Abstract
This article aims at showing heterogeneity in the degree of exchange rate pass-through to import prices in major advanced economies at three different levels: (1) across destination markets; (2) across types of exporters [distinguishing developed economy (DE) from emerging economy (EE) exporters] and (3) over time. Based on monthly data over the period 1991–2007, the results show first that large destination markets exhibit the lowest degree of pass-through. The degree of pass-through for goods imported from EEs is also significantly lower than for those from DEs. Regarding the evolution over time, no clear change in pricing behaviours can be identified and the well-identified decline in the exchange rate pass-through between the 1980s and 1990s appears to have stopped during the period considered.
Notes
Marazzi et al. (2005) detect a particular step down in the pass-through coefficient around the time of the Asian financial crisis and document a shift in the export pricing behaviour of emerging Asian firms around that time.
The lower share of the UK imports coming from EEs does not necessarily imply that the UK is less open to those countries than the US or the euro area. It may rather reflect the fact that, owing to geographical and institutional reasons, the UK sources most of its imports in the euro area countries.
In this article, the naming convention for the groups of economies is as follows: major advanced economies refer to the US, the euro area, Japan, the UK and Canada; DEs refer to the IMF classification of World Economic Outlook group named “Advanced Economies” (this group obviously includes the above major advanced economies) less the “Newly industrialised economies”; emerging economies (EEs) refer to the IMF classification of World Economic Outlook group named “Emerging and Developing Economies” plus the “Newly industrialised economies”. An alternative grouping, however, envisages the case where the countries belonging to the group “Newly industrialised economies” are considered as DEs.
Some countries are small but very attractive to foreign firms, owing to a favourable geographical position (e.g. part of a large economic area) or good accessibility (e.g. large portuary infrastructures) These factors make their potential much larger than what their GDP would suggest.
Empirical results are also available for Canada in Dees et al. (2008), where the degree of pass-through is found to be very large. However, the import price measures in the case of Canada may artificially bias the pass-through rate to the upside. Indeed, the Canadian import price series suffer from measurement problems in that a number of Canadian import prices is constructed by multiplying US producer prices by the Canadian US dollar exchange rate. Owing to these problems, we have decided not to show here the results for Canada.
As Marazzi et al. (2005), we use CPIs rather than PPIs because the CPI data are available for more countries and in longer time series. However, robustness checks using PPI based on a shorter sample are available upon request. Overall, the results presented here remain whatever price indices used.
As some EEs have moved during the period to DEs (like the Republic of Korea or Singapore), we also perform some robustness checks with an alternative grouping. Results based on alternative groupings are available in Dees et al. (2008). Overall, the conclusions presented in this article are robust to alternative groupings.
To test whether \(R=1\) is a valid assumption, we re-estimate Eq. (1 ), with various values for \(R\), varying between 0.9 and 1 with a step of 0.01. Looking at the value of the likelihood function, we verify that \(R=1\) corresponds to the optimum of the likelihood function, validating our assumption.
When estimating the model using higher order VARs, the results remain very similar.
Contrary to Bache (2006), the VAR is estimated together with the rational expectations equations with ML. Bache fixes the VAR coefficients at their OLS estimates prior to the ML estimations.
Robustness checks using alternative groupings (i.e. Korea and Singapore included in the DE countries) are not shown here but are reported in Dees et al. (2008).
De Bandt et al. (2008) are, however, sceptical about such specification and rather favour estimations including cointegrating relationships. As we have not been able to find any cointegrating relationships among the variables of our sample, we have disregarded such specifications.
It is somehow abusive to call it “long-run pass-through” and it would rather be more correct to call it “pass-through after \(s\) periods”.
This lag specification is consistent with Marazzi et al. (2005), who used two quarters. Alternative specifications omitting the lagged-dependent variable have been also estimated. Different lag structures have also been tested. Overall, the results obtained are very close to those presented in the article. The results of these alternative estimations are available upon request.
