are more likely to relocate to a highbenefit state than to a low-benefit state.
Little research has been done on how
welfare affects the locational choices of immigrants. Borjas (1996) concludes that immigrant
welfare recipients are more clustered in highwelfare states than are other immigrants and
natives. The effect is particularly pronounced
among new immigrants, who may have lower
interstate migration costs than natives or earlier
immigrants. His findings are driven by California, where 45 percent of new immigrants
who receive welfare live, versus 29 percent of
new immigrants who do not receive welfare, 12
percent of natives who receive welfare, and 10
percent of natives who do not receive welfare.
Buckley (1996) claims that the settlement patterns of new immigrants are positively correlated with state welfare generosity; this research
is discussed in greater detail below.
In this article, I estimate the determinants
of new immigrants’ destinations, focusing on
whether immigrants respond to differences in
the generosity of public assistance payments
across states. The results indicate that the location of other immigrants is the primary determinant of new immigrants’ destinations. However,
since earlier immigrants are clustered in highwelfare states, it appears that new immigrants
are attracted by welfare generosity unless the

FEDERAL RESERVE BANK OF DALLAS

presence of other immigrants and constant
immigration patterns across states are controlled for. In the next section, I summarize the
basic characteristics of immigrants’ locational
choices. I then discuss the results of an econometric analysis of the determinants of immigrants’ destinations.

Immigrants are highly clustered . . .
In the 1980s, the United States experienced its greatest wave of immigration since the
turn of the century. More than 10 million persons were granted permanent-resident status
between 1980 and 1992, including more than
2.65 million aliens who adjusted to legal status
under the Immigration Reform and Control Act
(IRCA) of 1986. In 1990, almost 8 percent of the
population was foreign-born, a considerable
increase over the rate of 4.8 percent in 1970.
The immigrant population is highly concentrated in a few states. In 1990, almost 33
percent of the foreign-born population lived in
California; 14.4 percent lived in New York, 8.4
percent in Florida, and 7.7 percent in Texas.
Even though these are the four most populous
states, immigrants also make up a larger proportion of the population in these states than in
other parts of the country.
New immigrants choose the same destinations as previous immigrants, possibly because
the presence of a foreign-born population
attracts other immigrants. Six states accounted
for the intended residences of almost threequarters of new immigrants in 1992: California,
Florida, Illinois, New Jersey, New York, and
Texas. California alone was the intended residence of more than 34 percent of new immigrants in 1992. In addition to having a high
proportion of the population that is foreignborn, these states have relatively high welfare
benefits, except for Texas and Florida (Table 1 ).

. . . and attracted to areas with
other immigrants
Previous research on the locational
choices of immigrants concludes that the presence of earlier immigrants affects the locational
choices of new immigrants. Using crosssectional data on immigrants from eleven
Western Hemisphere countries in 1987, Dunlevy
(1991) finds that the number of immigrants is
positively correlated with a state’s stock of
immigrants. Dunlevy also finds that immigrants
are attracted to urbanized states; he does
not estimate the effect of welfare on settlement patterns. Bartel (1989) finds that ethnic
stock is the primary determinant of male immi-

3

ECONOMIC REVIEW SECOND QUARTER 1997

Table 1

Concentration of Locational Choices

State
California
Florida
Illinois
New Jersey
New York
Texas
Other

Percent of
immigrants
in 1992

Percent of
foreign-born
in 1990

Maximum AFDC
and food stamps
benefits in 1991

34.6
6.3
4.5
5.0
15.3
7.8
.6

21.7
12.9
8.3
12.5
15.9
9.0
3.5

$853
571
629
671
787
461
624

teristics that are likely to influence locational
choices, such as education and occupation,
are not available in the cross-tabulated data.
Therefore, I examine the effect of state-level
variables on the number of immigrants from a
particular country but cannot control for other
characteristics of those immigrants that might
affect the choice of destination. The next section
uses an analysis of covariance model to examine
the determinants of immigrants’ destinations.

NOTES: “Other” is the average of the remaining forty-two mainland states and the District of
Columbia. AFDC and food stamps are the maximum for a three-person family with one
parent.

The empirical model

SOURCES: U.S. Immigration and Naturalization Service, U.S. Bureau of the Census, and U.S.
House Ways and Means Committee.

In the general model estimated here using
ordinary least squares (OLS), the number of
immigrants is regressed on state-level economic
and demographic conditions, or

grants’ location. In the cross-sectional data
she uses, generous public assistance payments
also appear to attract male immigrants, a
surprising result since single males and most
male-headed households are not eligible for
AFDC.5
In this article, I focus on the destinations
of immigrants from eighteen countries who
arrived in the United States in 1982 and 1992.6
These countries are the source countries of
the majority of immigrants, accounting for 68.3
percent of immigrants in 1992 and 63.8 percent
in 1982. Mexico was the source country of
the largest number of immigrants in 1992, and
Vietnam was the largest in 1982. The data do
not include new refugees but do include refugees converting to permanent-resident status
and immigrants converting from illegal to legal
status under the IRCA; about 163,000 immigrants (primarily from Mexico) converted to
legal status under the IRCA in 1992.
The next section presents the empirical
model used to estimate the determinants of
locational choice. The model is based on individuals choosing the utility-maximizing location.
(See the box titled “A Model of Locational
Choice” on page 6.) I use the model to estimate
how state economic conditions and demographic characteristics affect the number of
immigrants “pulled” to a state; the model does
not focus on source country conditions that
might “push” immigrants to move because such
factors are likely to affect the number of immigrants who leave but not specific destinations
within the United States.
I use aggregate data on immigration levels
to the United States published by the U.S.
Immigration and Naturalization Service (INS).
The data are annual aggregate cross-tabulations
by destination state and country of birth for
major source countries. Individual-level charac-

(1)

Ijkt = α + Xkt – 1β + Fjkt – 1γ + Djk δ
+ Kk σ + Jjt ρ + ηjkt ,

where j indexes the country of origin, k the
destination state, and t the year (1982 or 1992).
The variable Ijkt is the number of persons immigrating from country j to state k in year t.7
The vector Xkt – 1 includes the unemployment
rate, per capita personal income less transfers,
the percentage of the population living in
metropolitan areas, the per capita tax level, the
percentage of employment in manufacturing
and agriculture, and the maximum combined
AFDC and food stamps benefits available for
a family of three in state k at year t – 1.8 All
monetary variables are deflated using the consumer price index for urban consumers. The
covariates are lagged for two reasons: to avoid
the possibility of endogeneity bias, which is
discussed in greater detail below, and to reflect
the information upon which immigrants are
likely to base decisions.9 All the variables except
the unemployment rate and the tax level are
expected to be positively correlated with the
number of immigrants. The error term ηjkt is
corrected for heteroscedasticity.
Fjkt – 1 is the proportion of the state population that was born in the same country as the
immigrant group. As discussed above, the presence of other foreign-born residents is a key
determinant of immigrants’ destinations. Immigrants are more affected by the size of the population from the same country of origin, not the
total foreign-born population. For example, 83
percent of Cuban immigrants in 1992 settled in
Florida, the state with the largest Cuban population. The variable is available only in census
years, 1980 and 1990, during the sample time
frame. Because of the likely importance of this
variable, I examine in detail the sensitivity of the

4

Table 2

Determinants of Immigrants’ Destinations
results to including it; it is expected to be positively correlated with the number of immigrants.
Djk is the distance in miles between the
largest city in the origin country and the largest
city in the destination state.10 The variable captures the psychological and monetary costs of
moving and is expected to be negatively
correlated with the number of immigrants.
Fixed effects are also included in some
specifications. The state effects Kk control for
time-invariant characteristics assumed to be
equally attractive for all immigrant groups,
such as climate and location. The vector Jj t
includes interactions of country and time effects
to capture any “push” effects from country j in
year t that are common to all states and any
changes in national immigration policy or
the business cycle. When the fixed effects are
included, the estimated coefficients show
correlations between changes in the number
of immigrants and changes in economic
and demographic variables within states and
countries of origin over time. Equation 1 is estimated using data on the number of immigrants
from eighteen countries to the forty-eight mainland states and the District of Columbia in 1982
and 1992.
This analysis offers several improvements
over previous research. By disaggregating the
data by country of origin, I can estimate the
sensitivity of specific groups to differences in
welfare across states. I can also better estimate
the importance of the stock of previous immigrants in a location by using country-specific
data on the number of previous immigrants.
Time-invariant state characteristics can be controlled for by including state fixed effects, the
importance of which is discussed below,
because two years of data are used. Buckley
(1996) uses panel data for the years 1985– 91
but does not control for state fixed effects; he
also uses a linearly interpolated measure of the
stock of immigrants since only decennial data
are available. Linear interpolation automatically
makes the immigrant stock covariate correlated
with the error term, leading to identification
problems in Buckley’s results.11
The estimation results are discussed in the
next section.

