of Interest Rates|Term Structure of Interest Rates < E4 -Money and Interest Rates

Keywords: JEL Classification Numbers:E40, E52, E58 Inflation targeting, interest rate rules, exchange rates, monetary policy

Under a flexible inflation targeting regime, should policymakers avoid any reaction to movements in the foreign exchange market? Using data for six advanced open economies explicitly targeting inflation, the paper examines empirically whether real exchange rate disequilibria systematically affect the conduct of monetary policy. Estimates indicate that monetary policy responses in inflation-targeting, open economies have changed significantly, as the institutional framework for the conduct of monetary policy has evolved. In particular, an explicit target for core inflation and a greater use of the expectation channel of monetary policy appear to be key features of the newest policy framework. In this context, central banks are unlikely to react to regular fluctuations in the exchange rate.

I. INTRODUCTION

In a number of industrial countries, an inflation targeting regime has been instrumental in taming inflation and stabilizing the economy, and appears to have gained considerable credibility over time. 1 Central banks have generally operated a flexible inflation targeting regime, smoothing interest rate adjustments over a "reasonable" time horizon, while taking into consideration both variability in output and variability in inflation. 2 In most cases, recent measures have further improved both the flexibility and the transparency of the policy framework, thus enhancing guidance to the markets. 3 An unsettled issue in inflation-targeting open economies remains, however, whether deviations of the real exchange rate from equilibrium should be taken into account in formulating monetary policy. Under a flexible inflation targeting regime, should policymakers focus solely on domestic variables and avoid any reaction to movements in the foreign exchange market? Or is it correct to claim that "a substantial appreciation of the real exchange rate [...] furnishes a prima facie case for relaxing monetary policy," as argued by Obsteld and Rogoff (1995, pp. 93)? The primacy of inflation targeting entails that, as soon as F o r P e e r R e v i e w macroeconomic indicators suggest that inflationary pressures begin to surfacing, the monetary authority should start a gradual policy tightening. Indeed, delays in rising interest rates might undermine the credibility of the inflation targeting framework itself. In practice, however, the room for maneuver of open economies' policymakers is likely to be constrained by the need to avoid an exchange rate appreciation that would damage the traded goods sector. Should this prospect make a case against an immediate policy tightening?

Using data for six advanced open economies explicitly targeting inflation (e.g., Australia, Canada, New Zealand, Norway, Sweden, and the United Kingdom), this paper examines empirically whether deviations of the exchange rate from equilibrium systematically affect the conduct of monetary policy. For this purpose, we first assess the role of the exchange rate as information variable into the policy decision-making process, and then test whether it also enters as a separate argument in a forward-looking interest rate reaction function.

Open economies targeting core inflation do not seem to react to regular fluctuations in the exchange rate. Nonetheless, in three out of six cases (Canada, Australia, and New Zealand)e.g., in those countries where exports of non-oil commodities play a major role in the economy-the real exchange rate emerges as an independent monetary policy target.

Tellingly, in all cases considered, exchange rate disequilibria appear to be valuable inputs for monetary policy decisions.

Estimates from rolling regressions also indicate that monetary policy responses in inflationtargeting open economies have varied significantly over time. As the institutional framework for the conduct of monetary policy has evolved over recent years, the parameterization of interest rate reaction functions has changed accordingly. The use of an explicit target for core inflation and a greater use of the expectation channel of monetary policy appear to be key features of the newest policy framework. We interpret this evidence as an indication that central banks' credibility has increased over time and is now well established. At the same time, putting private sector perceptions about the stability of monetary policies at center stage highlights the importance of central bank communication.

The paper is organized as follows. Section II briefly reviews the standard framework of analysis of forward-looking monetary reaction functions. The model is generalized to an interest rate rule explicitly allowing for real exchange rates to act both as information variables and as monetary policy targets. Inter alia, alternative targets for inflation and a range of proxies for the output gap are here examined. Section III reports the main empirical results from estimating standard forward-looking rules as well as augmented forwardlooking Taylor rules, which takes into account possible exchange rate targeting. For each country, the actual and the implied value of the policy interest rate under the standard and the

