Please use the BUS401 Case3 2019 template to complete this assignment.

U.S. Nurse Labor Market Dynamics Are
Key to Global Nurse Sufficiency
Linda H. Aiken

Objectives. To review estimates of U.S. nurse supply and demand, document trends
in nurse immigration to the United States and their impact on nursing shortage, and
consider strategies for resolving the shortage of nurses in the United States without
adversely affecting health care in lower-income countries.
Principal Findings. Production capacity of nursing schools is lagging current and
estimated future needs, suggesting a worsening shortage and creating a demand for
foreign-educated nurses. About 8 percent of U.S. registered nurses (RNs), numbering
around 219,000, are estimated to be foreign educated. Eighty percent are from lower-
income countries. The Philippines is the major source country, accounting for more
than 30 percent of U.S. foreign-educated nurses. Nurse immigration to the United States
has tripled since 1994, to close to 15,000 entrants annually. Foreign-educated nurses are
located primarily in urban areas, most likely to be employed by hospitals, and somewhat
more likely to have a baccalaureate degree than native-born nurses. There is little
evidence that foreign-educated nurses locate in areas of medical need in any greater
proportion than native-born nurses. Although foreign-educated nurses are ethnically
more diverse than native-born nurses, relatively small proportions are black or His-
panic. Job growth for RNs in the United States is producing mounting pressure by
commercial recruiters and employers to ease restrictions on nurse immigration at the
same time that American nursing schools are turning away large numbers of native
applicants because of capacity limitations.
Conclusions. Increased reliance on immigration may adversely affect health care in
lower-income countries without solving the U.S. shortage. The current focus on facili-
tating nurse immigration detracts from the need for the United States to move toward
greater self-sufficiency in its nurse workforce. Expanding nursing school capacity to
accommodate qualified native applicants and implementing evidence-based initiatives
to improve nurse retention and productivity could prevent future nurse shortages.

Key Words. Nurse migration, U.S. immigration

The United States plays a pivotal role in the global migration of nurses. It has
the largest professional nurse workforce of any country in the world, num-
bering almost 3 million in 2004 (USDHHS 2006). The United States has
almost one-fifth of the world’s stock of professional nurses and about half of

r Health Research and Educational Trust
DOI: 10.1111/j.1475-6773.2007.00714.x

1299

English-speaking professional nurses. With a nurse labor force of this size,
even modest supply–demand imbalances exert a strong pull on global nurse
resources. A looming projected shortage of nurses in the United States that
could reach 800,000 by 2020 (USDHHS 2002) is thus cause for concern
among other countries also experiencing nurse shortages.

The United Kingdom has been more in the spotlight in recent years than
the United States regarding its international nurse recruitment policies and
practices because, at one point, nurse migrants outnumbered domestically
educated nurses among nurses entering the U.K. workforce (Aiken et al. 2004;
Ross, Polsky, and Sochalski 2005). However, the United Sates recently be-
came the world’s largest importer of nurses, with almost 15,000 foreign-edu-
cated nurses passing the licensing exam for registered nurses (RNs) in 2005
(National Council on State Boards of Nursing [NCSBN] 2006), compared with
an estimated 13,000 foreign-educated nurses entering the United Kingdom
(see Buchan 2007). Moreover, economic constraints in the National Health
Service in the United Kingdom have recently limited immigration, while de-
mand for nurses in the United States continues to grow. Altogether in 2000,
just over 300,000 U.S. nurses were foreign born. After taking into account
foreign-born nurses who immigrated before the age of 22, and thus probably
received their nursing education in the United States, close to 219,000 nurses
currently residing in the United States are estimated to have received their
nursing education abroad, constituting close to 8 percent of the nation’s RN
stock (Table 1).

Table 1: Registered Nurses by Nativity and Citizenship Status, U.S. 200

0

Frequency % All RNs

Born in the United States or Territories 2,369,185 88
Born abroad of American parent 18,249 1
Immigrated before age 21 83,807 3
U.S. citizen by naturalization (immigration age 211) 124,968 5
Not a U.S. citizen (immigration age 211) 93,752 3
Estimated number of foreign-educated RNs 218,720 8

Source: Author’s calculation from the U.S. Department of Commerce, Bureau of the Census,
Census of Population and Housing (2000), Public Use Micro data, 1% sample.

RNs, registered nurses.

Address correspondence to Linda H. Aiken, Ph.D., R.N., The Claire M. Fagin Leadership Pro-
fessor of Nursing, Professor of Sociology, Director, Center for Health Outcomes and Policy
Research, University of Pennsylvania, Center for Health Outcomes and Policy Research, 420
Guardian Drive #332R NEB, Philadelphia, PA 19104-6096.

1300 HSR: Health Services Research 42:3, Part II ( June 2007)

The United States is the destination of choice for many migrating nurses
from both developed and lower-income countries because of high wages,
opportunities to pursue additional education, and a high standard of living
(Kingma 2006). Nurses do not emigrate from the United States in substantial
numbers or permanently. The prolonged recent nurse shortage and the large
shortage projected for the future have motivated more aggressive nurse re-
cruitment abroad by hospital employers and commercial recruiting firms
(Brush, Sochalski, and Berger 2004). Almost 34,000 foreign-educated nurses
took the NCLEX-RN registered nurse license exam in 2005, with 44 percent
passing the exam, suggesting a great deal of interest among foreign-educated
nurses in working in the United States (NCSBN 2006). Increasingly, physi-
cians and others in developing countries are retraining as nurses because of the
potential opportunities for migration. Since 2000, 3,500 Filipino doctors have
retrained as nurses and left for nurse jobs abroad, and 4,000 Filipino doctors
are currently in nursing schools.

This paper (1) examines trends and projections in the U.S. RN work-
force; (2) reviews recent trends in nurse immigration; (3) explores the extent to
which nurse immigration contributes to national health care workforce goals,
including diversity and care of underserved populations; and (4) considers
policy options that could contribute to solving nurse shortages in the United
States, while minimizing adverse impact on global human resources for
health.

ESTIMATED NEED FOR RNS IN THE UNITED STATES

An estimated 703,000 new jobs for RNs will be created between 2004 and
2014; RNs are second among the top 10 occupations with the largest job
growth (U.S. Department of Labor, Bureau of Labor Statistics 2005). The
average age of RNs was 46.8 years in 2004, and has been increasing steadily
since 1980 (USDHHS 2006). Some 478,000 nurses can be expected to retire
between 2002 and 2012. New jobs plus retirements lead to predictions of 1.1
million additional nurses needed to be added to the stock of RNs between
2002 and 2012 to maintain a steady state. Meeting that target would require
graduations of around 110,000 RNs a year between 2002 and 2012.

Figure 1 portrays the overall upward trend in nursing school enrollments
and graduations since 1958, as well as cyclical increases and decreases. The
increase in enrollments and graduations from 1965 to 1976 were associated
with significant federal investments in nursing education over the decade,

Global Nurse Sufficiency 1301

following the introduction of Medicare and Medicaid. Enrollments in nursing
schools in the absence of federal subsidies have been very sensitive to market
conditions for nurses. Applications to nursing schools declined for 6 consec-
utive years from 1995 through 2000 during a period of highly publicized nurse
layoffs associated with employer adaptations to managed care.

Graduations

fell from over 95,000 in 1994 to below 70,000 by 2001, causing the domestic
production of nurses to fall behind the 2012 target by about a cumulative total
of 110,000 nurses between 2002 and 2005. Indeed, Buerhaus, Staiger, and
Auerbach (2004) suggest that most of the increase in nurses over this period
was from foreign-educated nurses and older nurses returning to the workforce.

The tight job market was short-lived, and escalating demand for nurses
was associated with real wage growth of 12.8 percent between 2000 and 2004,
the first significant upturn in real wages since 1988 (USDHHS 2006). The
nursing school applicant pool grew in response to improved employment
opportunities and increased wages. Graduations in 2003 were back to 73,000,
and between 2003 and 2004 graduations increased by 26 percent to about
88,000 (NLN 2005), and to close to 99,000 in 2005 (NCSBN 2006). The
applicant pool became so robust by 2004 that many qualified applicants for
RN programs were turned away. The American Association of Colleges
of Nursing reported that more than 30,000 applicants seeking baccalaureate

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Graduations

Enrollments

Year

Sources: NLN (enrollments 1958 –1996, 1999, 2002 – 2003; 2005, graduations 1958 – 2005;
National Council State Boards of Nursing (2005 first-time U.S.-educated NCLEX-RN test
takers to estimate graduations).
Dotted line indicates imputed values for missing data, years 1997, 1998, 2000, and 2001.
*Data on enrollments available for years 1958 – 2003.

Figure 1: Graduations and Enrollments in Registered Nurse Programs,
1958–2005.n

1302 HSR: Health Services Research 42:3, Part II ( June 2007)

education could not be accommodated and the National League for Nursing
estimates that as many as 150,000 applicants were turned away from all nurs-
ing programs because of shortages of faculty, resources, space, and clinical
education opportunities (AACN 2005; NLN 2005). While these estimates are
possibly inflated by multiple applications, there is little doubt that the number
of qualified applicants to nursing schools is much larger than the number of
positions.

The single greatest challenge to expanding domestic production of
RNs——a large, qualified applicant pool——is not presently a problem. If the
large applicant pool can be sustained, and if most qualified applicants could be
accommodated in nursing schools, it would be feasible to graduate most of the
125,000 nurses a year needed to meet projected future needs, assuming that
nurses currently in the workforce could be retained until retirement age. To
date, however, the federal response to the nurse shortage has been minimal,
and employers have not been fully engaged in participating in efforts to
increase the domestic supply of nurses.

TRENDS IN U.S. NURSE IMMIGRATION

Health care organizations in the United States have actively recruited profes-
sional nurses from abroad for more than 50 years in response to cyclical nurse
shortages in hospitals and nursing homes (Brush and Berger 2002; Aiken et al.
2004). Until the early 1990s, the inflow of RNs trained abroad generally did not
exceed 3,000–4,000 a year (Buerhaus, Staiger, and Auerbach 2004). Between
1994 and 2005, however, the annual number of foreign-educated nurses pass-
ing the NCLEX-RN exam tripled to almost 15,000 in 2005. Two concurrent
factors shaped recent trends in nurse migration: changes in the demand for
nurses in the hospital sector and changes in immigration policies.

Demand for nurses fell in the hospital sector in the early 1990s as health
care organizations adapted to increased penetration of managed care and
predictions of decreased inpatient days. Sixty percent of hospital CEOs re-
ported reducing nurse staffing by attrition and layoffs, and use of supplemental
and foreign-educated nurses declined (Aiken, Clarke, and Sloane 2001; Aiken
et al. 2001). These changes were short lived, and, within a few years, hospitals
began adding nurse positions. Enrollments in nursing schools, however, had
fallen significantly in the interim, and nurses were in short supply, motivating
greater interest in international recruitment. Buerhaus, Staiger, and Auerbach
(2003) peg the beginning of the current nurse shortage, the longest lasting in

Global Nurse Sufficiency 1303

recent decades, at 1998, the point at which nurse immigration began its steady
increase. Foreign-born nurses accounted for about a third of the increase in
employed nurses between 2000 and 2002.

Changes in immigration policy may have also had an impact on the
trends in nurse immigration. In 1995, the H-1A visa for temporary employ-
ment for foreign RNs was allowed to sunset. It is difficult to know how much of
the decline in nurse immigration in 1996 and beyond was due to the visa
change, versus lower employer demand for nurses. Additionally, in 1996, the
Immigration and Naturalization Service (INS) required foreign-educated
nurses to complete a screening program to qualify for permanent residence
through employment. The INS did not issue rules for the implementation of
the screening provision until 1999 when the Commission on Graduates of
Foreign Nursing Schools (CGFNS) won a lawsuit requiring the rules to be
issued. The more recent increase in immigration may be due to more aggres-
sive recruitment of nurses from abroad and more creative utilization of ex-
isting immigration provisions. In an unusual decision, Congress in 2005
approved the ‘‘recapture’’ or carryover of 50,000 unused employment-based
visas for nurses (and physical therapists) that had been authorized in previous
years but not filled. Hospital employers urged Congress in 2006 to make
available 90,000 additional unused employment-based visas for which
skilled professionals, such as nurses, qualify. Representatives from the
American Hospital Association (AHA) note that the additional visas for nurs-
es approved by Congress in 2005 will have little impact on ameliorating the
national shortage of hospital nurses but could bring temporary relief to
some institutions (Fong 2005). Data from the U.S. Census reveal that 63 per-
cent of foreign-born RNs residing in the United States in 2000 were U.S.
citizens by naturalization suggesting that the majority of nurses who come to
the U.S. stay.

Data Sources

There is no ideal source of information on nurse immigration to the United
States and, thus, this paper relies on multiple sources, each with some lim-
itations. The four main data sources are the U.S. Department of Homeland
Security, the National Sample Survey of Registered Nurses (NSSRN), the U.S.
Population Census, and the NCSBN. This paper focuses on RNs (professional
nurses) only.

Immigration statistics reported by the U.S. Department of Homeland
Security do not include complete information on occupational status except

1304 HSR: Health Services Research 42:3, Part II ( June 2007)

for those entering the United States on an occupational visa. Many nurses
enter the country on other types of visas, for example, as family members or
students. Thus, Department of Homeland Security statistics appears to sig-
nificantly underestimate the number of foreign-educated nurses entering the
United States.

