A COUNTRY RISK INDEX: ECONOMETRIC FORMULATION AND AN APPLICATION TO MEXICO

A COUNTRY RISK INDEX: ECONOMETRIC FORMULATION AND AN APPLICATION TO MEXICO

MICHAEL MELVIN and DON SCHLAGENHAUF'

D ynamic factor analysis is used to estimate a monthly country risk index for Mexico. This method extracts the unobsemable risk information contained in deviations from interest rate parity and allows for hypothesis tests regarding the important determinants of such risk. The results suggest that the ratios of imports to reserues and debt to exports are important determinants of Mexican country risk. The estimated risk index correctly antici- pates the Mexican capital controls and financial crisis of August 1982. In addition, the index significantly leads the country risk rating published by Institutional Investor based on commerchl bank surueys.

I. INTRODUCTION

Country risk refers to the risks in international lending arising from the economic, political, legal, and social conditions existing in a foreign country. While such risk is often referred to as political risk, this term is somewhat misleading unless interpreted as meaning the risk associated with investment opportunities in another political jurisdiction.

The importance of understanding the nature of risk in international lending has been underscored by the well-publicized events of recent years. A proliferation of debt reschedulings and questionable loans has moved the issue of country risk analysis from the back rooms of international banking to the front pages of the popular press. Yet for all of the recent popular interest, our technical knowledge of country risk remains woefully inadequate.

Economic models of country risk are generally concerned with predicting the probability of debt default or rescheduling due to country-specific factors. Unfortunately, there are few observations on countries that actually default so that the resulting probabilities from LOGIT analyses of the problem are at best mildly suggestive, and at worst, dangerously misleading. The market perception of country risk should be a continuous variable, rising and falling as new information becomes available. Trying to gauge the his- torical degree of country risk associated with a country's debt liabilities from a retrospective view of whether or not actual debt reschedulings occurred is unsatisfactory.

'Arizona State University. We would like to thank Mark Watson for advice as well as for his program, which we modified for use in this stydy. Chin Duu Shiau provided helpful research assistance. Comments on an earlier draft were provided by John Bilson, Craig Hakkio, and seminar participants at Arizona State University, The Claremont Graduate School and U.C.L.A.

601 Economic Inquiry Vol. XXIII. October 1985

602 ECONOMIC INQUIRY

We propose to infer the risk associated with a country from a time domain dynamic factor analysis framework using deviations from interest rate parity. The statistical technique is designed to yield maximum likelihood estimates of unobserved variables, such as the unobserved risk associated with a country. Once we estimate the risk index for a country, we have a time series for the risk factor that so often appears in studies of international capital flows or international currency substitution. While past researchers have either avoided the issue of specifying empirical proxies for country risk or else used dummy variables, we offer a preferred alternative-a time series that continuously varies as the risk associated with a particular country changes.

The next section briefly discusses previous approaches to estimating the magnitude and determinants of the risk associated with a country. Section I11 presents the basic model framework for determining risk. Section IV covers the econometric methodology. The estimated model is discussed in section V. The evidence presented there allows an evaluation of the menu of variables likely to be important determinants of risk. Section VI offers a discussion of the risk index along with a comparison of our index and the Znstitutionul Znuestor country risk rating system. Finally, section VII presents a summary along with our major conclusions.

11. PREVIOUS APPROACHES

When economists think of country risk research, they generally think of the studies that seek to explain the probability of debt rescheduling or default, or the studies that examine the sovereign risk involved in Eurocur- rency loans. We will present a brief overview of each approach and indicate the importance of our research in contrast.

Limited Dependent Variable Studies Studies using limited dependent variables generally explore the determi-

nants of debt rescheduling or defaults. Researchers use a sample of countries over some time period, with some (usually very few) of the countries expe- riencing debt repayment difficulties. The dependent variable is generally a binary series with a value of 1 assigned to problem countries and zero assigned to all others. The statistical analysis considers a set of explanatory variables which allow inference regarding those characteristics associated with rescheduling countries apart from the rest of the sample. Researchers generally use either discriminant or LOGIT analysis. Discriminant studies include Frank and Cline (1971), Sargen (1977), and Saini and Bates (1978). Representative LOGIT studies are Feder and Just (1977), Saini and Bates (1978), Mayo and Barrett (1978), and Feder, Just, and Ross (1982).

Table 1 lists the variables found to be significantly related to the probability of debt rescheduling or default.