II. THEORETICAL BACKGROUND

Extensive academic work on monetary policy tends to characterize conduct in terms of interest rate rules and consequences in stylized models embedding these rules. 4 According to this framework, short-term money market rates are set to stabilize domestic variables-such as price inflation and real output-around their equilibrium path. Several contributions within the so-called New Keynesian synthesis have shown that-under quite general conditions-a simple, inward-looking, interest rate rule can be regarded as an optimal policy response for a closed economy. 5 Less attention has been paid to the choice of monetary policy objectives in an open economy context, given that an open economy is isomorphic to a closed economy whenever the exchange rate pass-through to import prices is complete. 6 In other words, under complete exchange rate flexibility, open economies' policymakers should also be focused uniquely on domestic targets. Unfortunately, there is extensive evidence that-in reality-departures from the law of one price for traded goods prices are large and pervasive. 7 Under these circumstances, policy choices are hardly independent of exchange rate dynamics and monetary conduct is liable to focus on more than just domestic 4 See, among others, Clarida, Galí, and Gertler (1999), [START_REF] Taylor | Discretion Versus Policy Rules in Practice[END_REF]Taylor ( , 2000)), and Woodford (2001). 5 See, for example, Taylor (1999) and references therein. 6 On this point, see [START_REF] Galí | Monetary Policy and Exchange Rate Volatility in a Small Open Economy[END_REF]. 7 See, for instance, Rogoff (1996) and Engel (1993Engel ( , 1999Engel ( , 2002)). Following a widespread approach in the literature of flexible inflation targeting, this paper assumes that central banks face a quadratic loss function over inflation and output. 10 Under standard conditions, this implies that in each period the monetary authority has a target for the nominal money market interest rate * t i , which is a function of the gaps between expected inflation and output from their respective targets:

( ) ( ) * * * , y t t t t k t k i i E E y + + = + + (1)
where * i is the desired nominal rate of interest when both inflation and output are at their target levels; ( ) y , is defined as the difference between the level of real output and its efficient level. The coefficients and measure the strength of (long-run) policy responses to deviations from the target variables. A parameter =0 suggests a pure inflation target, whereby monetary 8 Corsetti and Pesenti (2001) and [START_REF] Monacelli | Monetary Policy in a Low Pass-Through Environment[END_REF] show that, with incomplete pass-through, optimal monetary policy is not purely inward looking. 9 See, for example, [START_REF] Clarida | Monetary Policy Rules in Practice: Some International Evidence[END_REF] and [START_REF] Chadha | Monetary Policy Rules, Asset Prices, and Exchange Rates[END_REF].

10 See, among others, [START_REF] Bernanke | Inflation Forecasts and Monetary Policy[END_REF] and [START_REF] Svensson | Inflation Forecast Targeting: Implementing and Monitoring Inflation Targets[END_REF].

[ ] * ( ) 1 ( ) , t t s t t i L i L i v = + + (2)
where ( ) L is generalized to a second-order polynomial, L is the lag operator, ˆˆ, , , , , % % % % % such that the residuals obtained from the estimation of equations ( 4) are orthogonal to the information set available, z t . Formally, 0

t t E = z
. This set of orthogonality conditions forms the basis of the estimates, using the Generalized Method of Moments (GMM). In addition, the validity of the set of instruments used can be tested by means of overidentifying restrictions, provided the number of instruments in z t is greater than the number of parameters to be estimated. United Kingdom. The baseline inflation measure is the annual core inflation rate ( CORE ), as reported by national monetary authorities. Because this measure is generally available only over recent periods, the series were extended backwards using the fourth differences in the log of CPI, as reported by the IFS database. Results are however also described using fourth differences in the log of CPI series ( CPI ) throughout the sample. Figure 1 plots the instrument interest rate for each of the six countries against measures of underlying and headline inflation. 14 Standard deviations of the countries' policy rates are estimated in the order of 1 percent, with the exception of New Zealand, where the volatility is slightly higher

(around 1.3 percent).
As for the output gap, the preferred indicator is the growth gap ( DGAP y ), given recent findings on the optimal policy response to potential output uncertainty [START_REF] Orphanides | The Unreliability Of Output-Gap Estimates In Real Time[END_REF]. Results are also reported for two alternative measures of the output gap: the Hodrick-

Prescott filter for the level of real output ( HP y ), and the real unit labor cost after adjusting for wage markup ( ARMC y ), constructed as documented in [START_REF] Galí | European Inflation Dynamics[END_REF].

