TESTING EXCHANGE RATE PASS-THROUGH EFFECT ON U.S. IMPORTED CRUDE OIL PRICES

Transcription

1 TESTING EXCHANGE RATE PASS-THROUGH EFFECT ON U.S. IMPORTED CRUDE OIL PRICES Jui-Chi Huang Pennsylvania State University, Berks Campus Tantatape Brahmasrene Purdue University North Central Colin M. Witman Pennsylvania State University, University Park ABSTRACT This study investigates the relationship between the U.S. dollar and price of U.S. imported crude oil as well as the rivalry and collusive pricing behavior in the U.S. crude oil market. In effect, the significance of the exchange rate fluctuations that are passed through to the prices of imported crude oil is determined from countries of the Organization of the Petroleum Exporting Countries (OPEC) and non-opec countries to the U.S. crude oil market. This study found the incomplete exchange rate pass-through occurring with months lagged and there was the pricing rivalry behavior. In addition, the OPEC countries passed the cost shocks to the consumers a few months faster than non-opec countries possibly due to the market power. INTRODUCTION Over the years, a weaker dollar and higher gasoline prices have led researchers especially economists to ponder the relevance of the correlation between the dollar and price of gasoline. The price of crude oil either imported or produced domestically drives the value of gasoline. The existing literature has provided a general connection between exchange rates and imported crude oil prices. The purpose of this research is to determine what amount of exchange rate fluctuations are passed through to the prices of imported crude oil. This study essentially connects the exchange rate changes to the imported crude oil prices on both OPEC and non- OPEC countries. It also links the exchange rate fluctuations with the wholesale and retail gasoline prices. The known research methodology in exchange rate pass-through, so called passthrough, provides the necessary tool and may offer new insights into the results acquired by the existing literature. When exchange rates change, the exporters may lose some of the trade value when converting the currencies. Depending upon the market structure of the destination country or the particular characteristics of the industry, the exporters can choose one of the following: (1) complete pass-through by passing the cost shock or the currency conversion loss completely into its destination-currency prices, (2) zero pass-through by absorbing the shock to keep its price unchanged or (3) incomplete pass-through by combining some of the previous two options. In other words, the more market power a firm has, the higher the exchange rate shock the firm is able to pass-through to its prices which means more pass-through. The U.S. crude oil market has a property of the monopolistically competitive market structure. So, the domestic and foreign International Journal of Business and Economics Perspectives Volume 4, Number 2, Fall

2 individual producers have their own monopolistic power but are competing rigorously in the market. The market power, pricing rivalry or even collusive pricing may be observed. This study investigates the relationship between the U.S. dollar and price of U.S. imported crude oil as well as the rivalry and collusive pricing behavior in the U.S. crude oil market. In effect, the significance of the exchange rate fluctuations that are passed through to the prices of imported crude oil is determined from countries of the Organization of the Petroleum Exporting Countries (OPEC) and non-opec countries to the U.S. crude oil market. LITERATURE REVIEW Numerous studies provide and support evidence of a near one-for-one positive relationship between the crude oil price and the price of whole/retail gasoline. According to Chouinard and Perloff (2007), the price of crude oil directly affects wholesale gasoline as 56 percent of the price is associated with crude oil; consequently, 32 percent of the price of the retail gasoline the consumer pays for at the pump is derived from the cost of crude oil. Borenstein, Cameron, and Gilbert (1997) also suggest that a one dollar increase in the price of crude oil may lead to a 55 cent price increase of retail gasoline. According to Yousefi and Wirjanto (2003), most OPEC counties will adjust their prices on the value of the dollar to maintain market share and secure the purchasing power of oil revenue. A 10 percent depreciation in the dollar will result in a price increase in dollars between 1.9 percent and 8.5 percent. In addition, the finding in Yousefi and Wirjanto (2005) also suggests that the reason incomplete exchange rate-pass through occurs in OPEC nations is because of the collusive nature of OPEC as well as the fact that oil revenues are priced in U.S. dollars. Goldberg and Knetter (1997) have confirmed that incomplete or zero exchange rate pass-through is possible because the exporters have to absorb some or all the exchange rate cost shocks. The possible sources of incomplete pass-through are: (1) market power and market structure, (2) the imported raw materials exposed to exchange risk, (3) the speed of price adjustment and asymmetric response to exchange rate fluctuations, (4) the choice of exchange rates and indices, (5) price rigidity, (6) multinational cooperation by related and non-related party trade, (7) market segmentation by trade costs, and (8) strategic and collusive pricing and rivalry. Gross and Schmitt (1996 & 2000) found that pricing rivalry among foreign automobile producers in the Swiss market existed. Several researchers such as Kravis and Lipsey (1977), Richardson (1978), Feinberg (1986), Giovannini (1988), Knetter (1989), Feinberg (1989), and Feinberg (1996) developed a theory of imperfect competition to model the pricing behaviors. They concluded that market power plays an important role in local price destabilization. The foreign exporters can pass the exchange rate shock to consumers by adjusting prices as frequently as they wish due to market power. The degree of pass-through is not only destination-specific but also sector-specific. Caselli (1996), Athukorala and Menon (1994), Athukorala (1991), Parsley (1993) and Menon (1991) found that the degree of pass-through varies both within and across industries due to the differences in product differentiation and market structure. In general, the ability of adjusting the markup in favor of export agents or passing cost shock to consumers is based on the competitiveness of destination markets following the exchange rate changes. The primary product sectors such as crude oil trade are relatively more competitive than the manufacturing 104 International Journal of Business and Economics Perspectives Volume 4, Number 2, Fall 2009