The NSSRN, a national probability sample of 35,724 RNs drawn from
50 states and the District of Columbia in 2004 (USDHHS 2006), has the
greatest detail on foreign-educated nurses residing in the United States. The
survey has been conducted every 4 years since 1977. The 2004 NSSRN es-
timated that 3.5 percent of RNs in the United States, or 100,791 RNs, were
trained abroad (Xu and Kwak 2005).

The U.S. Population Census, conducted every 10 years, is another
source of information on foreign-born nurses. Its major limitation is the ab-
sence of data on countries in which professional education took place. Anal-
yses of the 2000 U.S. Population Census (1 percent Public Use Data file) reveal
over 300,000 foreign-born RNs. After deleting foreign-born nurses who im-
migrated before age 21, and, thus, probably received their nursing education
in the United States, Census data suggest that close to 218,000 nurses in the
United States are likely to have been educated abroad.

The NSSRN appears to significantly underestimate the number of for-
eign-educated nurses in the United States. Also, it finds little change in the
number of foreign-educated nurses between 2000 and 2004, despite evidence
from the NCSBN of more than a tripling of the number of foreign-educated
nurses who passed the licensing exam over that period, most of whom pre-
sumably immigrated. The undercount of foreign-educated nurses in NSSRN
may result from the geographic concentration of foreign-educated nurses in
five states.

The NCSBN reports on an annual basis the number of nurses taking and
passing the licensing exam (NCLEX-RN) by country of nursing education.
The NCSBN data provide proxy measures of immigration potential (numbers
taking the test the first time) and of the number of new foreign-educated nurses
entering the United States (numbers passing the exam). Licenses to practice
nursing are issued by the states, however, and NCSBN does not have a com-
plete listing of newly licensed foreign-educated nurses from every state. The
actual number of nurses immigrating is less, by an unknown amount, than the
number passing the exam.

In this paper, NCSBN data are used to estimate annual trends in nurse
migration. The NSSRN is used to explore the individual characteristics and
qualifications of foreign-educated nurses and their practice patterns. The U.S.

Global Nurse Sufficiency 1305

Census is used to estimate the number of foreign-educated nurses residing in
the United States.

Primary Source Countries

Primary source countries and regions can be viewed in two ways: (1) the
countries/regions that have contributed the largest number of nurses to the
U.S. stock of RNs and (2) the countries that provide the largest number of
nurses currently. Table 2 examines the current population of U.S. nurses born
abroad and presumed to have been educated abroad because they migrated as
adults. Close to a third of the estimated 218,720 foreign-educated nurses in the
United States are from the Philippines. The second most important source
regions for foreign-born nurses are the Caribbean and Latin America, which
have contributed almost 50,000 nurses. Western developed countries includ-
ing Canada, Western Europe, Australia, and New Zealand rank third with a
total of almost 33,000 nurses. Overall, almost 60 percent of foreign-educated
nurses have become citizens. Nurses from Western developed countries are
less likely to become citizens.

Table 3 presents a snapshot of current nurse immigration to the United
States by the 10 source countries contributing the largest number of nurses in
2005. As noted earlier, the number of nurses passing the NCLEX-RN is used

Table 2: Foreign-Educatedn Registered Nurses by Region of Birth and Cit-
izenship Status, U.S., 2000

Place of Birth

Citizen

Yes No Total

The Philippines 52,745 27,994 80,739
Caribbean and Latin America 30,707 16,214 46,921
Western Europe, Canada, Australia, and New Zealand 8,864 23,663 32,527
Asia 13,674 7,762 21,436
Sub-Saharan Africa 5,527 6,726 12,253
India 6,992 6,268 13,260
Eastern Europe, Russian Federation 5,649 4,949 10,598
Other 810 176 986
Total 124,968 93,752 218,720

Source: Author’s calculation from the U.S. Deptartment of Commerce, Bureau of the Census,
Census of Population and Housing (2000), Public Use Micro data, 1% sample.
nForeign-educated estimated by eliminating RNs born abroad who immigrated to the United
States before age 22.

RNs, registered nurses.

1306 HSR: Health Services Research 42:3, Part II ( June 2007)

as a proxy for the number of nurses who immigrate. The pass rate for first time
NCLEX-RN test takers is also included, as it suggests the relative difficulty of a
migration by country, and possibly is a reflection of the comparability of
education and/or English language comprehension of nurses from various
countries. The first time pass rate for U.S. nurses in 1995 was 87 percent. The
Philippines continues to contribute more nurses than any other source coun-
try, although almost half fail to pass the NCLEX-RN the first time. This rela-
tively low pass rate has been consistent for Philippine test takers and is not a
new phenomenon to be attributed to the rapid growth in nursing schools in the
Philippines. Three other countries contribute over 1,000 nurses a year: India,
South Korea, and Canada. The remaining source countries, even among the
top 10, contribute o300 a year. Nigeria is the only sub-Saharan African
country among the top 10 source countries for the United States.

Demographic Characteristics

The demographic and work characteristics of native- and foreign-educated
nurses are compared in Table 4. Average age is very similar for both groups,
close to 43 years of age in 2000, suggesting that immigration has had little
impact on lowering the average age of U.S. nurses. The majority of foreign-
educated nurses are married, as are native-born nurses. Foreign-educated
nurses are somewhat more likely to have a baccalaureate degree, or higher,
and to work full time than native-born nurses. Overall, foreign-educated

Table 3: Top 10 Source Countries for NCLEX-RN Exam Passers and
First-Time Pass Rates for 2005

Source Country Number Passing First-Time NCLEX Pass Rate %

The Philippines 6,852 55
India 1,927 72
South Korea 1,587 72
Canada 1,168 69
China 288 65
Nigeria 275 34
United Kingdom 226 61
Taiwan 222 53
Cuba 217 32
Russian Federation 144 44
Top 10 total/average 12,906 60
Total all countries/average 14,750 44

Source: Author’s analysis of National Council of State Boards of Nursing data.
RNs, registered nurses.

Global Nurse Sufficiency 1307

nurses have higher incomes than native-born nurses, but the difference seems
to be explained by the longer hours worked by foreign-educated nurses
(Lowell and Gerova 2004) and, possibly, by their concentration on the two
coasts, where nurses’ wages are higher.

Geographic Distribution

Figure 2 shows the uneven distribution of foreign-educated nurses by state.
California, New York, New Jersey, Florida, and Illinois have the highest density
of foreign-educated nurses, with foreign-educated nurses comprising as much as
29 percent of the nurse workforce in California and 24 percent in Florida. These
states all report acute shortages of nurses, particularly in hospitals. Both Cal-
ifornia and Florida have particularly low nurse to population ratios. California’s
shortage has been exacerbated by enactment of legislation that took effect from
January 1, 2004, mandating minimum licensed nurse staffing ratios in all hos-
pitals. Nurse workloads cannot exceed five patients per nurse on medical and
surgical units and two in ICUs (Coffman, Seago, and Spetz 2002).

Urban/Rural Location

Foreign-educated nurses are less likely to reside in rural areas than are native-
born nurses, as noted in Table 4, and they are much more likely to reside in
central city locations. Less than 2 percent of foreign-educated nurses reside
outside of metropolitan areas, compared with 18 percent of native-born nurs-
es. Thus, foreign-educated nurses are not likely to have had much of an impact
on rural–urban health services gaps or nurse shortages in rural areas. This is
noteworthy given the immigration provisions that favor visas for nurses to
work in rural nurse shortage areas. Foreign-educated nurses are twice as likely

Table 4: Characteristics of U.S. Registered Nurses by Nativity, 2000

Native Born Foreign Born

Age (average) in years 42.5 42.8
Female (%) 92.4 90.9
Married (%) 68.1 70.2
Hold BSN (%)n 43.6 48.9
Work full-time (%)n 58.0 72.5
Work outside metropolitan area (%) 17.8 1.5
Work in metropolitan area, not central (%) 60.3 52.7
Central city (%) 21.9 45.9

Source: Author’s calculation from the U.S. Department of Commerce, Bureau of the Census,
Census of Population and Housing (2000), Public Use Micro data, 1% sample.
nAuthor’s calculation from 2000 National Sample Survey of Registered Nurses.

1308 HSR: Health Services Research 42:3, Part II ( June 2007)

as native-born nurses to reside in central city locations where safety-net hos-
pitals and other services for the poor tend to be located.

Employment Setting

Foreign-educated nurses are more likely to work in hospitals and in intensive
care (not shown), settings in which the nurse shortage has been greatest (see
Table 5). Almost 72 percent of foreign-educated nurses work in hospitals,
compared with 59 percent of native-born nurses. An estimated 15,000 foreign-
educated RNs work in nursing homes and extended care. While foreign-
educated nurses are only slightly more likely to practice in nursing homes and
extended care than native-born RNs (9 versus 7 percent), the numbers are
important in a sector of health care that is experiencing 15 percent vacancy
rates for RNs (National Commission on Nursing Workforce for Long-Term
Care 2005). Foreign-educated nurses are distributed across clinical specialties,
much like native-born nurses, except for their higher presence in intensive
care. Foreign-educated nurses are no more likely to practice in psychiatry, for
example, than native-born nurses, and are less likely to be employed in pri-
mary care.

Source: Author’s calculation from the U.S. Department of Commerce, Bureau of the
Census, Census of Population and Housing, 2000, Public Use Micro data, 1% sample

5 – 8%

2– 3%

8 – 14%

3 – 5%

15 – 29%

0– 1%

Figure 2: Proportion of all Registered Nurses that Are Foreign-Born by State,
2000.

Global Nurse Sufficiency 1309

Workforce Diversity

Ninety percent of U.S. nurses are white, and blacks and Hispanics are under-
represented in relation to their proportion of the U.S. population. As noted in
Table 5, foreign-educated nurses are less likely to be white than native-born
nurses, but not much more likely to be black or Hispanic. The major difference
in ethnicity between native and foreign-educated nurses is that half of all for-
eign-educated nurses are Asian compared with 1 percent of native-born nurses.
Thus, while nurse immigration contributes somewhat to ethnic and cultural
diversity of the U.S. nurse workforce, it does not contribute substantially to
increasing the number of black and Hispanic nurses, the major underrepre-
sented minorities. This finding must be viewed in the context of thousands of
qualified American applicants to nursing school being turned away, including
black and Hispanic applicants. If achieving greater representation of blacks and
Hispanics in the nurse workforce is the objective, efforts to accommodate the
American applicant pool would appear be a worthy strategy, and one without
adverse consequences for lower-income countries.

Professional Values

U.S. nurses have expressed concerns about the employment of nurses edu-
cated abroad, particularly in relation to their competence to practice in a
highly technological environment and their ability to communicate. To some
extent, competence and communications issues have been addressed by
CGFNS via examinations in nursing knowledge and English proficiency (Xu,
Xu, and Zhang 1999). Nurses’ concerns have persisted about the extent to

Table 5: Characteristics of Registered Nurses by U.S. Education and Foreign
Education

United States (%) Foreign-Educated (%)

Employment setting
Hospital 58.7 71.6
Nursing home, extended care 6.9 9.2
Community health 13.1 8.4
Ambulatory care 9.8 5.2

Ethnic background
White (non-Hispanic) 90.1 32.4
Black 4.8 8.1
Hispanic 1.9 3.7
Asian/Pacific Island 1.4 54.5

Source: Author’s calculation from 2000 National Sample Survey of Registered Nurses.

1310 HSR: Health Services Research 42:3, Part II ( June 2007)

which differences in cultural values held by nurses from more collectivistic
cultures such as the Philippines, which comprise the majority of new nurse
immigrants to the United States, would undermine U.S. nurses’ quests for
increased autonomy, control, and professional status (Brush 1994, 1999).
However, Flynn and Aiken (2002) confirmed that U.S.- and foreign-educated
nurses share a set of core nursing values that embrace professional nursing
practice models, such as Magnet Hospitals (McClure and Hinshaw 2002), and
include professional values, such as clinical autonomy and collegiality with
physicians. Indeed, the Flynn and Aiken study found that the absence of a
positive professional nurse practice environment has the same negative out-
comes of job dissatisfaction, burnout, and turnover among foreign-educated
nurses as among native-born nurses. Health care organizations with poor work
environments are likely to have problems retaining foreign-educated nurses
once their contract obligations have been fulfilled, just as they experience high
turnover of native-born nurses.

NURSE IMMIGRATION REQUIREMENTS

The United States has stringent requirements for licensing nurses compared
with most other countries. A VisaScreent certificate must be received before
the U.S. Citizenship and Immigration Services will issue an occupational visa.
To obtain the certificate, the following are required: (1) a credentials review of
the applicant’s professional education and licensure to ensure comparability
with U.S. requirements, especially to make certain that nursing education was
at the post-secondary level; (2) successful completion of required English lan-
guage proficiency examinations; and (3) successful completion of either the
CGFNS qualifying examination or the NCLEX-RN examination. As a point
of comparison, the United Kingdom does not have a licensure examination,
and credential reviews are up to employers. The CGFNS has a comprehensive
program of services for foreign-educated nurses, designed to help ensure
safety in patient care, as well as to facilitate the application process for grad-
uates of foreign nursing schools who wish to immigrate to the United States
(Davis and Nichols 2002). CGFNS undertakes the required credentials review
and offers tests of nursing knowledge and English proficiency. CGFNS offers
its nursing knowledge examination in many locations throughout the world;
the exam provides nurses interested in migrating to the United States a good
indication of their likelihood of passing the required licensure exam (NCLEX-
RN), which until 2005 was offered only in the United States. For the first time,

Global Nurse Sufficiency 1311

in 2005 the NCLEX-RN was offered in three sites outside the United States:
London, Hong Kong, and Seoul. In 2006, NCSBN planned to expand test sites
to Australia, India, Japan, Mexico, Canada, Germany, and Taiwan, in order to
lessen the financial burdens on qualified candidates who intend to apply for
licensure in the United States.