Covariate

(2)

(3)

(4)

95.432
(37.605)

48.643
(24.521)

29.716
(47.761)

30.608
(40.051)

.690
(.305)

.270
(.422)

–1.937
(2.023)

–.671
(1.377)

21.300
(4.050)

2.356
(6.022)

– 84.304
(63.989)

– 82.828
(57.329)

–.392
(.137)

–.095
(.226)

.106
(.348)

–.862
(.707)

Manufacturing
employment share

–13.170
(4.854)

15.832
(12.022)

– 8.303
(22.371)

–19.931
(29.456)

Agriculture
employment share

849.604
(316.976)

346.170
(120.647)

– 282.895
(301.400)

– 252.222
(212.232)

3.144
(1.746)

.955
(.764)

10.891
(6.954)

5.703
(3.759)

6.478
(2.482)

—

6.615
(2.537)

Unemployment rate
Income
Metropolitan population
Taxes

Welfare
Foreign-born population
(*1,000)

—

Distance

.008
(.041)

.142
(.034)

–.985
(.267)

–.003
(.264)

Fixed effects

No

No

Yes

Yes

Adjusted R-squared

.060

.481

.187

.555

NOTES: The dependent variable is the number of persons immigrating from one of eighteen
countries to one of forty-nine states in 1982 or 1992, a total of 1,764 observations. See
the text for details of the data. Heteroscedasticity-corrected standard errors are shown
in parentheses.

of immigrants. As shown in the first column of
Table 2, a $1 increase in a state’s maximum
combined AFDC and food stamps payment is
correlated with an increase of three in the number of immigrants. As expected, higher income,
a more metropolitan population, and a more
agricultural economy are positively correlated
with the number of immigrants; the tax level is
negatively correlated with the number of immigrants. Surprisingly, a manufacturing-oriented
economy appears to discourage immigrants,
and a high unemployment rate appears to
attract immigrants. Many of these relationships
are not robust to using other specifications, as
discussed below.
Welfare payments are not correlated with
the number of immigrants when the percentage
of the population comprised of earlier immigrants from the same country is controlled for.
The estimated coefficient reported in column 2
implies that the number of immigrants increases
by almost 6,500 when the percentage of the
population from the same country increases
by one point. The substantial increase in the
goodness of fit, as measured by the R-squared,
indicates the importance of other immigrants
in determining locational choice. Agricultural
employment remains positively correlated with

The results
The estimation results clearly indicate the
importance of controlling for the stock of previous immigrants and for differences across
states and countries. When immigrant stock and
fixed effects are not controlled for, welfare payments are positively correlated with the number

FEDERAL RESERVE BANK OF DALLAS

(1)

5

ECONOMIC REVIEW SECOND QUARTER 1997

A Model of Locational Choice
This box presents the derivation of the empirical model, which is based on individuals (or households) choosing the utility-maximizing location. The specific destination chosen by an immigrant depends on a multitude of characteristics, including
those of the individual, the individual’s country of origin, and all potential destinations. An individual should choose the utility-maximizing location, which depends on
location-specific amenities, individual characteristics, and previous location. Using
similar notation to that of Blank (1988), individual i ’s expected utility in location k at
time t, given that the individual lived in location j at time t – 1, can be expressed as

given year and for changes in immigration
policy. Only the distance between the country
of origin and the state is negatively correlated
with the number of immigrants when the
foreign-born population is not controlled for,
as shown in column 3. The foreign-born population is the only variable well correlated with
the number of immigrants when it is included
as a covariate, as shown in column 4.
The sensitivity of many of the estimated
coefficients to the inclusion of the fixed effects
indicates that immigrant settlement patterns
within states do not change significantly over
time in response to changes in economic conditions and welfare payments. When the fixed
effects are not included, other variables proxy
for the unchanging settlement patterns of immigrants. The only variable that appears to affect
the number of immigrants settling in a state over
time is the stock of previous immigrants.
Using other measures of welfare generosity, such as SSI and Medicaid, yields results
similar to those reported in Table 2. The locational choices of older immigrants are more
likely to depend on SSI benefits, for which the
impoverished elderly qualify, than on AFDC
levels, which require the presence of dependent children in the household. When equation
1 is estimated using the combined maximum
SSI and food stamps payment for an individual living alone as the measure of welfare
benefits, the results do not indicate that the
number of immigrants depends on SSI payments when the presence of other immigrants
and fixed effects are controlled for.12 Similarly,
the results do not change when average
Medicaid benefits are added to the maximum
AFDC and food stamps benefits.13 After fixed
effects are controlled for, only the percentage
of the population that is born in the same
country is significantly correlated with the number of immigrants.
The results are also robust to modifying
the dependent variable in order to examine the
determinants of the distribution of new immigrations across states. Equation 1 was estimated
using the fraction of all immigrants from a
country going to a state instead of using the
number of persons immigrating to the state, or

Uijkt = U (Xikt , Fijkt, Djk ),

(B.1)

where Xikt is location-specific amenities in location k at time t. Xikt includes variables
that affect an individual’s expected income, such as average earnings, the unemployment rate, and welfare benefits. Fijkt is a vector of household characteristics that affect
a person’s utility of living in location k at time t, given that the person lived in j at time
t – 1. These characteristics do not change across locations but may be associated
with different utility levels across locations. For example, a person from the Philippines
will likely have higher utility living in a location where other Filipinos live. Djk reflects
time-invariant monetary and psychological costs of moving from location j to location
k, which are assumed to be expressible in the same utility units as Xikt and Fijkt .
Research on individuals’ locational choices typically assumes that utility can be
expressed as a linear combination of variables, or

Uijkt = Xikt α + Fijkt β + Djk γ + eijkt ,

(B.2)

where eijkt is an error term assumed to be orthogonal to the covariates.
A person chooses the utility-maximizing location at time t, conditional on living
in location j at time t – 1. The conditional probability of individual i choosing location
k from N possible locations is then
(B.3)

Pr (kit | jit

– 1)

= Pr (Uijkt = MAX(Uij 1t , Uij 2t , . . ., UijNt )).

A multinomial logit model is usually used to estimate the effect of location-specific
amenities, individual characteristics, and previous location on the probability that an
individual chooses a certain location.
The above model can be used to estimate determinants of individuals’ locational
choice, but in this article I use aggregate data on immigration levels to the United
States. Equation B.3 can be aggregated across individuals to generate a model that
can be applied to aggregate data. The number of individuals moving to a location is
the number of individuals whose utility is maximized at that location, or
(B.4)

Ijkt = ∑ Pr(Uijkt = MAX(Uij 1t , Uij 2t , . . . , UijNt )),
i

where Ijkt is the number of immigrants moving to location k from location j at time t.
The number of immigrants moving to a location is assumed to be a linear function of
the variables that affect individuals’ locational choice, or
(B.5)

Ijkt = Xkt α + Fjkt β + Djk γ + ηjkt ,

where ηjkt is an error term assumed to be uncorrelated with the covariates. The
model estimated also includes state, time, and country-of-origin fixed effects in some
specifications.

the number of immigrants. The unemployment
rate and the distance from the country of origin
are positively correlated with the number of
immigrants, but these results are also sensitive
to the inclusion of additional controls.
None of the variables that reflect economic conditions, including the welfare variable, is well correlated with the number of
immigrants when fixed effects are included in
the regression. As discussed above, the state
fixed effects control for state characteristics that
are fixed over time and the country-of-origin
time effects control for factors that push immigrants to immigrate to the United States in a

(1′ )

I jkt

∑I

= α + X kt − 1β + F jkt − 1γ
jkt

k

+D jk δ + K k σ + Tt θ + η jkt ,
where all variables are as defined above. This
dependent variable may better capture the
determinants of immigrants’ settlement patterns

6

Table 3

Determinants of Immigrants’ Destinations
among the states, conditional on immigrants’
decisions to come to the United States, because
it avoids any level effects associated with the
large differences in the number of immigrants
across countries.14
As shown in Table 3, the results do not
change. After fixed effects are controlled for,
only the foreign-born percentage of the population is well correlated with the percentage of
immigrants settling in a state.
The regression results thus indicate that
welfare benefits do not affect the number of
new immigrants settling in a state. These results
are robust to a variety of sensitivity and specification checks discussed in the appendix.
The percentage of the population comprised
of earlier immigrants from the same country is
the only factor that affects immigrants’ locational choices over time. However, the effect
of welfare payments on locational choices may
differ across immigrants based on their country
of origin. This possibility is investigated next.