While the studies summarized in Table 1 have been instructive, there are several problems associated with this line of research, so caution must be used in interpreting the results. Reviews of this literature have pointed to the

MELVIN & SCHLAGENHAUF COUNTRY RISK INDEX 603

TABLE 1

Authors Important Determinants of Rescheduling Probabilities

Frank and Cline

Sargen

Saini and Bates

Feder and Just

Mayo and Barrett

Feder, Just, and Ross

debt-service ratio, debt amortization to total debt ratio, imports to reserves ratio

inflation rate, debt-service ratio

current account balance, inflation rate

debt amortization to total debt ratio, debt-service ratio, imports to reserves ratio, per capita income, capital inflows to debt service payments ratio, GDP growth

disbursed debt outstanding to exports ratio, foreign reserves to imports ratio, gross fixed capital formation to GDP ratio, imports to GDP ratio, reserve position in IMF to imports ratio, inflation rate

debt-service ratio, CNP to U.S. CNP ratio, reserves to imports ratio, exports to GNP ratio, debt-service for noncommercial banks, debt-service for commercial banks

small number of actual reschedulings as a major problem. For instance, Walter (1983) states:

. . . because there are only a few observations of “difficulties” in debt- service available in any one year compared with the number of “non- problem” countries, the weights assigned to the former are inordinately large and a single country marginally falling into one or the other cate- gory may significantly affect the estimated parameters [p. 2721.

McDonald (1982) also takes issue with the dependent variable of these studies on two grounds:

First, the relative frequency of reschedulings is quite low, a factor that is likely to lower the power of the estimation methods used. Second, there is the question of the appropriate definition for the dependent variable. Reschedulings have reflected a diverse set of circumstances. Not all reschedulings have been associated with debt-servicing difficulties. In other cases, arrears existed for some time before reschedulings occurred, leaving the researcher the difficulty of deciding the timing of the rescheduling variable [p. 6241.

Thus, the limited dependent variable approaches have widely recognized shortcomings. An alternative approach to empirical exploration of country risk issues has focused on the determinants of spreads in the Eurocurrency markets.

Eurocurrency Spreads The interest rate on loans in the Eurocurrency market is stated as a spread

over LIBOR (the inter-bank interest rate in London). Other things being equal, the greater the spread, the greater the risk associated with a loan. It

604 ECONOMIC INQWIRY

is on this basis that researchers have investigated the determinants of the spread on loans in the Eurocurrency market. Studies of Eurocurrency spreads include Feder and Just (1977b), Feder and Ross (1982), and Ed- wards (1984). Table 2 presents a listing of variables found to be significant in determining Eurocurrency spreads.

TABLE 2 ~

Authors Variables Significantly Related to Eurocurrency Spreads

Feder and Just debt-service ratio, export fluctuations, imports to reserves ratio, import to GNP ratio, per capita GNP, CDP growth, loan duration

country risk rankings from the Institutional Investor survey

debt to output ratio, debt-service ratio, reserves to GNP ratio

Feder and Ross Edwards

These studies suffer from a timing problem in that we can only infer the risk associated with a country at the time a loan is made. New Eurocurrency loans are not made on a continuous monthly or quarterly basis to a country. Furthermore, the variables significantly related to the spread over LIBOR are not available on a timely basis for analyzing country risk; for example, GNP and external debt are available with a considerable lag for developing countries. Thus, there is a need for another approach that offers a more timely assessment of risk.

Finally, our goal in generating a country risk index is more than producing a tool to be used by practitioners in assessing risk. We also want to use the index to gain a greater understanding of the nature of international capital flows or any of a host of other empirical applications of such an index. Thus, the paucity of observations associated with actual debt reschedulings or actual Eurocurrency loans leads us to look to more continuous approaches.

Perhaps the study closest to the spirit of ours is that of Dooley and Isard (1980). They seek to establish that the interest differential due to country risk “depends essentially on the gross supplies of debt outstanding against different governments and the distribution of world wealth among residents of different political jurisdictions” (p. 371). What is interesting to us, is that they examine the differential between the interest rate on Euromark deposits in Zurich and the interbank rate in Frankfurt between January 1970 and December 1974. This interest differential should reflect the country risk associated with German assets, and so we have a series that will allow a view of risk for the complete 1970-74 period. However, since the interest differential will also exist due to German capital controls, such controls must be accounted for before infemng anything about political risk. Dooley and Isard include proxy variables for capital controls in their interest differential equation and then assume that the significance of the debt and wealth variables also included indicates the role of wealth and debt in capturing the

MELVIN & SCHLAGENHAUF: COUNTRY RISK INDEX 605

country risk effect. We view the Dooley and Isard study as a step forward for empirical work on country risk. However, Eurocurrency deposit interest rates only exist for the major industrialized countries, so we must look else- where to infer country risk for less developed countries. The approach developed below offers a significant additional step in improving empirical work and, consequently, our understanding of country risk.