The North American Free Trade Agreement (NAFTA) facilitated the
migration of Canadian nurses to the United States. Contrary to expectations,
however, NAFTA has not substantially influenced the migration of nurses from
Mexico. Most nursing education in Mexico takes place at the secondary-school
level, which does not meet U.S. requirements for licensure. English language
proficiency is another barrier. A total of 77 Mexican nurses took the NCLEX-
RN exam for the first time in 2003 and the pass rate was 17 percent compared
with 2,126 Canadian nurses who had a pass rate of 75 percent (NCSBN 2006).

POLICY AND PRACTICE IMPLICATIONS

The United States lacks a national capacity to monitor nurse labor market
dynamics and has no national nurse workforce policy, despite dire predictions
about impending shortages. Indeed, health workforce policy was not ranked
among the top 10 policy priorities in a recent survey of experts by the Com-
monwealth Fund (2004). The implications of health care cost containment
policies for nursing supply and demand are rarely, if ever, considered pro-
spectively. Immigration policies are not part of a broader strategy to ensure
sufficient availability of nurses to meet national needs. There is little coherence
between international development and immigration policies.

Unlike many other countries where the government fully funds nursing
students to become qualified nurses, U.S. nurses pay for their own education,
helped by tax subsidies to public educational institutions and limited schol-
arship and student loan programs. In recent years, out-of-pocket costs of
higher education have increased significantly. Enrollments in nursing schools
are thus sensitive to nurse labor market dynamics, as exemplified by the
reduction in graduations between 1995 and 2001 of up to 25,000 nurses a year.
Public policy, at a minimum, should establish the capacity to monitor changes
in nurse labor market dynamics, consider how changes might impact on long-
term availability of nursing services, and offer suggestions when indicated for
public and/or private sector responses.

In view of future projected large shortages of nurses, public policy in-
terventions and private sector actions warrant consideration. They fall into

1312 HSR: Health Services Research 42:3, Part II ( June 2007)

four interrelated categories: (1) transition to greater self-sufficiency in domestic
nurse labor; (2) diminishing the rate of growth in demand for nurses by im-
proving retention and achieving greater productivity; (3) managed nurse mi-
gration; and (4) achieving more coherence between international
development and immigration policies.

Self-Sufficiency

The United States has the capacity, in terms of human and economic resour-
ces, to become largely self-sufficient in its nurse workforce. There are large
numbers of Americans who want to become nurses, thousands more than can
be accommodated by nursing schools because of faculty shortages and other
capacity limitations. The United States has a large enough labor pool and
enough resources to expand higher education to increase nurse supply.
Moreover, greater representation in nursing by blacks, Hispanics, and men
could be achieved by expanding nursing school capacity at a time when the
applicant pool is strong.

Expanding nurse supply is in the interest of nurse employers. Indeed,
hospitals, the major employers of nurses, have been making significant in-
vestments in nurse education through tuition support in exchange for work
commitments for new graduates, facilitating access to education for their nurse
employees, providing space in their facilities for nursing education, and en-
abling their employed nurses with graduate education to serve as clinical
faculty for nursing schools (Cheung and Aiken 2006). However, hospitals are
unlikely to substantially increase their support of nursing education beyond
current levels without provisions to include these costs in reimbursement
rates. The State of Maryland is currently implementing such an initiative
where the State Health Services Cost Review Commission increased hospital
rates by a tenth of 1 percent, raising about $100 million over 10 years to be
used in solving the hospital nursing shortage by investing in the expansion of
nursing faculty and nursing school enrollments.

Investments by hospitals in nursing education have helped nursing
schools expand enrollments substantially over the past 5 years resulting in the
highest number of new nurse graduates ever, at close to 100,000 a year.
However, most estimates suggest that increases in enrollments will have to be
considerably higher to avert the nurse shortage projected to occur in a decade
when increasing demand collides with increasing retirements of an aging
nurse workforce (AACN 2005). Title VIII funding under the Public Health
Service Act provides an annual federal appropriation ($149.68 million in FY

Global Nurse Sufficiency 1313

2006) for nurse workforce development programs, including advanced prac-
tice in nurse training (i.e., nurse practitioners, nurse anesthetists, nurse ad-
ministrators), grant support to increase nursing workforce diversity and
improve retention, and loan repayment and scholarship funds. These funds
were only modestly increased by the Nurse Reinvestment Act, passed in 2002
as a response to the nursing shortage. In 2004, 82 percent of applicants for the
loan repayment program and 98 percent of the applicants for scholarships
were turned away due to insufficient funding (AACN 2005). The federal Nurse
Training Act in 1974 is credited with increasing the nation’s nurse to popu-
lation ratio by 100 percent in the period 1974–1983 (Eastaugh 1985). In
today’s dollars, a comparable investment would require over $400 million a
year in addition to the existing funding levels for Title VIII, an amount that
would be insignificant as a percent of federal health expenditures.

Times of nurse shortage tend to lead to calls for shortened education for
nurses. There is ample evidence, however, that a more educated nurse work-
force is associated with better patient outcomes and higher nurse productivity
(Aiken et al. 2003; Estabrooks et al. 2005). Indeed, the recent position state-
ment of the American Organization of Nurse Executives (AONE), represent-
ing the employers of nurses, which favors baccalaureate education (Bowcutt
2005), can be expected to aid in the transition to a more highly educated nurse
workforce. Innovations in nursing education have created options to produce
nurses with a baccalaureate degree in about 1 year for students who hold a
baccalaureate or higher degree in another field. These accelerated programs
are among the most popular in nursing schools today and speak to the high
level of interest of Americans in becoming nurses. In many states, the length of
associate degree programs has increased to 3 years or more to meet the re-
quirements for practice, thus, reducing the difference in the time it takes to
produce nurses with associate versus baccalaureate degrees. Nursing schools
need incentives and/or mandates to create more efficient educational path-
ways that will permit the extensive network of community colleges to par-
ticipate in baccalaureate nursing education, thus producing a larger cadre of
nurses with baccalaureate degrees without extending substantially the time it
takes students to complete their education.

Improving Retention and Increasing Productivity

Nurse turnover rates, particularly in hospitals and nursing homes, are high.
Additionally, more nurses are opting to work in jobs outside of hospitals and
nursing homes and in nonclinical roles. A substantial body of research links poor

1314 HSR: Health Services Research 42:3, Part II ( June 2007)

work environments with nurse job dissatisfaction, burnout, turnover, and in-
creased costs (Aiken et al. 2002; Vahey et al. 2004; Waldman et al. 2004). Every
blue-ribbon expert committee convened over the past 25 years to make rec-
ommendations on how to solve the U.S. nursing shortage recommended mod-
ifications in nurses’ practice environments to retain nurses in clinical roles and
facilitate their productivity (AHA 2002; The Joint Commission 2002; Kimball
and O’Neil 2002; Steinbrook 2002). Progress has been slow but growth of the
Magnet Recognition Program suggests that positive change is occurring (Aiken
2002). Ultimately the long-term solution to the projected future shortage of
nurses is to redesign work, particularly in hospitals, to enable nurses to be more
productive in their care of patients (The Joint Commission 2005), potentially
resulting in the need for fewer nurses than would be required in the absence of
changes in work design and better organizational support of nursing care. Im-
proving work environments is largely an agenda that must be taken on within
health care organizations by management and clinicians, although there is the
possibility that new payment incentives, such as Pay for Performance, might
provide an impetus for change.

Managed Nurse Migration

The projected size of the long-term nurse shortage in the United States appears
too large to be resolved primarily through recruitment of nurses from other
countries. To place the projected shortage of 800,000 nurses in perspective,
Canada currently has a nurse workforce of fewer than 250,000 nurses and a
projected shortage of 100,000 (see Little 2007). India, a country with a very
large overall workforce and therefore the potential to produce large number of
nurses for export, currently has a low density of nurses relative to its popu-
lation, and the majority of its current 600,000 nurses (Chen and Evans 2004)
would not meet U.S. educational standards. Nevertheless, Indian
nurses, as well as nurses from other low-income countries, are interested in
working in the United States. Entrepreneurial activities to produce more
nurses abroad for export to the United States can be expected.

At present, there is little ‘‘management’’ of international nurse recruitment
that would ideally include provisions to balance the rights of individual nurse
migrants and their families, the interests of their countries of origin, patient
concerns about quality and communication, and employers’ needs. The services
of CGFNS are designed to contribute to achieving such a balance but many
important aspects of nurse immigration remain largely unaddressed. Given the
decentralized and largely private nature of health care in the United States,

Global Nurse Sufficiency 1315

private sector entities should develop and enforce ethical and quality standards
for nurse recruitment and involvement of U.S. educational institutions in training
nurses for export to the United States. The Joint Commission sets and enforces
quality standards in settings that employ international nurses and has a program
of accreditation for supplemental staffing firms. The Joint Commission is an
example of a type of private sector organization that could play a more active
role in protecting the public interest in international nurse recruitment. Nursing
education accreditation bodies could play more central roles in the oversight of
educational programs preparing nurses for export to the United States, especially
those involving American nursing schools. Another potential strategy to manage
the ethical and quality dimensions of nurse recruitment would be through pro-
visions to the Medicare requirements for provider conditions of participation.
There could be other strategies, as well, but the point is that insufficient attention
has been given to managing the ethical and quality concerns pertaining to the use
of international workers in health care, which is bound to increase in the future.

International Development and Immigration Policies

Employers, professional associations, and other stakeholders with interests in
having an adequate supply of nurses in the future should work together with
the federal government to promote more coherence between international
development and foreign aid and immigration policies. With modest, targeted
investments, the U.S. foreign aid funds could have a substantial impact on
expanding the capacity of low-income countries with high illness burden to
increase their production of nurses and improve nurse retention. The focus
should be on developing a self-sustaining nursing educational infrastructure
with capacity to produce nurse leaders and faculty, as well as clinical nurses.

Research suggests that the differentials in wages between low-income
source and high-income destination countries are so large that small increases
in source country wages do not affect migration. Thus, nonwage interventions
in source countries are likely to be more successful in retaining nurses (Vujicic
et al. 2004). Creating safer and more rewarding professional nurse roles and
settings in source countries could be very important in stemming the flight of
nurses. Initiatives are needed to find ways to make professional nursing more
attractive in source countries. A recently completed demonstration supported
by the United States Agency for International Development involving ‘‘twin-
ning’’ is an exemplar worthy of replication (Aiken 2005). The Nursing Quality
Improvement Program paired U.S. hospitals that had been accredited for nurs-
ing excellence in the Magnet Recognition Program with hospitals in Russia and

1316 HSR: Health Services Research 42:3, Part II ( June 2007)

Armenia, countries that have historically underinvested in professional nursing.
Over a 3-year period involving the exchange of nurses and hospital managers
between the countries, the professional roles of nurses in the participating Rus-
sian and Armenian hospitals expanded, nurses were more satisfied with their
jobs, patients were more satisfied with their care, and adverse patient outcomes
were reduced. This model needs to be replicated in countries experiencing
more nurse emigration to know whether increased job satisfaction would trans-
late into greater retention within source countries. This model and others like it
are worthy of investment by the United States and other developed countries as
a strategy to reduce the ‘‘push’’ factors associated with the lack of professional
roles and opportunities for nurses in low-income countries.

The United States clearly plays an important role in global nurse mi-
gration because of the size of its nurse workforce and its ever-growing demand
for more nurses. The size of its projected future shortage of nurses, if not
contained by increases in domestic production of nurses and policies to
dampen growing demand, threatens to undermine health care delivery in the
United States as well as in low-income countries whose nurses would migrate
to the United States in significant numbers. By developing and implementing
an action plan to ensure the availability of enough nurses to meet future needs
in the United States, we will ensure access and quality of care for our own
citizens in addition to making a very important contribution to global health.

ACKNOWLEDGMENTS

This research was supported by the Robert Wood Johnson Foundation and
the Agency for Healthcare Research and Quality. The author gratefully ac-
knowledges the analytic assistance of Tim Cheney, the provision of data by the
NCSBN, and the comments of an anonymous reviewer.

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Journal of International Money and Finance 48 (2014) 271e290

Contents lists available at ScienceDirect

Journal of International Money
and Finance

journal homepage: www

.

elsevier.com/locate/jimf

External balances, trade flows and financial
conditions

Martin D.D. Evans
Department of Economics, Georgetown University, Washington, DC 20057, USA

a r t i c l e i n f o

Article history

:

Available online 12 June 2014

Keywords:
Global imbalances
Foreign asset positions
Current accounts
Trade flows
International asset pricing

JEL codes:
F31
F3

2

F34

E-mail address: evansmdd@gmail.com.

http://dx.doi.org/10.1016/j.jimonfin.2014.05.018
0261-5606/© 2014 Elsevier Ltd. All rights reserved

a b s t r a c t

This paper studies how changing expectations concerning future
trade and financial conditions are reflected in international
external positions. In the absence of Ponzi schemes and arbitrage
opportunities, the net foreign asset position of any country must,
as a matter of theory, equal the expected present discounted value
of future trade deficits, discounted at the cumulated world sto-
chastic discount factor (SDF) that prices all freely traded financial
assets. I study the forecasting implications of this theoretical link
in 12 countries (Australia, Canada, China, France, Germany, India,
Italy, Japan, South Korea, Thailand, The United States and The
United Kingdom) between 1970 and 2011. I find that variations in
the external positions of most countries reflect changing expec-
tations about trade conditions far into the future. I also find the
changing forecasts for the future path of the world SDF are re-
flected in the dynamics of the U.S. external position.