Covariate

(2)

.298
(.064)

.248
(.056)

.042
(.075)

.042
(.071)

.904
(1.024)

.460
(.981)

1.414
(2.870)

2.500
(2.738)

Metropolitan population

.085
(.010)

.065
(.008)

–.028
(.098)

–.026
(.093)

Taxes

–.002
(.0004)

–.002
(.0003)

–.001
(.001)

–.001
(.001)

Manufacturing
employment share

–.062
(.019)

–.031
(.015)

.077
(.072)

.067
(.062)

Agriculture
employment share

2.073
(.383)

1.541
(.319)

–.197
(.385)

–.171
(.322)

Welfare

.010
(.002)

.008
(.002)

.004
(.009)

–.001
(.008)

—

6.846
(1.142)

—

5.673
(1.101)

–.001
(.001)

.001
(.001)

–.001
(.001)

.001
(.001)

Fixed effects

No

No

Yes

Yes

Adjusted R-squared

.174

.322

.436

.536

Unemployment rate
Income

Foreign-born population
(*1,000)
Distance

Differences across immigrants
To test whether the effect of welfare on
locational choices differs across immigrants based
on their country of origin, variables interacting
country-of-origin dummy variables with the welfare variable were included in equation 1. The
specification also included all the variables measuring state-level economic and demographic
conditions, including the foreign-born variable,
the distance variable, and the fixed effects.
The results indicate substantial differences
across immigrant groups. As shown in Table 4,
welfare benefits are positively correlated with
the number of immigrants from China, El
Salvador, the Philippines, the former Soviet
Union, and Vietnam. The coefficients for China,
the Philippines, and Vietnam are significant
at the 5 percent level, and the other two are
significant at the 10 percent level. Except for
persons from the Philippines, these immigrants
are more likely than other immigrants to be
converting from refugee to legal permanentresident status, a finding that raises the possibility that refugees’ locational choices are
influenced by welfare even though immigrants’
choices are not. Refugees are more likely than
nonrefugee immigrants to participate in the
welfare system (Borjas 1994), so it is not surprising that their locational choices are more
responsive to differences in welfare payments
across states.
Other interesting findings include the
estimated coefficient for immigrants from
Mexico, which is the largest in magnitude but is

FEDERAL RESERVE BANK OF DALLAS

(1)

(3)

(4)

NOTES: The dependent variable is the fraction of persons immigrating from one of eighteen
countries to one of forty-nine states in 1982 or 1992, a total of 1,764 observations. See
the text for details of the data. Heteroscedasticity-corrected standard errors are shown
in parentheses.

Table 4

Responsiveness of Immigrants to Welfare Differences
By Country of Origin
Country
Canada
China
Colombia
Cuba
El Salvador
Germany
Guyana
Haiti
India
Iran
Jamaica
Mexico
Philippines
Poland
South Korea
Former Soviet Union
United Kingdom
Vietnam

Estimated
coefficient

Standard
error

–12.612
7.826
3.984
6.463
6.427
–.466
5.821
4.323
6.259
5.584
4.917
22.454
10.694
1.444
5.338
7.573
–.362
15.289

6.792
3.779
3.975
5.125
3.718
4.058
4.180
4.119
3.900
3.978
4.064
16.688
4.438
4.083
3.924
4.034
3.885
7.098

NOTES: Shown is the coefficient on a variable measuring the maximum AFDC and food stamps
payment interacted with an indicator variable of country of origin. The dependent variable is the number of persons immigrating from one of eighteen countries to one of
forty-nine states in 1982 or 1992. See the text for details of the data and specification.

7

ECONOMIC REVIEW SECOND QUARTER 1997

not significantly different from zero. The number of immigrants from Canada is negatively
correlated with welfare benefits. The F -test
statistic of whether all of the coefficients displayed in Table 4 are equal is 3.37, which rejects
the hypothesis that they are equal at the 1 percent level.

2

3

Conclusions
Much of the motivation for eliminating
most immigrants’ access to federally funded
public assistance benefits was concern that persons migrate to the United States because of the
availability of welfare benefits. The 1996 welfare
law makes noncitizens ineligible for food
stamps and SSI payments and allows states to
discontinue AFDC, Medicaid, and other public
assistance benefits to noncitizens. Several states
intend to continue extending benefits to noncitizens, whereas others are likely to cut off
benefits, widening the already substantial differences in welfare benefits across states. These
differences in policy create concern that immigrants will move in response to interstate differentials and that states that continue to allow
immigrants to receive welfare payments will
become welfare magnets.
In this article, I find little evidence to support the contention that new immigrants will
choose their destinations based on welfare generosity. New immigrants are attracted to areas
with large immigrant populations. Because
earlier immigrants are disproportionately
located in high-welfare states, it may appear
that high welfare benefits attract immigrants.
However, immigrants do not respond to interstate differentials in welfare generosity but
rather to differences in the sizes of the foreignborn populations. Immigrants are also attracted
to a specific subset of states—namely California,
New York, Florida, and Texas—and do not
respond to changes in welfare benefits within
states over time. The recent historical evidence
gives little reason to be concerned that new
immigrants will choose their destinations based
on the welfare differentials created by the new
welfare law.

4

5

6

7

8

9

Notes

1

10

I thank Lori Taylor and Jason Saving for helpful
comments.
Refugees are eligible for benefits the first five years
they are in the United States, and legal immigrants
who have worked in the United States for at least ten
years without receiving any federal means-tested benefits remain eligible for federally funded benefits. The
eligibility rules for immigrants who have received U.S.

11

8

citizenship are the same as for natives.
Previously, the federal government partially reimbursed
states’ AFDC and Medicaid costs. The 1996 law
replaces these federal payments with block grants to
states, and the AFDC program was replaced by
Temporary Assistance for Needy Families (TANF).
See Hutt (1996). Current welfare statistics generally do
not distinguish between legal and illegal immigrants.
Although illegal immigrants have always been barred
from receiving federally funded welfare benefits, the rule
has not been enforced until now. Similarly, a provision
stating that the income of an immigrant’s sponsors is
used in determining an immigrant’s eligibility for public
assistance is now supposed to be enforced. The law
also requires states to report known illegal aliens to
the U.S. Immigration and Naturalization Service. States
can enact laws to continue benefits to illegal immigrants under state-funded programs.
Douglas Besharov, a senior fellow at the American
Enterprise Institute, quoted in Havemann (1996).
Adult males were present in less than 10 percent of
AFDC-recipient households in 1979, according to
Blank (1985).
The countries are Canada, China, Colombia, Cuba,
El Salvador, Germany, Guyana, Haiti, India, Iran,
Jamaica, Mexico, the Philippines, Poland, South
Korea, the former Soviet Union, the United Kingdom,
and Vietnam. The immigrant data are for fiscal years,
which run from October of the preceding year through
September of the given year.
The immigration data are from the INS publication
Statistical Yearbook. The data include the country in
which immigrants were born, which is assumed to be
the country of origin.
The unemployment rate and manufacturing employment data are from the Bureau of Labor Statistics (BLS)
publication Employment and Earnings. The income data
are from the Bureau of Economic Analysis publication
Survey of Current Business. The metropolitan population and tax data are from the U.S. Bureau of the
Census publication Statistical Abstract. The agricultural employment data are from the BLS publication
Employment and Wages. The immigrant stock data are
from the 1980 and 1990 censuses.
All fiscal-year variables are lagged one year, and all
annual variables are lagged two years to avoid any
overlap in the time periods of the dependent variable
and the covariates.
The distance data were graciously supplied by Jeff
Gorham of the U.S. Department of Transportation,
Bureau of Airline Statistics.
Any shock in the number of immigrants in a given year
will be reflected in the next census count of immigrant
stock; a linear interpolation of the immigrant stock will
therefore make the covariate correlated with the error
term. Although Buckley (1996) recognizes this problem and attempts to correct it using two-stage least

12

13

squares estimation, the equation he estimates is
unidentified.
The SSI data are from the U.S. House Ways and
Means Committee. All results not included in tables
here are available from the author on request.
The Medicaid data are average payments per recipient
and are from the Statistical Abstract.