111. MODEL TO BE ESTIMATED

Znterest Rate Parity and Country Risk We will infer the risk associated with a country from a model of inter-

national return differentials. The return differential between U.S. and foreign securities is determined in part by country risk.

(1) where S is the spot exchange rate and F is the forward rate. The right-hand- side of (1) is the forward premium on foreign exchange, and the left-hand- side is the interest differential between domestic ( i ) and foreign ( iF) securities. When interest rates on Eurocurrencies are used for the rates in (l), careful studies indicate that IRP holds quite well. Such deposits may be issued by a single bank in one country so that the only difference is the currency of denomination of the deposits.

If instead of comparing Eurocurrency rates, we use money market securities in two different countries, we find that IRP does not hold as well. As Aliber (1973) argued, if securities are issued in different political jurisdictions, they will differ in political risk, where such risk covers “the probability that the authority of the state will be interposed between investors in one country and investment opportunities” (p. 1453). It follows, then, that deviations from IRP calculated from the domestically issued securities of two different countries will contain information on such risk.

There is no reason why covered interest arbitrage would equalize expected returns on assets that are non-comparable in terms of risk. The forward premiums should be set according to the interest differential on offshore market assets, so that the non-comparability of the domestically issued assets will be reflected in the interest differential deviating from the forward premium. However, this deviation may reflect more than simply the country risk existing. We know that transaction costs, taxes, and existing capital controls can also be reflected in the existing interest differential.

Model Specification

(2) where fl, is the deviation from interest rate parity at time t , CR, is the value of the country risk index at t , CC, is a variable measuring the effect of existing capital controls, and a, PI, and P2 are parameters to be estimated.

To illustrate, consider the interest rate parity relation (IRP):

(i - i F ) / ( l + i F ) = ( F - S ) / S

We specify the deviation from interest rate parity as

a, = aCR, + PI + &CC, + e ,


The CR effect may be thought of as inducing a variable risk premium. If R, measures the deviation from interest rate parity between U.S. and Mexican securities, then if the risk associated with lending to Mexico increases, there should be an increase in n,. We have no prior information regarding changes in transaction costs, and effective tax rate changes are difficult to measure, so we use the constant term as our proxy for these. This is reasonable in that Mexican corporate tax rates were unchanged over the sample and U.S. corporate tax rates dropped but slightly over the lowest marginal brackets.

The capital controls should widen the deviation from interest rate parity. In the Dooley and Isard study, capital controls were in the form of higher reserve requirements on foreign-owned deutschemark deposits in German banks. Such controls are analogous to a tax placed on foreign depositors or investors. The controls Mexico imposed in August 1982, effectively halted remittances to foreign investors. Then in September, Mexican Foreign In- vestment Commission approval was required for the purchase of foreign exchange used for dividends, royalties, and other remittances abroad. Ac- cording to the Economist Intelligence Unit’s Quarterly Economic Review of Mexico, approvals from the Foreign Investment Commission were subject to long delays. Therefore, the capital controls in Mexico involved uncertainty regarding the bureaucratic delay in securing approval for the repatria- tion of funds. Lack of free capital mobility should increase the deviation from interest rate parity as Mexican assets must offer a higher yield to com- pensate for the extra lag in repayment.

The problem in estimating (2) is that CR is unobservable. Suppose we had data on country risk. Then we could specify an equation for country risk, suggesting that CR evolves over time according to the following (the exact functional form is an empirical question):

(3) where Z, is a matrix of variables likely to be related to changes in the perceived risk associated with loans made to a country, and + and (Y are parameter vectors to be estimated. The parameters allow tests of the significance of variables suggested by previous researchers as important determinants of country risk.

While we want to estimate equations (2) and (3) jointly, we must first address the crucial issue of how to treat the unobserved CR variable. The next section discusses an econometic approach for estimation with unobservable variables.