© 2014 Elsevier Ltd. All rights reserved.

1. Introduction

This paper studies how changing expectations concerning future trade and financial conditions are
reflected in international external positions. Economic theory links a country’s net foreign asset (NFA)
position to agents’ expectations in a precise manner. In the absence of Ponzi schemes and arbitrage
opportunities, the NFA position of any country must equal the expected present discounted value of
future trade deficits, discounted at the cumulated world stochastic discount factor (SDF) that prices all
freely traded financial assets. In practice this means that changes in observed external positions of
countries across the world should reflect changing expectations about future trade flows and future
financial conditions represented by the world SDF, or some combination of the two. The aim of this

.

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M.D.D. Evans / Journal of International Money and Finance 48 (2014) 271e290272

paper is to assess whether this is in fact the case. More specifically, the paper examines the extent to
which changing expectations about future trade and financial conditions are reflected in the evolving
external positions of 12 countries between 1970 and 2011.

To undertake this analysis, I present a new analytic framework that links each country’s current NFA
position to its current trade flows, expectations of future trade flows, and expectations concerning future
returns on foreign assets and liabilities in an environment without arbitrage opportunities or Ponzi
schemes. This framework incorporates several key features. First it accommodates the secular increase in
international trade flows and national gross asset/liability positions that has taken place over the past 40
years. The secular growth in both trade flows and positions greatly exceeds the growth in GDP on a global
and country-by-country basis. Between 1970 and 2011, the annual growth in trade and positions exceeds
the growth in GDP by an average of 2.6 and 4.8 percent, respectively, across the countries studied.1

The second key feature concerns the identification of expected future returns. As a matter of logic,
expected future returns on a country’s asset and liability portfolios must affect the value of its current
NFA position, so pinning down these expectations is unavoidable when studying the drivers of external
positions. This is easily done in textbook models where the only internationally traded asset is a risk
free bond, but in the real world countries’ asset and liability portfolios comprise equity, FDI, bonds and
other securities, with risky and volatile returns. Pinning down the expected future returns on these
portfolios requires forecasts for the future returns on different securities and the composition of the
portfolios. To avoid these complications, I use no-arbitrage conditions to identify the impact of ex-
pected future returns on NFA positions via forecasts of a single variable, the world SDF. SDFs play a
central role in modern finance theory (linking security prices and cash flows) and appear in theoretical
examinations of the determinants of NFA positions (see, e.g., Obstfeld, 2012). A key step in my analysis
is to show how the world SDF can be constructed from data on returns and then used to pin down how
expectations concerning future financial conditions are reflected in external positions.

In the empirical analysis I study the external positions of 12 countries (Australia, Canada, China,
France, Germany, India, Italy, Japan, South Korea, Thailand, The United States and The United Kingdom).
I first show how the world SDF can be estimated from data on returns and discuss how the estimates
can be tested for specification errors. Next I turn to the identification of expectations. In theory,
external positions reflect expectations concerning the entire future paths of trade flows and the world
SDF, so we need to forecast over a wide range of horizons. For this purpose I use VARs e a common
approach in the literature following Campbell and Shiller (1987). I then compare the present values of
future trade flows and the world SDF based on the VAR forecasts with external positions. If the actual
expectations embedded in the external positions are well represented by the VAR forecasts, the present
values computed from those forecasts should be strongly correlated with the external positions. This
implication is borne out by my empirical findings using the VAR forecasts for trade flows. Forecasts of
trade flows far into the future are strongly correlated with the external positions of 10 countries I study.
Evidence on the role of expected future financial conditions is less clear cut. While VAR forecasts for the
world SDF suggest that there have been persistent and sizable variations in the prospective future
financial conditions that are relevant for the determination of external positions, the forecasts are only
weakly correlated with the positions of many countries. One notable exception to this pattern is the
U.S., whose external position is strongly correlated with the forecasts.

These findings add to a growing empirical and theoretical literature on international external
adjustment. The analytic framework I present is most closely related to the work of Gourinchas and Rey
(2007a). They derive an expression for a country’s NFA position from a “de-trended” version of the
consolidated budget constraint (that governs the evolution of a country’s NFA position from trade flows
and returns), that filters out the secular growth in trade flows and positions mentioned above. Thus
their analysis focuses on the “cyclical” variations in NFA positions, rather than the “total” variations.
Similarly, Corsetti and Konstantinou (2012) use the consolidated budget constraint to derive an
approximation to the current account that includes deterministic trends in the log ratios of con-
sumption, gross assets and gross liabilities to output to accommodate the long-term growth in trade

1 This feature of the data has proved to be a challenge for researchers studying the determinants of external positions, see e.g.,
Gourinchas and Rey (2007a) and Corsetti and Konstantinou (2012) discussed below.

M.D.D. Evans / Journal of International Money and Finance 48 (2014) 271e290 273

flows and positions (relative to GDP).2 On the theoretical side, Pavlova and Rigobon (2008), Tille and
van Wincoop (2010) and Devereux and Sutherland (2011) all study external adjustment in open
economy models with incomplete markets. In these models changing NFA positions primarily reflect
revisions in expected future trade flows and the world risk-free rate because the equilibrium risk
premia on foreign assets and liabilities are (approximately) constant. In contrast, the framework I use
allows for variations in the risk premia on assets and liabilities to also affect NFA positions.

My analysis also extends a related literature on international returns. Early papers in this literature
(Obstfeld and Rogoff, 2005; Lane and Milesi-Ferretti, 2005; Meissner and Taylor, 2006; Gourinchas and
Rey, 2007b) estimated that the return on U.S. foreign assets was on average approximately three
percent per year higher than the return on foreign liabilities. Subsequent papers by Curcuru, Dvorak,
and Warnock (2008) and Lane and Milesi-Ferretti (2009) argued that these estimates were biased
upward because of inaccuracies in data. In their recent survey, Gourinchas and Rey (2013) show that
alternative treatments of the data can produce average return differentials between U.S. foreign assets
and liabilities that differ by as much as 1.1 and 1.8 percent, depending upon the sample period. My
analysis shifts the focus away from average U.S. returns in two respects. First, I use the returns on the
assets and liabilities of major economies to estimate the world SDF. Second I model how conditional
expectations concerning the world SDF are related to external positions. Gourinchas and Rey (2007a)
also consider the short-horizon (one quarter) forecasting power of the (cyclical) U.S. external position
for returns on its NFA portfolio, and the return differential between equity assets and liabilities. Here I
study forecasting power of external positions over longer horizons.

The remainder of the paper is structured as follows: Section 2 describes the data. I present the
analytic framework in Section 3. Section 4 describes how I estimate the world SDF and compute long-
horizon forecasts. I present the empirical results in Section 5. Section 6 concludes.

2. Data

I examine the external positions of 12 countries: the G7 (Canada, France, Germany, Italy, Japan, the
U.S. and the U.K.) together with Australia, China, India, South Korea and Thailand. Data on each
country’s foreign asset and liability portfolios and the returns on the portfolios come from the data-
based constructed by Lane and Milesi-Ferretti (2001), updated in Lane and Milesi-Ferretti (2009),
available via the IMF’s International Financial Statistics database. These data provide information on
the market value of the foreign asset and liability portfolios at the end of each year together with the
returns on the portfolios from the end of one year to the next. A detailed discussion of how these data
series are constructed can be found in Lane and Milesi-Ferretti (2009). I also use data on exports,
imports and GDP for each country and data on the one year U.S. T-bill rate, 10 year U.S. T-bond rate and
U.S. inflation. All asset and liability positions, trade flows and GDP levels are transformed into constant
2005 U.S. dollars using the prevailing exchange rates and U.S. price deflator. All portfolio returns are
similarly transformed into real U.S. returns. The Lane and Milesi-Ferretti position data is constructed on
an annual basis, so my analysis below is conducted at an annual frequency.3 Although the span of
individual data series differs from country to country, most of my analysis uses data spanning
1970e2011.

2 A related literature on external adjustment focuses attention on current account balances. For example, Lane and Milesi-
Ferretti (2012) examine how changes in current account balances between 2008 and 2010 relate to pre-crisis current ac-
count gaps estimated from a panel regression model. Similar empirical models of current account determination can be found
in Chinn and Prasad (2003), Gruber and Kamin (2007), Lee et al. (2008), Gagnon (2011) and others. Current accounts also
remain a focus in current multilateral surveillance frameworks used by the International Monetary Fund and the European
Commission (see, e.g., IMF, 2012; EU, 2010).

3 Ideally, we would like to track international positions and returns at a higher (e.g. quarterly) frequency, but constructing the
market value of foreign assets and liabilities for a large set of countries is a herculean task. For the U.S., Gourinchas and Rey
(2005) compute quarterly market values for four categories of foreign asset and liabilities: equity, foreign direct investment,
debt and other, by combining data on international positions with information on the capital gains and losses. In Evans (2012) I
revise and update their data to 2012:IV. Corsetti and Konstantinou (2012) also work with quarterly U.S. position data which
they impute from the annual Milesi-Ferretti data using quarterly capital flows. For a discussion of the different methods used to
construct return data, see Gourinchas and Rey (2013).

M.D.D. Evans / Journal of International Money and Finance 48 (2014) 271e290274

The Web Appendix describes the characteristics of the data in detail. Here I simply note several
prominent features. First, for many countries, variations in the ratios of net exports and NFA to GDP are
highly persistent. Second, the cross-country dispersion in the ratios has widened in the last decade.
Third, gross financial positions (i.e., the sum of foreign assets and liabilities) and trade (i.e., the sum of
export and imports) have grown much faster than GDP. Averaging across all the countries, trade grew
approximately 2.6 percent faster than GDP, while foreign asset and liability positions grew 4.8 percent
faster. There have also been swings in global trade growth and position growth that are much larger
than global business cycles. In light of these facts, the next section presents an analytic framework that
links a country’s current external position to prospective future trade and financial conditions while
accommodating the growth in trade and positions.

3. Analytic framework

3.1. NFA positions

The framework I develop contains three elements: (i) the consolidated budget constraint that links a
country’s foreign asset and liability positions to exports, imports and returns; (ii) a no-arbitrage condition
that restricts the behavior of returns; and (iii) a condition that rules out international Ponzi schemes.

I begin with country’s n’s consolidated budget constraint:

FAn;t � FLn;t ¼ Xn;t � Mn;t þ RFAn;tFAn;t�1 � RFLn;tFLn;t�1: (1)

Here FAn,t and FLn,t denote the value of foreign assets and liabilities of country n at the end of year t,
while Xn,t and Mn,t represent the flow of exports and imports during year t, all measured in real terms
(constant U.S. dollars). The gross real return on the foreign asset and liability portfolios of country n
between the end of years t�1 and t are denoted by RFAn;t and RFLn;t, respectively. Equation (1) is no more
than an accounting identity. It should hold true for any country provided the underlying data on po-
sitions, trade flows and returns are accurate. Notice, also, that FAn,t and FLn,t represent the values of
portfolios of assets and liabilities comprising equity, bond and FDI holdings, and that RFAn;t and R

FL
n;t, are

the corresponding portfolio returns. These returns will generally differ across countries in the same
year because of cross-country differences in the composition of asset and liability portfolios.

Next, I introduce the no-arbitrage condition. In a world where financial assets with the same payoffs
have the same prices and there are no restrictions on the construction of portfolios (such as short sales
constraints), there exists a positive random, Ktþ1; such that

1 ¼ Et
h
Ktþ1Ritþ1

i
; (2)

where Ritþ1 is the (gross real) return on any freely traded asset i. Here Et½:� denotes expectations
conditioned on common period-t information. The variable Ktþ1 is known as the stochastic discount
factor (SDF). This condition is very general. It does not rely on the preferences of investors, the ra-
tionality of their expectations, or the completeness of financial markets.4 I assume that it applies to the
returns on every security in a country’s asset and liability portfolios, and so it also applies to the returns
on the portfolios themselves; i.e.