Figure 2 seems to confirm that the three output gap series are, overall, positively correlated. 15, 16 Finally, for all countries, misalignments from PPP are proxied by the logs of 14 In the case of Australia and New Zealand, both inflation measures correct for the large effects of the goods and services taxes (introduced in 2000 in Australia and in 1986 in New Zealand). As a result, headline inflation in these countries tends to resemble more closely core inflation. the real effective exchange rates based on CPI, given that real effective exchange rates series based on unit labor costs were not available for all countries. Estimation results yield parameter values broadly consistent with previous findings reported by the literature for inflation targeting countries. In particular, for Norway, New Zealand, Sweden, and the United Kingdom, for each of the specifications considered, the estimate of is always correctly signed, strongly significant, and greater than unity, while the estimate of 16 In all cases, pairwise correlations between the Hodrick-Prescott output gap ( HP y ) and the growth gap measure ( DGAP y ) are statistically significant, ranging between 0.46 (for the United Kingdom) and 0.65 (for New Zealand). For all countries, the adjusted real unit labor cost ( ARMC y ) exhibits the least synchronized behavior with the other two gap measures; in the case of Sweden and the United Kingdom, it appears to be uncorrelated with the HP filter, although positively and significantly correlated with the growth gap. is not statistically different from zero at conventional significance levels. This implies that central banks in these countries have responded only to deviations of expected inflation from target, not to the expected output gap, which is consistent with a pure inflation targeting regime. 17 As for Australia, the estimate of is strongly significant and greater than unity, but there is also some evidence that monetary policy aims to stabilize expected business cycle fluctuations. The evident outlier is Canada, for which monetary policy responses to both inflation and output gap are much stronger than in other countries, though the parameters are estimated with far less precision. Likely, shifts in the behavior of the Canadian monetary authority may not be described accurately by the framework at hand. Indeed, the close link with the United States may have forced the Bank of Canada to have a watchful eye on the U.S. Federal Reserve's behavior, whereas our current interest rate reaction function does not account for any systematic response to the U.S. monetary policy stance.

III. EMPIRICAL RESULTS

For all specifications and for each country, the overidentifying restrictions cannot be rejected, with the Hansen test supporting the validity of the information set used. Given that the instrument set used comprises four lags in the log of the real effective exchange rate, the result of this test can be interpreted as follows: under the null hypothesis, the central bank adjusts the interest rate according to the reaction function (3), with the expectations of future inflation and output based on the relevant information available at time t. The assumption 17 For the United Kingdom, this holds true in five out of six specifications, while the estimate of becomes significant when the output gap is proxied by the HP filter and price changes are measured by core inflation. The same exception remains valid in Table 2, when deviations from PPP are also allowed for.

k k k = = =
). The results of the GMM estimation are reported in Table 2, using alternative measures of inflation and output gap. In all countries except the United Kingdom, there is some evidence that-over the sample-real exchange rate movements have direct explanatory power in characterizing interest rate changes. In Norway and in Sweden, deviations from PPP play a role in explaining interest rate movements only if the monetary authority is believed to target headline rather than core inflation. Only in those economies that are commonly lumped together as the "commodity currencies" (e.g., Canada, Australia, and New Zealand), the real exchange rate yields significant (and correctly signed) parameter estimates, even when the central bank is assumed to stabilize core inflation. However, it is worth noticing that in countries where exports of non-oil commodities play a major role, exchange rate fluctuations are likely to reflect changes in the equilibrium exchange rate due to commodity prices shocks rather than deviations of the REER from equilibrium [START_REF] Chen | Commodity Currencies[END_REF]. Under these circumstances, monetary authorities are unlikely to smooth exchange rate volatility through changes in the interest rate [START_REF] Clinton | On Commodity-Sensitive Currencies and Inflation Targeting[END_REF]. Unfortunately, our empirical framework cannot distinguish between different sources of exchange rate fluctuations, as it implicitly assumes a time-invariant REER equilibrium. Overall, the inclusion of exchange rate disequlibria do not seem to affect appreciably the model's interest rate predictions. To aid interpretation of the results, Figure 3 shows, for each country, the actual interest rate and its estimated value implied by the baseline standard forward-looking Taylor rule (Table 1) and by the preferred augmented interest rule specification allowing also for exchange rate responses (Table 2). The interest rates implied by the estimated rules characterize well the behavior of the actual rates. Indeed, both specifications of the reaction function satisfactorily trace the dynamics of the interest rates.

