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Exchange Rate Volatility and U.S. Poultry Exports: Evidence From Panel Data
Yan Yuan
Graduate Research Assistant
and
Titus O. Awokuse*
Assistant Professor
Dept. of Food and Resource Economics
University of Delaware
Newark, DE 19717, USA.
Email: [email protected]
This is a selected paper for the annual meetings of the American Agricultural Economics
(AAEA) meetings in Montreal, Canada, July 2003.
______________________________
* Copyright 2003 by Titus O. Awokuse. All rights reserved. Readers may make verbatim
copies of this document for non-commercial purposes by any means, provided that this
copyright notices appears on all such copies.
Exchange Rate Volatility and U.S. Poultry Exports: Evidence From Panel Data
Abstract
Very little research exists on the potential impact of exchange rate volatility on agricultural
trade. This paper evaluates the effects of exchange rate volatility on U.S. poultry exports
using the gravity model on panel data. We find that exchange rate volatility has a negative
effect on the U.S. poultry export but only statistically significant for the model in which we
use the variance of spot exchange rate as the measurements. Consistent with previous studies,
foreign incomes are also a very important determinant of poultry trade.
KEYWORDS: exchange rates, volatility, agricultural trade, poultry exports, panel data
2
Exchange Rate Volatility and U.S. Poultry Exports: Evidence From Panel Data
Introduction
The U.S. poultry industry, the world’s largest producer and exporter of poultry meat,
is very dependent on exports to foreign markets. Poultry production in 2001 totaled 42.43
billion pounds, accounting for 24 percent of world total output. In 2000, U.S. poultry exports,
at 6.4 billion pounds, accounted for about 33 percent of world poultry trade and about 60
percent were shipped to the Asian market (Foreign Agricultural Service, USDA). Imports are
anticipated to increase in all the largest import markets, including Russia, China, Japan, Hong
Kong, Mexico, Canada and the Middle East. Shipments of turkey to Russia, the second
largest export market of the US, nearly tripled compared with 1999, accounting for almost 12
percent of U.S. turkey exports. Exports to Hong Kong in 2000 were 43 million pounds, up 28
percent from the previous year. Most of the growth in world poultry trade is expected to
come from expanded shipments of relatively low-priced poultry parts. As markets have
opened to increased poultry trade, the United States has benefited by selling chicken breasts in
the domestic market while exporting dark meat and less valuable cuts to foreign markets
where they are preferred over breast meat. This strategy has been especially beneficial for the
United States, as large markets have developed for leg meat in Russia, wings and feet in Hong
Kong and Mainland China.
However, a country with a domestic preference for dark meat, such as China, could
reverse this marketing pattern and attempt to export white meat products to developed
countries with a preference for those products. Given that about 18 percent of total poultry
production is being exported, the U.S. poultry industry is affected by economic conditions in
3
foreign markets, transmitted via exchange rates fluctuations. In the past few years, Asian and
Russian market destinations have experienced significant level of economic uncertainty and
volatility in prices for poultry meat. Foreign demand uncertainty can create a tremendous
pressure on the profit margin for poultry meat producers. For instance, the Asian financial
market crisis in 1997 and the Russian poultry meat embargo in 2001 can have significant
income ramifications for foreign poultry meat importers. The Asian crisis started with a wave
of currency devaluations, stock market plunges, and business failures in several countries
across the East Asian region. Exchange rate, the price of a currency in terms of another
currency, is probably the single most important variable in determining the level of trade. The
fluctuations in the real exchange rate result in the rise and fall of the prices of U.S.
agricultural products in terms of the local currency in foreign countries. The Asian crisis led
to the depreciation of the foreign currencies (appreciation of U.S. dollar). A depreciated
currency in Asia raises the price of U.S. goods abroad and may consequently lead to a
reduction in the volume of U.S. export sales.
Historically, fluctuations in he exchange rates have accounted for and about 25 percent
of the change in U.S. agricultural value (Economic Research Service, USDA).
domestic consumption and demand for imports declined dramatically.
Thus,
A severe and
prolonged financial crisis abroad could substantially lower U.S. agricultural export earnings
and shift trade patterns in some Asian countries.