14

The variables interacting the country and time fixed
effects control for differences across countries in the
number of immigrants, but they also capture other unmeasured variables. The interactions are omitted in the
results shown in Table 3, but including them does not
change the reported results. A dummy variable for the
year 1982 (a time fixed effect) is included here instead.

Appendix
Sensitivity and Specification Checks
The regressions results indicate that immigrants’ locational choices have not been affected
by changes in welfare benefits within states. The
estimated coefficients on the welfare variable are
imprecisely estimated, however, and may be subject to bias from several sources. The estimates
may be subject to multicollinearity or endogeneity
problems. In addition, a failure to control fully for
differences across states in the cost of living may
bias the estimates. Finally, California, a potential
outlier because of its large number of immigrants
and high welfare benefits, may be driving the
results. The sensitivity of the results to each of
these potential problems is examined.
Multicollinearity may underlie the large standard errors estimated for many of the variables,
making it difficult to determine what affects immigrants’ locational choice. An examination of the
correlations between the covariates shows that per
capita income, taxes, and maximum welfare benefits are highly correlated.1 Equation 1 was therefore
reestimated without the income and tax variables,
and the results are similar to those reported in
Tables 2 and 3. The welfare variable is not correlated with the number of immigrants after controlling
for the stock of previous immigrants in equation 1
and fixed effects in equation 1′.2
The estimates reported in Tables 2 and 3
are also potentially subject to endogeneity bias
because the number of immigrants may affect state
economic conditions. In particular, policymakers
may cut welfare benefits in response to high levels
of immigration. If welfare and immigration are
simultaneously determined, the estimated coefficient on the welfare variable may be biased and
inconsistent. However, the specification estimated
here should not be subject to endogeneity bias
because the right-hand-side variables are lagged.
One of the usual means of correcting for endogeneity bias in a panel is first-differencing the data
and then using an instrumental variables estimator
in which the instrument is lagged values of the
right-hand-side variables (Holtz-Eakin, Newey, and

FEDERAL RESERVE BANK OF DALLAS

Rosen 1988). The method used here is a reducedform version of the same procedure.
Another specification concern is that equation
1 may not fully control for cost-of-living differences
across states that affect immigrants’ locational
choices. Equation 1 does not contain variables that
explicitly capture differences in the cost of living
across states or within states over time; however,
the state fixed effects control for time-invariant
differences across states. To control more fully for
differences within states, equation 1 was reestimated and a variable that measures the real median
rent in the state was included. Housing, the second
largest expenditure category for poor households,
is likely to be the largest source of within-state
variation in the cost of living over time.3 Controlling
for housing costs does not significantly affect the
estimated coefficient on the welfare variable in any
of the specifications.
The large number of persons migrating to
California and the state’s high welfare benefits
may drive the estimation results that find a positive
correlation between immigration and welfare in
some specifications. Borjas (1996) finds that
welfare is not correlated with the distribution of
immigrants across states when California is
omitted from his analysis. If California is omitted
from the data used here, the results indicate a
weaker correlation between the number of immigrants and welfare. The estimated coefficient on
the welfare variable is not positive and significant
in any of the specifications.
1

The partial correlation between income and welfare benefits is
0.50, and the partial correlation between taxes and welfare benefits is 0.46. The Belsley condition number for the regressions
results reported in Table 2, column 2, is 27.6, which is above the
acceptable level.
2 The condition number for the equation corresponding to Table 2,
column 2 without the income and tax variables is 11.4, indicating
that multicollinearity is not a problem in the reestimated specification.
3 The median rent in a state is from the 1980 and 1990 censuses.
The average poor family spent more than 22 percent of income
on shelter in 1992 – 93, compared with about 16 percent for a
nonpoor family (Federman et al. 1996).

9

ECONOMIC REVIEW SECOND QUARTER 1997

References

Havemann, Judith (1996), “District Could Become Welfare Oasis As Neighbors’ Benefits Dry Up,” Washington
Post, September 15, A13.

Bartel, Ann P. (1989), “Where Do the New United States
Immigrants Live?” Journal of Labor Economics 7 (October): 371– 91.

Holtz-Eakin, Douglas, Whitney Newey, and Harvey S.
Rosen (1988), “Estimating Vector Autoregressions with
Panel Data,” Econometrica 56 (November): 1371– 95.

Blank, Rebecca (1988), “The Effect of Welfare and Wage
Levels on the Locational Decisions of Female-Headed
Households,” Journal of Urban Economics 24 (September): 186 – 211.

Hutt, Katherine (1996), “Legal Immigrants Facing Grim
Prospects Without Welfare,” Associated Press, August 9.

——— (1985), “The Impact of State Economic Differentials on Household Welfare and Labor Force Participation,” Journal of Public Economics 28 (October): 25 – 58.

U.S. Bureau of the Census (various years), Statistical
Abstract of the United States (Washington, D.C.: Government Printing Office).

Borjas, George J. (1996), “Immigration and Welfare
Magnets,” Paper presented at Workshop on the Effects
of Immigration on African-Americans, University of Texas
at Austin, November.

U.S. Bureau of Economic Analysis (various years),
Survey of Current Business (Washington, D.C.: U.S.
Department of Commerce).

——— (1994), “The Economics of Immigration,” Journal
of Economic Literature 32 (December): 1667– 717.

U.S. Bureau of Labor Statistics (various years), Employment and Earnings (Washington, D.C.: U.S. Department
of Labor).

Buckley, F. H. (1996), “The Political Economy of Immigration Policies,” International Review of Law and Economics
16 (March): 81– 99.

——— (various years), Employment and Wages (Washington, D.C.: U.S. Department of Labor).
U.S. House of Representatives Ways and Means Committee (various years), Background Material and Data
on Major Programs within the Jurisdiction of Committee
on Ways and Means (Washington, D.C.: Government
Printing Office).

Dunlevy, James A. (1991), “On the Settlement Patterns of
Recent Caribbean and Latin Immigrants to the United
States,” Growth and Change 22 (Winter): 54 – 67.
Federman, M., T. I. Garner, K. Short, W. B. Cutter, J. Kiely,
D. Levine, D. McGough, and M. McMillen (1996), “What
Does It Mean to Be Poor in America?” Monthly Labor
Review 119 (May): 3 –17.

U.S. Immigration and Naturalization Service (various
years), Statistical Yearbook of the Immigration and
Naturalization Service (Washington, D.C.: Government
Printing Office).

Gramlich, Edward M., and Deborah. S. Laren (1984),
“Migration and Income Redistribution Responsibilities,”
Journal of Human Resources 19 (Fall): 489 – 511.