The simple visual inspection of the models' predictions suggests that the contribution of real exchange rate disequilibria is not sufficient to distinguish between the two models.

Interestingly, even for Australia and New Zealand-where deviations from PPP play a slightly greater role in explaining interest rate movement, given our preferences for measuring inflation and output gap-it is the standard Taylor rule to provide better fit over the latest quarters of the sample. This suggests that central banks have been recently inclined to ignore real exchange rate misalignments.

A more flexible approach to inflation targeting implies that central banks can decide to apply a somewhat longer period for bringing inflation back to target. The horizon for achieving the inflation target implicitly provides some indication of how much weight the central bank gives to stability in the real economy. Considerable emphasis on stability in the real economy-at the expenses of somewhat greater and more persistent deviations from the inflation target-implies a relatively longer horizon. A precondition for a longer monetary policy horizon is that financial market participants are confident that inflation will be low and Monetary authority's scope of maneuver tends to increase as the inflation target succeeds in providing an anchor for the formation of inflation expectations and, in particular, for the wage bargaining process. This creates a role for regular central bank communication to help markets filter macroeconomic news. However, in situations where there is a risk that inflation may deviate considerably from the target over a lengthy periods, or confidence in monetary policy is in jeopardy, a rapid and pronounced change in the interest rate may remain appropriate.

The results confirm the view that central banks tend to smooth the adjustment of interest rates over several quarters, thereby increasing the predictability of monetary policy conduct. However, the extent to which central banks rely on smoothing appears to differ across countries and over time. In particular, for Australia, Norway, and Sweden, the coefficient on the first lag appears to be close to one, while the second lag displays a significant corrective behavior, signaling more elongated and predictable interest rate movements in response to changes in the macroeconomic environment and, hence, a greater use of the expectation channel of monetary policy. Previous work in this area indicates that the strength of the expectation channel relates to the degree of forward-looking behavior in the rest of the economy, which-in turn-can be seen as the policymaker's reward for ensuring monetary stability. 18 Figure 4-plotting parameter estimates from rolling regressions over successive 18 On this point, see also [START_REF] Bayoumi | Deconstructing the Art of Central Banking[END_REF]Sgherri (2004a, 2004b). forty-quarter windows-shows that, in this respect, Norges Bank (and, to a lesser extent, the Swedish Riksbank) has enjoyed the greatest confidence gains over recent times, possibly in connection with its latest switch to a longer adjustment horizon. At the other end of the spectrum is New Zealand (and, to a lesser extent, Canada), for which the degree of smoothing seems to be lower.

Central banks' response to real exchange rate misalignments have varied over time. Indeed, the sample period 1984Q1-2004Q2 transcends several different monetary regimes for almost all countries in the sample. Rolling-window estimates of the exchange rate responses (Figure 5) suggest that, even if the level of the implied instrument rate is very similar to the one implied by the standard forward-looking Taylor rule, central banks in each of the six countries have effectively targeted the exchange rate at some point over the sample. Norway and Sweden, for example, have been concerned about exchange rate misalignments until the first half of the 1990s. Over the last decade, however, interest rate responses to deviations of exchange rates from target have become statistically insignificant. Conversely, for the United Kingdom, exchange rate disequilibria have been important in determining the interest rate target around the sterling devaluation of 1992 and during the protracted sterling appreciation of the mid-to late 1990s. As suggested below, the statistical significance of the central bank's reaction to exchange rate disequilibria may in fact reflect the size of the disequilibria themselves, rather than the formal monetary regime actually in place. 19 19 A similar argument is suggested by [START_REF] Chadha | Monetary Policy Rules, Asset Prices, and Exchange Rates[END_REF]. Inflation targeting has evolved over time across a number of dimensions, notably the degree of flexibility and the approach to communication. The evidence seems to confirm that, across advanced open economies, the regime has recently become more flexible, allowing greater scope for inflation to vary around the target and, as a result, for broader macroeconomic goals to be taken into account. As central banks have started to smooth the adjustment of interest rates over a longer horizon, the predictability of monetary policy conduct has also increased. In addition, monetary authorities have gradually become more and more transparent, improving the scope of their communication and delivering it in more varied forms.

