Empirical Perspectives on Long-Term External Debt

Empirical Perspectives on Long-Term External Debt

Philip R. Lane∗Trinity College Dublin and CEPR

This version: July 2000

Abstract

In this paper, we attempt to paint a statistical portrait of the determination of

external debt for a set of low- and middle-income countries. The goal is to facili-

tate thinking about the role played by international capital ßows in the development

process. Empirically, we Þnd that external debt is strongly increasing in the level of

initial output. This remains true even when we control for variation in productivity

and creditworthiness. In addition, we Þnd a positive association between trade open-

ness and the level of external debt. We argue these results may lend some support

to theories of constrained access to international credit markets.

JEL Codes F21, F34, O10.

∗I thank Robert Barro, Xavier Sala-i-Martin, Raquel Fernandez, Linda Tesar, Andy Rose, Gian Maria

Milesi-Ferretti, three anonymous referees and participants in seminars at Princeton University, University

of Miami, INSEAD, the World Bank, the Inter-American Development Bank, the International Mone-

tary Fund, the Federal Reserve Bank of New York, the NBER Summer Institute, FEMES, LACEA, the

CEPR/CEPII/CNR/CRS conference in Alghero-Sardegna and the Columbia Macro Lunch for comments

and suggestions.

1 Introduction

The goal of this paper is to empirically examine the determination of external debt for

a set of low- and middle-income countries. This topic is of interest because access to

international capital markets in principle allows countries that are far from their steady-

states to grow more quickly, by breaking the link between domestic savings and domestic

investment. For instance, the US during the nineteenth century and Korea and Singapore

during recent decades each relied heavily on external debt in order to fuel accelerated rates

of growth.1 Moreover, integration into external Þnancial markets also enables countries

to smooth consumption in the face of temporary shocks and to enter into international

risk-sharing arrangements.

In this paper, we investigate the Þrst of these roles. As a guide to the empirical work

that constitutes the heart of this paper, we follow the sizeable literature that emphasizes

imperfections in international credit relationships that limit borrowing countries� access to

credit. One important distortion is repudiation risk: a country may like to borrow in order

to converge more quickly to its steady-state; however, it will prefer not to repay its debt

obligations. This problem is greater in international credit transactions than in domestic

loans because cross-border enforcement of repayments is more costly, especially loans to

sovereign governments.2 Moral hazard is another problem: if the borrower can hide some

of its actions from creditors, it may secretly choose to consume or invest in inappropriate

projects some proportion of the debt it takes on.3

Our primary focus is on the stock of external debt. As illustrated by the long-running

controversy regarding savings-investment correlations, it is difficult to interpret data on

international ßows of capital: for example, a positive correlation between capital inßows

and economic growth would be predicted both by theories of constrained access to interna-

1In contrast, in the well-known empirical work of Mankiw, Romer and Weil (1992), the authors explicitly

assume that domestic savings equals domestic investment, rejecting any role for international capital ßows.2See Kletzer (1994), Eaton and Fernandez (1995) and Obstfeld and Rogoff (1996, Chapter 6) for surveys

on this topic.3See Gertler and Rogoff (1990) and Obstfeld and Rogoff (1996, Chapter 6).

1

tional credit markets (an increase in output expands the debt ceiling available to borrowing

countries) and by theories of perfect capital mobility (a fast-growing country borrows both

to accelerate convergence to its steady-state and to smooth consumption during the tran-

sition path).4 As discussed below, theories of credit rationing have additional implications

for the stock of external debt and it is along this dimension that the predictions of this

literature can be most clearly explored.

In order for international borrowing and lending to take place in the presence of repudia-

tion risk, it is necessary that creditors have some ability to �punish� countries that default.

One possibility is that some fraction of current output (or capital) is put up as collateral,

as in the models of Cohen and Sachs (1986) and Barro, Mankiw and Sala-i-Martin (1995).5

Another is that creditors can impose sanctions, such as disruption of trade or the seizure

of external assets, in the event of default. In either case, all else equal, the punishment is

plausibly an increasing function of a country�s current level of output, since a richer country

can offer more collateral and would suffer a greater decline in total production if sanctions

were imposed.

Accordingly, this literature predicts a positive association between the volume of exter-

nal debt and the level of output. As is developed in section 2.1 below, other factors that

affect the costs and beneÞts of repudiation will also affect the equilibrium level of debt.

Such factors may include the level of productivity, trade openness, political instability and

other proxies for creditworthiness. In section 3, we explore empirical speciÞcations for

the level of external debt that incorporate these potential determinants, using data over

1970-95.

The organization of the paper is as follows. Using a simple framework, in section 2, we

develop from some basic theory the speciÞcations to be employed in the empirical work.

Section 3 describes the data, some summary statistics and implements the speciÞcation

determining the level of external debt. Section 4 discusses the limitations of the empirical

4On the international capital ßows literature, see Feldstein and Horioka (1980) and Tesar (1991).5Marcet and Marimon (1992) also examine growth under repudiation risk. They assume that the

defaulter�s punishment is permanent exclusion from international markets.

2

approach and considers some potential future directions for research on this topic. Section

5 concludes.

2 Theory

2.1 Debt Limits

To Þx ideas, we consider the per capita production function

Yit = Aitf(Kit) f1 > 0 f11 < 0 (1)

where Ait and Kit denote the levels of productivity and the capital stock in country i at

time t.