It is widely believed that the adoption of the floating exchange rate system after the
collapse of the Bretton-Woods system increased the level of uncertainty associated with
exchange rates. The potential effects of exchange rates uncertainty on the level of exports has
been a hotly debated issue which still persist in the literature. The controversy about the role
4
of exchange rate fluctuations on trade flows has been fueled by the ambiguous and conflicting
nature of the existing empirical evidence. The popular assertion has been that exchange rate
volatility increases risk and uncertainty and thereby hinders the flow of trade. Theoretical
considerations have traditionally been unambiguous in suggesting that increased uncertainty
should reduce the level of trade.
For example, Clark (1973), Ethier (1973), Hooper and Kohlhagen (1978) and
Cushman (1983,1986) have provided useful insights into the ways in which exchange rate
volatility may impede international trade flows. However, recent work by Dellas and
Zilberfarb (1993) and Viaene (1992) has provided a theoretical basis for positive effect of
exchange rate variability on trade. Dellas and Zilberfarb (1993) model nominal un-hedged
trade contracts as standard risky assets that can be analyzed in a conventional asset portfolio
framework, within this framework the trade effects of an increase in exchange rate volatility
will, in general, depend on the risk aversion parameter of the model. They indicate that
existing work on the effects of exchange rate uncertainty on trade has employed a restrictive
version of the portfolio choice model, which leads to an unambiguously negative relation.
Giovannini (1988) discusses the case where increases in exchange rate volatility do not
necessarily lead firms to restrict supply, and where, if export prices are invoiced in domestic
currency, expected profits might actually increase as a result of increased exchange rate risk,
leading firms to reduce their export prices. De Grauwe (1988) argues that the effect of
exchange rate volatility on export depends on the degree of risk aversion. De Grauwe (1988)
asserted that a very risk-averse exporter who worries about the decline in his or her revenue
might export more when risk is high. Klein’s (1990) study examined the effects of real
exchange rate volatility on specific categories of bilateral exports from the United States over
5
the period 1978 to 1986, from which he concludes that real exchange rate volatility may
stimulate export supply by risk-neutral firms through its effects on their expected profits. It
may be considered that increased risk associated with volatility will induce risk-averse agents
to direct their economic resources to less risky activities.
Accordingly, risk-averse exporters may be expected to reduce their trade volumes in
response to uncertainty generated by greater exchange rate variability, since profits from
international trade decline due to unanticipated exchange rate changes. Forward markets can
be used for hedging but there are both limitations and costs associated with their use.
Caporale and Doroodian (1994) point out that the size of contracts can be a limitation since in
the case of US-Canadian trade they must average US $1 million per contract before hedging
can take place. A further limitation is that customers must keep minimum deposits, usually for
multiples of 30 days. These factors indicate the difficulties for trading firms in planning the
volume and timing of their international transactions to make optimal use of forward markets.
The empirical studies, however, have failed to provide unambiguous evidence in
favor of the theoretical predictions. For example, Kenen and Rodrik (1986), Thursby and
Thursby (1985), Koray and Lastrapes (1989), Pritchett (1991), Savvides (1992) Pozo(1992),
Chowdhury (1993), Arize (1997), Frankel and Wei, Eichengreen and Irwin(1998) found that
the risk associated with this exchange rate volatility has reduced the level of exports. Bailey,
Tavlas, and Ulan (1986), Gotur (1985), and Hooper and Kohlhagen (1978) found that the
exchange rate uncertainty does not have a significant effect on trade. In contrast, Asseery and
Peel (1991) found a positive relationship between exchange rate volatility and exports. The
empirical studies do not yield a definite result that increased uncertainty has reduced
international trade. Rather, an IMF (1984) survey of the effects of exchange rate variability
6
on world trade concluded that the “the large majority of empirical studies on the impact of
exchange rate variability on the volume of international trade are unable to establish a
systematically significant link between measured exchange rate variability and the volume of
international trade, whether on an aggregate or on a bilateral basis.”