10

Inflation’s effect on economic activity, and
ultimately on people’s well-being, is a primary
concern of monetary policymakers and has been
the focus of much study. For instance, analysts
have questioned whether a permanent change
in the inflation rate raises or lowers the rate of
economic growth.
In this article, I review both theoretical
and empirical literature on this subject. I begin
with the theoretical literature, which examines
the relationship between monetary policy and
welfare, the level of output, and the rate of
output growth. Like Stein (1970) and Orphanides
and Solow (1990), I find that equally plausible
models yield qualitatively different predictions
for the relationship between the inflation rate
and per capita output. However, when the inflation rate is initially steady at zero and then
increases permanently, there is no ambiguity—
the average person suffers a welfare loss. Thus,
policymakers may face a dilemma: reducing inflation may raise the average person’s welfare,
but the growth rate of per capita output may fall.
I next survey the empirical literature on
the correlation between inflation and per capita
output growth. The preliminary evidence shows
a significant negative correlation. However,
recent studies have raised doubts about this
relationship, showing that the correlation may
not be robust. In particular, researchers have
shown that inflation is not significantly related
to per capita output growth when either a
common set of control variables is included
in the regressions or a different measure of
the trend rate of output growth is used. Notably,
the formal statistical analyses fail to find a
significant positive correlation between inflation and per capita output growth. Thus, with
all the caveats, the evidence suggests a nonpositive relationship between inflation and output growth.
The neoclassical growth model is the framework for analysis in this article. Adoption of this
framework makes it easier to account for the
qualitative differences in the relationship between inflation and output growth. As in previous surveys, there is still disagreement about
the direction in which output moves in
response to a change in inflation, even in the
neoclassical economies. What distinguishes the
model economies is the role for fiat money. In
some cases, the researcher highlights money’s
transactions features, whereas others focus on
money as a store of value. My review suggests
that money’s different roles are key determinants of the direction output growth takes in
response to a change in inflation.

Output, Growth,
Welfare, and
Inflation:
A Survey
Joseph H. Haslag
Senior Economist and Policy Advisor
Federal Reserve Bank of Dallas

F

ormal statistical analyses
fail to find a significant

positive correlation between
inflation and per capita
output growth.

FEDERAL RESERVE BANK OF DALLAS

11

ECONOMIC REVIEW SECOND QUARTER 1997

more, thus driving down the real interest rate.
Greater saving means greater capital accumulation and thus faster output growth.
Neoclassical economies. Tobin’s (1965) contribution to the inflation–output growth literature is a study of the issue in the context of the
neoclassical growth model. Tobin follows Solow
(1956) and Swan (1956) in making money a
store of value in the economy. Hence, people
can save for future consumption by either holding money or acquiring capital. In Tobin’s setup,
people hold a fraction of their income to meet
their transaction needs, despite capital’s offering
a higher rate of return.
To formalize the portfolio mechanism, consider the following simplified version of Tobin’s
economy. The model is characterized by the
following two equations:

I also review some recent developments in
the inflation –output growth literature. Several
researchers have raised the issue of whether
permanent changes in the inflation rate can permanently affect the rate of output growth. In the
neoclassical model, long-run growth is driven
by perpetual technological advancement. Because
inflation does not drive technological advancement, movements in the inflation rate potentially
affect the growth rate only along the transition
path from one steady-state value of the capital–
labor ratio to the next. In short, inflation may
have permanent effects on output level but not
on output growth rates. The endogenousgrowth literature, led by Romer (1986) and Lucas
(1988), shows that economies can unboundedly
grow in equilibrium without exogenous technological change. In view of the Romer and Lucas
results, it is natural to wonder whether differences in inflation rates account for any of the
differences in growth across countries.
The first section of the article reviews the
various mechanisms through which inflation
affects capital accumulation in the neoclassical
setting. Next, it briefly surveys the theoretical
studies on inflation and growth. The third section is an overview of the empirical results on
the correlation between inflation and growth.
The final section summarizes the survey.

(1)

k t +1 = (1 – δ)kt + it , and

(2)

it = sk f (kt ),

where k is the capital stock; i is gross investment spending; f (k ) is the production technology, using capital as the sole input; δ is the
constant rate of capital depreciation; and sk is
the fraction of output saved to acquire additional capital stock.
Equations 1 and 2 describe how this
economy operates by characterizing how capital
evolves over time and by specifying the equilibrium condition, respectively. In equilibrium, saving, characterized as a known fraction of output,
equals gross investment; that is, st = sk f (kt ) = it .
In steady state, the capital stock is constant over
time, so that equation 1 reduces to δk = sk f (k).
In Figure 1, which depicts the equilibrium for
this simple economy, the steady state occurs

Theories on inflation and growth
Persistent inflation is a post–World War II
phenomenon. Before then, the history of price
indexes shows bouts of inflation followed by
periods of deflation. In other words, the price
level cycles showed no discernible upward or
downward trend.1
In the absence of persistent inflation, the
early inflation–output growth theories were built
on such cyclical observations. Economic expansions generally coincided with inflation, and
contractions typically coincided with deflation.2
Theory, therefore, sought to account for a positive
correlation between inflation and output growth.
The textbook aggregate demand – aggregate
supply framework could account for a positive
correlation between inflation and output growth.
In that theory, the chief mechanism is a positive
association between aggregate demand and the
growth rate of money. Inflation and faster output
growth are joint products of faster money growth.
Mundell (1963) was the first to articulate a
mechanism relating inflation and output growth
through something other than the excess demand for commodities. In Mundell, an increase
in inflation immediately reduces people’s wealth.
To accumulate the desired wealth, people save

Figure 1

Steady State in the Neoclassical Economy
f (k )

δk

sk f (k )

0

12

k0

k

where the δk line intersects the sk line.
Figure 2 depicts the portfolio mechanism.
Consider a once-and-for-all increase in the inflation rate from π0 to π1 (π1 > π0 ), which is
equivalent to saying that the return to money
has fallen. In Tobin’s portfolio mechanism, people
will substitute away from money, with its lower
return, and toward capital. In Figure 2, this
substitution is depicted by a shift in the sk line
to sk′. As Figure 2 shows, the portfolio mechanism results in a higher steady-state capital
stock (from k 0 to k 1).
As Figure 2 shows, once the economy has
achieved steady state, there is no growth. Instead, Tobin’s framework shows that a permanently higher inflation rate permanently raises
the level of output. However, the effect on output growth is temporary, occurring during the
transition from steady-state capital stock, k 0, to
the new steady state with capital stock, k 1.3
Indeed, growth in the neoclassical economy is
driven by exogenous technological advancement—upward shifts in the f (k) curve— not by
a once-and-for-all change in the inflation rate.
Within the neoclassical setup, the next major
development in the study of inflation effects
comes from Sidrauski’s (1967) superneutrality
result. In Sidrauski’s study, people choose the
saving ratio to maximize their happiness, as
opposed to Tobin’s assumption that saving is a
fixed ratio of output. Money has an implicit
transaction feature in Sidrauski. Formally, this is
reflected in the notion that people’s happiness is
directly related to their holdings of real money
balances.4 The main result in Sidrauski’s economy
is that an increase in the inflation rate, for example, does not affect the steady-state capital
stock. Thus, neither output nor output growth is
related to changes in the inflation rate.5
Why is Sidrauski’s result different from
Tobin’s? People’s saving behavior plays a crucial
role in determining whether inflation affects output growth. In Tobin’s model, the portfolio
mechanism describes how people move from
money to capital when inflation rises. In
Sidrauski’s economy, people’s saving ratio falls
in response to an increase in inflation, as do
their real money balances. Indeed, people
match their decline in saving dollar for dollar
with a decline in money balances. Capital is
unchanged in the Sidrauski model.
To demonstrate that the Tobin effect does
not depend on the assumption that saving is a
constant fraction of output, I review several
model economies in which a Tobin effect is
present and people choose their saving rate
optimally.