In models of repudiation risk under certainty, credit rationing typically takes the form

of a ceiling on debt. The upper bound Dmaxit is determined so that a borrower will not

default on a lower level of debt but will on a higher level. In the default calculus, the

gain to not repaying debt is set against the punishment imposed by angry creditors. This

punishment is typically modelled as increasing in the level of output of the debtor country:

the greater the level of output, the greater the total damage that can be imposed by trade

sanctions, the seizure of goods and the retention of collateral.6 Accordingly, we write

Dit ≤ Dmaxit = λitY

θit (2)

We allow the relationship between the level of output and the debt ceiling to be nonlinear

(θ 6= 1). One reason is that the punishment may not rise one-to-one with increases in

output. Another is that in debt models that generate credit ceilings by virtue of a moral

hazard problem in the debtor�s allocation of borrowed funds, domestic net worth (output)

can have a nonlinear effect on the equilibrium amount of lending (Gertler and Rogoff 1990,

Lane 1999).

6See Kletzer (1994), Eaton and Fernandez (1996) and Obstfeld and Rogoff (1996, Chapter 6) for surveys

of the related literature.

3

Although the factor λit is often assumed to be a constant in the theoretical literature,

we allow λit to vary across countries and over time by modelling λit as positively depending

on the levels of productivity Ait, creditworthiness Rit and the world interest rate rWt7

λit = λ (Ait,Rit,rWt) λ1 > 0, λ2 > 0, λ3 < 0 (3)

The level of productivity matters for λit if there is an intertemporal dimension to punish-

ment in the event of default, for example if sanctions persist for some time in the wake

of default. The rationale is that the more productive is a country, the more costly is the

lower rate of return on investment that is imposed by persistent sanctions and the more

reluctant will be such a country to default.

Creditworthiness refers to other factors that affect a country�s incentives to default. An

important consideration is the extent of trade openness of a debtor country. The classic

punishment considered in the sovereign debt literature is the imposition of trade sanctions

on the offending country (Bulow and Rogoff 1989). The more open is an economy, the

more costly is the loss of opportunities to engage in international trade; the more open is

an economy, the easier is it to disrupt trade and seize tradable goods and assets from the

offending country.8

Another factor that plausibly affects creditworthiness is political instability. The mech-

anism is that the more unstable is a country, the shorter is the expected time in office for

the ruling government and the greater the temptation to reap the immediate beneÞts of

default and discount the future costs of creditor sanctions. Finally, it is straightforward

that the higher is the world interest rate, the greater is the cost of servicing a given stock

of external debt and the smaller is the set of proÞtable domestic investment opportunities,

raising the temptation to default.9

7Obstfeld and Rogoff (1996, Chapter 6, pp 381-387) analyze a two-period model of borrowing constraints

that delivers a reduced form similar to equation [3].8Lane (1998) develops a theoretical growth model in which traded goods serve as collateral for interna-

tional borrowing and traces out the evolution of output and debt during the convergence process.9In earlier studies, Eaton and Gersowitz (1981) and Sachs (1985) also identiÞed some of these factors

as inßuencing sustainable debt levels.

4

For the purposes of empirical implementation, we consider the functional form for equa-

tion [3]

log λit = λA logAit + λR logRit + λr log rWt + ελit (4)

where ελit is a residual term. In turn, we take it that the levels of productivity and

creditworthiness can be related to sets of proxy variables XAit and XRit plus residual terms

logAit = β 0AXAit + εAit (5)

logRit = β 0RXRit + εRit (6)

and the world interest rate can be captured by a time effect

log rWt = φt (7)

By equations [2]-[7], we can write

logDmaxit = log λit + θ log Yit

= λAβ0AXAit + λRβ

0RXRit + λrφt + θ log Yit + λAεAit + λRεRit + ελit (8)

or

logDmaxit = γ0

AXAit + γ0RXRit + θ log Yit + τ t + εit (9)

Equation [9] forms the basis for the econometric work in section 3.

3 Empirics

3.1 Data and Sample Selection

As listed in Table A.1, eighty-seven developing countries are included in the study, with

the availability of external debt data determining the selection of countries.

Total external debt and GDP, both in constant 1987 US dollars, are taken from the

World Bank�s World Development Indicators. This data set also provides exports and

imports in current US dollars, which are converted into constant dollars using the US

5

GDP deßator. All these variables are converted into per capita form, using popula-

tion data from the same source. Our trade openness measure (OPEN) is constructed

as OPEN=0.5*(exports+imports)/GDP.

In selecting proxies for cross-country variation in productivity levels, we follow the

recent work of Hall and Jones (1997).10 We consider the economic variables they employ:

a measure of human capital (EDUC), government anti-diversion policy (GADP), the form

of economic organization (ECO) and a proxy for liberal economic policies (LIB).11

The EDUC variable is average years of schooling among the adult population.12 The

GADP measure is a weighted average of country ratings in Þve categories of the Interna-

tional Country Risk Guide: (i) law and order; (ii) bureaucratic quality; (iii) corruption;

(iv) risk of expropriation; and (v) government repudiation of contracts. It is scaled between

0 and 1 where a high value indicates a country that more severely restricts rent-seeking ac-

tivity. ECO follows Freedom House (1994) in classifying countries into six categories from

pure statist (0) to capitalist (1). LIB is the Sachs and Warner (1995a) index measuring the

fraction of years from 1950-94 that a country followed liberal economic policies, meaning

(i) nontariff barriers covering less than 40 percent of trade; (ii) average tariff rates less than

40 percent;(iii) a black market premium less than 20 percent in the 1970s and 1980s; (iv) a

non-socialist economic system; and (v) the government does not monopolize major exports.