3 product sectors. Controlling all other factors, the pass-through coefficients on average should be lower in primary product sectors than manufacturing sectors. THEORETICAL MODEL The exchange rate pass-through behavior comes from the pricing adjustment due to change in trade value affected by currency exchange fluctuations. A depreciation of the U.S. dollar creates the reduction of the trade value. Thus, profit or loss occurs for the source country producers while the trade is denominated by the U.S. dollar. The upward pricing may be due to the trade value loss adjustment due to U.S. dollar depreciation during the previous contract period. As previously noted, the mathematical model must be calibrated as a modification of the one developed in recent study of Ghosh and Rajan (2009) which assumed a monopolistic competition in export pricing. This is consistent with earlier models of Campa and Goldberg (1999), Goldberg and Knetter (1997), and Knetter (1989). To simulate the effect of the exchange rate fluctuations passing through to the prices of imported crude oil, the export pricing equation is constructed as follows: P i t = β 0 + β 1 E i t-k + β 2 P t + β 3 C i t-k + ε t Where i is foreign cross sections; t is time; k is time lags (1, 2, ); β 0 is a constant; P i is imported prices; E i is bilateral exchange rates or source country currency price of destination currency; P is destination competing price; C i is costs of imported products; ε t is a disturbance term. The partial derivatives imply that: ( P i t/ E i t-k) = β 1 < 0; ( P i t/ P t ) = β 2 > 0; ( P i t/ C i t-k) = β 3 > 0. The parameter β 1, the coefficient of E i t-k, is the degree of exchange rate pass-through. As a result, the predicted value of β 1 varies between 0 and 1 which can be interpreted as: β 1 = 0 means zero pass-through; β 1 = 1 implies complete pass-through; 0 > β 1 > 1 indicates an incomplete pass-through. In addition, the market rivalry behavior or even collusive pricing can be detected by the coefficient of domestic competing price (P t ) which is β 2. If β 2 = 1, a strong rivalry or collusive pricing occurs. Sample and Data Collection RESEARCH METHODS The sample includes monthly data from January 1996 to January 2009 of five source countries of the U.S. crude oil imports. The U.S. imported crude oil prices (USD per barrel) as International Journal of Business and Economics Perspectives Volume 4, Number 2, Fall