CR, = +CR,-, + aZ, + u,

IV. ECONOMETRIC ISSUES

A number of empirical approaches have been suggested to deal with unobserved variables. Some of the more frequently employed approaches are to use survey data, the ex post value of the series, or a generated regres-

MELVIN & SCHLAGENHALIF: COUNTRY RISK INDEX 607

sor approach (see Pagan 1984). In terms of the problem at hand, neither survey data on country risk nor ex post measures of country risk are available at regular intervals through time. As a result, we approach the country risk problem in terms of a time-domain dynamic-factor analysis framework as formulated by Engle and Watson (1981) and Watson and Engle (1982, 1983).' With this approach, a statistical model is specified as if the data were available on the unobserved variable or variables. Then, a joint distribution of the observable variables can be derived. This serves as a likelihood function for estimating the unknown parameters. In addition, this framework allows for estimates of the values of the unobserved variables to be obtained in an index form. We use this series to measure country risk.

To apply this framework, the economic model is specified in "state-space" form (see Mehra 1974). By using this formulation, general maximum likelihood estimates are available based on the Kalman filter recursive algorithm. The state-space model takes the general form

(4) x , = &,-, + yzl, + 0, (5 ) y, = a x , + Pz2, + e, and

(6)

where x , is a i X 1 state vector of unobserved variables; zl is a k X 1 vector of observable exogenous and lagged dependent variables, 22 is a L X 1 vector of observable exogenous and lagged dependent variables; y, is a p X 1 vector of measurement variables containing information on the unobserved variables; 0, is a m X 1 vector of disturbances; e, is a p X 1 vector of disturbances; and 4, y, a and f l are coefficient matrices. Equation (4) is known as the transition equation and describes the evolution of the unobserved variables. Equation (5) is the measurement equation. This equation describes the relationship between the unobserved variables and the measured y variables.

If 7, represents the innovation in y,, and H , denotes the variance of q,, Harvey (1982) has shown that the log likelihood for equations (4) - (6) can be written as

T L ( B ) = constant - 1/2 C (log I H , ~ + ~,'~,-lr],),

where 8 is the vector of unknown parameters. The innovations and their variances are easily calculated with the Kalman filter.

In theory the above maximization problem is straightforward. However,

I --I (7)

1. Geweke (1977), Brillinger (1975), and Sargent and Sims (1977) have proposed frequency domain procedures for identifying unobserved components in multivariate time series.

so8 ECONOMIC INQUIRY

in practice this maximization problem is complex due to the fact that the number of parameters is usually large, and each evaluation of the log likelihood function uses an appreciable number of calculations. Two methods for maximizing the likelihood function have been suggested in the literature. Pagan’s (1980) generalization of the method of scoring, which uses only first derivatives to produce asymptotically efficient estimates, can be employed. Alternatively, Watson and Engle (1983) suggest that the EM algorithm of Dempster, Laird, and Rubin (1977) can be employed. Since the EM algorithm is a derivative-free method designed to maximize a likelihood function in the presence of missing observations, we use that approach in this paper.

The EM algorithm consists of two steps which are iterated to convergence. The estimation step constructs the estimate of the missing observations conditional on the observed data and the parameters (using specified starting values for the first iteration). The filter is a recursive method for finding E[x,IY,] where Y, = ( y f , Y , - ~ , . . . , Zl,, Zl , - l , . . . ,22,, 22,-1 . . .). If we de- fine XfI,+ = E ( x , I Yt-i), y,l,-i = E ( y f lY,- i ) , Ptlf-, = var (X, IY,-,) and H, = var (y, lY,-,), then the Kalman filter for this problem is

(8)

(9)

(10)

(11)

Xflt-1 = &f-ll,-l + Y Z l ,

Ptlt-1 = M t - l p - l + ’ + Q Yflf-I = aXfIf-1 + P&

H, = c ~ P ~ ~ , - ~ a ’ + R

(13) P,I, = P,,,-l - P,l,-l a’ 4 - l cUP,I,-l *

Equations (8) through (11) generate the means and covariance matrix of the joint distribution of (y,x,) conditional on Y f . These values can then be sub- stituted into (12) and (13) to form x,~, and P,l,. In order to use this recursive algorithm, initial values for x and P are required. Following Watson and Engle, we employ a vague prior to start the algorithm by specifying starting values of zero and lo00 for xo,o and Polo respectively.