Tests for equality of pass-through across destination markets is available in Dees et al. (2008).
As in the estimations we now use two aggregated measures for the exchange rate and for the price level (i.e. distinction between DE and EE countries), we check whether possible co-movements of theses variables among high- and low-cost trading partners do not cause severe multi-collinearity. Dees et al. (2008) show that in all cases, the estimations should not be subjected to multicollinearity, except for the highest order lags in the models for the euro area and Japan.
More details about these robustness are available upon request.
To improve the readability of Fig. 5, we report only pass-through coefficients averaged over 1 year (i.e. the 1998 figures corresponds to the average of estimates over windows going from 1992M1–1998M1 to 1992M12–1998M12).
This assumption has some empirical support. Using microdata for traded goods prices at the docks for the US, Gopinath and Rigobon (2008) find that the stickiness of prices invoiced in foreign currencies is similar to the stickiness of prices invoiced in dollars.
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Acknowledgments
The authors thank Menzie Chinn, Linda Goldberg, Chiara Osbat and three anonymous referees for helpful comments and suggestions. Any remaining errors are the responsibility of the authors. Any views expressed represent those of the authors and not necessarily those of the European Central Bank, the Eurosystem, or the Bank of Canada.
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Appendices
Appendix 1: Theoretical framework
Import prices are modelled following Betts and Devereux (1996, 2000). It is assumed that part of the exporters price their exports in the currency of the importing country (LCP) and the remaining exporters price their products in their own currency (PCP). Moreover, frictions in the price setting process à la Calvo (1983) are introduced, i.e. only part of the exporters are allowed to change their price in the current period. The aggregation of pricing behaviours over these two types of exporters gives an import price Euler equation where import prices depend on expected future import price inflation, current and expected future change in foreign exchange rates and on the real marginal costs of the exporters.
An importer aggregates the various types of exports.
1.1 Aggregate imports
An importing firm aggregates the products of the exporter firms. The goods are produced in a number of varieties defined over a continuum of unit mass. Varieties of goods by PCP exporters are indexed by \(j\in [0,\alpha )\) and those of LCP exporters by \(j\in [\alpha , 1]\). Aggregate imports \( M \) is defined by:
where \(M^{i}\) is the imports coming from the \(i\) exporter (\(i=P,L\)) and \(1/(1+\rho )\) is the constant elasticity of substitution between the individual goods.
The aggregate import price \(P\) is defined by
where \(P^{i}\) is the import price corresponding to goods produced by exporter \(i\) ( \(i=P,L\)), \(S\) is the bilateral exchange rate between the exporting country and the importing country. Assuming symmetric equilibria and log-linearising the price equation (6) around the steady-state gives
The cost minimisation implies the following demand functions:
1.2 Exporter price behaviours
Assuming imperfect competition, exporters price their products by taking into account the demand function (8). All firms share the same cost function \(C(j)\), assumed to be homogenous of degree one in output. They also share the same discount factor \(R_{t,t+k}\). Firms are assumed to change their price level when they receive a random “price-change signal” (see Calvo 1983). Probability of receiving a price-change signal is given by \(1-\zeta \) (\(\zeta \in \left[ 0,1\right] \)). It is assumed to be identical to all (both LCP and PCP) firms.Footnote 20 As there is a continuum of firms, \(1-\zeta \) also represents the share of firms that has received such a signal and, consequently, got an opportunity to change their prices. The average time between price changes is given by \(1/(1-\zeta )\). The firms’ maximisation problem is as follows:
where \(\Pi _{t+k}^{i}\left[ \overline{P}_{t}^{i}(j)\right], i=P,L\) is momentary profits of a firm type \(i\).
1.3 PCP firms
Given the momentary profits of PCP firms
the first-order condition of the profit maximizing problem (9) is given by
where \(MC(j)=C^{\prime }(j).\)
The aggregate price level \(P_{t}^{P}\) evolves according to the following equation of motion:
Assuming symmetric equilibrium and log-linearising the Eqs. (11) and (12) gives the Euler equation for PCP firms in high-cost countries.