Although with some significant differences across countries, the paper finds that exchange rates are generally not key for systematic monetary responses in inflation-targeting, open economies. More precisely, if a country attempts to target core (rather than headline) inflation, the exchange rate does not seem to enter as a separate argument in interest rate rule. -18 -At the same time, however, exchange rates are found to be valuable inputs into the monetary policy decision-making process, as information variables.

As stressed by Taylor (2001), there may be two reasons why central banks' actions in open economies are better described by interest rate rules not entailing a direct response to the exchange rate. First, if the policy rule is based on (rational) expectations of future inflation and output, a short-run appreciation of the exchange rate at time t will be expected to translate into lower output and inflation in the future. As such, a policy rule like equation (3) will entail a decline in interest rates today, even though the exchange rate does not appear directly in the rule. Second, in some situations, changes in the real exchange rate might reflect changes in productivity that should not be offset by the monetary policy. As such, while fluctuations in the exchange rate may not have much consequences on inflation expectations-and thus have little impact on the interest rate through this channel-these movements may result in dangerous swings in interest rates if there were a strong direct reaction to them. Finally, our findings may be consistent with the view that, while committed to a flexible inflation targeting regime, central banks may still act in response to exchange rates on occasions when there is a need to smooth out high volatility in foreign exchange markets that could destabilize domestic inflation. Indeed, to detect unsystematic interest rate responses to abrupt corrections in the exchange rate (or in any other asset price), a nonlinear framework of analysis could be more helpful than a standard linear framework like the one used in this paper. 3). Estimates are obtained by GMM with Heteroskedastic and Autocorrelation Consistent covariance matrix, obtained by nonlinear three-stage least squares. The Bartlett kernel is used to weight the covariances in order to ensure the covariance matrix to be positive semidefinite. The Newey-West fixed bandwidth is used, so that the weights given by the kernel do not change with the autocorrelation in the data. The sample period is 1984Q1 to 2004Q2. The instruments set includes a constant, the log-difference of a world commodity price index, plus 4 lags of output gap, fourth differences in prices, interest rate, and log real effective exchange rate. The forward-looking horizon is one quarter for each target variable. J-test is the test for overidentifying restrictions (Hansen, 1982), which is distributed as a ; 2 under the null. For this test, only p-values are reported. HAC-consistent standard errors are given in parentheses 1/ The estimated parameters refer to equation (4). Estimates are obtained by GMM with Heteroskedastic and Autocorrelation Consistent covariance matrix, obtained by nonlinear three-stage least squares. The Bartlett kernel is used to weight the covariances in order to ensure the covariance matrix to be positive semidefinite. The Newey-West fixed bandwidth is used, so that the weights given by the kernel do not change with the autocorrelation in the data. The sample period is 1984Q1 to 2004Q2. The instruments set includes a constant, the log-difference of a world commodity price index, plus 4 lags of output gap, fourth differences in prices, interest rate, and log real effective exchange rate. The forward-looking horizon is one quarter for each target variable. J-test is the test for overidentifying restrictions (Hansen, 1982), which is distributed as a ; 2 under the null. For this test, only p-values are reported. HAC-consistent standard errors are given in parentheses.

F o r P e e r R e v i e w

  augmented monetary reaction function are shown. Finally, changes in central banks' behavior over time are analyzed by presenting results from rolling regressions, in which parameter estimates are reported over successive forty-quarter windows. The results are open to several interpretations, which are discussed in the concluding section.

  The dataset comprises quarterly data from January 1984 to June 2004 for six inflation targeting, open economies: Australia, Canada, New Zealand, Norway, Sweden, and the

  . Indeed, financial market confidence in the inflation target provides central banks with greater scope for promoting stability in the real economy.

  now a well established framework for the conduct of monetary policy.The experience of advanced open economies has been that the period of inflation targeting has delivered favorable economic outcomes. According to surveys, medium-term inflationary expectations in these countries remain well anchored, thereby contributing to stabilizing inflation around the target and amplifying the effects of monetary policy itself.Expectations concerning inflation and economic stability are indeed of crucial importance for both wage-price formation and the stability of the foreign exchange market.