The Kalman filter generates minimum mean square error estimates of r , using data up through t . It is preferable to generate the minimum mean square error estimates of x,, using all available data. This estimate of x,, known as the “smoothed estimate, can be derived by using the recursion

(14) xflT = Xflr + A(xt+lIT - X,+llr)

( 15) A, = P,lf4’P,+ll,

(16) ptlT = PtI, + A,[Pt+ll, - Pf+llfIA:. The second step of the E M algorithm-the maximization step-calculates

the maximum likelihood estimators of the parameters conditional on the observed and newly created unobserved data. The Kalman filter equations


can also be used to derive a likelihood function which can be used to estimate the unknown parameters of the model. In this case, the likelihood function can be written in terms of the innovations in the filter, y, - yf,f-l or 7, . The joint distribution of (yo, . . ., yT) can be expressed as the product of all conditional distributions. Thus, the log likelihood function is

T

L = log f ( y J + Clog [ f ( Y , i y,-,)i. ,=I

Given that each of the conditional densities is normal with specified mean and variance and a proper prior is available for y, the log likelihood can be written as (7).

The completion of the maximization step yields a new set of estimates for the parameters. These estimates can then be used in the estimation stage to generate a new estimate of the x series. This iterative process continues until convergence is achieved.2

V. ESTIMATING THE MODEL

In the context of the econometric framework described in section IV, we specify the measured variable containing information on country risk to be deviations from interest rate parity on US. and foreign issued securities. Specifically, the measurement equation analogue to equation (5) is equation (2):

(2) The transition equation, in our case, describes the evolution of the unobserved country risk variable as originally specified in equation (3)3:

Q, = aCR, + P I + &CC, + e,.

(3) C R , = r$CR,-, + 72, + v,.

While the model could be applied to any pair of countries for which data are available, we believe that the U.S.-Mexico relationship provides an interesting and instructive first application of the model.

Since Mexican peso forward rates are available in a consistent form since October 1978, we apply the model to the dollar-peso sample over the period October 1978 to March 1984. This is a most interesting period, as it includes the period of rapid growth in Mexican external debt as well as the 1982 imposition of capital controls and freezing of Mexdollars in Mexican banks.

2. The information matrix is calculated using the parameter estimates from the converged EM algorithm. Engle and Watson (1981) show that the ijth element of the information matrix can be calculated by the following formula:

Z i i = ~ Y s t r ( H ; ' 6 H / 6 O i H ; ' S H / S O i ) + E ( ( 6 q , / S O i ) H ; ' ( 6 q , / 6 0 i ) ) . t

3. Estimating the model without a lagged CR term reveals significant autocorrelated errors. It is the presence of such dynamic factors in time series data that rules out the use of standard factor analysis techniques.


The interest rates used to compute n are 30 day certificates of deposit in U.S. and Mexican banks. The capital controls variable is a dummy variable equal to zero until August 1982, when the value changes to one to reflect the capital controls imposed on the economy since that time.

The choice of 2, variables is limited by the data available on a monthly basis. Variables suggested by past studies which could be assembled on a monthly basis for Mexico include: the ratio of imports to reserves (ZMR), the debt-export ratio (DEX), the Mexican inflation rate (ZNF), and the growth rate of industrial production (GZP). The data are described in more detail in the appendix.

TABLE 3 Dynamic Factor Analysis Model of Country Risk

Equation

C R

Constant

cc

C R Equation

CR,-,

I M R

DEX

I N F

CIP

.090 (5.21)

.214 (4.42)

. a 3 (2.79)

.697 (5.86)

.378 (2.28) .263

( 1.73) -.114

(-1.15)

.133 ( 1.03)

.092 (5.28)

239 (3.96) .200

(2.79)

.735 (6.06) .238

(1.68) .m

( 1.43)

log L 117.73 115.73 t statistics in parentheses

The estimate of the complete model is presented in column (1) of Table 3. In the n measurement equation, country risk and capital controls both have a positive effect on deviations from interest rate parity, as expected. Since n compares returns on C.D.’s in the U.S. with C.D.’s in Mexico, the imposition of capital controls in Mexico drives a wedge between the eff ec- tive returns on the respective investments. Beyond this capital controls ef-


fect, as the country risk associated with Mexican investment opportunities varies, so does the observed deviation from interest rate parity. Ceteris paribus, the greater the Mexican country risk, the less comparable are Mexi- can and U.S. assets.