FEDERAL RESERVE BANK OF DALLAS

Figure 2

The Tobin Effect
f (k )
δk

sk′f (k )(Π = Π1)

sk f (k )(Π = Π0)

0

k0

k1

k

One example is a study by Freeman and
Huffman (1991) in which they specify an
economy populated by heterogenous people;
specifically, people are identifiable by their level
of wealth. To consume in the future, people can
either hold money or hold capital.6 The rate of
return on money is strictly less than the rate of
return on capital, but people willingly hold money
because a flat fee must be paid to acquire capital. The fixed cost means that capital’s after-fee
real return is inversely related to the size of the
capital stock purchase. In other words, small
savers will prefer to hold money. Provided that
people are identical except for their wealth
holdings, Freeman and Huffman derive a breakeven value for saving, w *, at which the return
to money is identical to the after-fee return to
capital. Correspondingly, people saving less
than w * will hold money balances, and people
saving more than w * will prefer capital.
Consider an increase in the inflation rate
in the Freeman–Huffman model. With a lower
return on their money holdings, some of the
small savers who had held money will now find
it more appealing to pay the fee and acquire
capital. The bottom line is that Freeman and
Huffman specify a simple portfolio-substitution
effect in an explicit, optimizing framework.
As in the Tobin economy, an increase in the
inflation rate results in a permanently higher
steady-state level of output.
Ireland (1994) also presents a model in
which a Tobin effect is present, but it results
from a consumption–saving decision rather
than a portfolio-substitution mechanism. Ireland
specifies two alternative payment forms: government money and credit. In using credit, people
must pay for an intermediary’s services. Two key
assumptions are made regarding the intermedi-

13

ECONOMIC REVIEW SECOND QUARTER 1997

Welfare considerations. Although a rise in
the inflation rate does not instigate a change in
the level of output in the Sidrauski model, it
would be incorrect to conclude that inflation
has no effect on people’s welfare. Here again,
Feenstra’s interpretation is useful for assessing
the welfare costs associated with an increase in
the inflation rate. As noted above, the composition of total output shifts away from the consumption good and toward financial services as
the inflation rate rises. Since people’s happiness
is directly related to the quantity of the consumption good, welfare is unambiguously lowered when the inflation rate goes up.
An increase in the inflation rate also reduces people’s welfare in the models presented
by Freeman and Huffman and by Ireland. In
Freeman and Huffman, all moneyholders suffer
when the inflation rate rises because the return
to money falls. With a lower real return, less
savings are available to acquire the consumption
good. In Ireland’s research, people’s consumption–saving decision is distorted by inflation.
People save more to avoid the increased costs
associated with purchasing the consumption
good with either financial services or with lower
yielding money.
Thus, even though output may rise in
response to an increase in the inflation rate, a
review of the neoclassical economies shows
that people’s welfare will fall.7 As such, the
theoretical evidence points to a conundrum:
if monetary policy raises the inflation rate, output could increase, but what the benevolent
policymaker seeks to maximize—people’s happiness—would fall.
The Stockman effect. Stockman (1981)
develops a model in which an increase in the
inflation rate results in a lower steady-state level
of output and people’s welfare declines. In
Stockman’s research, money is a complement to
capital, accounting for a negative relationship
between the steady-state level of output and
the inflation rate.
Stockman’s insight is prompted by the fact
that firms frequently put up some cash in financing their investment projects. Sometimes the cash
is directly part of the financing package, whereas
other times, banks require compensating balances. Stockman models this cash investment
feature as a cash-in-advance restriction on both
consumption and capital purchases. Since inflation erodes the purchasing power of money
balances, people reduce their purchases of both
the cash good and capital when the inflation
rate rises. Correspondingly, the steady-state level
of output falls in response to an increase in the

ary’s cost function: at a given date t, costs increase with the amount of credit, and for a given
quantity of credit, costs decline over time. The
latter assumption is designed to capture financial innovations, while the former assumption is
crucial for people to want government money.
The inflation rate affects the composition
of consumption financed by credit and by government money. With a cash-in-advance restriction, people must acquire money balances
one period prior to their actual expenditures.
Consequently, with an increase in the inflation
rate, people buy less with money because its
purchasing power erodes at a faster rate. In this
economy, time also plays an important role.
Instead of substituting away from money and
toward more credit, people may wait and consume more when financial innovations have
lowered the cost of using credit. Capital is the
means by which people can practice such
patience. Over the near term, greater capital
accumulation yields temporarily faster growth.
Eventually, people will draw down their capital
reserves at a faster rate to enjoy more consumption. Hence, a rise in inflation initially
results in output growing faster than trend, but
then output grows slower than trend at some
future date. In the long run, output grows at the
same trend rate, regardless of the inflation rate.
The research by Sidrauski and Freeman
and Huffman shows that money can play a
decisive role in terms of output’s long-run response to an increase in the inflation rate.
Sidrauski identifies money as a means of payment, whereas Freeman and Huffman see money
as competing with capital as a store of value.
Feenstra (1986) offers an interpretation of the
Sidrauski model that makes the distinction clear.
According to Feenstra, an increase in the inflation rate causes people to economize on their
money balances. Moreover, the composition of
output shifts from the consumption good to
financial services. As in Sidrauski’s model, total
output—the sum of consumption and financial
services—is unchanged. An increase in the inflation rate, therefore, does not affect the level of
total output but does affect its composition.
Thus, the Feenstra interpretation shows that how
we pay for total output—in this case, the ratio
of output to money—may respond to the inflation rate, but the overall level of economic
activity is unaffected. In Freeman and Huffman,
because money is a competing store of value,
a rise in the inflation rate makes capital more
attractive. Inflation induces people to produce
more total output, not just change output’s
composition.

14

The literature review shows that models in
the neoclassical framework can yield very different qualitative results with regard to inflation’s
effect on the steady-state level of output. Depending on money’s role, an increase in the
inflation rate can result in less output (the Stockman effect), more output (the Tobin effect), or
no change in output. The theoretical review
does, however, reveal one consistent result:
people’s welfare is inversely related to changes
in the inflation rate.
Endogenous growth models. Kaldor (1961)
observed persistent differences across countries
in terms of growth rates of per capita output.
This observation stimulated efforts by Romer
(1986) and Lucas (1988) to specify economies
that could grow unboundedly.
One feature accounts for the chief difference between the endogenous growth models
and the neoclassical economies. In the neoclassical economy, the marginal product of capital
declines as more capital is accumulated. In the
simplest versions of the endogenous growth
models, per capita output continues to increase
because the marginal product of capital does
not fall below a positive lower bound. Indeed,
for unbounded growth, the marginal product of
capital must be greater than the rate at which
people discount future consumption.9 The basic
intuition is that only if the rate of return on
capital is sufficiently high will people be induced to continue accumulating it.
Several studies have looked at the effect
inflation has on output growth. The studies reviewed here find that an increase in the inflation
rate retards growth. As with the Stockman effect,
a welfare loss accompanies a rise in the inflation
rate. In the endogenous growth models, the
distortionary effects identified above are compounded by the reduction in growth. As will be
seen, the way in which money is introduced has
a great bearing on the size of the inflation rate
effects on output growth.
The earliest versions of the endogenous
growth economies find that the inflation rate
effects on growth will be small. Gomme (1993)
studies an economy similar to the one specified
by Cooley and Hansen; that is, an inflation rate
increase results in a decline in employment. In
Gomme’s research, efficient allocations satisfy
the condition that the marginal value of the last
unit of today’s consumption equals the marginal
cost of the last unit of work. With a rise in the
inflation rate, the marginal value of today’s last
unit of consumption falls. Accordingly, the efficiency condition is satisfied provided people
work less. With less labor, the marginal product

inflation rate. Insofar as money acquisition is
necessary for capital accumulation, Stockman
presents a model in which money and capital
are complementary goods. The term Stockman
effect generally applies to all theoretical results
in which output is inversely related to the inflation rate.
Inflation and labor. The Stockman effect
can also operate through effects on the labor–
leisure decision. Greenwood and Huffman (1987)
develop the basic labor–leisure mechanism, and
Cooley and Hansen (1989) identify the implications for capital accumulation.
In Greenwood and Huffman’s research,
people hold money to purchase the consumption good and derive utility from both consumption and leisure. Fiat money is valued because
there is a cash-in-advance constraint on the consumption good. Greenwood and Huffman show
that the return to labor falls when the inflation
rate rises. Cooley and Hansen simplify the mechanism, noting that people substitute away from
the cash good—consumption —and choose to
enjoy more leisure. Consequently, people facing
an increase in the inflation rate will substitute
away from consumption and toward leisure.
Cooley and Hansen (1989) extend the
Greenwood–Huffman mechanism to consider
capital accumulation.8 The key assumption is
that the marginal product of capital is positively
related to the quantity of labor. Thus, when
labor quantity declines in response to a rise in
the inflation rate, the return to capital falls and
the steady-state quantities of capital and output
decline. As Cooley and Hansen show, the level
of output permanently falls in response to an
increase in the inflation rate. The mechanism
described by Cooley–Hansen emphasizes labor’s
role in determining the response of steady-state
output to inflation.
With an increase in the inflation rate, the
typical person suffers a welfare loss in the
Stockman and Cooley and Hansen setups. In the
Stockman economy, inflation distorts people’s
decisions regarding the purchase of all cash
goods, including capital. With less wealth, people
can afford a smaller stream of consumption
spending, making them worse off. In the Cooley–
Hansen setup, people respond to an increase in
the inflation rate by wanting less of the cash
good and more of the credit good, leisure. While
more leisure partially offsets the loss of the
consumption good, the main point is that an
increase in the inflation rate has distorted people’s
choices. In effect, the Cooley–Hansen resident
consumes too much leisure and too little of the
consumption good, resulting in a welfare loss.