The use of aggregate trade data ignores the fact that the impact of exchange rates may
vary across sectors because different sectors have different degrees of openness to
international trade, and because different sectors have different industry concentration levels
and make different use of long term contracts, constraining the income, price and exchange
rate risk elasticities to be equal across sectors. Given the different nature of the markets in
which trade occurs, it is likely that volatility will impact differently on different sectors and
aggregating across those sectors means a loss of important information. The use of aggregate
data may contribute to the array of conflicting results derived. This is because using national
trade data implicitly assumes the impact of exchange rate volatility is uniform between
countries and commodities both in terms of direction and magnitude. If this assumption is
incorrect, then the examination of aggregate trade data is likely to dilute the true nature of the
relationship and lessen the probability of deriving a significant result.
Recognizing this possibility, researchers have adopted trade models, which focus on
disaggregated trade data in the form of bilateral and sectoral trade flows. Cushman (1988a)
attempted to establish if bilateral trade flows analysis during the floating period alone could
verify any of the significant effects by using the U.S. bilateral trade flow from 1974-1983.
The use of bilateral trade flows data does not appear to significantly enhance the results when
compared to those obtained using aggregate trade flows. It is possible to take this
disaggregating process one step further and examine the commodity specific trade data.
7
Bini-Smaghi (1991) support the hypothesis that variability has decreased trade both in
terms of price and quantity by using intra-European Monetary System (EMS) manufacturing
trade for the period 1976-1984. Klein (1990) disaggregated U.S. bilateral exports to seven
major industrialized countries for the period 1978-1986 into nice categories of traded goods.
Six yielded a significant relationship between the volatility estimator and trade, among which
five indicated a positive relationship. McKenzie (1998) analyzed the impact of exchange rate
volatility on both aggregate and sectoral trade flow data for the Australian economy over the
period 1988-1995. Whereas the aggregate trade data revealed limited and conflicting evidence
of a relationship, a significant relationship could be established when the data was
disaggregated into various commodity sub-groupings.
There has been little research on the impact of exchange rate variability on agricultural
trade. Earlier work on agricultural trade and exchange rates, focused on the impact of
changes in the level of real exchange rate and agricultural exports. Examples of this work
include Batten and Belongia(1986), Haley and Krissoff (1987), and Bessler and Babula
(1987). Reflecting the earlier research in the general literature, empirical research relating to
short-run exchange rate volatility and agricultural trade flows has produced ambiguous
conclusions. For example, Pick found that exchange rate risk had no effect on U.S. trade
flows to other developed countries, though it did have a negative effect on U.S. exports to
developing countries. In contrast, Klein found that short-run real exchange rate volatility
negatively affected U.S. agricultural exports compared to other sectors. Maskus(1986) also
found that the sector most affected by short-run volatility was agriculture, though his
empirical model, like Klein, focused on U.S. bilateral trade flows only. Anderson and Garcia
(1989) found significant negative effects of exchange rate volatility on U.S. exports of
8
soybean to three developed countries.
More recently, Langley et al. (2000) found that
exchange rate volatility had a positive impact on Thailand’s exports of poultry, but not on
aggregate agricultural exports. Attention has been on short-run exchange rate volatility. The
effects of medium to long run exchange rate variability have been ignored even though this is
arguably more likely to have a more significant impact on trade.
The aim of this paper is to provide a contribution to the empirical debate on the
relationship between exchange rate volatility and trade based on panel data of the US poultry
export to 49 countries from 1976 to 2000. Following Cho, Sheldon and McCorriston (2002),
we attempted to study the relationship between medium to long run exchange rate uncertainty
and U.S. poultry export. A gravity model is applied to these data, which allows for crosscountry determinants of trade including income, distance, unit export price and exchange rate
uncertainty.
Gravity Models
The so-called gravity equation came into use in social sciences in the 1860s when H.
Carey (1871) first applied Newtonian Physics to the study of human behavior. More recently,
gravity model studies have achieved empirical success in explaining various types of interregional and international flows, including labor migration, commuting, customers, hospital
patients, and international trade.
Tinbergen (1962) and Pöyhönen (1963) are the first researchers to propose the gravity
model. In its basic form, the amount of trade between two countries is assumed to positively
relate with their sizes, as measured by their national incomes, and negatively relate with the
transportation costs between them, as measured by the distance between their economic
9
centers. Linnemann (1966) included population as an additional measure of country size to
allow for non-homothetic preferences in the importing country, known as the augmented
gravity model. It is also common to specify the augmented model using per capita income,
which captures the same effects. Gravity equations become popular in the trade literature on
account of its empirical success in predicting the bilateral trade flows of different
commodities and under different circumstances. Deardorff (1984) writes that gravity models
are “extremely successful empirically” judging by their ability to explain variance in bilateral
trade volumes. Leamer and Levinsohn (1997) hold that gravity models “have produced some
of the clearest and most robust empirical findings in economics.”