What causes country risk to vary? The CR equation allows inferences regarding the likely causal variables. Column (1) reports' C R as a function of an autoregressive factor and I M R , D E X , ZNF, and GZP. The ratio of imports to reserves exerts a positive influence on perceived country risk. As imports rise in relation to the Mexican stock of international reserves, the market perceives a greater element of risk attached to Mexican investments. The ratio of Mexican debt to exports appears to exert a positive effect also. Therefore, as the ratio of debt to exports increases, the risk attached to Mexican assets also increases.

Curiously, Mexican inflation appears to exert a negative influence, al- though the effect of inflation is statistically insignificant. Our prior belief would call for higher inflation to be associated with greater country risk. The effect of GIP is also statistically insignificant. We infer that changes in the growth of industrial production are not important in evaluating the risk associated with a country.

If we omit the ZNF and GZP variables, we estimate the model reported in column (2) of Table 3. The likelihood ratio statistic -2logh based on models (1) and (2) of Table 3 is equal to 4.00, which is an insignificant value of X' with two degrees of freedom. We cannot reject the null hypothesis of the ZNF and GZP coefficients being jointly zero.

The second model of Table 3 is our preferred country risk model, with ZMR and D E X identified as important variables for evaluating country risk. The likelihood ratio statistic on the joint significance of ZMR and D E X yields a value of 4.98, which is a significant value of chi-square with two degrees of freedom. The relatively low individual t-statistics are likely due to correla- tion between I M R and D E X . The collinearity of ZMR and D E X is certainly a plausible result. The identification of the import-reserves and debt-export ratios as key variables for assessing country risk is a result shared by several of the previous researchers mentioned in section 11. However, the unique feature of our study is the generation of the CR variable in the estimation process. Our model allows the country risk factor to be estimated for each month in the sample. This adds a new dimension to the study of country risk and the specification of such risk proxies in related work in international finance.

VI. THE RISK INDEX

A Country Risk Zndex for Mexico Figure 1 presents the estimated country risk index for Mexico over the

1978-84 period. This index is the estimated unobserved CR factor in the model described in the preceding section, and is the maximum likelihood estimate based on the data and estimated parameters of the model. Note the


U N T

R Y

R I s K

YERR

FIGURE 1 Country Risk Index For Mexico


relatively stable nature of the index in the 1978-80 period. In mid-1981, the index begins to increase. There is a brief period of decrease followed by an increase in the spring of 1982. In August and September of 1982, Mexico froze dollar denominated bank deposits in Mexican banks, nationalized commercial banks, and imposed foreign exchange controls. Note that country risk was high prior to August but fell with the imposition of the controls and then remained at high levels through November. By the end of the year, the risk index fell to quite a low level and remained low through the remainder of the sample.

The risk index should be thought of as mirroring market uncertainty regarding the repayment prospects of Mexican deposits and loans. The increase in the index occumng in mid-1981 may be traced to the Mexican response to a softening in the world market for oil. In the analysis of the events leading to the Mexican financial crisis of 1982, Ize and Ortiz (1984) view the drop in oil exports as a factor contributing to the building pressure on the Mexican authorities:

In retrospect, it appears that the fall in oil prices hurt the Mexican economy more because of the government's attitude towards the fall than because of its sheer magnitude, which was relatively modest (2.5 dollars per barrel). Instead of acknowledging a weaker market and simply cutting the price of crude in line with other producers, &e authorities refused to lower the price of Mexican oil for several months. The result was a severe decline in the volume of exports, which from May to August averaged only 55 percent of their value during the first four months of the year [p. 61.

The oil price effect contributed to an all-time high current account deficit of approximately 12 billion dollars in 1981. The model estimates reported in Table 3 indicate that country risk tends to increase as the ratio of debt to exports increases. Even with a constant external debt, the dramatic decline in exports in mid-1981 would increase the risk associated with lending to Mexico.

As oil exports recovered quickly during the fourth quarter of 1981, we would expect the country risk index to fall, ceteris paribus, and this indeed occurs. However, in the spring of 1982, the index begins to rise again. Why should we associate greater risk with Mexico beginning in March and April of 1982? Ize and Ortiz claim that public confidence in the government's ability to contain the crisis was severely jolted by a large wage increase in March 1982.4 The government had announced an adjustment program aimed at reducing government spending and strengthening the economy, but with the wage increase ". . . the public realized that no serious attempt of adjusting internal spending could be possible, given the size of the wage increases, and that by further delaying the day of reckoning, the final neces-

4. The minimum wage increased 30 percent in March closely following a 34 percent increase in January. This rise of 64 percent since the first of the year was considerably above the realized inflation rate.