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a permanent increase in the inflation rate stimulates, retards, or has no effect on the level of
output. In short: (1) if money is a complement to
capital, inflation and the output level are negatively related; (2) if money and capital are substitutes, inflation and the level of output are
positively related; and (3) if money is primarily
a medium of exchange and some substitute payment medium exists, inflation and the output
level are independent. Whereas the neoclassical
models predict that the inflation rate affects the
level of output, the newer literature asks how a
rise in the inflation rate can affect the growth
rate of per capita output. In the endogenous
growth setting, research shows that money’s
role determines whether the quantitative effects
are large or negligible.
Theories are useful insofar as they account
for some observed phenomenon. In the next
section, I review the literature on the empirical
evidence relating inflation to growth.

of capital is permanently reduced, resulting in a
slower rate of capital accumulation. Gomme
calculates the effect a permanent change in the
inflation rate would have in this economy. He
finds that eliminating a moderate inflation rate
(for example, 10 percent) results in only a very
small (less than 0.01 percentage point) gain in
the growth rate of output.
Jones and Manuelli (1995) use fiscal policy
distortions as the mechanism through which
inflation might affect growth. Jones and Manuelli
specify a model in which the tax code includes
a nominal depreciation allowance. With a rise in
the inflation rate, the discounted value of depreciation tax credits falls; hence, the effective tax
on capital income is higher. People accumulate
capital at a lower rate because of the reduction
in after-tax real returns. Correspondingly, there
is a reduction in output growth. As in Gomme,
Jones and Manuelli calculate the inflation rate
effect, finding that the growth rate reduction
will be quite small. In both Gomme and Jones
and Manuelli, inflation does not directly influence capital accumulation. Instead, the capital
accumulation response is a second-order effect.
Alternative models examine how inflation
might directly affect capital accumulation and
hence output growth. Marquis and Reffert (1995)
and Haslag (1995) specify economies in which
capital and money are complementary goods.
Marquis and Reffert examine inflation rate
effects in a Stockman economy: there is a cashin-advance constraint on capital. In Haslag’s
research, banks pool small savers but are
required to hold money to satisfy a reserve requirement. The reserve requirement is binding
because money offers a return strictly below that
of capital. In a reserve requirement economy,
the equilibrium return to deposits is then a
weighted sum of returns to money and capital.
Thus, an inflation rate increase drives down the
return to deposits, resulting in deposits being
accumulated at a slower rate. Since capital is a
fraction of deposits, capital accumulation and
output growth both slow. In both the Marquis
and Reffert and Haslag studies, the inflation rate
effects on growth are substantially greater than
those calculated in Gomme and Jones and
Manuelli. For instance, Haslag finds that economies with 10 percent inflation will grow 0.2
percentage point slower than economies with
zero inflation.10
Economic theory reaches a striking variety
of conclusions about the responsiveness of output (or the growth rate of output) to changes in
the inflation rate. In the neoclassical models,
money’s role in the economy determines whether

The empirical evidence on
inflation and growth
The chief aim of this section is to identify
the relationship between inflation and growth.
More specifically, Is the secular trend in the
inflation rate systematically related to the secular
trend rate of output growth?
Table 1 summarizes the findings of the
empirical papers cited in this article. Clearly, a
majority of studies find that inflation and growth
are systematically and negatively related. However, Levine and Renelt (1992), Bullard and
Keating (1995), and Ericsson, Irons, and Tryon
(1993) fault this conclusion. Levine and Renelt
contend that the inflation–output growth relationship is simply too tenuous. Bullard and
Keating and Ericsson, Irons, and Tryon question
whether the early studies use the correct notion
of trend.
Figure 3 plots the average values for the
inflation rate and per capita real GDP growth
rate across countries. The sample consists of
average rates of inflation and per capita real
GDP growth for eighty-two countries. The sample
means are based on annual observations spanning the period 1965 –90. The plot shows a
weak negative correlation between per capita
output growth and the inflation rate. The countries with lower than average growth rates tend
to be the ones that have higher than average
inflation rates. The notion of trend applied in
these data is multiyear averages. (The issue of
what constitutes trend is examined in greater
detail later in this survey.)
In the literature, regression analysis is a

16

Table 1

Empirical Evidence on the Inflation–Growth Relationship
Author(s)

Samples

Methodology

Synopsis of results

Kormendi and
Meguire

46 countries
1948 –77, varying
periods

Cross-country regression using sample
means

Negative and significant
relationship between output
growth and inflation exists.

Fischer

73 countries

Comparison of sample
means from fastand slow-growing
groups

Inflation in fast-growth group
is lower than in slow-growth
group.

DeGregario

12 Latin America
countries, 1950 – 85

Cross-country regression using 6-year averages, nonoverlapping

Negative and significant
relationship between output
growth and inflation exists.

Gomme

82 countries
1949 – 89, varying
periods

Cross-country simple
correlations using
annual data

Output growth and inflation
are negatively correlated.

Bullard and
Keating

58 countries

Regressions for
each country

Inflation has no significant
long-run effect on the level
of output.

Ericsson, Irons,
and Tryon

G–7 countries

Regressions for
each country

Inflation has no significant
long-run effect on output
growth.

frequently used tool. Examples include Kormendi
and Meguire (1985), Fischer (1991), DeGregario
(1993), and Gomme (1993).11 In general, these
studies find that the correlation between inflation and per capita output growth is negative
and significant. Thus, the more formal analyses
are consistent with the ocular econometrics used
in analyzing Figure 3: countries with higher than
average inflation typically experience slower
than average output growth.
In addition to regression analysis, Fischer
employs simple nonparametric methods to look

at inflation and growth. He calculates the average inflation rates for two smaller groups of
countries; namely, those that grow at least one
standard deviation faster than the average rate
and those that grow, at most, one standard
deviation slower than the average rate. Fischer
reports that the slow-growth countries have an
average inflation rate slightly above 30 percent,
while the fast-growth countries average only 12
percent inflation.
An obvious concern is whether the inflation–output growth relationship is robust. Sarel
(1996) and Judson and Orphanides (1996) ask
whether the relationship between inflation and
growth is linear. The idea is that a 1 percentage
point increase in a low inflation rate may have
a smaller effect on output growth than a 1 percentage point increase in a moderate to high
inflation rate. Both studies find that the effect of
an increase in the inflation rate depends on
whether the initial rate is high or low. Specifically, an inflation rate increase does retard output growth when the inflation rate is moderate
or high (defined as an inflation rate exceeding
10 percent) but is not significantly related to
output growth when inflation is low (less than
10 percent). Thus, the cross-country evidence
suggests that the inflation– output growth relationship is robust but most likely depends on
the initial inflation rate.
Other researchers have questioned whether
a systematic relationship between the inflation

Figure 3

Cross-Country Plots of Inflation Versus GDP
Inflation
(Percent)
80
70
60
50
40
30
20
10
0
–1

0

1

2

3

4

5

6

7

Per capita GDP growth (Percent)
SOURCE: International Financial Statistics.