In light of this deÞnition, the Sachs-Warner index is properly interpreted as a measure of

liberal economic policies and is clearly distinct from a country�s level of trade openness,

10I thank Andy Rose for this suggestion.11Of course, some of these variables could also improve a country�s access to credit via other channels.

For instance, a government that maintains liberal policies may also be one that is reluctant to default on

its external debt. Since our regressions are reduced-form, we cannot disentangle these various effects. Hall

and Jones also employ variables such as distance from the Equator and the international languages spoken

in a country as determinants of productivity but it is harder to interpret the economic meaning of these

variables.12The data are based Barro and Lee (1993) but extended by Hall and Jones to a larger number of

countries. We also explored the human capital stock data used by Benhabib and Spiegel (1994). This

alternative measure gave similar results.

6

which is heavily inßuenced by geographical and size considerations.

In addition, we include a proxy for natural resource endowments (NR), which is the

share of fuels, minerals and metals in merchandise exports, from the World Bank�s World

Development Indicators.13 A high natural resource endowment may have ambiguous ef-

fects on the level of external debt. On the one side, it may permit an increase in the credit

ceiling facing a country if natural resources serve as particularly good collateral for inter-

national loans or make a country particularly vulnerable to trade sanctions: the natural

downstream purchasers of many commodities are globally dispersed and it would impose

a high efficiency cost to domestically process and consume such resources. On the other

side, Sachs and Warner (1995b), Hall and Jones (1997), Lahiri (1997) and Lane and Tor-

nell (1997) argue that natural resource endowments may reduce productivity by switching

factors from more dynamic sectors or stimulating an increase in distortionary rent-seeking

activities, which would have the effect of lowering the credit ceiling. Although this view has

empirical support ex-post, natural resources ex-ante may have been perceived as raising

the productivity of capital.

In some speciÞcations, we also include a control for the fraction of external debt that

is lent at concessional terms (CONC), as countries that face cheaper credit terms, all else

equal may be able to borrow more: this variable is taken from the World Bank�s Global

Development Indicators.14 Finally, as an additional proxy for creditworthiness, we consider

a measure of political instability (INS) which is a common factor recovered from a host of

instability proxies considered by Annett (1997).15

13This has the advantage of having time series variation. The mining variable used by Hall and Jones

generates similar results.14A concessional loan is one with a grant-equivalent element of 25 percent or more. In the data, there is

a signiÞcant inverse relationship between the share of concessional debt and the level of output per capita

(the correlation is -0.45).15The deÞnition is INS=0.61*WARCIV+0.57*COMPOL+0.29*ASSASS +0.1*CRIS+0.07*REVOLS

+0.06*COUPS+0.1*CABCHG+0.01*CONSTCG. where WARCIV is a dummy variable for the occur-

rence of a civil war, COMPOL is a dummy variable for genocidal incidents, ASSASS is the number of

assassinations per thousand population, CRIS is the number of major government crises, REVOLS is the

7

Since we need stock data, we focus on external debt. 16 We do not distinguish between

private and public debt, since the identity of the borrower reveal little information. For

instance, a government may borrow only to relend domestically to private Þrms. Alterna-

tively, a private Þrm may borrow overseas but the government may assume responsibility

for its debts in the event of a crisis, even if no formal guarantee is in place. The joint nature

of public and private debt is supported by the fact that the credit rating for a private Þrm

in a developing country is (almost) never below that of its government.

3.2 Basic Facts

Table 1 describes the evolution of sample means for the levels of external debt per capita,

GDP per capita and openness over the time period 1970-95. Average external debt in-

creased rapidly during the second half of the 1970s, at a time when average output also

grew quickly. The 1980s saw a continued increase in external debt, especially in the Þrst

half of the decade, but at a slower rate of growth, with output growth modest during this

period. Finally, the 1990s has seen an actual decline in average external debt and a pick

up in the average output growth rate.17 These trends are reßected in the ßuctuations in

number of attempted forced or illegal changes in the top government elite, RIOTS is the number of violent

demonstrations involving more than one hundred citizens and physical force, COUPS is the number of

actual extraconsitutional or forced changes in the top government elite and/or its effective control of the

country�s power structure, CABCHG is the number of times per year that a new premier is named and/or

50 percent of the cabinet posts are occupied by new ministers, and CONSTCHG is the number of basic

alterations in a state�s constitutional structure. The source for these data is Easterly and Levine (1997).

We use the 1970s score for the subperiods 1970-74 and 1975-79 and the 1980s score for the subperiods

1980-84, 1984-89 and 1990-95. (We also have data on the 1960s for some countries. Using lagged values

for INS gives generally similar results but substantially reduces the number of available observations.)16In any event, over the 1970-95 period, there is a strong correlation between changes in external debt

and other types of capital inßow. For instance, in the pooled data, the correlation between changes in

external debt and the World Bank�s measure of total net private capital inßows is 0.59. However, there

has been a substantial rise in non-debt ßows during the 1990s (World Bank 1997c).17The fall in external debt in the 1990s partly reßects a sharp rise in non-debt external liabilities (World

Bank 1997c). Our focus on stock data precludes an analysis of non-debt liabilities in this paper.

8

the ratio of external debt to GDP in Table 1. While trade openness increased during the

1970s, it actually fell during the 1980s but has expanded considerably during the 1990s.