4 collected from the website of the Energy Information Administration (EIA) reveal the landed cost of the crude oil. It should be noted here that the top five exporting countries accounted for 64 percent of United States crude oil imports in February 2009 while the top ten sources accounted for approximately 84 percent of all U.S. crude oil imports. The top ten sources of U.S. crude oil imports for February 2009 were (numbers in parentheses represents million barrels per day): Canada (1.913), Mexico (1.219), Saudi Arabia (1.099), Venezuela (0.960), Angola (0.671), Iraq (0.554), Nigeria (0.457), Brazil (0.365), Kuwait (0.251), and Ecuador (0.243). The prices for the U.S. competing crude oil are proxied by the West Texas Intermediate s first purchase crude oil prices as obtained on EIA s website. The monthly data on bilateral exchange rates and the crude oil production costs of the source country proxied by the respective country s producer price index (PPI) are collected from the International Financial Statistics online database of the International Monetary Fund. This study attempted to include as many OPEC and non-opec countries as the data available. The five source countries under study are Canada, Mexico, Colombia, United Kingdom (non-opec members) and Venezuela (OPEC member). The EIA only provides the landed cost of the crude oil for eight selected source countries: Canada, Mexico, Colombia and United Kingdom for the non-opec countries; and Angola, Nigeria, Saudi Arabia and Venezuela for the OPEC countries. For the purpose of this study Saudi Arabia is removed due to the fixed exchange rate regime. Angola and Nigeria are also eliminated because the PPI data are not available. The sample begins in January 1996 because the landed cost of the crude oil for Colombia is not available prior to January The data sample ends in January 2009 when the latest landed cost of the crude oil are available. Moreover, there is missing data in the landed cost of the crude oil for United Kingdom and Colombia. Due to the time series consideration, the missing data is constructed using the smoothing technique of averaging two ends of the missing observations. Data exploration is shown in Appendix. Given the limited length of 145 months, estimating separate exchange rate pass-through equations for each source country gives imprecise results. This problem can be reduced by pooling the data. The two groups, OPEC and non-opec, will be studied separately. The OPEC data will be a time series study while the non-opec data will be under a panel data analysis. Data Analysis The data is analyzed according to the following estimation procedures: unit root tests for stationarity, cointegration tests, specification tests and the dynamic ordinary least squares regression method (DOLS). Unit root tests for stationarity. The time series data may contain a unit root and give a spurious regression estimation result. The unit root tests for stationarity of time series are employed prior to cointegration tests. These tests determine the existence of a unit root in each series. The series are examined whether they are stationary or integrated in the same order. If the two variables are non-stationary in level, but stationary in first difference i.e. I(1), cointegration test can be performed. The six panel unit root tests used in this study are similar, but not identical. The difference is on the basis of whether there are restrictions on the autoregressive process across cross-sections. Cointegration tests. According to Engle and Granger (1987), a linear combination of two or more nonstationary series may be stationary. If such a stationary linear combination exists, the 106 International Journal of Business and Economics Perspectives Volume 4, Number 2, Fall 2009

5 nonstationary time series are said to be cointegrated. The stationary linear combination may be interpreted as a long-run equilibrium relationship among the variables. The purpose of the cointegration test is to determine whether a group of non-stationary series is cointegrated or not. Specification tests. To understand the characteristics of the data, specification tests such as Breusch and Pagan (BP) Lagrangian multiplier, the incremental F test and the Hausman specification test are employed to determine whether the data have unobserved, but constant, variation across the cross-sectional units (the fixed effects model assumption) or there is random variation (the random effects assumption) across the cross-sectional units. Dynamic ordinary least squares regression method. The estimation is undertaken using the dynamic ordinary least squares (DOLS) methodology developed by Stock and Watson (1993). The DOLS methodology has been widely used in panel cointegration analysis with nonstationary data for bias correction in finite samples. The DOLS method involves regressing the dependent variable with independent variables itself and also the leads and lags of the first differences of the independent variables to correct the serial correction and endogeneity problems. The DOLS-specific empirical equation is shown in the model section where all variables are first differenced and in natural logarithm but k is time lags and leads (i.e., -2, -1, 0, 1, 2, ). This empirical equation is used for two testing groups: four non-opec country crude oil imported prices and one OPEC country crude oil imported prices. Unit Root Tests for Stationarity EMPIRICAL RESULTS The variables in the sample period exhibit unit root. In Table 1, all variables from four non-opec countries are nonstationary when tested via the Levin, Lin, and Chu (2002), IPS (Im, Persarn & Shin, 2003) and other panel unit root tests except for P t under IPS method. For Venezuela, the only OPEC country in this study, all variables in Table 2 are nonstationary using Augmented Dickey Fuller (ADF) and Phillips Perron (PP) unit root tests. The six panel unit root tests for four non-opec countries (Table 1) and the two individual unit root tests for the OPEC country (Table 2) all show that they are stationary in the first difference with the exception of E i t under Hadri test. Overall, the first differences of E i t for four non-opec countries are stationary because five out of six panel unit root tests show no unit root and the non-stationary firstdifferenced E i t under Hadri test is only at 10 percent significance. According to Hlouskova and Wagner (2006), the Hadri test appears to over-reject the null of stationarity, and may yield results that directly contradict those obtained using alternative test statistics even though Hadri s panel unit root test experiences significant size distortion in the presence of autocorrelation when there is no unit root as found in Table 1. Therefore, the concern over autocorrelation of the differenced series is minimal. Co-integration Tests Two panel cointegration tests Pedroni residual cointegration test and Johansen Fisher panel cointegration test are employed for four non-opec countries. The Pedroni test reveals in Table 3 that out of seven different tests only panel and group ADF-statistics accept the null hypothesis of no cointegration. Furthermore, the Johansen Fisher test in Table 4 also shows that International Journal of Business and Economics Perspectives Volume 4, Number 2, Fall

