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

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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 F32 F34

E-mail address: [email protected].

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

.

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 0.129 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

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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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. 2000

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

0

50

100

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N u

m b

e r

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o u

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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.

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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.

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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.

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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.

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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,

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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

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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

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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

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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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1320 HSR: Health Services Research 42:3, Part II ( June 2007)