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In Bullard and Keating’s research, the
cross-country evidence shows that there is no
systematic long-run relationship between inflation and the level of output. Bullard and Keating
(1995) identify trend inflation and output for
fifty-eight countries. Here, trend is associated
with long-run relationships between series with
stochastic trends. The authors do not pool
results across countries. Instead, they estimate
separate regressions for each country, permitting each country’s long-run relationships and
short-run dynamics to be different. In their
examination of the long-run relationship,
Bullard and Keating find that permanent
changes in the inflation rate are not systematically related to the level of output.15

rate and growth even exists. Levine and Renelt
(1992) argue that one must first control for a set
of essential growth determinants before testing
for a systematic relationship between inflation
and output growth. Levine and Renelt find that
after including measures of physical and human
capital accumulation rates, the inflation rate is
not significantly related to per capita output
growth.12 Thus, Levine and Renelt conclude that
the inflation – output growth relationship is
fragile. The implication is that policymakers
should not assume that a rise in the inflation
rate will, on average, slow growth.
The Levine–Renelt criticism may overstate
the weakness in the inflation–output growth
relationship. The theoretical literature shows that
inflation effects operate through movements
in capital accumulation —both physical and
human. If one controls for capital accumulation
directly in the regression —as Levine and Renelt
do—it is less likely that inflation will be significantly related to output growth. Barro and
Sala-i-Martin (1995) show that per capita output
growth is the sum of total factor productivity
growth and growth in both physical and human
capital. Based on this growth accounting expression, it is difficult to imagine how inflation,
or any policy variable, could be significantly related to per capita output growth in regressions
that include measures of physical and human
capital accumulation rates.
Even before Levine and Renelt’s investigation, researchers were wary of putting too much
faith in the inflation–output growth relationship.
In particular, high-inflation countries are also
likely to experience highly volatile inflation
rates. If only inflation is included in the estimated
regression equation, it is impossible to determine whether it is the inflation rate or inflation
uncertainty that is determining growth.13
The final issue is whether multiyear averages should be used to measure the trend rate of
output growth.14 Statistical methods permit the
extraction of trend from annual observations.
The implication is that much greater countryspecific variation in the trend will occur when
the dataset has a time series of trend rates than
when the trend is single valued. Greater variability in the time series highlights the basic
trade-off facing a researcher; potentially, too
much of the high-frequency (read business cycle)
movement in the series will be incorporated
into the trend measure. Consequently, regressions with more variable trend rates of output
growth potentially pollute the attempt to identify the relationship between long-run output
growth and inflation.

Summary and conclusions
In this article, I survey the theoretical and
empirical literature examining the relationship
between movements in the inflation rate and
output, output growth, and welfare. In the theoretical literature, an inflation rate increase unambiguously reduces the average person’s welfare.
However, inflation’s qualitative effect on the
level of output is ambiguous. I suggest that
inflation’s effect on output depends on why
people hold money. If the researcher emphasizes money as a substitute for capital, a rise in
inflation raises the long-run level of output. If
the researcher emphasizes money’s role as a
complement to capital, a rise in inflation results
in lower long-run levels of capital.
The most recent theoretical research has
studied inflation’s effect on growth rates. These
theories generally find that a rise in inflation
either results in slower growth or has no impact
on the growth rate.
In the empirical literature, research attempts to find the relationship between the
trend rate of per capita output growth and the
trend inflation rate. In this article, empirical
results differ, owing mostly to the notion of
trend applied. Many cross-country studies use
multiyear averages as the measure of trend.
Early studies show that high-inflation countries
tend to grow slower that low-inflation countries.
More recent studies suggest that countries with
inflation rates above 10 percent tend to exhibit
a negative relationship between inflation and
growth, whereas in countries with average inflation rates below 10 percent, there is no significant relationship. Studies that use the trend rate
of growth each year fail to find a significant
relationship between per capita output growth
and inflation.
Thus, the survey produces two uncontested

18

findings. First, there is no empirical evidence
that there is a positive relationship between the
secular trend rate of inflation and the secular
trend rate of output growth. Second, economic
theory tells us that an inflation rate increase
makes the average person worse off.
11

Notes
1

2

3

4

5

6

7

8

9

10

For example, the U.S. producer price index in 1943
was slightly below its 1810 value.
Fischer’s (1926) original study established the negative comovement between inflation and the unemployment rate. With Okun’s law, the negative association
between inflation and unemployment is a positive relationship between inflation and output growth.
The capital stock monotonically approaches its steady
state in the neoclassical economy. Under different
conditions, the capital stock could cyclically converge
to its steady state. With cyclical convergence, the
capital stock could exhibit periods in which it rises
and falls as it approaches the new steady-state level.
Hence, growth could either rise or fall in response to
a rise in the inflation rate.
Rather than interpreting real money balances as something that makes people happier, the money-in-theutility-function specification is a proxy for some transaction technology. Feenstra (1986) shows that money
in the utility is functionally equivalent to a cash-inadvance payment technology.
See Abel (1985) and Koenig (1987) for details on the
capital–labor ratio along the transition path.
More precisely, people can hold deposits that are
used to finance capital.
In much of this research, the optimal inflation rate is
equal to the person’s time rate of preference — the
Friedman rule. Akerlof, Dickens, and Perry (1996)
argue that a moderate steady inflation rate permits
maximum employment. Inflation substitutes for the
desire to avoid lowering nominal wages. Akerlof
et al. compare outcomes by the effect on employment
and output, not welfare. Consequently, their findings
do not overturn the welfare implications reported in
this article.
Cooley and Hansen are primarily interested in the
business-cycle properties of an economy in which
the inflation rate changes. Interestingly, they find that
the business-cycle properties are not substantially
affected by changes in the inflation rate. My interest
here is in the features of their model related to
inflation’s effect on the steady-state levels of capital
and welfare.
The assumption that the marginal product of capital
does not always diminish is based on the common
definition of capital, which includes physical quantities
— buildings and machines — and human features,
such as accumulated knowledge.
The impact of the inflation rate on growth depends on

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12

13

14

15

the size of the reserve requirement. With a 15 percent
reserve requirement, an economy with 10 percent
inflation grows at a rate 0.67 percentage point slower
than an economy with zero inflation. With only a
5 percent reserve ratio, the effect on growth is only
0.2 percentage point.
These studies differ primarily in terms of the variables
included in their regressions. Kormendi and Meguire,
for example, include measures of fiscal policy,
whereas Fischer includes measures of physical and
human capital accumulation. Details on the countries
sampled and the time periods are in Table 1.
Levine and Renelt’s baseline regression includes
the investment share of real GDP, the initial (1960)
level of real GDP per capita, the initial secondaryschool enrollment rate, and the annual rate of population growth.
Tommassi (1994) models the effect of inflation uncertainty on economic activity. In Tommassi, inflation
uncertainty results in people putting more effort into
activities that are not counted in national income
accounts.
Ericsson, Irons, and Tryon (1993) identify three methodological problems with the typical cross-country
regressions: aggregation over countries, aggregation
over time, and the use of growth rates instead of
output levels. Aggregation over countries lumps lowinflation countries with high-inflation countries. Ericsson
et al. argue that the systematic relationship owes
almost entirely to the inclusion of a small group of
African and Latin American countries. In aggregation
over time, the unit of observation is average inflation
over periods as long as several decades. Ericsson
et al. show that contemporaneously uncorrelated
variables can be either positively or negatively related
when averaged data are used. Finally, using firstdifferences as the unit of observation, the authors
point out, imposes an unnecessary restriction on the
dynamic relationships in the data.
In Bullard and Keating, the first-difference in the inflation rate and output growth is a stationary series. The
interpretation is that the sample mean is the best forecast of output growth over an infinite horizon. Bullard
and Keating’s forecasting equations, in which output
growth eventually returns to its long-run average value,
are consistent with the neoclassical theory that a
permanent change in the inflation rate can have only
temporary effects on output growth. Interestingly,
Bullard and Keating find evidence that the transition
phase exhibits cyclical convergence, as opposed to
the monotonic convergence predicted by the neoclassical models.

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