6 there is a co-integrating vector relationship since the null of the maximum of zero co-integrating vectors is rejected. Therefore, there is a strong co-integration among variables. The data for one OPEC country is also tested for the co-integration as displayed in Table 5. The result also indicates a strong co-integrating relationship among variables since the null of the maximum of zero co-integrating vectors is rejected. Given that all the variables are stationary in their first differences (Table 1 & 2), the evidence of panel co-integration among variables (Table 3-5) prevents the possibility of the estimated relationship being spurious. Specification Tests Table 1 Panel Unit Root Tests (4 Non-OPEC Countries) a ADF LLC PP Breitung IPS b Hadri c Variable (level) P i t *** (0.1687) (0.4093) (0.3681) (0.8436) (0.1866) (0.0000) E i t *** (0.8928) (0.6098) (0.8580) (0.9999) (0.8578) (0.0000) P t * ** *** (0.1000) (0.1257) (0.3754) (0.9932) (0.0466) (0.0000) C i t *** (0.1184) (0.5080) (0.3129) (0.7568) (0.3146) (0.0000) Variable (1 st difference) p i t *** *** *** *** *** (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.5213) e i t *** *** *** *** *** * (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0995) p t *** *** *** *** *** (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.4384) c i t *** *** *** *** *** (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.7307) Notes: a. The upper case series are level variables while the lower case variables are first differenced. b. Abbreviations: ADF (Augmented Dickey Fuller); LLC (Levin, Lin, & Chu); PP (Phillips Perron); IPS (Im, Pesaran, & Shin). c. The null hypothesis of ADF (Fisher χ 2 ), LLC (t-statistic), PP (Fisher χ 2 ), Breitung (t-statistic) and IPS (Wstatistic) is unit root (non-stationarity). d. The null hypothesis of Hadri tests uses stationarity (no unit root). e. The lag length is modeled by Schwarz s automatic selection of maximum lags from zero to five lags. f. The p-values are given in parentheses. ***, **, and * indicate statistical significance at 1, 5, and 10%, respectively. The Breusch and Pagan (BP) Lagrangian multiplier test is employed to test the random effect assumption of the data. The result accepts the null hypothesis of the random effects that unit-specific residual or variation does not exist [χ 2 (1) = 1.77; P-value > χ 2 = ]. It indicates that the fixed effects model regression is not appropriate. 108 International Journal of Business and Economics Perspectives Volume 4, Number 2, Fall 2009

