World Development, Vol Printedin Great Britain.

Effects

20, No. 8, pp. 1173-1181, 1992 03055750)(/92 $5.00 + 0.00 Pergamon Press Ltd

of Exchange Rate Risk on Exports:

Crosscountry Analysis

MOHSEN BAHMANI-OSKOOEE

and

NABIL LTAIFA* University of Wisconsin, Milwaukee

Summary. - In this paper we investigate the effects of exchange rate uncertainty on the aggregate exports of 19 developed and 67 developing countries using cross-sectional data. Exchange rate uncertainty is found to be detrimental to the exports of both developing and developed countries. Developed countries’ exports, however, are found to be less sensitive to exchange risk than that of developing countries. Furthermore, within the developing countries. those who fixed their exchange rates to one major currency were found to be subject to less risk than the other developing countries.

1. INTRODUCTION

One area in international finance that still attracts the attention of many researchers is the effects of exchange rate uncertainty on trade volume. On the one hand, Makin (1976), Hooper and Kohlhagen (1978), Gotur (1985), and Bailey et al. (1986, 1987) found that exchange rate uncertainty does not have a significant effect on the trade flows, supporting the proponents of floating rates. On the other hand, Cushman (1983), Akhtar and Hilton (1984), and Kenen and Rodrik (1986), are examples of studies which conclude that exchange rate uncertainty is detri- mental to international trade, supporting the view expressed by the opponents of flexible exchange rates.’ All these studies have used time-series data and have investigated the reponse of either bilateral or manufactured trade of industrial countries to a measure of exchange rate variability.

Developing countries have received little attention. Coes (1981) looked at Brazilian ex- ports and has found that after adopting the crawling peg in 1968, a decrease in exchange rate uncertainty had a positive effect on the Brazilian exports. Gupta (1980) analyzed the export supply of five developing countries and found that short- term volatility had negative effects on export supply in two cases. Finally, Rana (1981) found that exchange rate volatility had negative effects on the import volumes of South Korea, Taiwan,

and the Philippines. All three studies used time- series data to reach their conclusions.2

If we are to draw a general conclusion from the aforementioned time-series studies pertaining to developed and developing countries, it is likely that while import volumes are affected adversely by exchange rate uncertainty, export volumes are not. Of course, recent theoretical developments in the literature support the notion that exchange rate uncertainty could have positive or negative effects on trade volume. For example, De Grauwe (1988) provided a theoretical argument showing that the effects of exchange rate uncer- tainty on exports depend on the degree of risk aversion. According to De Grauwe, a very risk- averse exporter who worries about the decline in his or her revenue may export more when risks are higher. On the other hand, a less risk-averse person may not be concerned with the worst case and consider the return on his or her export less attractive under a very risky environment and decide to export less.

Brada and Mendez (1988) are perhaps the only authors who have utilized pooled bilateral export data across 30 countries. They found that risk is greater, and trade lower, between countries whose currencies are not fixed against each

*We would like to thank constructive comments of an anonymous referee. We alone, however. remain responsible for any error. Final revision accepted: September 20, 1991.

1173

1174 WORLD DEVELOPMENT

other, although floating rates lead to a higher volume of trade. This trend is argued to be due to the fact that effects of exchange risk on trade under floating rates are less than the effects of restrictive commercial policies under fixed rates. They drew such a conclusion, however, without using a proxy for exchange rate variability. Rather. they relied on dummy variables that were assigned to countries which either fixed or floated their exchange rates. Therefore, Brada and Mendez (1988) is a major contribution which shows that exchange rate uncertainty is detrimen- tal to hiluteral exports among 30 countries using a dummy variable procedure and not a measure of exchange rate variability.

The primary purpose of this study is to assess the effects of exchange rate uncertainty on the uggregute export volumes of 86 countries. Our study differs from those in the literature. First, unlike many previous studies (except Brada and Mendez. 198X) that have used time-series data. we utilize cross-sectional data. Second, we use aggregate export volumes, whereas previous studies have either used bilateral trade flows or trade in manufactured goods. Third, our measure of exchange risk is more comprehensive and is based upon the real effective exchange rate rather than a nominal effective rate or real bilateral exchange rate. Notice that this point is one major difference between our work and that of the Brada and Mendez (1988) who did not use a measure of exchange rate variability at al1.3 Finally, unlike other studies thus far we include 19 developed and 67 developing countries in our analysis for a total 86 countries.