1 ¼ Et
h
Ktþ1RFAn;tþ1

i
and 1 ¼ Et

h
Ktþ1RFLn;tþ1

i
: (3)

Equations (1) and (3) enable me to derive a simple expression for a country’s NFA position. First I
multiply both sides of the budget constraint in (1) by the SDF and then take conditional expectations.
Applying the restrictions in (3) to the resulting expression and simplifying gives

4 For a textbook discussion of SDFs, see Cochrane (2001); or in an international setting, Evans (2011).

M.D.D. Evans / Journal of International Money and Finance 48 (2014) 271e290 275

Et Ktþ1NFAn;tþ1 ¼ Et Ktþ1 Xn;tþ1 � Mn;tþ1 þ NFAn;t: (4)

� � � � ��

Rearranging this expression and solving forward using the Law of Iterated Expectations we obtain

NFAn;t ¼ Et

X∞
i¼1

Dtþi
�
Mn;tþi � Xn;tþi

�
þ Et lim

i/∞
DtþiNFAn;tþi; (5)

where Dtþi ¼
Qi

j¼1Ktþj.
The last term on the right-hand-side on (5) identifies the expected present value of the country’s

NFA position as the horizon rises without limit using a discount factor determined by the world’s SDF.
To rule out Ponzi-schemes, I assume that

Et lim
i/∞

DtþiNFAn;tþi ¼ 0; (6)

for all countries n. For intuition, suppose a debtor country (i.e. a country with NFAn,t < 0) decides to simply roll over existing asset and liability positions while running zero future trade balances. Under these circumstances, the country's asset and liability portfolios evolve as FAn;tþi ¼ RFAn;tþiFAn;tþi�1 and FLn;tþi ¼ RFLn;tþiFLn;tþi�1 for all i>0. Since Et½Ktþ1Xtþ1� identifies the period �t value of any period tþ1
payoff Xtþ1, (4) implies that the value of claim to the country’s net assets next period is just
Et½Ktþ1NFAn;tþ1� ¼ Et½Ktþ1ðXn;tþ1 � Mn;tþ1Þ� þ NFAn;t ¼ NFAn;t. This same reasoning applies in all future
periods, i.e., Etþi½Ktþiþ1NFAn;tþiþ1� ¼ NFAn;tþi for all i > 0, so the value of a claim to the foreign asset
position t periods ahead is Et½DtþtNFAn;tþt� ¼ Et½Dtþt�1Et�1½KtþtNFAn;tþt�� ¼ :: ¼ NFAn;t: Taking the
limit as t/∞ gives NFAn;t ¼ Etlimi/∞½DtþiNFAn;tþi� < 0: Thus, the country's current NFA position must be equal to the value of a claim on rolling the asset and liability positions forward indefinitely into the future. Clearly then, no country n can initiate a Ponzi scheme in period t when Etlimi/∞DtþiNFAn;tþi � 0. Moreover, since

P
nNFAn;t ¼ 0 by market clearing, if

Etlimi/∞DtþiNFA~n;tþi > 0 for any one country, ~n, then at least one other must be involved in a Ponzi
scheme. Thus, the restriction in (6) prevents any country from adopting a Ponzi scheme in period t.

We can now identify the determinants of a country’s NFA position by combining (5) and the no-
Ponzi restriction (6):

NFAn;t ¼

Et
X∞
i¼1

Dtþi
�
Mn;tþi � Xn;tþi

�
: (7)

This equation states that in the absence of Ponzi schemes and arbitrage opportunities, the NFA position
of any country n must equal the expected present discounted value of future trade deficits, discounted
at the cumulated world SDF. As such, it describes the link between a country’s current external position
and the prospects for future trade flows (i.e. exports and imports) and future financial conditions,
represented by the future SDF’s in Dtþi.

Several aspects of equation (7) deserve note. First, the equation is exact; i.e., it contains no approxi-
mations.ItmustholdunderthestatedconditionsforaccurateNFAandtradedatagivenmarketexpectations
and the world SDF. Second, (7) holds whatever the composition of the country’s asset and liability portfolios
(i.e. whatever the fractions held in equity, bonds, etc.), and however those fractions are determined (by
optimal portfolio choice or some other method). Third, the equation applies simultaneously across all
countries. If news about prospective future financial conditions anywhere change expectations concerning
futureworldSDFs,itaffectstheNFApositionofallcountriesthatanticipaterunningfuturetradesurplusesor
deficits. Equation (7) also takes explicit account of risk. It states that a country’s NFA position is equal to
the value of a claim to the future stream of trade deficits in a world where those deficits are uncertain.

Finally, it is worth emphasizing that the expected future trade flows and SDF on the right-hand-side
of (7) represent the proximate determinants of the country’s NFA position. More fundamental factors,
such as demographic trends, fiscal policy or productivity growth, can only affect the NFA position
insofar as they impact on these expectations. Moreover, since the same SDF applies to all countries,
such fundamental factors can only account for cross-country differences in NFA positions insofar as
they impact prospective future trade flows.

M.D.D. Evans / Journal of International Money and Finance 48 (2014) 271e290276

3.2. Forecasting implications

Equation (7) implies that all variations in a country’s NFA position reflect revisions in expectations
concerning future trade deficits and the world SDF. Consequently, NFA positions should have fore-
casting power for future trade flows and/or SDFs. To investigate this empirical implication, we must
overcome two challenges: The first concerns the identification of the world SDF, Kt. Section 4 describes
how I estimate Kt from data on returns. The second arises from fact that the present value expression in
(7) includes forecasts for DtþiMn;tþi and DtþiXn;tþi with Dtþi ¼

Qi
j¼1Ktþj for all i>0 rather than forecasts

for Mn,tþi, Xn,tþi and Ktþi separately. To meet this challenge, I use a standard approximation.
To approximate the present value expression for each country’s NFA position, I first rewrite (7) as

NFAn;t ¼ Mn;tEt
X∞
i¼1

exp
�Xi

j¼1Dmn;tþj þ ktþj
�
� Xn;tEt

X∞
i¼1
exp
�Xi

j¼1Dxn;tþj þ ktþj
�
; (8)

where kt ¼ ln Kt is the log SDF, and D is the first-difference operator. (Throughout I use lowercase
letters to denote the natural log of a variable.) This transformation simply relates the NFA position to
the current levels of imports and exports and their future growth rates, Dmn;tþi and Dxn;tþi, rather than
the future levels of exports and imports shown in (7).

Next, I approximate the two terms involving expectations. If dt is a random variable with mean
E½dt� ¼ d < 0; then a first-order approximation to dtþj around d produces

Et
X∞
i¼1
exp
�Xi

j¼1dtþj
�
¼ Etexpðdtþ1Þ þ Etexpðdtþ1 þ dtþ2Þ þ …

x
r

1 � r þ rEtðdtþ1 � dÞ þ r
2
Etðdtþ1 � dÞ þ r3Etðdtþ2 � dÞ þ …:

¼ r
1 � r þ

1
1 � r Et

X∞
i¼1

r
iðdtþi � dÞ;

(9)

where r ¼ exp(d) < 1. To apply this approximation, I make two assumptions:

E
�
Dmn;t

�
¼ E

�
Dxn;t

�
¼ g; and (A1)

g þ k ¼ d < 0; with E½kt� ¼ k; (A2) where E½:� denotes unconditional expectations. Under assumption A1 the mean growth rate for imports and exports are equal. This will be true of any economy on a balanced growth path and appears consistent with the empirical evidence for the G7 countries. To interpret assumption A2, note that in the steady state the log risk free rate r satisfies 1 ¼ E½expðktÞ�expðrÞ. Thus d ¼ g þ kx g � r � 12 V½kt�, where V½:� denotes the variance, so A2 will hold provided V½kt� > 2ðg � rÞ: The mean growth rate for
trade across the countries in the dataset is approximately 6.5 percent, which is well above any
reasonable estimate of the mean risk free rate of close to 1 percent. Clearly then, A2 will only hold if the
variance of the log SDF exceeds roughly 0.11 ¼ 2(0.065�0.01). This volatility bound is easily exceeded
by estimates of the log SDF derived below.

Applying the approximation in (9) to the expectations terms in (8) and simplifying the result gives

NFAn;t ¼
r

1 � r

�
Mn;t � Xn;t

�
þ 1
2ð1 � rÞ

�
Mn;t þ Xn;t

�
Et
X
i¼1

∞
r
i�Dmn;tþi � Dxn;tþi�

þ 1
1 � r

�
Mn;t � Xn;t
�
Et
X
i¼1

∞
r
i�Dtn;tþi � g� þ 11 � r

�
Mn;t � Xn;t
�
Et
X
i¼1

∞
r
iðktþi � kÞ;

(10)

where Dtn;t ¼ 12 ðDmn;t þ Dxn;tÞ. This expression identifies the three sets of factors determining a
country’s NFA position in a clear fashion. The first term on the right-hand-side identifies the influence

M.D.D. Evans / Journal of International Money and Finance 48 (2014) 271e290 277

of the current trade balance. This would be the only factor determining the NFA position in the sto-
chastic steady state where import growth, export growth and the log SDF followed i.i.d. processes
because the terms involving expectations would equal zero. As such, this first term identifies the
atemporal influence of trade flows on the NFA position. The remaining terms on the right-hand-side
identify the intertemporal factors that were present in (7). In particular they make clear how expec-
tations concerning future trade flows and financial conditions, represented by the world SDF, are
(approximately) linked to a country’s current NFA position.

The influence of future trade and financial conditions on external positions can be further clarified
with a simply transformation of (10). For this purpose, I define country n’s external position by

NXAn;t ¼
NFAn;t

Mn;t þ Xn;t
� r
1 � r TDn;t where TDn;t ¼

Mn;t � Xn;t
Mn;t þ Xn;t

:

In words, the country’s NXA position is defined as the gap between its current NFA position and the
steady state present value of the future trade deficits, all normalized by the current volume of inter-
national trade. Combining this definition with (10) gives

NXAn;t ¼
1

2ð1 � rÞ Et
X∞
i¼1

r
i�Dmn;tþi � Dxn;tþi� þ 11 � r TDn;tEt

X∞
i¼1

r
i�Dtn;tþi � g�

þ 1
1 � r TDn;tEt

X∞
i¼1

r
iðktþi � kÞ: (11)

Equation (11) provides us with the (approximate) link between a country’s current external position
and expectations concerning future trade flows and the SDF that forms the basis for the empirical
analysis below. For intuition, consider the effects of news that leads agents to revise their forecasts for
future trade deficits upwards. If there is no change in the expected future path of the SDF, according to
(7) there must be a rise in assets prices and/or a fall in liability prices that produces a rise in NFA if
investors are to avoid participation in a Ponzi scheme. This link is represented by the first two terms on
the right-hand-side of (11).

The third term on the right-hand-side of (11) identifies how news concerning the future financial
conditions, as reflected by the SDF, affects a country’s external position. To illustrate the economic
intuition behind this term, consider the effect of news that lowers agents’ forecasts of the future SDF
but leaves their forecasts for future trade flows unchanged. Under these circumstances, (7) shows that
future trade deficits are discounted more heavily so the country’s current NFA position is more closely
tied to the value of a claim on its near-term deficits. Thus the NFA positions of countries
currently running trade deficits deteriorate while the NFA positions of those running current trade
surpluses improve. These variations in NFA are reflected one-to-one in NXA.

Equation (11) contains expectations conditioned on the common information set of agents in period
t, much of which is unavailable to researchers. To take this into account, let Ft denote a subset of agents’
information at t that includes NXAn,t and TDn,t. Taking expectations conditioned on Ft on both sides of
(11) and applying the Law of Iterated Expectations, we find that

NXAn;t ¼
1
2
PV

�
Dmn;t � Dxn;t

�
þ TDn;tPV

�
Dtn;t � g

�
þ TDn;tPVðkt � kÞ; (12)

where PVðytÞ ¼ 11�r
P∞

i¼1r
iE½ytþijFt�. This equation takes the same form as (11) except the agents’ ex-

pectations are replaced by expectations conditioned on Ft. Conditioning down in this manner doesn’t
affect the link between the country’s external position and the expectations because information used
by agents is effectively contained in Ft via the presence of NXAn,t and TDn,t.

The implications of (12) for forecasting are straightforward. NXA should have forecasting power for
any stationary variable ytþk insofar as expected future values of that variable, E½ytþkjFt�, are correlated

M.D.D. Evans / Journal of International Money and Finance 48 (2014) 271e290278

with the present value terms on the right-hand side of (12). Suppose, for the sake of illustration, that yt
is independent of the trade flows and that country n’s long-run trade deficit is equal to TDn. Then a
projection of ytþk on NXAn,t (i.e. a regression without an intercept) would produce a projection coef-
ficient equal to

E
�
ytþkNXAn;t

�

E
h
NXA2n;t

i ¼ 1
1 � r E

2
64TDn;t X∞

i¼1
r
iE½ðktþi � kÞjFt�ytþk

E
h
NXA2n;t

i
3
75

¼ TDn
1 � r

X∞
i¼1

r
iℂV½E½ktþijFt�; E½ytþkjFt��

E
h
NXA2n;t

i ;
where ℂV½:; :� denotes the covariance. Notice that in this case the size of the coefficient depends on
both the long run trade deficit, TDn, and the covariance between the expectations of ytþk and ktþi over a
range of horizons i. In the empirical analysis below, I examine the forecasting power of NXAn,t for future
trade flows with yt ¼ Dmt � Dxt and yt ¼ Dtt, and future financial conditions with yt ¼ kt at particular
horizons k. I also study the forecasting power of NXAn,t for trade and financial conditions over a range of
horizons (i.e. for all k�1) using time series estimates of PVðDmn;t � Dxn;tÞ; PVðDtn;t � gÞ and
PVðkt � kÞ.

4. Empirical methods

4.1. Estimating the world SDF

In a fully specified theoretical model of the world economy the world SDF would be identified from
the equilibrium conditions governing investors’ portfolio and savings decisions. Fortunately, for our
purposes, we can avoid such a complex undertaking. Instead, I adopt a “reverse-engineering” approach
in which I construct a specification for the SDF that explains the behavior of a set of returns; the returns
on the asset and liability portfolios for six of the G7 countries.5 This approach is easy to implement and
allows us to empirically examine how prospective future financial conditions are reflected in external
positions.

