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Augustin Cournot Doctoral Days Strasbourg April 06th - 08th 2005 Exchange rate movements and export prices An empirical analysis Isabelle Méjean ∗ ∗ CREST and EUREQua, University of Paris I. Email: [email protected] MOTIVATIONS A new generation of “Incomplete pass-through” models... Following Betts and Devereux (1996) Incomplete pass-through of exchange rate movements into import prices Results : Link between the extent of the exchange rate pass-through and the expenditure switching effect ⇒ Impact on the international monetary transmission and the exchange rate determination ... Constructed on a set of stylized facts : Large and persistent deviations from the LPU, even for traded goods (Engel, 1999) Weak sensitivity of import prices to exchange rate movements (Goldberg and Knetter, 1997) Pricing-to-Market behaviours observed on disaggregated prices (Knetter, 1989, 1993) LIMITS Theoretically : No unified microfounded explanation of the incomplete pass-through phenomenon ⇒ Nominal price rigidities in local currency vs optimal pricing-to-market behaviours Empirically : Aggregate estimates ⇒ Composition and/or endogeneity biases (Imbs and al., 2002) Lack of microeconomic results confirming the influence of market structures and the elasticity of demand (see Bacchetta and van Wincoop, 2005) OBJECTIVE Robust Exchange Rate Pass-Through Estimates Estimations on disaggregated data to limit the risk of composition bias Supply-sided approach Estimations on export prices with a low frequency, allowing an interpretation in terms of strategic behaviours Macroeconomic Interpretation Estimations on a large sample of industries, to evaluate the global consequences of those strategies ⇒ Main results Large pass-through of exchange rate movements into export prices. High heterogeneity across industries. MODEL A basic model of profit maximization... Price decisions of a monopolist from country i in a given industry k, producing goods sold in N separated markets (j = 1...N ) under constant returns : M ax ijk ijk ijk ijk ij ijk Πijk = M ax [P −M C ] D (P S ,Z t t t t t t t ) ...Leading to a non structural theoretical relation pijk t = (1 + β ijk )mcijk t εηZ ijk ijk + η β zt + β ijk sij t εP S with : β ijk εηP S = − ijk η − 1 + εηP S εηP S = εηZ ∂ ln ηtijk ∂ ln(Ptijk Stij ) = ∂ ln ηtijk ∂ ln(Ztijk ) EMPIRICAL METHOD Estimated equation For each exporting industry located in a given country (for a each (i,k)) : pjt = αt + γ j + β j sjt + εjt Method Panel model with time and individual fixed effects Test on the fixed vs random effect hypotheses Within transformation ⇒ White correction for heteroscedasticity and autocorrelation of the error term Homogeneity assumption : β j = β, ∀ j DATA OECD’s International Trade by Commodities Statistics Database 6 Exporting countries : Germany, the USA, France, Italy, Japan, the UK Importing countries restricted to OECD’s Around 2000 5-digit SITC industries, restricted to the 500 largest ones to increase the accuracy of results Period 1988-1998 (annual frequency) F.O.B. export unit-values Nominal bilateral exchange rates 52 53 -0.25;0.23 -0.30;0.33 41 59 -0.29;0.19 -0.38;0.30 -0.20;0.10 -0.18;0.08 76 65 -0.28;0.18 -0.24;0.10 67 51 (b) Range of the estimated coefficients ignoring the tails (10% of the coefficients ignored) (a) Fraction of estimated coefficients that are significantly different from 0 at the 5% level standard error Share of significant coefficients (%)(a) Share of negative significant coefficients (%) Interquartile range of coefficients(b) Interquartile range of significant coefficients -0.89;0.23 -0.59;0.13 69 52 -0.38;0.32 -0.31;0.22 62 57 Tab. 1 – Averaged statistics on the distribution of the sector-based nominal exchange rate pass-through (restricted to the 500 largest industries) USA UK DEU FRA ITA JAP Mean estimated 0.030 0.041 0.020 0.026 0.046 0.034 Exporter-specific kernel estimates of the distribution of estimated long-run pass-throughs (Normal kernel function with bandwidth=0.8) Why is the estimated pass-through so high? Hypotheses : Differences in the estimated pass-through obtained from export and import prices Upward composition bias when using aggregate prices rather than sectorial data Sensitivity of the results to the sample of importing countries Substitution across goods Benchmark USA Euro Zone High Volatility Large Partners Large Shares -.25;.22 -.24;.15 -.23;.19 -.76;.68 -.24;.23 -.29;.31 -.28;.19 . -.18;.17 -.85;.48 -.28;.25 -.26;.27 -.24;.13 -.19;.06 -.51;.50 -.19;.08 -.18;.06 -.18;.08 DEU j j j j -.95;0,26 -.69;.19 -.98;.08 -.58;.19 -.57;.13 -.59;.13 ITA -.38;.23 -.34;.21 -.71;.50 -.35;.19 -.30;.16 -.31;.22 JAP where DGS is a dummy variable computed so that it equals 1 when the observation concerns an importing country of the studied group. The composition of groups is the following : 1. the United States, 2. Euro Zone, i.e. Austria, Belgium, Finland, France, Germany, Ireland, Italy, the Netherlands, Portugal and Spain, 3. “Volatile” countries, i.e. Greece, Hungary, North Korea, Mexico, Poland and Turkey, 4. the 5 largest partners (in terms of exported value) of each industry- and exporter-specific sample, 5. importing countries in which the share of the exporter in the total imported value is large (with respect to the mean market share of the studied exporter in the sample of destination markets). pt = αt + γ + βst + βGS DGS st + υt j -.29;.17 -.24;.11 -.62;.47 -.25;.10 -.24;.07 -.24;.10 FRA of the common pass-through coefficients (β) UK (a) (a) 90% of the total distribution of coefficients taken into account. Except for the “benchmark” estimation, estimations are based using (5) : 0. 1. 2. 3. 4. 5. USA Tab. 2 – Interquartile ranges