Section 2 presents our model and related theoretical analysis. Estimation results are re- ported in section 3. A conclusion and summary are provided in section 4.

2. THE MODEL

As indicated in the previous section, the primary purpose of this study is to investigate the effects of exchange rate uncertainty on export volume. Therefore, in its very simple form, our hypothesis could be formulated in a log-linear format as follows:

LogX,=a+eLogo,+c, (1)

where X, = real exports of country i,

0, = measure of exchange rate varia- bility of country i,

6 = error term.

If exchange rate volatility is to deter exports, we would expect estimate of e < 0.

Although we can estimate equation (1) by employing data on X and a from as many countries as possible, this practice will leave other determinants of exports out of our exercise and render the results biased. Therefore, we need to identify other determinants of exports that are suitable to cross-sectional studies.

All previous studies have included a scale variable in their models. In export demand models, such as the one portrayed by equation (1) the scale variable is usually world income. For two major reasons, however, we replace world income with domestic real income. First, con- structing world income for, for example, country i requires obtaining data on the share of imports or exports of country i from or to its major trading partners. Such shares are not readily available for as many countries as are used in this paper. Second, since most of the countries included in this paper are less-developed coun- tries (LDCs), domestic real income (output) is more appropriate than world income due to the fact that there are studies that argue that high- income LDCs tend to export more.’ Therefore, we will extend equation (I) to include Log Y,, where Y, will be a measure of country ith output.

The second variable that we need to enter into equation (1) is a price term. In time-series analysis the usual practice is to include either export price indexes or exchange rates. In a cross-sectional analysis, however, we cannot include either variable due to the fact that both variables are heterogeneous across countries. To make the price term a homogenous variable, we rely upon rate of devaluation (DEV) of each country’s exchange rate against the US dollar.5 A country that experiences a higher rate of devalua- tion or depreciation in its exchange rate is expected to export more.h

Finally, following Brada and Mendez (1988). we also include population of each country, POP, in the model. They have indicated that the effects of population on exports are usually ambiguous. The ambiguity arises from the fact that a country with a large population could export more due to excess supply of labor, most of which could be concentrated in the exporting sector. if that densely populated country, how- ever, consumes most of its exportables at home, it could eventually end up exporting less.

Including all the variables in equation (1) yields a simple model as follows:

Log X, = (i + b Log Y, + c Log POP, + d DEV, + e Log 0, + E, (2)

According to theoretical arguments that WC provided above, it is expected that estimate of h > 0, c 3 0. d > 0. and e < 0. Equation (2) is the

EXCHANGE RATE RISK AND EXPORTS 1175

basis of our empirical analysis to which we turn next.

3. ESTIMATION RESULTS

TO estimate equation (2), each variable was constructed for each of the 86 countries from time-series data covering 1973-80.’ The con- structed variables were then used in a cross- sectional regression analysis of equation (2). In what follows we first discuss the method of constructing each variable in equation (2) and then present the regresison analysis of the cross- sectional data.

(a) Vuriables

The dependent variable, X. for each country is defined as that country’s real exports in US dollars. The nominal exports in US dollars during 1973-80 is deflated by the US export price index using annual observations.x The mean of these deflated figures is used as a measure of Xi in equation (2).

The income variable, Y, for each country is defined as each country’s real output in 1980 US dollars. To construct this variable we took average of each country’s real GNP figures (1980 = 100) in domestic currency during 1973-80 and then converted that average to US dollar using the period average exchange rate in 1980.”

The DEV variable is defined as the rate of devaluation if positive and revaluation if nega- tive. For each country the annual period average exchange rates (defined as number of units of domestic currency per US dollar during 1973-80) are collected. The rate of change of these exchange rates from one year to another are calcuated. The mean of these rates are used for the DEV variable.

The population variable, POP, is the mean population figure for each country during 1973- 80.

Finally, the exchange rate variability measure, o, is defined as the standard deviation of percentage changes in the real effective exchange rate. Fortunately, this measure for 1973-80 is calculated by Lanyi and Suss (1982, p. 554). We, therefore, borrow their measure and use it in this paper. Table 1 reports data on all these variables for 86 countries.

(b) Cross-country regression results

We can now estimate equation (2) using cross-

country data reported in Table 1. Coefficient estimates of equation (2) in general and its spec- ial forms in particular are reported in Table 2.