7 Table 2 Unit Root Tests (1 OPEC Country) ADF PP Variable (level) P i t (0.1202) (0.3499) E i t (0.8109) (0.8280) P t (0.1882) (0.3404) C i t (1.0000) (0.3404) Variable (1 st difference) p i t (0.0000)*** (0.0000)*** e i t (0.0000)*** (0.0000)*** p t (0.0000)*** (0.0000)*** c i t (0.0010)*** (0.0022)*** Notes: a. The upper case series are level variables while the lower case variables are first differenced. b. The null hypothesis of ADF (Fisher χ 2 ) and PP (Fisher χ 2 is unit root (non-stationarity). c. Abbreviations: ADF (Augmented Dickey Fuller); PP (Phillips Perron). d. The p-values are given in parentheses. ***, **, and * indicate statistical significance at 1, 5, and 10%, respectively. Table 3 Panel Cointegration Tests (4 Non-OPEC Country Panel) With Constant With Constant & Trend Panel v-statistic (0.0000)*** (0.0000)*** Panel ρ-statistic (0.0000)*** (0.0000)*** Panel PP-Statistic (0.0000)*** (0.0000)*** Panel ADF-Statistic (0.1448) (0.1879) Group ρ-statistic (0.0000)*** (0.0000)*** Group PP-Statistic (0.0000)*** (0.0000)*** Group ADF-Statistic (0.2021) (0.2790) Note: a. The null hypothesis is no cointegration among variables. b. Abbreviations: ADF (Augmented Dickey Fuller); PP (Phillips Perron). c. The p-values are given in parentheses. ***, **, and * indicate statistical significance at 1, 5, and 10%, respectively. Table 4 Unrestricted Cointegration Rank Tests (4 Non-OPEC Country Panel) Fisher Trace Statistic Fisher Maximum Eigenvalue Statistic r = 0 a (0.0000) *** (0.0000) *** r = (0.0002) *** (0.0000) *** r = (0.8639) (0.8274) r = (0.6695) (0.6695) Note: a. r denotes the maximum number of cointegrating vectors. b. The p-values are given in parentheses. ***, **, and * indicate statistical significance at 1, 5, and 10%, respectively. International Journal of Business and Economics Perspectives Volume 4, Number 2, Fall

8 Table 5 Unrestricted Cointegration Rank Tests (1 OPEC Country) a Trace Statistic Maximum Eigenvalue Statistic r = 0 b (0.0261) ** (0.0507) * r = (0.2497) (0.2294) r = (0.5935) (0.6829) r = (0.2068) (0.2068) Note: a. The tests allow for quadratic deterministic trend with a constant term. b. r denotes the maximum number of cointegrating vectors. c. The p-values are given in parentheses. ***, **, and * indicate statistical significance at 1, 5, and 10%, respectively. By using the incremental F test, the null hypothesis of the random effects that unitspecific residual is zero cannot be rejected [F(3, 540) = 0.08; P-value > F = ]. This test also shows that the fixed effects model is not suitable for the data. Furthermore, the Hausman specification test is also used to identify the appropriate model assumption for the panel regression. The null hypothesis that there is no difference in the coefficients estimated by the efficient random effects estimator and the consistent fixed effects estimator is examined. The insignificant P-value [χ 2 (28) = 0.02; P-value > χ 2 = ] accepts the null hypothesis of no difference. Therefore, the random effects estimator is less biased than the fixed effects estimator. Dynamic Ordinary Least Squares Regressions Based on the diagnostic testing results, the pooled dynamic ordinary least squares (DOLS) regression method with the random effects specification is used to analyze our data. According to the DOLS-specific empirical equation, various leads and lags of independent variables are used. The results for two groups are showed in Table 6. Only the lead and lagged regressors with significant estimates are reported in Table 6. PRACTICAL IMPLICATIONS In light of this research, the results in Table 6 affirm the practical implications of the exchange rate pass-through, market power and the rivalry behaviors or collusive pricing as described below. Exchange Rate Pass-Through and Market Power Non-OPEC Countries. For the four non-opec countries (Canada, Mexico, Colombia, & United Kingdom), the exchange rate did not have any impact on the pricing of these four countries crude oil exports to the U.S. until the ninth month after the exchange rate change. The estimated coefficient of e i t-9 in Table 6 is statistically significant at 5 percent level. In other words, there was a zero pass-through in the first eight months. That implies the crude oil exporters of these four non-opec countries were unable to pass any cost shocks from currency exchange fluctuations to the U.S. market. This is due in part to non-collusive pricing at least 110 International Journal of Business and Economics Perspectives Volume 4, Number 2, Fall 2009