The tight connection between the levels of external debt and output is revealed by

the scatter diagram in Figure 1, which pools the data from the Þve subperiods 1970-74,

1975-79, 1980-84, 1985-89 and 1990-95.18 The scatter plot clearly reveals a strong positive

association between the quantity of external debt per capita and the level of output per

capita.19

3.3 Econometric Analysis

In the econometric work, it is a maintained hypothesis that credit constraints bind (Dit =

Dmaxit ). Although it would be desirable to allow that credit constraints may be slack for

some countries, at least for some time periods, there is no persuasive estimation procedure

that could incorporate this ßexibility. Eaton and Gersowitz (1981) attempt to estimate

such a model, using the maximum likelihood procedure of Maddala and Nelson (1974),

but their identiÞcation assumption is that a country with a high growth potential is more

likely to default and hence faces a lower debt limit, which runs counter to our theoretical

approach. (In their model, there is no capital accumulation and international borrowing

is only for consumption-smoothing purposes.) In any event, using this procedure, they

Þnd that the data indicates that the vast majority of developing countries are in practice

credit-constrained. In light of these difficulties, our more restrictive approach is an efficient

way of handling the data.

We Þrst examine the determination of the level of external debt before turning to the

speciÞcations for the dynamic evolution of external debt in section 4.

18Gertler and Rogoff (1990) also presented a graph showing a positive relationship between external debt

and the level of GDP. However, their data was for a cross-section using only 1980 data and, unlike this

study, they did not attempt to control for any additional determinants of external debt.19Similarly strong scatter relationships hold for each of the individual subperiods.

9

3.4 Results

3.4.1 1970-95 Cross-Section

We begin by looking at the cross-sectional data averaged over the entire period 1970-95.

We restrict the sample to the 68 countries which have complete data on external debt and

output for the entire period. Column (1) contains the simple regression of the log of per

capita external debt on the log of per capita output. In line with the scatter diagram in

Figure 2, there is a tight Þt to the relationship between these variables, with 70.5 percent

of the cross-sectional variation in the level of external debt explained by the variation in

the level of output per capita.

In column (2), trade openness is added to the speciÞcation. (Only 50 countries in the

sample have complete trade data for 1970-95.)20 This does not alter the contribution of per

capita output in explaining the level of external debt but trade openness itself is also highly

signiÞcant, with external debt responding more than proportionately to an increase in trade

openness. This result Þts well the credit constraint story: greater trade openness makes a

country more vulnerable to the threat of trade sanctions in the event of default; in turn,

increased vulnerability means that such a country is more unwilling to default and hence

represents a better credit risk.21 Moreover, the openness result holds even if openness

is measured at the beginning of the sample period, such that the correlation cannot be

attributed to reverse causation, whereby an indebted country endogenously becomes more

open in order to run the trade surpluses required to service the debt. The results on per

capita output and trade openness remain robust in all the remaining speciÞcations in Table

20When incomplete data exist, one can either restrict the sample to those countries with complete data

or run regressions with different sample sizes according to the speciÞcation. We took the latter route, on

the basis that giving up degrees of freedom is expensive when the total number of observations is already

small.21Of course, openness may also improve access to credit indirectly by raising productivity. Levine and

Renelt (1992) point out that it is hard to detect an effect of trade openness on productivity: openness

raises growth mainly by increasing investment. This is fully consistent with the mechanism described here,

since easier access to credit facilitatess a faster investment rate.

10

2.

In column (3), we begin to control for cross-sectional variation in levels of productivity

across country by including the human capital proxy (EDUC); in column (4), additional

productivity controls are introduced (GADP, ECO, LIB). In column (5), the natural re-

source variable (NR) is added to the speciÞcation. Regional dummies for sub-Saharan

Africa (SSA) and Latin America (LAM) are added to the speciÞcation in column (6).

We introduce the control for concessional access to credit (CONC) in column (7) and the

political instability index (INS) in column (8).

Across columns (3)-(8), the level of human capital generally enters positively and sig-

niÞcantly. According to the estimates in column (4), restrictions on rent-seeking activity

(GADP) actually exerts a negative inßuence on the level of external debt, even though such

restrictions might be expected to relax the debt ceiling imposed by lenders. However, this

effect is no longer signiÞcant, once additional controls are introduced in columns (5)-(8).

Holding Þxed the other variables, the form of economic organization (ECO) and the degree

of policy liberalization (LIB) are not individually signiÞcant.

According to the point estimates in columns (5)-(8), countries well endowed with natural

resources (NR) have higher levels of external debt but the estimates are imprecise for this

cross-section. The sub-Saharan Africa dummy (SSA) and the Latin American dummy

(LAM) are both insigniÞcant.

In columns (7)-(8), the share of debt that is lent at concessional terms (CONC) does not

appear to have a signiÞcant effect on the total quantity of debt.22 Finally, in column (8),

more politically unstable countries (INS) have lower levels of debt, which is consistent with

such countries being poor credit risks, but the effect is insigniÞcant for this cross-section.

3.4.2 1970-95 Panel

We turn to panel estimation in Table 3. We consider data over Þve subperiods 1970-74,

1975-79, 1980-84, 1985-89 and 1990-95. In column (1), we report the pooled regression of

22One reason is that this variable is highly (negatively) correlated with the output per capita variable.

11

per capita external debt on per capita output. The output coefficient estimate of 0.858

is very similar to the cross-sectional estimates and output explains 47.4 percent of the

variation in external debt in the pool. Time effects, intended to control for shifts in world

interest rates and capital market conditions, are included in column (2). This has little effect

on the output coefficient estimate and the time dummies themselves are highly signiÞcant.

The estimated time dummies indicate a generally increasing level of debt relative to output

over the time period 1970-95.