sary adjustment would eventually have to be much more severe” (Ize and Ortiz, p. 10). Note that the risk index remains high in the months prior to the new policies announced in August 1982. In May, President Lopez Portillo urged Mexicans to stop buying dollars. In the same speech, he ruled out the use of currency controls. It has often been the case that the more frequently the government claims that it will not do something, the greater the likelihood of government action counter to the promises. This seemed to be the case in Mexico. According to Lawrence Rout in the June 17,1982 edition of the WaU Street Journal,

The more the government insists, the more the public doubts. When the central bank issued a pamphlet in April outlining why it wouldn’t put in controls, it only convinced more people that controls were possible. And when the bank reissued the pamphlet with a glossy cover, many Mexi- cans believed controls were imminent [p. 321.

The country risk index in Figure 1 appears to capture the public perception of the coming events. When the capital controls were imposed and dollar bank deposits were frozen in August, the index falls. This makes sense, in that actual controls were now in place to drive a wedge between the return on U.S. and Mexican assets.

However, in the fall, the index rose to a high level again, only to drop, once and for all, in December. The early fall was a time of great uncertainty regarding the direction of Mexican policy. In mid-September, foreign exchange controls were tightened. The September-October period was one of rumors of strong disagreements within the government over economic policy. In addition, there were constant rumors that key government officials either had resigned or would soon do so. In October, the Mexican situation became still more threatening when the Mexican Treasury released a memo to the international banking community stating that it needed to bor- row 8.31 billion doUars by the end of 1983. This need was predicated on the assumption of no principal repayment on the existing external debt-a policy that had not been approved by the creditor banks. Compounding the problem was the forthcoming change in the federal administration. The lame-duck administration was characterized by inaction in the final months as bureaucrats were waiting to learn of the new administration’s policies. As a result, firms requiring government approval, as in the case of foreign investment, found such approval very slow in coming, if any decision at all was rendered.

By mid-November, the situation began to turn. On November 10, a tenta- tive agreement with the IMF was announced involving a $3.84 billion loan along with a program requiring government austerity. Political risk dropped sharply in December following President de la Madrid‘s inaugural address on December 1. The new President indicated that Mexican policy would be changed to restore a sound economy. Even more important than the Presi- dent’s promises was his appointment of Miguel Mancera to be Director of the central bank. Mancera had resigned the Director’s position on Septem-

MELVIN & SCHLAGENHALIF: COUNTRY RISK INDEX 615

ber 1 in opposition to the foreign exchange controls that were imposed. Mancera’s appointment was viewed as a repudiation of past policy. Also in December, a debt restructuring plan and tiered exchange rates were announced. When the exchange rate tiering began, the free market rate floated to the previous black market rate of Ps150 = $1. There also was a preferential rate of Ps95 = $1, which was used for crucial imports and interest payments on foreign debt.

The (at least partial) restoration of free market forces in Mexican financial links with the rest of the world appears to have had a settling effect on the market as the estimated country risk index fell in December 1982 and then remained quite low for the remainder of the sample.

An important feature of a useful country risk index is that it serve as a predictor of government activities that threaten to lower the return on investment. The index reported in Figure 1 indicates that a high degree of risk existed in the summer and fall of 1981 and in the spring and early summer of 1982. As a harbinger of actual changes in government policy, we must consider the index as successfully anticipating the change in policy of August 1982.

A Comparison With Znstitutional lnvestor Rankings lnstitutional lnvestor publishes rankings of country credit risks every

March and September. A sample of between 75 and 100 banks are asked to rank countries on a scale of 0 to 100 (100 being the most credit-worthy ranking). The responses are then weighted according to bank size and the perceived sophistication of the rating system used. The ratings given Mexico over the 1979 (the beginning of the survey) to 1984 period are presented in Table 4. Notice that Mexico is among the top 25 countries in the survey until 1982 when the rankings begin to drop. Not until March of 1983 is Mexico placed among the poorer risks in the market. This suggests that the lnstitutional lnuestor ratings are more a record of events that have already passed than an index of uncertainty regarding the future. Estimating a simple regression of the change in the lnstitutional lnvestor rating on the value of our country risk index in the previous period indicates that our index leads the change in the published rating. If we use the PR values from March and September of each year in order to correspond to the dating of the bank survey, we estimate

(17) ARATZNG = -6.91 - 4.07CR R e = .57. (-3.57) (-2.59) D.W. = 1.55

(t-statistics in parentheses)

Thus when CR rises, say in September, the Institutional Znvestor ranking will tend to decrease between September and the following March. If the goal of a country risk index is to anticipate change, then the CR series appears to satisfy this goal. The series correctly anticipated the Mexican events of August 1982, and the series leads the lnstitutional lnvestor ratings which record bankers’ views on the current state of country credit risk.