9 within eight months after the shock. The exporters from these countries somehow managed to pass the cost shocks to the new contract pricing in the ninth month. TABLE 6 The DOLS-Specific Exchange Rate Pass-Through Regression Results (January 1996 January 2009) Four Non-OPEC Panel Data (Panel DOLS with Random Effects) One OPEC Time Series Data (DOLS) Constant (0.352) (0.861) e i t (0.345) (0.032)** e i t (0.956) (0.109)* e i t (0.022)** (0.358) p t (0.000)*** (0.000)*** p t (0.000)*** (0.000)*** p t (0.000)*** (0.384) c i t (0.795) (0.104)* c i t (0.089)* (0.242) R Sample Size Notes: a. The lower case variables under the regression equation are first differenced. b. The p-values are given in parentheses. ***, **, and * indicate statistical significance at 1, 5, and 10%, respectively. OPEC Country. In the third column of Table 6, the exchange rate pass-through happened sooner for the one OPEC country (Venezuela) at the third month. The estimated coefficient of e i t- 3 is statistically significant at 10 percent level. These findings provide some evidence that the OPEC countries possess more market power or control than the non-opec countries. They pass through the exchange rate shock to the U.S. consumer quicker. The average pass-through estimation (β 1 ) across the four non-opec source countries and OPEC country are and percent, respectively (e i t-9 and e i t-3 in Table 6). This means that the degree of the OPEC country s third month pass-through is less than the four non-opec countries ninth month pass-through. Rivalry Behaviors The incomplete pass-through may be explained by hedging the trade value in the future market, data aggregation, country varying characteristics, market share competition and noncollusive pricing. However, it is evident that the rivalry behaviors or collusive pricing for both groups occurred during the sample period. The coefficients (β 2 ) of the U.S. domestic crude oil competing prices (p t+k ), a lead and a lag, are all significant at 1 percent level. Their estimated coefficients of p t are 0.86 and 0.82, respectively. The implication is that there was strong price competition or even collusive pricing at a nearly one for one response between foreign crude oil exporters under study and the U.S. domestic producers. International Journal of Business and Economics Perspectives Volume 4, Number 2, Fall

10 DISCUSSION AND CONTRIBUTIONS This study makes important contributions in the literature of exchange rate pass-through to the U.S. import product prices. This study essentially connects the exchange rate changes to the imported crude oil prices on both OPEC and non-opec countries. Besides the finding that there appears to be a long-run relationship between the exchange rate and the U.S. imported crude oil price, the amount of exchange rate fluctuations passing through to the prices of imported crude oil are further examined. (1) Non-OPEC Countries. As previously noted, the second column of Table 6 shows that the average pass-through estimation (β 1 ) across the four non-opec source countries is percent. The estimated degrees of pass-through in the U.S. crude oil market contribute to support the finding of Marazzi, Sheets, Vigfusson, Faust, Gagnon, Marquez, Martin, Reeve, and Rogers (2005) in various U.S. imported products. They found that there has been a continuous decline in exchange rate pass-through to U.S. import product prices from above 50 percent during the 1980s to roughly 20 percent between 1995 and In contrast to recent findings, a survey of exchange rate pass-through by Goldberg and Knetter (1997) found evidence of 60 percent overall exchange rate pass-through in the United States during the floating exchange rate period from 1971 to 1990s. (2) OPEC Country. The average pass-through estimation (β 1 ) OPEC country is percent. This Venezuela s pass-through estimate supports the finding of Yousefi and Wirjanto (2003) of 19.3 percent during Their estimated degrees of exchange rate pass-through to the traded crude oil prices of three OPEC source countries (Venezuela, Iran, & Saudi Arabia) during the period of 1973 through the 1990s are between 19.3 and 84.6 percent. Further, the estimated degrees of exchange rate pass-through to the traded crude oil prices of four OPEC source countries (Indonesia, Nigeria, Iran, & Saudi Arabia) from 1989 to 1999 are between 34.9 and 75.8 percent (Yousefi & Wirjanto, 2005) while they vary from 7 to 76 percent in eight OPEC country data (Algeria, Indonesia, Iran, Libya, Nigeria, Qatar, Saudi Arabia, & United Arab Emirates) between 1974 and 1989 (Yousefi & Wirjanto, 2004). (3) By using the dynamic ordinary least squares method (DOLS) with leads and lags empirical specification, this study is able to detect the delayed pass-through pricing while it could have been concluded as zero exchange rate pass-through in the existing pass-through literature with non-dynamic regression methods. CONCLUSIONS Crude oil exporters to the U.S. market have tried to maintain the market share due to competition and pricing rivalry by passing nothing to prices initially after the exchange rate shock. They also wanted to protect the value of crude oil trade revenue by passing some of the currency conversion loss to the U.S. consumers. These two main objectives of their crude oil pricing intertwine. The testing of exchange rate pass-through to prices of imported crude oil from four non-opec countries and one OPEC country to the U.S. market yielded plausible results. The imported crude oil prices from the four OPEC countries are unresponsive to the exchange rate movements in the first eight months but are responsive marginally in the ninth month. Furthermore, the imported crude oil prices from the one OPEC country are unresponsive to the exchange rate movements in the first two months but are responsive marginally in the third month. However, the pass-through diminished quickly beyond the responsive months. 112 International Journal of Business and Economics Perspectives Volume 4, Number 2, Fall 2009