In column (3), country Þxed effects are also included in the speciÞcation. This is an

atheoretical way to control for cross-country variation in productivity and creditworthiness

and the inclusion of country Þxed effects substantially raises the overall explanatory power

of the speciÞcation. In the presence of country Þxed effects, the output coefficient estimate

is halved but remains highly signiÞcant. In this case, only the �within country� variation in

external debt is explained so it is unsurprising that the output coefficient estimate changes

signiÞcantly. For this reason, Quah (1996) and Barro (1997) criticize the Þxed effects

estimator in international studies, since it rules out explaining the sources of variation

across countries, which is at least as interesting as the within country variation.

Accordingly, in Table 4, we drop Þxed effects estimation in favor of including speciÞc

controls for variation in productivity and creditworthiness. All speciÞcations include time

effects and otherwise the format in columns (1)-(7) are as in columns (2)-(8) of Table 2.23

The strong positive association between output and the level of external debt consistently

shows up across the speciÞcations in columns (1)-(7). The range of output coefficient

estimates (0.563-0.848), together with the small standard errors, indicates that the level of

external debt rises less than proportionately with output. In the context of the theoretical

discussion in section 2, this may signify that the punishment that can be imposed by

creditors in the event of default does not increase one-for-one with the level of output.

Alternatively, a nonlinear relationship between debt and output is also possible in models

of international borrowing in the presence of moral hazard. A third interpretation is that

23The time effects are not reported to conserve space. They are generally signiÞcant across the speciÞ-

cations.

12

the external debt constraint may not be binding for the richest countries in the sample.

The positive partial correlation between trade openness and the level of external debt

is also consistently signiÞcant across the different speciÞcations in Table 4, with a near

unitary elasticity of external debt with respect to openness. Educational attainment is

similarly a robustly positive predictor of the level of external debt. These effects are in line

with open and productive countries enjoying higher debt ceilings.

In columns (3)-(4) of Table 4, the level of external debt is signiÞcantly negatively as-

sociated with government anti-diversion policies but the signiÞcance is lost once regional

dummies are introduced in columns (5)-(7). Unlike the cross-sectional estimates, the natu-

ral resource endowment variable is now consistently signiÞcant across the speciÞcations in

columns (4)-(7). As pointed out in section 2, this result is open to several interpretations.

On the one side, a large natural resource endowment may raise productivity. However, this

view faces the problem that the growth performance of resource-rich countries has been

poor. On the other side, dependence on primary commodity exports may increase a coun-

try�s vulnerability to sanctions, improving its creditworthiness by reducing the probability

of default.

Although the sub-Saharan African dummy is again insigniÞcant in columns (5)-(7) of

Table 4, the Latin American dummy now enters positively and is signiÞcant. A higher level

of external debt among the Latin American countries is unlikely to be explained by these

countries enjoying unusually high productivity or superior reputations for repayment and

so is strictly anomalous, within the context of the theory advanced in section 2. We explore

further this result in the next subsection.

The share of concessional debt remains insigniÞcant in columns (6)-(7) but political

instability now has a signiÞcantly negative inßuence on the level of external debt in column

(8) of Table 4. This is in line with the theoretical prediction that such countries Þnd it

more tempting to default on debt and hence face lower debt limits. Of course, political

instability also plausibly reduces the productivity of capital by raising the risk of economic

disruption (Alesina and Perotti 1996).

13

3.4.3 Sub-Period Cross-Sections

The purpose of this subsection is to investigate whether the relationships in the panel data

also hold in shorter time intervals and to isolate the effects of unusual episodes such as the

1980s debt crisis that might involve factors that cannot be incorporated into our simple

theoretical framework. In Tables 5-9, we run cross-sectional speciÞcations for each of the Þve

subperiods in the sample. Table 5 presents the results for the 1970-74 cross-section. Across

columns (1)-(8), the level of output again is the dominant factor in explaining variation in

the level of external debt. Trade openness is signiÞcant in columns (2)-(4) but becomes

marginally insigniÞcant once the sample size shrinks in columns (5)-(8).24 Educational

attainment is signiÞcant in the speciÞcations in columns (3)-(5) but the estimate loses

its precision once regional dummies are included in columns (6)-(8). None of the other

regressors are individually signiÞcant for this subperiod.

Broadly similar results obtain for the 1975-79 cross-section in Table 6. However, the

evidence on trade openness becomes more robust and, in column (3), there is marginal

evidence in favor of a negative relationship between government anti-diversion policies and

the amount of external debt. In contrast to the 1970-74 time period, the natural resources

variable is now a signiÞcant factor in explaining the level of external debt.25 This Þts

well with the terms of trade boom enjoyed by exporters of primary commodities in the

mid-1970s, relaxing their debt ceilings.

The 1980-84 cross-section in Table 7 marks the beginning of the �debt crisis� episode.

It is noteworthy that the estimated coefficient on output falls relative to the estimates for

the 1970s and the coefficient on trade openness increases sharply relative to the estimates

for the 1970s. We can interpret these changes as reßecting a general tightening of lending

conditions and an increased attention to �fundamentals� on the part of creditors. Another

24If we run the speciÞcation in column (4) for the sample size in columns (5)-(8), the trade openness

variable is insigniÞcant, indicating the loss in signiÞcance is attributable to the change in sample size.25This remains true even if we omit the six countries that have natural resources data for 1975-79 but

not 1970-74. (The countries are Argentina, Chile, Dominican Republic, Papua New Guinea, Paraguay and

Togo.)

14

novel Þnding for this time period is the new signiÞcance of the Latin American dummy

variable in columns (6)-(8). Finally, the estimated negative effect of political instability on

the level of external debt becomes more precise for this time period, as shown in column

(8). These effects generally persist in the cross-sectional results for 1985-89 in Table 8 with

the exception that the coefficient on political instability is halved in column (8) and is

insigniÞcant.