1.4 LCP firms
Given the momentary profits of LCP firm
the first-order condition of the profit maximizing problem (9) is given by
where \(MC(j)=C^{\prime }(j).\)
The aggregate price level \(P_{t}^{L}\) evolves according to the following equation of motion:
Assuming symmetric equilibrium and log-linearising the Eqs. (15) and (16) gives the Euler equation for LCP firms.
1.5 Aggregate import prices
Using the aggregation equation (7) and Euler equations (17) and (13), the aggregated import price equation is as follows:
The Euler equation is the equation to be estimated. The unknown parameters are the discount factor (\(R)\), the percentage of firms that can change their price (\(1-\zeta \)), the share of firms that price in local currency (\(\alpha \)).
Appendix 2: Statistical annex
1.1 Import price series
1.1.1 United States
Import price index in US dollar excluding petroleum products, not seasonally adjusted. Source: Bureau of Labour Statistics.
1.1.2 Euro area
Unit value index in euros for manufactured products (SITC 5–8), seasonally adjusted. Source: Eurostat.
1.1.3 Japan
Import price index in Japanese Yen for “Other primary products & manufactured goods” (i.e. excluding foodstuffs & feedstuffs, textiles, metals & related products, wood, lumber & related products, petroleum, coal & natural gas, chemicals & related products and machinery & equipment), not seasonally adjusted. Source: Bank of Japan.
1.1.4 United Kingdom
Import price index in British Pound for manufactures less erratics (SITC 5–8), not seasonally adjusted. Source: National Statistics.
1.2 Foreign price series
Foreign price series are derived from an aggregation of headline CPIs for 27 countries (Source: IMF International Financial Statistics—series 64). The weights are computing using country-specific import shares (time-varying computed as 3-year moving-average). The share are computed using bilateral trade weights (Source: IMF Direction of Trade Statistics). As the weights are available on an annual frequency, they have been linearly interpolated to obtain monthly weights.
The 27 countries considered are listed below:
10 DEs (high-cost): euro area, US, Japan, UK, Canada, Switzerland, Norway, Sweden, Australia and New Zealand.
17 emerging markets and developing economies (low-cost): Argentina, Brazil, Chile, China, Egypt, India, Indonesia, South Korea, Malaysia, Mexico, Peru, Philippines, South Africa, Saudi Arabia, Singapore, Thailand and Turkey. As in ECB (2006), the low-cost countries also include 2 newly industrialised economies (i.e. South Korea and Singapore) that were considered as emerging markets for most of the period.
1.3 Exchange rates
Effective exchange rates are derived from an aggregation of nominal exchange rates in national currency (Source: IMF International Financial Statistics—series are converted in national currency using the value of the national currency in USD). The weights are computed similarly to foreign price series using the same geographic coverage and the same weighting schemes (see above).
1.4 Commodity price index
The commodity price index include prices of raw materials belonging to categories 0 (food and live animals), 1 (beverages and tobacco), 2 (crude materials, inedible, except fuels), 3 (mineral fuels, lubricants and related materials), 4 (animal and vegetable oils, fats and waxes) and 68 (non-ferrous metals) in the SITC (Revision 3) classification. (USD). Source: HWWA.
1.5 Output gaps
Output gaps are computed as deviations of industrial production (Source: IMF International Financial Statistics) from a trend derived from a Hoddrick–Prescott filter (smoothing parameter = 14,400).
Appendix 3: Stationarity tests
See Table 6.
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Dees, S., Burgert, M. & Parent, N. Import price dynamics in major advanced economies and heterogeneity in exchange rate pass-through. Empir Econ 45, 789–816 (2013). https://doi.org/10.1007/s00181-012-0656-3
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DOI: https://doi.org/10.1007/s00181-012-0656-3