Let ertþ1 denote a k � 1 vector of log excess portfolio returns, eritþ1 ¼ ritþ1 � rTBtþ1, where ritþ1 denotes
the log return on portfolio i and rTBtþ1 is the log return on U.S. T-bills. I assume that the log of the SDF is
determined as

ktþ1 ¼ a � rTBtþ1 � b’ðertþ1 � E½ertþ1�Þ: (13)
This specification for the SDF contains kþ1 parameters: the constant a and the k � 1 vector b. In the
“reverse-engineering” approach values for these parameters are chosen to ensure that the no-arbitrage
conditions are satisfied for the specified SDF. More specifically, I find values for a and b such that the
portfolio returns for the asset and liability portfolios of the six G7 countries and the U.S. T-bill rate all
satisfy the no-arbitrage conditions.

Consider the condition for the i’th portfolio return: 1 ¼ Et½expðktþ1 þ ritþ1Þ�: Taking unconditional
expectations we can rewrite this condition as

1 ¼ E
h
exp

�
ktþ1 þ ritþ1

�i
xexp

�
E
h
ktþ1 þ ritþ1

i
þ 1
2
V
h
ktþ1 þ ritþ1

i
:

(14)

5 Unfortunately, the data needed to compute the returns on Canada’s foreign asset and liability positions is not available from
the IMF database before 2006, so I use the returns of the other six G7 countries.

M.D.D. Evans / Journal of International Money and Finance 48 (2014) 271e290 279

When the log returns are normally distributed the second line holds with equality because (13) implies
that ktþ1 and ritþ1 are jointly normal. Otherwise, the second line includes an approximation error.

Next, I substituting for the log SDF from (13) in (14) and take logs. After some re-arrangement this
gives

a þ

E
h
eritþ1

i
þ 1
2
V
h
eritþ1

i
þ 1
2
b0V½ertþ1�b ¼ ℂV

h
eritþ1; er

0
tþ1

i
b: (15)

This equation must hold for the T-bill return (i.e., when ritþ1 ¼ rTBtþ1, or eritþ1 ¼ 0 ) so

a þ 1
2
b0V½ertþ1�b ¼ 0: (16)

Imposing this restriction on (15) gives

E
h
eritþ1
i
þ 1
2
V
h
eritþ1

i
¼ ℂV

h
eritþ1; er
0
tþ1

i
b:

This equation holds for each of the k portfolio returns. So stacking the k equations we obtain

E½ertþ1� þ
1
2
L ¼ Ub; (17)

where U ¼ V½ertþ1� and L is a k � 1 vector containing the leading diagonal of U.
Finally, we can solve (16) and (17). Substituting the solutions for a and b in (13) produces the

following expression for the log SDF:

ktþ1 ¼ �
1
2
m’U�1m � rTBtþ1 � m’U�1ðertþ1 � E½ertþ1�Þ: (18)

By construction, equation (18) identifies a specification for the log SDF such that the unconditional
no-arbitrage condition, 1 ¼ E½expðktþ1 þ ritþ1Þ�, holds for the k log portfolio returns and the return on
U.S. T-bills. This specification would also satisfy the conditional no-arbitrage condition,
1 ¼ Et½expðktþ1 þ ritþ1Þ�, if log returns were independently and identically distributed. However, since
this is not the case, we need to amend the specification to incorporate conditioning information.

Consider condition 1 ¼ Et½expðktþ1 þ ritþ1Þ�: Let ut be a valid instrument known to market partici-
pants in period t. Multiplying both sides of the no-arbitrage condition by exp(ut) and taking uncon-
ditional expectations produces, after some re-arrangement

1 ¼ E
h
exp

�
ktþ1 þ ri;utþ1

�i
; (19)

where ri;utþ1 ¼ ritþ1 þ ut � lnE½expðutÞ�: Notice that (19) takes the same form as (14) used in the con-
structions of the log SDF in (18). The only difference is that (19) contains the adjusted log return on

portfolio i, ri;utþ1; rather than the unadjusted return r
i
tþ1: This means that we can reverse engineer a

specification for the log SDF that incorporates conditioning information if we add adjusted log returns

to the set of returns. Specifically, let

eri;u
j

tþ1 ¼ ritþ1 � rTBtþ1 þ u
j
t � lnE½expðu

j
t� denote the log excess

adjusted return on portfolio i using instrument ujt. If ertþ1 now represents a vector containing er
i
tþ1 and

eri;u
j

tþ1, the log SDF identified in (18) will satisfy the non-arbitrage condition

1 ¼ E
h
exp
�
ktþ1 þ ritþ1

�

ujt
i
;

for all the portfolio returns i and instruments ujt included in ertþ1.

M.D.D. Evans / Journal of International Money and Finance 48 (2014) 271e290280

Three aspects of this reverse engineering procedure deserve comment. First, equation (18) doesn’t
necessarily identify a unique SDF that satisfies the no-arbitrage conditions for a set of returns. Indeed,
we know as a matter of theory that many SDF exist when markets are incomplete. Rather the speci-
fication in (18) identifies one specification for the SDF that satisfies the no-arbitrage conditions. Second,
this reverse engineering approach makes no attempt to relate the SDF to underlying macro factors. This
complex task is unnecessary if our aim is simply to identify how prospective future financial conditions
affect external positions. The third aspect concerns the use of instrumental variables to control for
conditioning information. In principle the conditional expectations of market participants that appear
in the no-arbitrage conditions equal expectations conditioned on every instrumental variable in their
information set. In practice, there is a limit to the number of instruments we can incorporate into the
log SDF specification. I chose instruments that have forecasting power for log excess portfolio returns
and I examine the robustness of my results to alternative specifications for the log SDF based on
different instrument choices.

I consider two empirical specifications for the log SDF. The first, denoted by bkIt, is estimated from
(18) without conditioning information. To assess whether the estimates satisfy the no-arbitrage

condition, 1 ¼ E½expðbkItþ1 þ ritþ1Þ

ujt�, I estimate regressions of the form:

exp
�bkItþ1 þ ritþ1

�
� 1 ¼ b1

�
fan;t � fln;t

�
þ b2

�
xn;t � mn;t

�
þ vtþ1; (20)

where xn,t, mn,t, fan,t and fln,t denote the logs of exports, imports, the value of foreign assets and foreign
liabilities, respectively, for country n. Panel A of Table 1 reports the estimation results for the log returns
on the asset and liability portfolios. Notice that the log ratios of assets-to-liabilities and export-to-
imports are valid instruments so the estimates of b1 and b2 should be statistically insignificant
under the null of a correctly specified SDF. As Panel A shows, this is not the case for the portfolio returns
of four countries. The log asset-to-liability ratio has predictive power for German, U.K. and U.S. returns,
while the log export-to-import ratio has power for the returns on Japanese assets.

In the light of these results, I incorporate conditioning information in my second specification for

the log SDF, denoted by bkIIt . Specifically, I now add the adjusted log return on U.S. assets,
ri;ztþ1 ¼ rAUS;tþ1 þ ðfaUS;t � flUS;tÞ � lnE½expðfaUS;t � flUS;tÞ�, where rAUS;tþ1 is the log return on U.S. assets, to
the set of returns used to estimate the log SDF in (18). This specification incorporates information
concerning the future value of the SDF that is correlated with variations in the U.S. NFA position. Thus,

Table 1
Forecasting returns.

Asset returns Liability returns

b1 b2 R
2 b1 b2 R

2

A: bkI
France 0.059 �0.210 �0.001 0.117 �0.205 0.003
Germany �0.428* 0.669 0.124 �0.442** 0.594 0.12

9

Italy �1.031 2.436 0.135 �1.009 2.667* 0.143
Japan 0.299 2.304** 0.098 0.327 2.374 0.106
United Kingdom �5.852** 0.324 0.183 �5.843** 0.437 0.177
United States �1.108** 0.216 0.132 �1.059** 0.252 0.115
B: bkII
France �0.188 �0.636 0.023 �0.116 �0.610 0.017
Germany �0.083 2.824 0.057 �0.091 2.862 0.059
Italy �0.653 �0.668 0.018 �0.653 �0.453 0.016
Japan 0.742 1.809 0.050 0.774 1.874 0.055
United Kingdom �4.595 2.237 0.052 �4.698 2.529 0.054
United States �0.229 0.515 0.022 �0.163 0.558 0.023

Notes: The table reports the OLS estimates of the regression (20) using the kIt specification for the log SDF in panel A and the k
II
t

specification in panel B. “**” and “*’’ indicate statistical significance at the 5% and 10% levels, respectively. All regression esti-
mated in annual data between 1971 and 2011.

Fig. 1. SDF Estimates.

M.D.D. Evans / Journal of International Money and Finance 48 (2014) 271e290 281

faUS;t � flUS;t should not have forecasting power for expðbkIItþ1 þ ritþ1Þ � 1 by construction. To check
whether the other instruments retain their forecasting power, I then re-estimate regression (20) with

bkIItþ1 replacing bkItþ1. Panel B of Table 1 reports these regression results. In contrast to Panel A, none of
the b1 and b2 coefficient estimates are statistically significant. Notice, also, that the R

2 statistics are (in
most cases) an order of magnitude smaller than their counterparts in Panel A. The asset-to-liability and
export-to-import ratios do not account for an economically meaningful fraction of the variation in

expðbkIItþ1 þ ritþ1Þ � 1. These findings appear robust to the choice of estimation period and instruments.
Re-estimating (20) over a sample period that ends in 2007 gives essentially the same results. I also find

statistically insignificant coefficients in regressions using bkIItþ1 as the log SDF when GDP growth rates
and/or lagged returns are used as alternate instruments.6

Fig.1 plots the two estimated SDFs, bKIt ¼ expðbkItÞ and bKIIt ¼ expðbkIIt Þ, together with the inverse of the
real return on U.S. T-bills, 1=RTBt . In the special case where the expected excess portfolio returns on
assets and liabilities are zero, equation (18) implies that the SDF is equal to 1=RTBt . Thus differences
between 1=RTBt and the estimated SDF’s arise because the SDFs must account for the expected excess
portfolio returns. As the plots clearly show, both estimates of the SDF are more volatile than 1=RTBt . In
fact, variations in the log return on U.S. T-bills contribute less than one percent to the sample variance
of bkIt and bkIIt . Changes in U.S. T-bill returns do not appear to have an economically significant impact on
estimates of the SDF that “explain” returns on asset and liability portfolios in major economies. The
plots in Fig. 1 also show that there are numerous episodes where the estimated SDFs are well above
one. Ex ante, the conditionally expected value of the SDF, EtKtþ1, identifies the value of a claim to one
real dollar next period. So safe dollar assets sold at a premium during periods where these high values
for the SDF were forecast ex ante.

6 Recall that specification for kt in (18) was derived using a log normal approximation to evaluate expected future returns.
Based of these regression estimates, there is no evidence to suggest that the approximation is a significant source of specifi-
cation error for bkIIt .

M.D.D. Evans / Journal of International Money and Finance 48 (2014) 271e290282

4.2. Estimating external positions

The estimates of the log SDF, bkIIt , allow us to pin down the discount rate r ¼ expðg þ kÞ used in
computing the NXA positions and the present value terms in equation (12). Recall that g is the un-
conditional growth rate for exports and imports, which I estimate to be 0.064 from the pooled average
of import and export growth across countries. My estimate of k computed from the average value of bkIIt
is �0.59. These estimates, denoted by bg and bk; imply a discount rate of r ¼ expðbg þ bkÞ ¼ 0:586. This is
the value I use to construct the NXA measures of each country’s external position.

Fig. 2 plots the NXA positions for each country in the dataset between 1980 and 2011. The upper
panel shows that the NXA positions for all but one of the G7 countries have remained between ±1
during the past 30 years. The one exception is the Japanese NXA position, which persistently increased
from 0.1 to 2.6 during the period. Variations in the NXA positions of countries outside the G7 are
generally larger. The plots in the lower panel of Fig. 2 show large improvements in the external po-
sitions of India and South Korea while Australia’s NXA position has remained largely unchanged. It is
also interesting to note that the steady improvement in the NXA position of China in the last twenty
years is not nearly as pronounced as the improvement in Japan’s position.7 Of course the time series for
the NXA positions reflect changes in NFA positions and trade deficits both measured as a fraction of
annual trade, NFAn;t=ðMn;t þ Xn;tÞ and ðMn;t � Xn;tÞ=ðMn;t þ Xn;tÞ. Plots for these variables are shown in
the Web Appendix.

4.3. Long-horizon forecasts

In principle, variations in the NXA positions could reflect revisions in the expectations concerning
the entire path for future imports, exports and the SDF. One way to investigate this possibility would be
to estimate regressions of realized present values; i.e.,

Pk
i¼1r

iytþi for yt ¼ fDmt � Dxt; Dtt � g; kt � kg,
on NXAn;t for some finite horizon k. For example, with r equal to 0.586, ri < 0:01 for i > 8, so a finite
horizon of eight or nine years ought to be sufficient for this purpose. Unfortunately, there are two well-
known econometric problems with this approach. First, the coefficient estimates may suffer from finite
sample bias when the independent variables are persistent and predetermined but not exogenous (see,
e.g. Campbell and Yogo, 2006). Second, the asymptotic distribution of the estimates provides a poor
approximation to the true distribution when the forecasting horizon is long relative to the span of the
sample (see, e.g. Mark, 1995), as it would be here with just a 40 year span.