In Table 9, we report the results for 1990-95, the most recent subperiod in the sample.

The basic picture remains the same for this time period: richer, more open and more

educated countries have higher levels of external debt. An important difference is that

resource-rich countries no longer have signiÞcantly higher external debt levels, in contrast

to the evidence from the 1970s and 1980s. In columns (6)-(7), both the sub-Saharan

African and Latin American dummies are positive and signiÞcant. However, once we include

the political instability measure in the speciÞcation in column (8), the magnitude of the

regional effects is much reduced and is no longer signiÞcant. Political instability itself

exerts a signiÞcantly negative inßuence on the level of external debt in column (8), as do

government anti-diversion policies.

From this cross-sectional analysis, it is apparent that the signiÞcance of the Latin Amer-

ican dummy in the panel regressions in Table 4 emanates from the debt crisis period of

the 1980s. Accordingly, as is also suggested by Sachs (1985), a plausible interpretation is

that the Latin American countries found it particularly difficult to reduce debt levels when

interest rates rose at the beginning of the 1980s.

4 Limitations and Extensions

Although the empirical work in section 3 has revealed some strong relationships in the data,

much remains to be done in future work. First, as we acknowledged earlier, a maintained

hypothesis has been that credit constraints bind. Although this is an efficient way to treat

the data, it would be desirable to implement a credible scheme that can discriminated

between rationed and non-rationed countries. Second, it would be interesting to obtain

15

additional empirical evidence that would identify the relative importance of reputation

and various types of penalties in encouraging debt repayment. Third, our focus has been

on the stock of debt. A stochastic model that allows for default risk would enable us to

study credit ratings and spreads on loans to developing countries (Ozler 1992). Since the

outstanding level of debt is itself a major determinant of risk premia, this would entail

modelling a joint process in which the stock of debt and the spread are both endogenously

determined.

Fourth, a potentially fruitful extension would be to broaden the analysis to include non-

debt ßows and investigate whether the composition of capital ßows inßuences overall access

to credit. For instance, is it more tempting to default on bank loans than to expropriate

foreign-owned businesses or conÞscate foreign-owned equity stakes in indigenous Þrms? (See

also Milesi-Ferretti and Razin 1996.) Similarly, subject to the caveats expressed earlier, it

would be worth investigating whether the criteria for sovereign and non-sovereign debt are

the same. Finally, �contagion� effects could also be studied, whereby the characteristics of

neighboring countries also matter in determining access to credit.

5 Conclusions

The empirical results in sections 3 and 4 provide considerable support for the predictions

of the literature that treats developing countries as having limited access to international

credit. In particular, the predominant relationship in the data is the tight positive asso-

ciation between the levels of external debt and output, which is a basic feature of many

models of international credit rationing. In addition, the signiÞcant positive inßuence of

trade openness on external debt is quite robust and has the natural interpretation that

more open economies are better credit risks. There is also some evidence of positive indi-

vidual effects of schooling and natural resource endowments on external debt. The latter

result in particular is worth further investigation, given that it admits multiple interesting

interpretations.

It should be further recognized that the support provided by this paper for the existence

16

of credit rationing can help explain the pattern noted by Levine and Renelt (1992) and

Sala-i-Martin (1996) that convergence was more rapid during the 1960s than subsequently.

The 1960s was a period of relatively closed international capital markets so that growth

performance during this period would have been driven by the underlying fundamentals

rather than by differential access to credit. Since the 1970s, in contrast, although richer

developing countries may have had a lower raw rate of return to capital, superior access to

credit has permitted a greater rate of investment so that, on net, the rate of catch-up of

the poorer developing countries has declined. Quah (1996) also attributes non-convergence

to unequal access to international capital markets. In his theory, however, access to credit

is randomly determined whereas in this paper, access to credit is modelled as increasing in

the levels of current output, productivity, trade openness and other factors that improve

creditworthiness.

Finally, although the emphasis in this paper has been on long-term capital ßows, the

existence of credit rationing will also limit the role of international borrowing and lending

in business cycle ßuctuations (Kehoe and Perri 1997).

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20

4 5 6 7 8 9 100

5

10

log(GDP-pc)

log

(Deb

t-p

c)

Figure 1: Scatter of (log) debt per capita against (log) GDP per capita, pooled data 1970-

95.

21

Table 1: Evolution of Key Variables

External Debt per capita GDP per capita (External Debt/GDP) Trade Openness1970-95 573.72 1122.29 51.1 31.661970-74 250.61 977.94 25.63 31.831975-79 472.60 1117.57 42.29 32.291980-84 654.42 1150.60 56.88 31.181985-89 776.60 1155.25 67.22 29.551990-95 714.40 1210.23 59.03 33.45

External debt and GDP Þgures are in 1987 constant US dollars. Data are from World Bank�s

World Development Indicators CD-ROM. 68 country subsample.