The theoretical background for the framework is found in a set of general equilibrium
models that derive specific predictions for bilateral trade. Anderson (1979) gave a theoretical
foundation based on the expenditure systems and introduced border taxes and transportation
costs as barriers to trade into the model derived from a general equilibrium framework. He
assumed complete specialization and identical preference, Cobb-Douglas and Constant
Elasticity of Substitution (CES) in the appendix. Bergstrand (1985) used CES preferences
over Armington-differentiated goods to derive a reduced form equation for bilateral trade
involving price indices. His CES preferences allowed for a different elasticity of substitution
among imports than between imports and domestic goods. His empirical estimates supported
the assumption that goods were not perfect substitutes and that imports were closer substitutes
for each other than for domestic goods.
More recently, there have been several empirical studies on this issue, based on the
gravity model of trade, and also making use of panel data. For example, Rose uses bilateral
trade for a panel of 186 countries, over the period 1970-90, finding a small, but statistically
10
significant negative effect of exchange rate volatility on trade.
De Grauwe and Skudelny
found a statistically significant negative impact of exchange rate volatility on trade in the
European Union, as did Dell’Ariccia. Cho, Sheldon and McCorriston (2002) studied the
effects of exchange rate uncertainty on agricultural trade in comparison to other sectors, in the
context of recent econometric work based on bilateral trade flows between several countries,
making use of the gravity model and panel data. One of the advantages of using panel data is
that unobservable cross-sectional effects can be accounted for either via fixed effects or
random effects specification. The use of panel data also captures changes in variables over
time such as income and changes in exchange rate uncertainty.
Consistent with the underlying micro-foundations, the empirical analysis uses a multicountry dataset of U.S. poultry exports with its trading partners together with other
fundamental economic data on prices, exchange rates and gross domestic products. The
gravity model is given as:
log X 1 j = β 0 + β 1 log Y1 + β 2 log Y j + β 3 log P1 j + β 4V + Eij
(1)
where
X1j – bilateral trade flow of the U.S. to importing country j;
Y1,Yj – the per capita income of the U.S. and country j;
P1j – unit export price of U.S. poultry;
V – exchange rate volatility;
E1j –distributed error term.
Distance, which reflects the transportation costs between U.S. and other countries, is
not included in this empirical model because the fixed effects estimation method of panel data
cannot capture the effects of time-invariant variables. Instead, we use unit export value
11
obtained by dividing the export value by volume to reflect it on the basis that the export value
includes the transportation costs. Before commenting specifically on he uncertainty measures,
one issue that should be noted is the frequency of the data. Because the aim is to capture
medium to long run uncertainty, annual exchange rate data were used.
Econometric Methodology
In this paper, fixed effects methods are employed for the unbalanced panel data.
Fixed effects models assume that the intercepts are different for individuals, i.e., countries in
this case, and the slope coefficients are constant when we control for time effects, which
means that the coefficients do not vary over time.
An alternative is the random effects
approach, whose intercept is assumed to be a random variable with a mean value. Random
effects method is not appropriate in this case because the assumption underlying the random
effects models is that the random error term, contained in the intercept, is a random drawing
from a much larger population. Therefore, fixed effects model is applied.
Ordinary Least Square (OLS) is applied to the pooling data obtained by averaging
over 1993-2000 and two sub-periods 1993-1996 and 1997-2000 for the purpose of
comparison. The model is specified as follows:
log X 1 j = β 0 + β 1 log Y j + β 2 LPOP + β 3 log P1 j + β 4 log XRAT + β 5 LDIST + Eij
(2)
The U.S. per capita income does not vary with the different cross sections and thus
excluded from the model.