To avoid these problems, I examine the relation between the NXA positions and
P∞

i¼1r
ibEtytþi, where

the conditional expectations bEtytþi are computed from VARs. Specifically, let the vector zt ¼ ½yt; :; ::�0
follow a p0 th. order VAR, which can be written in companion form as Zt ¼ AZt�1 þ Ut, where Zt stacks
the zt vectors appropriately. I estimate the present value for yt by

dPVðytÞ ¼ 11 � r
X∞
i¼1

r
ibEtytþi ¼ r1 � r ı1 bA

�
I � rbA��1Zt; (21)

where ı1 is a vector that picks out the first row of Zt (i.e., yt¼ı1Zt) and bA denotes the estimated com-
panion matrix from the VAR. [The 1=ð1 � rÞ term is included for compatibility with the expression for
the NXA position in equation (12)]. I compute present values for trade flows where yt ¼ Dmn;t � Dxn;t
or yt ¼ Dtn;t � bg from VARs estimated country-by-country, and for the log SDF with yt ¼ kIIt � bk using a
single world-wide specification. In all these calculations r ¼ expðbg þ bkÞ ¼ 0:586.

I estimate the present value terms involving future trade flows (i.e., dPVðDmn;t � Dxn;tÞ anddPVðDtn;t � bgÞ) from VARs that include the import-export growth differential Dmn,t�Dxn,t, trade growth
Dtn,t, and the log export-to-import ratio xn,t�mn,t. Below I report results based on first-order VARs
estimated separately for each country, n; higher-order VARs give very similar results. In addition, I

7 The span of the sample period is much too short for unit roots tests to provide reliable information on the whether the true
process for each country’s NXA position is stationary. On the other hand the economic logic embedded in equation (12) implies
that NXAn,t is indeed a stationary process, and so my analysis in Section 5 proceeds under this assumption.

Fig. 2. NXA positions.

M.D.D. Evans / Journal of International Money and Finance 48 (2014) 271e290 283

considered estimates that included NXAn,t and the log return on U.S. T-bills, rTBt ; in the VARs. The results
presented below are robust with respect to the presence of these variables.8

I also use a VAR to compute the present value of the log SDF, dPVðbkIIt � bkÞ. In this case the VAR in-
cludes bkIIt � bk, the log return on U.S. T-bills, rTBt ; the U.S. inflation rate, pUSt ; the spread between the real

8 The Web Appendix examines the time series predictability of the import-export growth differential and the trade growth
differential across the countries in the sample. It also documents the results of Grange Causality tests from the estimated VARs.

M.D.D. Evans / Journal of International Money and Finance 48 (2014) 271e290284

yields on ten and one year U.S. T-bonds, sprUSt , and the average rate of real GDP growth across the G7,
DyG7t . In addition, I use the VAR to compute the present value of the log return on U.S. T-bills,dPVðrTBt � brTBÞ, where brTB is the sample average of rTBt . Comparing dPVðbkIIt � bkÞ with dPVðrTBt � brTBÞ proves
useful when we examine how future financial conditions are reflected in the NXA positions below.

5. Results

5.1. Forecasting future trade flows

I begin by examining the short-horizon forecasting power of the NXA positions for trade flows.
Panel A of Table 2 reports slope coefficients, (heteroskedastic-consistent) standard errors and R2 sta-
tistics from regressions of ytþ1 on a constant and NXAn,t for each of the countries, n, over the full sample.
Columns I and II show estimates where ytþ1 ¼ Dmn;tþ1 � Dxn;tþ1 and ytþ1 ¼ ðDtn;tþ1 � bgÞTDn;t are the

Table 2
Forecasting trade flows.

A: short horizon forecasts

I II III

Forecast
variables

Dmn;tþ1 � Dxn;tþ1 ðDtn;tþ1 � bgÞTDn;t Dmn;tþ1 � Dxn;tþ1 þ ðDtn;tþ1 � bgÞTDn;t
coeff std R2 coeff std R2 coeff std R2

Canada 2.407 (1.811) 0.042 0.141 (0.186) 0.014 1.345 (0.885) 0.055
France �0.698 (0.646) 0.028 0.187*** (0.037) 0.386 �0.163 (0.323) 0.006
Germany 4.693 (3.818) 0.036 �0.802*** (0.280) 0.170 1.544 (1.991) 0.015
Italy 5.121 (3.683) 0.046 0.222 (0.236) 0.022 2.782 (1.848) 0.054
Japan 2.338 (1.947) 0.035 �0.210 (0.186) 0.031 0.959 (0.992) 0.023
United
Kingdom

1.925 (1.806) 0.028 �0.419*** (0.096) 0.325 0.543 (0.883) 0.009

United States 0.877 (1.822) 0.006 �0.064 (0.199) 0.003 0.374 (0.919) 0.004
Australia �1.279 (4.853) 0.002 �0.308 (0.316) 0.023 �0.947 (2.374) 0.004
China 10.311** (4.929) 0.131 �0.920*** (0.351) 0.192 4.235* (2.395) 0.097
India 0.518 (0.848) 0.009 �0.028 (0.099) 0.002 0.231 (0.457) 0.006
South Korea 2.603** (1.108) 0.124 �1.276*** (0.171) 0.588 0.025 (0.574) 0.000
Thailand 8.945* (5.356) 0.065 �1.688*** (0.656) 0.142 2.785 (2.635) 0.027

B: Long-Horizon Forecasts

Forecast
Variables

I II III

dPVðDmn;t � Dxn;tÞ dPVðDtn;t � gÞTDn;t dPVðDmn;t � Dxn;tÞ þ dPVðDtn;t � gÞTDn;t
coeff std R2 coeff std R2 coeff std R2

Canada 1.923 (1.244) 0.058 0.144 (0.386) 0.004 2.067 (1.478) 0.048
France �1.123*** (0.315) 0.246 �0.405*** (0.130) 0.199 �1.528*** (0.443) 0.234
Germany 7.750*** (1.530) 0.397 3.233*** (0.699) 0.354 10.982*** (2.213) 0.387
Italy 0.021 (2.105) 0.000 0.237 (0.814) 0.002 0.258 (2.908) 0.000
Japan 3.848*** (0.933) 0.304 0.607 (0.429) 0.049 4.455*** (1.309) 0.229
United
Kingdom

5.004*** (0.725) 0.550 2.281*** (0.302) 0.594 7.284*** (0.900) 0.627

United States 3.522** (1.585) 0.112 1.039*** (0.361) 0.175 4.562** (1.898) 0.129
Australia 5.338*** (1.980) 0.157 2.021** (0.969) 0.100 7.359*** (2.498) 0.182
China 12.564*** (2.117) 0.548 2.911*** (0.651) 0.408 15.475*** (2.683) 0.534
India 2.647*** (0.281) 0.695 1.923*** (0.289) 0.531 4.569*** (0.487) 0.693
South Korea 3.216*** (0.449) 0.568 �0.002 (0.251) 0.000 3.214*** (0.650) 0.385
Thailand 13.923*** (2.012) 0.551 4.706*** (0.836) 0.448 18.628*** (2.526) 0.582

Notes: The table reports OLS estimates of the slope coefficients, (heteroskedastic-consistent) standard errors and R2 statistics
from regressions of the variables shown at the top of each panel on NXAn,t and (an unreported constant). Each row reports
estimates for country n. “***”, ”“**” and “*’’ indicate statistical significance at the 1%, 5% and 10% levels, respectively. All re-
gressions estimated in annual data between 1971 and 2011.

M.D.D. Evans / Journal of International Money and Finance 48 (2014) 271e290 285

forecast variables, respectively. These are the trade flows that appear in the present value terms that
determine the NXA position of country n in equation (12). The estimates in column III use the com-
bination of trade flows that appears on the right-hand side of (12).

The results in Panel A of Table 2 show that information contained in the NXA positions concerning
future near-term trade flows differs considerably across countries. Among the G7, there is no evidence
that the NXA positions contain information about next year’s import-export growth differential; none
of the estimated slope coefficients are statistically significant at conventional levels. By contrast, the
NXA positions of China and South Korea appear to have reasonably strong forecasting power for the
differential. In both cases an increase in the NXA position forecasts a rise in Dmn;tþ1 � Dxn;tþ1. Ceteris
paribus, this is consistent with equation (12). For perspective on the size of coefficient estimates, the
value of 10.3 implies that an increase in the Chinese NXA position of 0.1 forecasts an increase in the
growth differential of approximately one percent.

External positions have more widespread forecasting power for trade growth. Column II shows that
six slope coefficients are statistically significant at the one percent level. According to (12), an increases
in NXAn,t should, ceteris paribus, forecast a rise in trade growth for current deficit countries and a fall in
growth for surplus countries. This prediction is not borne out in five of the six countries with significant
coefficients. Finally, column III shows the forecasting power of the NXA positions for the combined
trade flows. Here there is very little evidence of any short-horizon forecasting power. With the
exception of China, none of the estimated slope coefficients are statistically significant at the 10 percent
level, and all the R2 statistics are extremely small.

All-in-all, the results in Panel A suggest that variations in prospective near-term trade flows play no
more than a minor role in driving variations in external positions. This doesn’t mean that future trade
flows are irrelevant. On the contrary, changes in external positions could reflect revisions in expec-
tations concerning the entire future path for trade flows (i.e. expectations well beyond the one year
horizon studied above). The results in Panel B of Table 2 allow us to examine this possibility. Here I
report the estimates from regressions of the VAR-based present values of trade flows on a constant and
NXAn,t. Notice that these are not forecasting regressions e the dependent variable is not the realized
present value of the future trade flows. Rather the regressions measure the degree to which changes in
the present value of future trade flows computed from VAR forecasts are reflected in NXAn,t variations.

9

If the forecasting information captured by the VARs is also embedded in agents’ expectations that are
reflected in the NXA positions, we should expect to find positive and statistically significant slope
coefficients.

The results reported in Panel B generally confirm this prediction. The slope coefficients in column I
are positive and highly statistically significant for nine countries. And, judging by the R2 statistics, the
variations in NXAn,t capture a sizable portion of the variance in the VAR-based present values for the
import-export growth differential. This evidence is consistent with notion that the information con-
tained in the long-term VAR forecasts for Dmn,tþi�Dxn,tþi is positively correlated with that used to form
the actual expectations embedded in the NXA positions. The estimates based on French data prove an
exception to this pattern. Here the slope coefficient is negative and highly statistically significant e a
counterintuitive finding. The estimates shown in Panel II continue this pattern. In this case the slope
coefficients are positive and highly statistically significant in seven countries, with France again proving
the exception. Column III shows how the VAR-based forecast for the combined future trade flows relate
to external positions. Again, the slope coefficients are positive and highly significant for most countries
(except France). It is alsoworth noting that the R2 statistics from these regressions are over 0.5 in the U.K.,
China, India, and Thailand. The time series variations in the NXA positions of these countries during the
past 40 years are quite informative about changes in the VAR forecasts of future trade flows.

Overall, the results in Table 2 are consistent with the view that changing expectations about trade
flows far into the future contribute to the year-by-year variations in the NXA positions of many

9 The VAR-based present values used as left-hand-side variables in these regressions include some sampling error. Impor-
tantly, the results reported in the table are derived from VARs that do not include NXAn,t, so there is no reason to suspect that
this sampling error contributes to the estimated regression coefficients. Furthermore, when I estimate regressions using VAR-
based present values that include NXAn,t in the VAR specification, I obtain very similar results.

M.D.D. Evans / Journal of International Money and Finance 48 (2014) 271e290286

countries. Expectations concerning near-term trade flows appear far less relevant. These results are
broadly consistent with the findings reported by Gourinchas and Rey (2007a). They estimate that
changing expectations concerning future trade flows account for approximated 30 percent of the
cyclical variations in the U.S. external position between 1952 and 2004. Here variations in the U.S. NXA
position are strongly correlated with the forecasts of future trade flows, but not as strongly as the NXA
positions of other countries.

5.2. Forecasting future financial conditions

I now consider the influence of prospective financial conditions on country’s external positions.
Panel A of Table 3 reports on the short-horizon forecasting power of the NXA positions for different
measures of future financial conditions. As above, the table shows slope coefficients, (heteroskedastic-
consistent) standard errors and R2 statistics from regressions of the forecast variable on a constant and
NXAn,t estimated over the full sample. Recall that variations in the expected log SDF only affect NXAn,t
insofar as the country is running a current trade surplus or deficit, so the forecast variables are
multiplied by the current trade deficit, TDn,t, to be consistent with the right-hand-side of (12).

Table 3
Forecasting financial conditions.