Figure 1 :

 1 Figure 1: Interest Rate, Core, and Headline Inflation (percent)

  Figure 3: Interest Rate: Actual versus Implied (percent)

Figure 4 :Figure 5 :

 45 Figure 4: Interest Rate Smoothing 1

Page 5 of 32 Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK

  Indeed, recent empirical studies provide evidence that exchange rates are statistically significant in interest rate rules depicting the reaction function of major economies. 9

	Submitted Manuscript
	stabilization. 8
	F o
	r
	P
	e e r
	R e v i e w

Page 6 of 32 Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK Submitted Manuscript

	policy is uniquely concerned about price stability and does not aim at stabilizing business
	cycle fluctuations. If <1, policy is attempting to accommodate inflationary shocks, which-
	over the long run-will lead to instability as real rates respond perversely to inflationary
	disturbances. 11
	However, central banks are likely to react gradually to expected deviations from targets, by
	smoothing their policy rate adjustments over several periods. 12 To account for this behavior, F o the interest rate rule (1) is modified by allowing for a second-order partial adjustment to the
	target rate, namely:	r
		P
		e e r
		R e v i e w

  rate can thus be captured by a constant included in the intercept term % of the "augmented interest rate rule" (4), implying that central banks attempt to correct expected misalignments from PPP. If the real exchange rate is expressed as the domestic price of foreign currency, the resulting monetary rule will stabilize it if > 0, as an appreciation of the real exchange rate will require a cut in the short-term interest rate. Under the augmented specification, the implied (ex-ante) equilibrium real interest rate will hence be identified only if both the inflation target and the equilibrium real exchange rate are known:

	which, in turn, allows direct inference of the policy responses, and , and derivation of the
	implied (ex-ante) equilibrium real interest rate,	r =	( 1	)	, if the inflation target is
	known. So far, the only innovation in this policy rule specification regards the inclusion of
	two (rather than one) lagged terms in the interest rate. As discussed below, this more flexible
	dynamic structure provides a better description of some of the changes in monetary responses
	F o It is under debate whether and how exchange rates (and asset prices, in general) should be over time. F o ( ) * * * 1 r e = + % %	.
	r taken into account in formulating monetary policy. 13 While it is unanimously recognized that r
	* t i is the exchange rates are useful indicators of inflationary pressures in the economy (because P e e r P changes in the exchange rate feed through into domestic prices and affect aggregate demand), central bankers have often been explicit about the fact that exchange-rate stabilization is not e e r
	target rate whose behavior is described by equation (1), and t v is a zero-mean interest rate shock. Combining equations (1) and (2) yields an expression for the standard forward-looking Taylor rule, e.g. [ ] ( ) ( ) { } ( ) 1 ( ) , y t t s t t t t k t k i L i L E E y v + + = + + + + R e v i e w a direct target of policy. To assess whether this is really how they act, the interest rate rule (3) (3) is further generalized to allow for policymakers' responses to exchange rate disequilibria: [ ] ( ) ( ) ( ) { } ( ) 1 ( ) , y e t t s t t t t t k t k t k i L i L E E y E e + + + = + + + + + % % % (4) R e v i e w
	where	e e + denotes the forward-looking real exchange rate. In line with recent empirical t k
	literature, purchasing power parity (PPP) is assumed to hold in the long run, so that the real
	exchange rate follows a persistent, albeit stationary, process. The equilibrium real exchange

13

See

Taylor (2001)

and

[START_REF] Goodhart | What Weight Should Be Given to Asset Prices in the Measurement of Inflation?[END_REF]

.

Under rational expectations, central banks form their forecasts of future inflation, output gap, and real exchange rate using all relevant information available at the time the interest rate is set. Let z t denote the vector of indicators comprising the central bank's information set at that time (i.e., t t z

). If the monetary authority adjusts the interest rate according to the augmented interest rate rule (4), while forming expectations of future variables in a fully rational manner, then there must exist a set of parameters { } 1 2

Table 1

 1

	reports GMM estimates of the parameters {	1	} 2 ˆ, , , in the standard forward-
	looking Taylor rule (3), where only expected inflation and expected output gap are
	considered as explanatory variables. The target horizon is assumed to be one quarter for both
	inflation and the output gap (i.e.	k	1 = = ), although results look qualitatively unaffected y k
	by this choice (not reported). The instrument set,

t z , includes a constant, a world commodity price index, and four lags of the policy rate, inflation, and the output gap. In estimating the model for Norway and Sweden, the 1993Q1 and the 1992Q4 interest rate observations, respectively, are dummied out as extreme and unsystematic monetary tightening episodes dealing with the 1992-1993 Exchange Rate Mechanism crisis.