Regression number one in Table 2 shows the estimate of equation (2) when all 86 observations are used. From these results we gather that all estimated coefficients carry their expected signs and they are all highly significant except DEV. It is evident that the variability measure of real effective exchange rate (a) exerts a significantly negative effect on exports. This finding is in line with Brada and Mendez (1988) who also found similar results using bilateral exports among 30 countries. The difference is that we used aggre- gate exports and not bilateral exports. Another difference is that we relied upon the variability measure of the real effective exchange rate, whereas, rather than using this measure they relied upon the dummy variable procedure. As for the other features of the first regression, it is clear that income and population variables do carry positive and highly significant coefficients, results that are in accordance with the theory and in line with Brada and Mendez. DEV is the only variable that carries an insignificant coefficient. Perhaps, we should have followed Brada and Mendez in excluding the price term (DEV) from our model. To see how the results would improve if we dropped DEL’, we turn to the second regression. This regression reports the results when DEV is dropped from the analysis. As can be seen, there is no substantial change in the conclusion that exchange rate uncertainty exerts a significantly negative effect on real exports.

In all areas of economics developed and less- developed countries are usually investigated sep- arately and our model is no exception. Thus, one question of interest is whether the regression coefficients are the same across developed versus less-developed countries. To this end, we apply the Chow test according to which we need to calculate the following F-statistics,.“’

(RRSS - URSS)I(k+l) F= (3)

URSSI(n,

where RRSS =

’ ’ + n2 - 2k - 2)

restricted sum of squared residuals obtained from the regression that uses all the observations from DCs and LDCs combined, S6 in our case, unrestricted sum of squared residuals obtained by run- ning separate regressions for DCs and LDCs and adding the sum of squared residuals from these two regressions,

URSS =

P

EXCHANGE RATE RISK AND EXPORTS 1177

Table I wt~rit~wtl

country X-: Yi- PO P:i: DEW

Jamaica Nicaragua Pakistan Panama Paraguay Rwanda Sudan Suriname Syrian Arab Republic Yemen Arab Republic

Other LDCs

Algeria Bangledcsh Bolivia Brazil Colombia Cyprus Fiii Gambia Ghana Greece India Indonesia Korea Kuwait Malaysia Malta Mauritania Mexico Morocco Nigeria Peru Philippines Portugal Singapore South Africa Sri Lanka Tanzania Thailand Tunisia Turkey Uruguay Yugoslavia

0.9X400 3.45010 2.07900 0.11610 3.940 0.66900 2.35200 2.32200 0.05812 2.980 1.94800 20.1056 74.4120 0.00134 2.900 0.34800 2.83500 1.74000 0.00000 1.590 0.30600 3.15413 2.82000 0.00000 4.x30 0.09600 1.12222 4.49000 0.01494 3.580 0.70800 8.02200 16.1850 0.05532 6.750 0.4s 100 0.92661 0.36700 0.00000 3.050 1.47 100 9.33146 7.82300 0.00393 4.630 0 .O I600 2.16745 5.49300 -0.00149 3.930

8.41200 U.65800 0.80400

lS.S2SO 2.99900 0.39300 0.25900 0.05800 1.2SHOO 3.90 100 7.73700

13.92X0 23.4690 15.1X90 X.77200 0.3SlOO 0.23400 7.32400 2.15500

lS.SS60 2.6X000 4.39600 3.44200

I 1 .X360 16.1290 0.96400 0.62700 4.67000 I .47600 2.63000 0.67300 7.01200

32.8203 17.3100 -0.00373 2.460 10.7176 Hf.7330 0.11630 IO.00 0.000005 5.10600 0.03124 5.850

201.S60 109.372 0.30952 3.390 27.8596 241.213 0.10474 3.330

2.36611 0.62000 0.00339 I.570 I .06OSS 0.59000 -0.00309 2.030 0.22034 0.54500 0,00825 4.780

20.X429 10.3710 0.15995 13.64 3X.2162 9.22900 o.osso3 4.390

135.759 619.626 0.003 19 3.960 47.292X 136.979 0.06871 5 460 47.6759 36.1190 0.06672 4.290 21.9852 1.11500 -0.01294 2.160 16.6852 12.4830 -0.01565 2.470 0.91sss 0.33300 -0.00676 1 .x50 0.69963 1.48400 0.00457 3.690

139.426 62.8490 0.10201 5.440 16.1304 1X.1640 - 0.00406 1.590 67.9890 71. ISXO -0.0203.5 4.0x0 15.0586 16.0700 0.36093 5.830 2X.9460 44.0790 0.01552 3.380 22.2083 9.54100 0.110x3 4.250