I: short horizon forecasts

I II III

Forecast variables ðbkIItþ1 � bkÞTDn;t �ðrTBtþ1 � rTBÞTDn;t ðbkIItþ1 þ rTBtþ1ÞTDn;t
coeff std R2 coeff std R2 coeff std R2

Canada �3.216 (3.567) 0.020 �0.017 (0.084) 0.001 �3.200 (3.562) 0.020
France �2.277*** (0.697) 0.215 0.008 (0.014) 0.008 �2.285*** (0.693) 0.218
Germany 13.258*** (4.172) 0.206 0.005 (0.092) 0.000 13.253*** (4.166) 0.206
Italy �6.327 (3.810) 0.066 0.222*** (0.079) 0.169 �6.549* (3.804) 0.071
Japan 3.439 (2.499) 0.046 �0.146** (0.058) 0.142 3.585 (2.477) 0.051
United Kingdom 6.548** (2.571) 0.143 0.252*** (0.072) 0.238 6.295** (2.589) 0.132
United States 1.834 (4.113) 0.005 0.039 (0.077) 0.006 1.795 (4.104) 0.005
Australia �2.234 (6.107) 0.003 �0.239** (0.099) 0.130 �1.996 (6.093) 0.003
China 6.216 (3.999) 0.079 0.347*** (0.088) 0.357 5.869 (3.992) 0.072
India 5.476** (2.469) 0.112 �0.034 (0.065) 0.007 5.510** (2.474) 0.113
South Korea 6.630*** (1.709) 0.284 �0.083* (0.043) 0.090 6.712*** (1.714) 0.288
Thailand 5.795 (7.428) 0.015 0.727*** (0.165) 0.332 5.068 (7.480) 0.012

II: Long-horizon forecasts

I II III

Forecast Variables dPVðbkIIt � bkÞTDn;t �dPVðrTBt � rTBÞTDn;t dPVðbkIIt þ rTBt ÞTDn;t
coeff std R2 coeff std R2 coeff std R2

Canada 2.525 (1.644) 0.057 �0.467*** (0.152) 0.195 2.992* (1.706) 0.073
France 0.812** (0.378) 0.106 �0.146*** (0.039) 0.268 0.959** (0.405) 0.126
Germany �2.153 (2.523) 0.018 0.340* (0.194) 0.073 �2.493 (2.604) 0.023
Italy 2.537 (2.183) 0.033 �0.558** (0.256) 0.109 3.095 (2.400) 0.041
Japan �0.374 (1.167) 0.003 0.299** (0.116) 0.145 �0.672 (1.262) 0.007
United Kingdom �1.371 (1.553) 0.020 0.070 (0.173) 0.004 �1.441 (1.661) 0.019
United States 5.293*** (1.752) 0.190 �0.692*** (0.156) 0.336 5.985*** (1.790) 0.223
Australia 4.777* (2.625) 0.078 �0.148 (0.300) 0.006 4.924* (2.801) 0.073
China 2.721 (1.997) 0.060 �0.318** (0.135) 0.160 3.040 (2.048) 0.071
India �3.247** (1.246) 0.148 0.558*** (0.150) 0.263 �3.805*** (1.341) 0.171
South Korea �3.168*** (0.901) 0.241 0.650*** (0.096) 0.543 �3.818*** (0.973) 0.283
Thailand 0.189 (4.080) 0.000 �0.338 (0.551) 0.010 0.527 (4.500) 0.000

Notes: The table reports OLS estimates of the slope coefficients, (heteroskedastic-consistent) standard errors and R2 statistics
from regressions of the variables shown at the top of each panel on NXAn,t and (an unreported constant). Each row reports
estimates for country n. “***”, ”“**” and “*’’ indicate statistical significance at the 1%, 5% and 10% levels, respectively. All re-
gressions estimated in annual data between 1971 and 2011.

M.D.D. Evans / Journal of International Money and Finance 48 (2014) 271e290 287

Column I shows the results when NXAn,t is used to forecast the one-year ahead deviation of the log
SDF from its unconditional mean multiplied by the current trade deficit, ðbkIItþ1 � bkÞTDn;t: Recall that,
ceteris paribus, an increase in the expected future SDF should raise (lower) the NXA position of a deficit
(surplus) country because future trade imbalances are discounted more heavily when valuing current
asset and liability positions. So, if revisions in expected near-term financial conditions are a source of
NXAn,t variations over the sample, and those expectations are reflected in actual conditions as repre-
sented by the SDF estimates, we should see positive and significant slope coefficients in the forecasting
equations. The estimates in Column I show that this is the case for four countries: Germany, the U. K.,
India and South Korea. NXAn,t does not appear to have significant near-term forecasting power across
the other countries, with the exception of France; where, once again, the significant negative coeffi-
cient is counterintuitive.10

Columns II and III provide further perspective on these findings. Here I show the results from
forecasting regressions that include the log return on U.S. T-bills, rTBtþ1: In the absence of arbitrage
opportunities 1 ¼ Et½expðbkIItþ1 þ rTBtþ1Þ�, which (approximately) implies that Et½ktþ1 þ rTBtþ1� ¼
�12 Vt½kIItþ1 þ rTBtþ1�; where Vt½:� denotes the conditional variance. Subtracting unconditional expecta-
tions from both sides and re-arranging using (18) gives

Et
�
ktþ1 � k

�
¼ �Et

h
rTBtþ1 � rTB

i
� 1
2

n
Vt½b0ertþ1� � E½Vt½b0ertþ1��

o
: (22)

Thus, changing expectations concerning the future SDF must either reflect revisions in expected future
T-bill returns and/or changes in perceived risk measured by the conditional variance of future excess
portfolio returns on asset and liabilities across the major economies.

Column II shows the regression results when the T-bill returns (multiplied by the trade deficit) are
the forecast variable. Here we see a different cross-country pattern of forecasting power. NXA positions
have forecasting power for near-term T-bill returns in Italy, Japan, Australia, China and Thailand; all
countries where NXAn,t appeared not to forecast the log SDF. When judged by the R

2 statistics, these
forecasting results are particularly strong in the Chinese and Thai cases. Column III shows results whenbkIItþ1 þ rTBtþ1 (multiplied by the trade deficit) is used as the forecast variable. Mathematically, the esti-
mated slope coefficients are equal to the difference between their counterparts in columns I and II, but
economically they show the extent to which changing perceptions concerning near-term risk is re-
flected in the NXA positions. Notice that the cross-country pattern of the coefficient estimates closely
corresponds to the pattern in column I. To the extent that NXAn,t variations reflect prospective near-
term financial conditions, revisions in perceived risk appear more important than expectations con-
cerning future returns on U.S. T-bills.

Of course NXAn,t variations may reflect revisions in expectations concerning the SDF further into the
future. To gauge the importance of variations in these long-horizon expectations, Fig. 3 plots the
estimated present value for the log SDF, dPVðbkIIt � bkÞ, and minus one times the estimated present value
of the return on U.S. T-bills, �dPVðrTBt � brTBÞ. The plotted series are computed from a VAR estimated
from the full sample. Alternative series derived from a VAR estimated on pre-crisis data (1971e2006)
follow a similar pattern. As the figure clearly shows, time series variations in the present value for the
log SDF follow a cyclical pattern and are much larger in magnitude than the changes in the present
value of the log return on U.S. T-bills. This means that the changing VAR forecasts for the log SDF largely
reflect revisions in perceived future risk, represented by the last term on the right-hand-side of (22).
For example, the sizable swings in the log SDF between 1998 and 2008 appear to reflect, in turn, a large
rise, fall, and rise again in expectations concerning the level of risk well into the future.

To what extent are these estimates of changing risk perceptions reflected in the NXA positions? To
address this question, Panel B of Table 3 reports estimates from regressions of the VAR-based present

10 Gourinchas and Rey (2007a) found that the U.S. external position had forecasting power for the return on the net asset
position and the return differential between equity assets and liabilities at the quarterly horizon between 1952 and 2004. One
possible reason for the difference between their findings and the U.S. forecasting results in Panel A is that NXAn,t exhibits a good
deal more persistence than the cyclical component of the U.S. external position they use.

Fig. 3. The Present Value of the log SDF.

M.D.D. Evans / Journal of International Money and Finance 48 (2014) 271e290288

values of the log SDF and T-bill returns on a constant and NXAn,t. As in Panel A, the dependent
variables in these regressions are multiplied by the trade deficit for consistency with the right-hand-
side of (12). The estimates in column I show that the variations in NXA are only weakly related to
those in dPVðbkIIt � bkÞTDn;t for many countries. The most notable exception is the U.S., where the
estimated slope coefficient is positive, highly statistically significant, and the R2 is 0.19. This finding
contrasts with the U.S. estimates in Panel A, where the coefficient is insignificant and the R2 statistic
is smaller that 0.01. It suggests that changes in the U.S. external position are in part a reflection of
changing perceptions concerning future financial conditions beyond the immediate future, particular
future risk. The NXA positions of three other countries also appear to reflect prospective future
financial conditions. The estimate slope coefficient on the French NXA position is positive and sig-
nificant, but the regression R2 is only 0.1, while those for India and South Korean are negative and
significant.

The cross-country pattern of statistical significance changes when we focus on forecasts for U.S. T-
bill returns. Column II shows that the NXA positions of many countries are quite closely related to
�dPVðrTBt � brTBÞTDn;t: the estimated slope coefficients are significant at the five percent level in eight
countries. To interpret these estimates, recall from Table 2 that most country’s NXA positions
appeared to reflect prospective future trade conditions. Their NXA positions will also reflect long-
term forecasts for U.S. T-bill returns insofar as they are correlated with their forecasts for future
trade flows. The estimation results in column II reflect these correlations and the importance of
expected future trade flows for the determination of NXA across countries. Finally, note that the
results in column III closely mirror those in column I. This is due to the fact that the changing VAR
forecasts for the future log SDF primarily reflect revisions in the forecasts of risk rather than U.S. T-
bill returns (see Fig. 3).

Overall, the results in Table 3 provide only limited support for the view that revisions in expecta-
tions about future financial conditions contribute significantly to the changing NXA positions across
countries. Although the VAR forecasts reveal sizable and persistent swings in the present value of the
log SDF, the NXA positions of most countries are not strongly correlated with this measure of pro-
spective financial conditions. The one notable exception to this pattern is the U.S., where variations in
the NXA position are strongly correlated with the estimated present value of the future SDF.

M.D.D. Evans / Journal of International Money and Finance 48 (2014) 271e290 289

6. Conclusion

In the absence of Ponzi schemes and arbitrage opportunities, the NFA position of any country must
equal the expected present discounted value of future trade deficits, discounted at the cumulated
world SDF. In this paper I investigated the forecasting implications of this theoretical insight. To do so, I
first developed a measure of a country’s external position, NXAn,t, that is simply linked to expectations
of future trade flows and the log SDF. I also showed how the SDF can be estimated from cross-country
data on returns. With these tools I then studied the near-term forecasting power of 12 country’s NXA
positions for trade flows and the SDF, and the statistical link between the NXA positions and VAR
forecasts for the paths of trade flows and the SDF far into the future.

Overall, my empirical findings support the prediction that the external positions of most countries
reflect (in part) expectations about the future path for trade flows. Evidence on the role of future
financial conditions is less clear cut. While the VAR forecasts for the log SDF suggest that there have
been persistent and sizable variations in the prospective future financial conditions that are relevant
for the determination of NXA positions, only the U.S. NXA position is strongly correlated with these
forecasts. This suggests that identifying the impact of future financial conditions on many country’s
NXA positions requires a more structural empirical investigation than the simple forecasting exercise
undertaken here. One possibility along these lines would be to extend the VAR methods pioneered by
Campbell and Shiller (1987) to allow for the nonlinearity between the trade deficits and the present
value terms in equation (12) e a possibility I leave for future work.

Appendix A. Supplementary data

Supplementary data related to this article can be found at http://dx.doi.org/10.1016/j.ijrefrig.2014.
05.029.

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• External balances, trade flows and financial conditions

1 Introduction
2 Data
3 Analytic framework
3.1 NFA positions
3.2 Forecasting implications
4 Empirical methods
4.1 Estimating the world SDF
4.2 Estimating external positions
4.3 Long-horizon forecasts
5 Results
5.1 Forecasting future trade flows
5.2 Forecasting future financial conditions
6 Conclusion
Appendix A Supplementary data
References

Running head: INTERNATIONAL BUSINESS MODULE 3 CASE 1

INTERNATIONAL BUSINESS MODULE 3 CASE 7

Trident University International

Student Name

Module 3 Case

BUS401 International Business

Professor’s Name

Date of Submission

International Business Module 3 Case

This is your 2-3 sentence introduction. No heading is required. Remember to always indent the first line of a paragraph (use the tab key). The margins, font size, spacing, and font type (bold or plain) are set in APA format. While you may change the names of the headings and subheadings, do not change the font or style of font. This introduction should provide a quick overview of the topic discussed.

Global market trend: United States

Global markets are affected by conditions created by international trade institutions and the international trade policies they formulate. These global market conditions vary based on the regional trade blocs.

International business professionals must track trends in global market conditions that impact the success of operations in one or more foreign markets. For example, the United States anti-dumping case against European Union steel industry, Japanese automotive import quotas, European Union agricultural tariffs.

Using mainly articles from Trident Library’s full-text databases like (Academic Search Complete, Business Source Complete and/or Proquest Central), research a current global market condition trend involving the United States. Examine the significance of the trend for international trade institutions and international trade policies. (2 pages)

 Global market trend: country #2

Using mainly articles from Trident Library’s full-text databases like (Academic Search Complete, Business Source Complete and/or Proquest Central), research a current global market condition trend involving another country that you have not previously researched in the course. Examine the significance of the trend for international trade institutions and international trade policies. (2 pages)

Since you are engaging in research, be sure to cite and reference the sources in APA format. The paper should be written in third person; this means words like “I”, “we”, and “you” are not appropriate. For more information see

Differences Between First and Third Person

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Conclusion

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References

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number,(issue number), pages. Retrieved from [insert name of library

database]

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Borgerson, J. L., Schroeder, J. E., Escudero Magnusson, M., & Magnusson, F. (2009).

Corporate communication, ethics, and operational identity: A case study of Benetton. Business Ethics: A European Review, 18(3), 209-223. Retrieved from Proquest.

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