Distance is included to reflect the transportation costs over
countries. In these models, volatility is not defined and thus the level of logarithm of
12
exchange rate is used instead. ARCH tests are then performed to test heteroskedasticity. By
checking the residuals against each explanatory variable, we think that population might cause
heteroskedasticity and the logarithm of population is used as weights to correct for it. The
results of the cross-sectional data analysis are presented in table 1 in the appendix.
Measuring Exchange Rate Uncertainty
As researchers were trying to establish a systematic relationship between exchange
rate volatility with actual trade data, one of the central issues is the appropriate measurement
of the uncertainty in exchange rate. McKenzie (1999) provides a comprehensive review of
the empirical literature to generate exchange rate volatility.
These include absolute
percentage change of the exchange rate (Thursby and Thursby 1985; Bailey, Tavlas and Ulan
1986), average absolute difference between the previous forward and current spot rate
(Hooper and Kohlhagen 1978), and the moving average of the standard deviation of the
exchange rate (Bailey, Tavlas and Ulan 1987; Caballero and Corbo 1989; Koray and
Lastrapes 1989; Klein 1990 etc.).
More recently, Auto-Regressive Conditional
Heteroscedasticity (ARCH) models are more frequently used.
In order to investigate whether different measurements of volatility have effects on the
empirical results, we employ four of them in our analysis. The first measure is the absolute
percentage change of the exchange rate, i.e.
V1 = et − et −1 et −1
(3)
The second measure is the variances of the spot exchange rate around its trend, which is
predicted from:
ln et = α 0 + α 1t + α 2 t 2 + ε t
(4)
13
Following Koray and Lastrapes(1989), the third one we use is the moving average of the
standard deviation of the exchange rate. The order m is set to two.
m

2
Vt = (1 m ) ⋅ ∑ (Z t + i −1 − Z t +i − 2 ) 
i =1


12
(5)
Data
As noted above, one of the key motivations is to examine the impact of medium and
long run exchange rate uncertainty on U.S. poultry exports; therefore annual exchange rate
data are used. The real exchange rate between the U.S. and other 49 countries is obtained
from PWT 6.1. The export volume and value over the period 1976-2000 are obtained from
Economic Research Service (ERS), U.S. Department of Agriculture (USDA). Economic
theory would suggest that the income level of the trading partners of the domestic country
should contribute to determination of a nation’s exports, and since the marginal propensity to
import with respect to income is positive, the expected sign on a nation’s trading partner’s
income should also be positive. It is proxied by the annual real GDP data of the trading
partners that was obtained from Penn World Tables (PWT) 6.1. Unit value of poultry exports
yields when we divide the export value by export volume.
Results
The unbalanced panel data contains 49 U.S. trade partners in poultry. The countries
are chosen according to their importance in poultry to the U.S. export, data availability and
the exchange rate regime. We do not include the fixed exchange rate regime into the analysis
because the interest lies in the effects of exchange rate volatility and exchange rates do not
change over time in the fixed exchange rate regime. In addition, we use balanced data over
14
the period 1993-2000 for the purpose of comparison. The results are shown in table 1. The
coefficients for exchange rate fro the three periods are negative but not statistically
significant. Coefficients for unit export value are significantly negative at 5 percent level for
all. Per capita GDP are significantly positive for two out of three periods and consistent with
economic theory. Although the coefficients for distance are negative but are not statistically
significant. R squares are 0.368, 0.284 and 0.322 for 1993-1996,1997-2000 and 1993-2000
respectively, which are not bad for cross-sectional data. In table 2, the results from the fixed
effects methods using the absolute changes (model 1), the moving standard deviation (model
2), the variance of spot exchange rate (model 3) as the uncertainty measures, are presented.
The results shown above proved one of our hypotheses that the measuring techniques
do make a difference in examining the effects of volatility of exchange rate on trade. By
employing the variance of spot exchange rate, we find that exchange rate volatility has an
adverse effect on the U.S. poultry export but the magnitude is small. This is consistent with
recent research using the gravity model by dell’Arriccia, Rose and Cho, Sheldon, and
McCorriston, who found small but statistically significant effects. For models 1 and 2,
exchange rate volatility reduces U.S. exports but not in a significant way. The impacts of
foreign income have significantly positive effects on U.S. poultry trade. With the income
increases in its largest markets, as Russia, Hong Kong and Mainland China, which still have
prospects of income increases, U.S. poultry exports have a great potential. Among the
dynamic models, only model 3 that uses variance around the spot exchange rate indicates
significant effects of exchange rate volatility.