X.70867 2.30500 ~0.01898 2.840 71 .s720 26.4190 0.00469 3.S60

3.32188 13.8990 0.16591 x.370 5.32012 16.4710 0.01207 5.120

26.7X77 39.6360 -0.0009X 2.710 6.66914 5 .x4900 -0.0047X 2.400

53.3839 41.7630 0.33853 5.540 11.7790 2.84400 0.47946 6.110 52.4640 2 1.6600 0.06565 4.400

*X = mean real exports in billions of 1980 US dollars. +Y = mean real output in billions of 1980 US dollars, *POP = mean population in millions. SDEV = mean rate of devaluation. ol) = variability measure of real effective exchange rate. As indicated in the text, the variability measure of real effective exchange rate is from Lanyi and Suss (1982, p. SS4, Table 4). To be consistent, countries are entcrcd according to Lanyi and Suss’s classification.

1178 WORLD DEVELOPMENT

Table 2. Crosscountry regression results

Explanatory variables

Regression number Sample Constant Log Y Log POP DEV Log o N* l?’ SSRt

DCs and LDCs

DCs and LDCs

DCs

4 DCs

5 LDCs

6 LDCs

0.6960 0.3570 0.4160

(1.57)$ (5.53) (3.73)

0.6349 0.3621 0.4084 (1.49) (5.69) (3.71)

- 1.2043 1.2135 -0.3466 (1.72) (4.55) (1.41)

-1.2811 1.2501 -0.3642 (1.99) (5.23) (1.56)

0.1107 0.2024 0.4383 (0.23) (2.92) (3.77)

-0.0081 0.2149 0.4254 (0.02) (3.14) (3.67)

0.3370 - 1.2263 86 0.60 125.035 (0.54) (3.49)

- 1.1566 86 0.60 125.500 (3.54)

-0.5927 -0.5093 19 0.94 1.48990 (0.35) (1.84)

-0.5704 19 0.94 1.50366 (2.68)

0.6351 -0.X611 67 0.43 87.6845 (1.07) (2.26)

-0.7292 67 0.43 89.2915 (2.02)

N is total number of observations. SSR is the sum of squared residuals. *N = total number of observations. tSSR = sum of squared residuals. $ = t-ratios in parentheses.

k = number of regressors which is four if DEV is included and three if it is excluded,

nl = number of observations used in the regression for DCs, 19 in our case,

n2 = number of observations used in the regression for LDCs, 67 in our case.

It appears that in order to apply the Chow test, we need to estimate our basic model for all countries combined as well as for DCs and LDCs separately. Table 2 also reports the results of this practice as indicated by the size of the sample. Using the information pertaining to the number of observations and sum of squared residuals reported in Table 2 for regression numbers one, three, and five which all include DEV an F- statistic of 6.11 was obtained. When we relied upon information pertaining to regressions two, four, and six which they exclude DEV, an F- statistic of 7.49 was obtained. Since our calcu- lated F-statistics are much higher than their critical values from the F table at any level of significance, we reject the null hypothesis that the regression coefficients are the same for DCs and LDCs. This indicates that we must concen- trate on DCs and LDCs separately. In doing so. from regressions four and six in Table 2 we

gather that the measure of exchange rate vari- ability exerts significantly negative effects on both the exports of DCs and LDCs. The only difference between the two samples is the effect of population on exports and the size of adjusted R2. As the results show. an increase in the populaton of DCs does not have a significant effect on their exports, whereas, an increase in the population of LDCs has a significantly positive effect on their exports. This finding seems intuitive due to the fact that most of LDCs exports are labor intensive, whereas, most DCs exports are capital intensive. These results imply that an increase in LDCs’ population or their labor supply will help boost their exports. Com- paring the size of adjusted R2, it appears that the model fits the DC sample better than the LDC sample. This is perhaps true of any model due to the fact that data are collected more accurately in DCs as composed to LDCs.