Table 2: Cross-Section, 1970-95

(1) (2) (3) (4) (5) (6) (7) (8)

C -0.241 -0.764 -0.475 -0.072 0.21 0.255 0.311 1.15(.494) (.527) (.543) (.52) (.5) (.574) (.71) (.99)

LOG(Y) 0.932 0.955 0.867 0.967 0.827 0.785 0.782 0.709(.074) (.076) (.091) (.09) (.095) (.112) (.115) (.128)

OPEN 1.5 1.47 1.6 1.75 1.72 1.72 1.48(.356) (.35) (.328) (.337) (.346) (.352) (.4)

EDUC 0.082 0.092 0.116 0.105 0.107 0.106(.048) (.049) (.045) (.05) (.055) (.056)

GADP -2.07 -1.16 -0.85 -0.86 -1.24(.77) (.756) (.816) (.834) (.92)

ECO -0.021 -0.021 -0.029 -0.031 -0.013(.048) (.045) (.046) (.049) (.051)

LIB 0.006 -0.042 -0.009 -0.001 0.11(.256) (.233) (.245) (.255) (.256)

FUEL 0.27 0.34 0.33 0.36(.21) (.23) (.24) (.245)

SSA 0.122 0.126 0.194(.167) (.172) (.193)

LAM 0.203 0.204 0.187(.177) (.18) (.183)

CONC -0.05 -0.2(.36) (.41)

INSTAB -0.262(.208)

ADJ.R2 0.705 0.769 0.778 0.81 0.832 0.83 0.826 0.812N 68 50 50 50 47 47 47 46

Dependent variable is log of per capita external debt in 1987 constant US dollars, averaged over

1970-95. See text for deÞnitions of variables. Ordinary least squares estimation.

22

Table 3: Panel Estimation I, 1970-95

(1) (2) (3)

C 0.109(.302)

LOG(Y) 0.858 0.83 0.441(.045) (.04) (.127)

DUM7074 -0.498(.269)

DUM7579 0.624 0.645(.12) (.064)

DUM8084 0.952 1.05(.119) (.064)

DUM8589 1.16 1.28(.117) (.065)

DUM9095 1.14 1.25(.119) (.068)

Country Fixed Effects? No No Yes

ADJ.R2 0.474 0.605 0.894N 394 394 394

Dependent variable is log of per capita external debt in 1987 constant US dollars. See text for

deÞnitions of variables. Ordinary least squares estimation.

23

Table 4: Panel Estimation II, 1970-95

(1) (2) (3) (4) (5) (6) (7)

C -0.75 -0.505 -0.208 0.138 0.176 0.46 0.993(.287) (.298) (.303) (.311) (.34) (.43) (.47)

LOG(Y) 0.848 0.753 0.815 0.677 0.618 0.6 0.563(.042) (.051) (.052) (.057) (.062) (.064) (.065)

OPEN 0.864 0.895 1.01 1.02 1.05 1.06 0.9(.191) (.19) (.19) (.2) (.2) (.2) (.208)

EDUC 0.101 0.125 0.141 0.107 0.101 0.105(.028) (.03) (.03) (.032) (.032) (.032)

GADP -1.58 -0.869 -0.289 -0.355 -0.67(.447) (.448) (.47) (.473) (.48)

ECO -0.011 0.003 -0.015 -0.019 -0.011(.029) (.03) (.029) (.029) (.029)

LIB 0.025 -0.015 -0.039 -0.029 0.027(.161) (.155) (.152) (.152) (.153)

FUEL 0.54 0.7 0.67 0.65(.12) (.121) (.12) (.123)

SSA 0.124 0.111 0.049(.108) (.109) (.113)

LAM 0.393 0.373 0.351(.108) (.109) (.109)

CONC -0.197 -0.277(.179) (.181)

INSTAB -0.242(.09)

ADJ.R2 0.641 0.663 0.679 0.708 0.741 0.741 0.747N 317 313 313 264 260 260 258

Dependent variable is log of per capita external debt in 1987 constant US dollars. See text for

deÞnitions of variables. Ordinary least squares estimation. Time Þxed effects are included but

not reported.

24


(1) (2) (3) (4) (5) (6) (7) (8)

C 1.5 -2.32 -1.79 -1.54 -0.507 -0.22 -0.15 0.29(.5) (.806) (.83) (.88) (1.03) (1.18) (1.42) (1.6)

LOG(Y) 0.702 1.08 0.927 0.958 0.767 0.707 0.702 0.674(.074) (.123) (.144) (.149) (.178) (.217) (.227) (.234)

OPEN 1.05 0.917 0.954 0.8 0.887 0.887 0.809(.44) (.434) (.44) (.513) (.548) (.555) (.578)

EDUC 0.148 0.189 0.209 0.18 0.177 0.179(.075) (.085) (.105) (.12) (.124) (.126)

GADP -1.49 -1.08 -0.75 -0.74 -1.04(1.2) (1.42) (1.66) (1.69) (1.79)

ECO 0.053 0.023 0.024 0.022 0.034(.081) (.094) (.1) (.1) (.11)

LIB -0.096 -0.08 -0.167 -0.17 -0.155(.47) (.51) (.56) (.57) (.577)

FUEL 0.39 0.46 0.458 0.46(.45) (.49) (.5) (.5)

SSA 0.103 0.108 -0.2(.4) (.41) (.45)

LAM 0.135 -0.13 0.102(.4) (.41) (.417)

CONC -0.055 -0.116(.6) (.62)

INSTAB -0.183(.33)

ADJ.R2 0.54 0.581 0.602 0.592 0.509 0.488 0.474 0.464N 77 59 59 59 48 48 48 48



25


(1) (2) (3) (4) (5) (6) (7) (8)

C -0.5 -0.693 -0.373 -0.104 0.264 0.403 1.18 2.01(.56) (.6) (.64) (.658) (.722) (.828) (0.98) (1.12)

LOG(Y) 0.928 0.938 0.845 0.904 0.734 0.661 0.609 0.568(.086) (.089) (.11) (.114) (.124) (.146) (.149) (.153)

OPEN 0.676 0.638 0.744 0.821 0.93 0.953 0.747(.398) (.396) (.398) (.451) (.475) (.469) (.506)