15
Concluding Remarks
The potential effect of exchange rate risk on trade has been a subject of controversy.
At a theoretical level, researchers have been able to show that the exchange rate volatility may
exert a positive or negative impact on trade. Similarly, the available empirical evidence
generally conflicting and inconclusive on the possible impact of exchange uncertainty on
bilateral trade flows. Previous studies commonly use aggregate or sectoral trade data, in
which they assume that the effects of exchange rate volatility are the same over sectors or
commodities as the degree of openness varies. The underlying assumptions might contribute
to the ambiguity of empirical results. This paper re-examines the impact of exchange rate
volatility on U.S. exports by using poultry data for 49 trading partners of the U.S. over 1976
to 2000.
One of the central issues of debate is how to measure the exchange rate volatility. As
McKenzie (1999) points out, no general accepted technique exists in the literature. In order to
capture the effects of different measurements of exchange rate volatility on trade, three
measures are employed in this paper. Using the augmented gravity equation, fixed effects
method is applied to panel data of ninety-one importer countries of U.S. agricultural products.
Fixed effects approach is used against random effects model in that the latter one assumes that
the sample is random chosen from a larger population, which does not fit in this case. We
find that exchange rate volatility has a negative effect on trade in all the three static models
and are statistically significant in two of them. The measure by the absolute percentage
changes and by variance around the spot exchange rate yield statistically negative effects in
the long run but the magnitude differs. Consistent with previous authors, we also find that
export volume is sensitive to foreign income and price changes.
16
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24
Table 1 – Pooling Data Regression Report
Dependent Variable: Export volume
1993-1996
1997-2000
1993-2000
Variables
Average
average
Average
-0.008
-0.004
-0.004
(-1.232)
(-0.802)
(-0.804)
0.060 **
0.029
0.046 **
(2.629)
(1.403)
(2.250)
-0.211**
-0.161 **
-0.186 **
(-4.720)
(-4.133)
(-4.475)
-0.023
-0.026
-0.027
(-0.680)
(-0.810)
(-0.895)
0.368
0.284
0.322
LnXRAT
lnYj
lnPj
LnD
R2
Note: * *stands for significance at 0.05 confidence level.
25
Table 2 – Static Panel Data Fixed Effects Model
Dependent Variable: Export Volume
Variables
DXRAT
lnN
lnYj
lnY1
lnPj
V1
Model 1
Model 2
Model 3
-0.3378**
-0.0211
-0.157**
(-3.401)
(-0.419)
(-2.502)
-0.0408
-0.032
-0.002
(-0.998)
(-0.764)
(-0.048)
1.2629**
1.178 **
1.364**
(3.440)
(3.093)
(3.675)
0.162
0.125
0.084
(0.4226)
(0.313)
(0.218)
-1.763**
-1.825 **
-1.796**
(-14.0128)
(-14.453)
(-14.267)
-0.355**
(-3.521)
-0.026
V2
(-0.521)
-0.028**
V3
(-3.287)
R2
0.796
0.812
0.796
** - significant at 5% level; * - significant at 10% level.
T-values are in parenthesis.
26
Table 3 – Dynamic Panel Data Fixed Effects Model
Dependent Variable: Export Volume
Variables
DXRAT
lnN
lnYj
lnY1
lnPj
lnXt-1
V1
Model 1
Model 2
Model 3
-0.027
-0.051
-0.092**
(-0.344)
(-1.311)
(-1.900)
-0.011
-0.010
0.0102
(-0.351)
(-0.314)
(0.308)
0.426
0.408*
0.532*
(1.486)
(1.384)
(1.839)
0.063
-0.003
-0.029
(0.213)
(-0.011)
(-0.098)
-1.315**
-1.335**
-1.324**
(-13.209)
(-13.407)
(-13.328)
0.561**
0.561
0.556**
(22.651)
(22.361)
(22.870)
0.007
(0.091)
0.007
V2
(0.170)
-0.014**
V3
(-2.098)
R2
0.878
0.889
0.878
** - significant at 5% level; * - significant at 10% level.
T-values are in parenthesis.
27
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