One more feature of the results in Table 2 deserves mention. It appears that the absolute size of estimated elasticity of exchange rate variability (e) is smaller for developed countries than for less-developed countries. This could be an indication that exporters in DCs arc subject to a smaller risk than LDCs. Two facts may explain this outcome. First, forward exchange contracts that are available for currencies of most indus- trial countries, but not for the currencies of

EXCHANGE RATE RISK AND EXPORTS 1179

LDCs, could provide a channel through which some of the exchange risk faced by exporters of DCs could be hedged. Second, as indicated before, the measure of exchange rate variability is based on fluctuation in the real effective exchange rate. The more volatile the real effec- tive exchange rate, the larger the variability measure, thus, the larger the risk. Indeed, Table 2 reveals that most LDCs have experienced a larger variability in their real effective exchange rate and this could be another reason the exchange risk is higher for LDCs than DCs. What contributes to volatility in the real effective exchange rate? The real effective exchange rate could change if there is a change in foreign price level, domestic price level, and nominal effective exchange rate. Although most LDCs during our sample period pegged their currency to a major currency or to a basket of currencies, they could not avoid fluctuation in their nominal effective exchange rate as long as major currency fluctuate against one another.” Furthermore, most LDCs had higher inflation rates than DCs. All these factors could contribute to volatility in the real effective exchange rates of LDCs and thus, to a higher risk.

Finally, the proposition that risk is smaller for the countries that fix their exchange rates to a major currency or to a basket of major currencies could be tested directly. In doing so, we concen- trade only on LDCs for which no forward con- tracts are available. In order to have enough degrees of freedom in our empirical analysis, we estimated the model using data from 23 LDCs which fixed their exchange rates to the US dollar and 32 LDCs which did not (see Table 1 for these two groups). Indeed the absolute value of the estimate of elasticity of exchange rate variability, e, was found to be smaller for the first group (-0.61) than for the second group (-1.08). This supports the standard textbook argument that in the absence of forward contracts, exchange rate risks are lower for countries with fixed exchange rates.

4. SUMMARY AND CONCLUSION

The effects of exchange rate uncertainty on trade flows are the subject of a debate between opponents and proponents of floating exchange rates. Opponents of floating rates argue that by introducing uncertainty about the future value of exchange rates, floating rates are detrimental to international trade. Proponents of floating rates respond to such a contention by arguing that any risk introduced by floating rates could be hedged either through forward exchange markets or money markets, leaving the trade flows un- affected.

Previous studies that have used time-series data to investigate the effects of exchange rate uncertainty on the trade flows, have provided mixed results. Generally, it has been shown that while import volumes are adversely affected by exchange rate uncertainty, export volumes are not.

A recent study deviated from the traditional way of using time-series data and used cross- sectional bilateral exports between 30 developed and less-developed countries. Using a dummy variable procedure and not a measure of ex- change rate variability, this study concluded that risk is greater and trade is lower between two countries whose currencies are not fixed against each other.

The purpose of this paper is to assess the effects of exchange rate uncertainty on the aggregate exports of developed and less- developed countries. Using cross-sectional aggre- gate export data and a measure of exchange rate volatility from 19 developed and 67 less- developed countries, we provide strong evidence supporting the notion that exchange rate uncer- tainty has reduced the volume of aggregate exports of both developed and less-developed countries. This was the conclusion for DCs and LDCs separately and for the groups combined.

NOTES

1. For a comprehensive survey of literature on fixed upon spot or forward exchange rate as the basis for vs. floating exchange rates see Artus and Young uncertainty. The nominal effective exchange rate based (1979). measure of exchange risk is used by Akhtar and Hilton

(1984) and Bailey, Tavahs and Ulan (1986) and risk 2. We had no access to Coes (1981). Gupta (1980), derived from bilateral exchange rate by Cushman

and Rana (1981). Thus, the conclusions from those (1983). studies reported here are due to Kenen and Rodrik (1986).

4. See for example, Feder (1983), Kavoussi (1984). 3. Theoretical studies such as Ethier (1973), Baron Jung and Marshall (1985), and Bahmani-Oskooee.

(1976) and Hooper and Kohlhagen (1978) have relied Mohtadi and Shabsigh (1991).

1180 WORLD DEVELOPMENT

5. As it will be made clear, the US dollar is used as a of those countries due to unavailability of the data on common denominator for all variables involved. This is some of the other variables used in our model. one reason for the exclusion of the US from cross- sectional regression analysis. 8. The source of data for all variables is IMF (1987).

6. Notice that since the DEV variable could be 9. For some countries real GNP was not available. negative or positive, it will enter the model at its actual Thus, it was replaced by real GDP. level rather than its log level.

10. The Chow test explained here is from Maddala 7. The choice of this period is due to availability of (lY77), p. 19X.

data on the variability of real effective exchange rate only for this period for 118 countries from Lanyi and 11. For more on this point see Bahmani-Oskooec Suss (19X2). We were able, however, to include only X6 (1985, 1986, lY91) and Warner and Kreinin (1983).

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