EDUC 0.084 0.125 0.131 0.109 0.101 0.111(.06) (.066) (.068) (.075) (.074) (.08)

GADP -1.63 -0.712 -0.164 -0.357 -1.05(.96) (.98) (1.1) (1.1) (1.2)

ECO 0.004 0.019 0.005 -0.005 0.029(.062) (.063) (.066) (.066) (.07)

LIB -0.137 -0.107 -0.11 -0.152 -0.164(.368) (.356) (.381) (.377) (.394)

FUEL 0.68 0.77 0.687 0.702(.26) (.27) (.277) (.279)

SSA 0.064 -0.004 -0.221(.264) (.265) (.314)

LAM 0.281 0.196 0.109(.261) (.265) (.273)

CONC -0.625 -0.764(.44) (.45)

INSTAB -0.36(.217)

ADJ.R2 0.612 0.645 0.651 0.656 0.657 0.65 0.658 0.671N 75 62 62 62 54 54 54 52



26


(1) (2) (3) (4) (5) (6) (7) (8)C 0.474 0.195 0.65 0.907 1.27 1.69 1.86 2.64

(.553) (.59) (.61) (.63) (.7) (.74) (.86) (.87)

LOG(Y) 0.831 0.832 0.697 0.753 0.61 0.436 0.426 0.434(.083) (.086) (.105) (.11) (.129) (.127) (.13) (.127)

OPEN 1.09 1.05 1.17 1.27 1.56 1.57 1.13(.086) (.42) (.43) (.46) (.424) (.429) (.405)

EDUC 0.125 0.139 0.167 0.092 0.09 0.084(.059) (.065) (.066) (.065) (.065) (.059)

GADP -1.38 -0.924 0.34 0.266 -0.56(.96) (1.04) (.98) (1.01) (.93)

ECO -0.015 0.011 -0.032 -0.035 -0.008(.062) (.064) (.059) (.06) (.054)

LIB 0.181 0.204 0.114 0.129 0.067(.34) (.355) (.334) (.339) (.305)

FUEL 0.46 0.72 0.7 0.714(.26) (.24) (.24) (.218)

SSA 0.123 0.115 -0.103(.236) (.239) (.223)

LAM 0.814 0.796 0.618(.214) (.221) (.207)

CONC -0.154 -0.3(.38) (.34)

INSTAB -0.361(.179)

ADJ.R2 0.563 0.6 0.621 0.622 0.64 0.717 0.716 0.77N 78 65 65 65 55 55 55 54



27


(1) (2) (3) (4) (5) (6) (7) (8)

C 0.976 0.757 1.03 1.39 1.28 1.43 1.59 2.04(.509) (.577) (.607) (.62) (.57) (.64) (.81) (1.0)

LOG(Y) 0.785 0.765 0.682 0.753 0.672 0.55 0.539 0.518(.077) (.088) (.106) (.11) (.115) (.131) (.136) (.141)

OPEN 1.26 1.19 1.41 0.914 1.29 1.32 1.11(.46) (.46) (.465) (.515) (.58) (.6) (.643)

EDUC 0.081 0.105 0.106 0.094 0.09 0.086(.059) (.063) (.058) (.06) (.063) (.064)

GADP -1.64 -0.615 0.175 0.139 -0.089(.971) (.875) (.9) (.917) (.98)

ECO -0.056 -0.023 -0.059 -0.064 -0.066(.062) (.06) (.059) (.061) (.062)

LIB 0.107 -0.068 -0.048 -0.031 0.047(.344) (.3) (.288) (.3) (.311)

FUEL 0.71 0.78 0.767 0.72(.23) (.225) (.233) (.244)

SSA 0.282 0.278 0.252(.192) (.194) (.21)

LAM 0.531 0.527 0.495(.22) (.222) (.232)

CONC -0.11 -0.232(.35) (.4)

INSTAB -0.18(.19)

ADJ.R2 0.56 0.566 0.572 0.594 0.708 0.735 0.73 0.711N 83 68 68 68 54 54 54 53



28


(1) (2) (3) (4) (5) (6) (7) (8)

C 1.34 1.16 1.36 1.76 1.64 1.53 1.95 2.77(.502) (.57) (.61) (.62) (.62) (.66) (.92) (1.05)

LOG(Y) 0.725 0.709 0.649 0.75 0.653 0.6 0.557 0.58(.075) (.087) (.106) (.109) (.12) (.123) (.139) (.137)

OPEN 1.01 0.935 1.27 1.58 1.53 1.6 1.05(.436) (.442) (.454) (.456) (.44) (.459) (.462)

EDUC 0.059 0.063 0.103 0.086 0.08 0.062(.059) (.06) (.058) (.065) (.066) (.061)

GADP -1.98 -1.36 -0.8 -0.85 -1.79(1) (.95) (.93) (.94) (.93)

ECO -0.052 -0.019 -0.04 -0.045 -0.022(.064) (.063) (.06) (.062) (.056)

LIB -0.025 -0.131 -0.046 -0.017 0.059(.324) (.3) (.285) (.29) (.272)

FUEL 0.4 0.445 0.4 0.41(.28) (.267) (.277) (.26)

SSA 0.37 0.388 0.224(.197) (.2) (.193)

LAM 0.488 0.493 0.301(.196) (.198) (.189)

CONC -0.25 -0.5(.38) (.4)

INSTAB -0.344(.18)

ADJ.R2 0.551 0.574 0.574 0.61 0.669 0.708 0.705 0.747N 76 59 59 58 53 53 53 51



29

Documents

Empirical Perspectives on Long-Term External Debt