TABLE 4 Institutional Inoestor Rating of Mexican Country Risk

Month Rating Standing Among Rated Countries

Sept. 1979 March 1980 Sept. 1980 March 1981 Sept. 1981 March 1982 Sept. 1982 March 1983 Sept. 1983 March 1984 SeDt. 1984

71.8 71.7 73.0 71.5 69.0 62.8 54.8 36.8 33.9 36.1 38.1

25 25 22 23 25 29 37 58 60 50 49

VI. SUMMARY AND CONCLUSIONS

We used a dynamic factor analysis model to estimate a country risk index for Mexico. Previous work relied on actual debt reschedulings or Eurocur- rency interest rate spreads to infer country risk. A major weakness of such approaches is the lack of data with any regular periodicity. As a result, past studies that investigated the determinants of country risk used cross-sections of countries rather than time series for a particular country. Trying to infer the degree of country risk associated with a country from infrequent Euro- currency loans, or whether or not there was a debt rescheduling, is unsatisfactory. We offer an alternative that yields a monthly index of the risk associated with a country.

The econometric framework is based on an approach used by Engle and Watson for extracting estimates of an unobservable variable. In our model, deviations from interest rate parity are assumed to be the measured variable containing information on country risk. Holding constant the effect of actual capital controls, we specify an equation relating deviations from interest rate parity to country risk. A second equation specifies the evolution of country risk over time. Our model includes an equation that describes the determinants of country risk and thus allows hypothesis tests of the importance of variables likely to be related to country risk.

Using data on dollar-peso deviations from interest rate parity along with additional variables for Mexican capital controls and determinants of country risk, the model was estimated over the 1978-84 period. The results suggest that the ratios of imports to reserves and debt to exports are important determinants of country risk. The estimated risk index appears to anticipate correctly the Mexican capital controls and sweeping policy changes of August 1982 and also appears to lead the country risk ratings appearing in the Institutional Investor.

Considering the variety of models in international finance calling for


political risk variables, it would seem that the index presented here offers a desirable alternative to the standard dummy variable representation.

DATA APPENDIX

The data set consists of monthly observations over the period October 1978 through March 1984. Sources as well as definitions are discussed by series. The data were gathered to be consistent with information actually known in each time period. This was done by going through individual issues of lnternationul Financial Statistics so that only the data published, and therefore known, each month is used. Thus, in any period t , when we observe interest rates and exchange rates, the independent variables determining the market’s perception of country risk will be lagged to reflect only what market participants know at t .

U.S. Interest Rate Data Source: CITIBASE data tape Series: The 1 month certificate of deposit rate. These data are monthly

averages of daily figures.

Mexican lnterest Rate Data Source: lndicadores Economicos Series: The 1 month certificate of deposit rate. These data are monthly

averages of daily figures.

Forward Discount on Peso Data Source: Data Resources, Inc. (original source is Bank of America) Series: The 1 month forward discount. These data are monthly averages

of daily bid quotes taken at 8:30 a.m.

Mexican Capital Controls Data Source: Various issues of IMF’s Annual Report on Exchange Rate

Restrictions and Economist Intelligence Unit, Quarterly Economic Review of Mexico.

Series: A dummy variable set at 0 until August 1982, then at 1 for rest of sample. In August 1982 the Mexican government imposed foreign exchange controls which were continued through the sample.

Total Reserves Minus Gold Data Source: International Financial Statistics Series: The sum of SDRs, reserve position in the IMF, and foreign

exchange held by the monetary authorities (line 1 1.d).

Mexican Price lndex Data Source: lnternational Financial Statistics Series: This series is the consumer price index (line a), and is a period

average.


Mexican Industrial Production Data Source: lntemational Financial Statistics Series: line 66.

Mexican Exports Data Source: lntemational Financial Statistics Series: line 70.

Mexican lmports Data Source: lntemutional Financial Statistics Series: line 70V.

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A COUNTRY RISK INDEX: ECONOMETRIC FORMULATION AND AN APPLICATION TO MEXICO

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