11 Moreover, the market power of the OPEC countries has shown that Venezuela s exporters can pass the cost shock to prices faster than those of non-opec member countries. LIMITATION OF THIS STUDY This study is limited by the data availability of the crude oil import prices to the U.S. and the related control variables. More comprehensive data of crude oil pricing and supporting data for all OPEC and non-opec countries should add further understanding of the pass-through and pricing rivalry behaviors in the U.S. market. RECOMMENDATION FOR FUTURE RESEARCH A few areas of research were identified as likely directions for future research as follows. The results in this study can be further generalized by investigating the crude oil trade to the non-u.s. markets. Other factors such as hedging behavior, institutional factors and related party oil trade may be included in the future research. The analysis could have been improved with multivariate time series analysis instead of the regression approach since the first differenced variables were stationary time series. REFERENCES Athukorala, P. (1991). Exchange Rates Pass-Through: The Case of Korean Exports of Manufactures. Economics Letters, 35 (1): Athukorala, P., & Menon, J. (1994). Pricing to Market Behavior and Exchange Rate Pass- Through in Japanese Exports. The Economic Journal, 104, Borenstein, S., Cameron, A. C., & Gilbert, R. (1997). Do Gasoline Prices Respond Asymmetrically to Crude Oil Price Changes? The Quarterly Journal of Economics, 112 (1): Campa, J. M., & Goldberg, L. S. (1999). Investment, Pass-Through and Exchange Rates: A Cross-Country Comparison, International Economic Review, 40 (2): Caselli, P. (1996). Pass-Through and Export Prices: an Empirical Test for the Leading European Countries. International Review of Applied Economics, 10 (2): Chouinard, H. H., & Perloff, J. M. (2007). Gasoline Price Differences: Taxes, Pollution Regulations, Mergers, Market Power, and Market Conditions, The B.E. Journal of Economic Analysis & Policy, 7 (1) Contributions, Article 8. Engle, R. F., & Granger, C. W. J. (1987). Co-Integration and Error Correction: Representation, Estimation, and Testing, Econometrica, 55, International Journal of Business and Economics Perspectives Volume 4, Number 2, Fall

12 Feinberg, R. M. (1986). The Interaction of Foreign Exchange and Market Power Effects on German Domestic Prices. Journal of Industrial Economics, 35(1): Feinberg, R. M. (1989). The Effects of Foreign Exchange Movements on U.S. Domestic Prices, Review of Economics and Statistics, 71, Feinberg, R. M. (1996). A Simultaneous Analysis of Exchange-Rate Passthrough into Prices of Imperfectly Substitutable Domestic and Import Goods. International Review of Economics and Finance, 5(4): Ghosh, A., & Rajan, R. S. (2009). Exchange Rate Pass-Through in Korea and Thailand: Trends and Determinants, Japan and the World Economy, 21, Goldberg, P., & Knetter, M. (1997). Goods Prices and Exchange Rates: What Have We Learned? Journal of Economic Literature, 35, Giovannini, A. (1988). Exchange Rates and Traded Goods Prices. Journal of International Economics, 24, Gross, D. M., & Schmitt, N. (1996). Exchange Rate Pass-Through and Rivalry in Swiss Automobile Market, Welwirtschaftliches Archiv, 132, Gross, D. M., & Schmitt, N. (2000). Exchange Rate Pass-Through and Dynamic Oligopoly: An Empirical Investigation, Journal of International Economics, 52, Hlouskova, J., & Wagner, M. (2006). The Performance of Panel Unit Root and Stationarity Tests: Results from a Large Scale Simulation Study, Econometric Reviews, 25, Im, K. S., Pesaran, M. H., & Shin, Y. (2003). Testing for Unit Roots in Heterogeneous Panels, Journal of Econometrics, 115 (1): Knetter, M. (1989). Price Discrimination by U.S. and German Importers, American Economic Review, 79 (1): Kravis, I. B., & Lipsey, R. E. (1977). Export Prices and the Transmission of Inflation. The American Economic Review, 67, Levin, A. L., Lin, C.-F., & Chu, C.-S. J. (2002). Unit Root Tests in Panel data: Asymptotic and finite-sample Properties, Journal of Econometrics, 108 (1): Menon, J. (1991). Exchange Rate Changes and the Pricing of Australian Manufactured Exports. IAESR Working Paper Series, 387, University of Melbourne. Marazzi, M., Sheets, N., Vigfusson, R., Faust, J., Gagnon, J., Marquez, J., Martin, R., Reeve, T., & Rogers, R. (2005). Exchange Rate Pass-through to U.S. Import Prices: Some New 114 International Journal of Business and Economics Perspectives Volume 4, Number 2, Fall 2009

13 Evidence, International Finance Discussion Papers series 833, Board of Governors of the U.S. Federal Reserve System. Parsley, D. C. (1993). Exchange Rate Pass-Through: Evidence from Aggregate Japanese Exports. Southern Economic Journal, 60 (2): Richardson, J. D. (1978). Some Empirical Evidence on Commodity Arbitrage and the Law of One Price. Journal of International Economics, 8, Stock, J. H., & Watson, M. W. (1993). A Simple Estimator of Cointegrating Vectors in Higher Order Integrated Systems, Econometrica, 61 (4): Yousefi, A., & Wirjanto, T. S. (2003). Exchange Rate of the U.S. Dollar and the J Curve: the Case of Oil Exporting Countries, Energy Economics, 25 (6): Yousefi, A., & Wirjanto, T. S. (2004). The Empirical Role of the Exchange Rate on Crude-Oil Price Formation, Energy Economics, 26 (5): Yousefi, A., & Wirjanto, T. S. (2005). A Stylized Exchange Rate Pass-Through Model of Crude Oil Price Formation, OPEC Review, 29 (3): About the Authors: Jui-Chi Huang is Assistant Professor of economics at Pennsylvania State University at Berks campus. He received a Ph.D. in economics from American University. He serves on a Board of Directors for Pennsylvania Economic Association. His current research interest is in the area of exchange rate risk and exchange rate pass-through. In addition to regularly presenting papers at professional meetings, he has published in selected refereed journals such as Asian-African Journal of Economics and Econometrics, Journal of Economics and Economic Education Research, Managerial Finance and the Journal of Business Economic Research. Tantatape Brahmasrene is Professor of Business and Economics in College of Business at Purdue University North Central. Dr. Brahmasrene is a 2001 J. William Fulbright Senior Scholar, a 2003 Fulbright Senior Specialists in Thailand and a 2007 Fulbright Senior Specialists in Kazakshstan. He holds a M.S. from Indiana State University, and a M.A. and Ph.D. from University of Cincinnati. He also earned two professional designations: Certified Financial Planner (1991) and Chartered Financial Consultant (1992). His current research interests are in the areas of contemporary business issues, applied economics, finance and international economics. His recent articles appear in selected refereed journals such as Journal of the Asia Pacific Economy, JT M, IJE, JIBR, SER, JEER, Managerial Finance, the CPA Journal and Journal of Contemporary Business Issues. Colin M. Witman is pursuing an Honors Program in Energy, Business, and Finance at Pennsylvania State University, University Park. His research interests include international economics and energy policy. International Journal of Business and Economics Perspectives Volume 4, Number 2, Fall

14 APPENDIX Non-OPEC (4) and OPEC (1) Country Exchange Rates vs. U.S. Imported Crude Oil Prices: Canada (CAD/USD) USD Prices of Crude Oil from Canada Mexico (MXN/USD) USD Prices of Crude Oil from Mexico Colombia (COP/USD) USD Prices of Crude Oil from Colombia UK (USD/GBP) USD Prices of Crude Oil from UK Venezuela (VEF/USD) USD Prices of Crude Oil from Venezuela U.S. WTI (West Texas Intermediate) First Purchase Crude Oil Price 116 International Journal of Business and Economics Perspectives Volume 4, Number 2, Fall 2009