Page 12 of 32 Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK

  that inflation and output gap forecasts are also based on available information from the foreign exchange market cannot be statistically rejected.

							Submitted Manuscript
	Next, the parameters {	1 % %	2	ˆˆ, , , , % %	}	for the six countries in our sample are estimated using
	an augmented interest rate rule such as (4). The target horizon for the three forward-looking
	variables-inflation, output gap, and real exchange rate-is still assumed to be one (i.e.,
	y	e	1	F o
					r
						P
							e e r
							R e v i e w

Page 18 of 32 Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK

	Submitted Manuscript
	F o r P e e r R e v i e w F o r P e e r R e v i e w F o r F o r P e P e e r e r R e v i R e v e w i e
	w

Table 1 :

 1 Forward-Looking Taylor Rule 1/

	Norway y=y DGAP ; = CORE	1	2			j-test	see
		0.585	0.062	1.731	0.002	0.884 1.010
	y=y HP ; = CORE	(0.111) (0.058) (0.099) (0.175)
		0.555	0.085	1.788	0.007	0.904 1.031
	y=y ARMC ; = CORE	(0.138) (0.066) (0.107) (0.291)
		0.613	0.064	1.739	0.504	0.873 1.003
	y=y DGAP ; = CPI	(0.109) (0.057) (0.188) (0.586)
		0.685	0.042	1.780	0.206	0.657 1.051
	y=y HP ; = CPI	(0.116) (0.073) (0.162) (0.311)
		0.699	0.060	1.738	0.671	0.614 1.051
	y=y ARMC ; = CPI	(0.134) (0.076) (0.183) (0.669)
		0.695	0.066	1.845	0.974	0.790 1.111
		(0.115) (0.073) (0.259) (0.740)
	Sweden y=y DGAP ; = CORE	1	2			j-test	see
		0.824 -0.055	1.951	0.321	0.573 0.974
	y=y HP ; = CORE	(0.054) (0.014) (0.178) (0.344)
		0.901 -0.063	1.900	0.917	0.704 0.925
	y=y ARMC ; = CORE	(0.080) (0.025) (0.223) (0.618)
		0.823 -0.064	1.916 -0.394	0.580 1.003
	y=y DGAP ; = CPI	(0.054) (0.014) (0.167) (0.448)
		0.936 -0.094	1.458	0.285	0.657 1.051
	y=y HP ; = CPI	(0.045) (0.014) (0.157) (0.414)
		0.960 -0.081	1.406	1.594	0.614 1.051
		(0

.053) (0.025) (0.196) (0.804)

	y=y ARMC ; = CPI
	0.949 -0.105	1.518 -0.830	0.790 1.111
	(0

.049) (0.013) (0.190) (

	F o r
	P
	e
	e r
	R e v
	i e 0.659) w (continued)

Table 1 :

 1 (continued)

	United Kingdom y=y DGAP ; = CORE	1	2			j-test	see
		0.771	0.013	1.838	0.732	0.570 0.980
	y=y HP ; = CORE	(0.127) (0.082) (0.327)	(0.773)
		0.591	0.124	1.900	1.416	0.665 0.968
	y=y ARMC ; = CORE	(0.108) (0.067) (0.243)	(0.470)
		0.777	0.029	1.799	0.667	0.585 0.987
	y=y DGAP ; = CPI	(0.125) (0.087) (0.361)	(0.982)
		0.604	0.166	1.724	0.144	0.763 0.872
	y=y HP ; = CPI	(0.080) (0.064) (0.203)	(0.391)
		0.642	0.181	1.522	0.872	0.838 0.899
	y=y ARMC ; = CPI	(0.085) (0.061) (0.320)	(0.901)
		0.611	0.163	1.759	0.294	0.743 0.872
		(0.084) (0.069) (0.217)	(0.377)
	Canada	1	2			j-test	see
	y=y DGAP ; = CORE
		0.835	0.129	5.755	9.318	0.732 1.006
	y=y HP ; = CORE	(0.094) (0.072) (6.372) (15.161)
		0.717	0.150	2.427	2.762	0.800 0.998
	y=y ARMC ; = CORE	(0.071) (0.050) (0.548)	(0.940)
		0.788	0.051	3.165	3.942	0.757 1.072
	y=y DGAP ; = CPI	(0.094) (0.057) (0.790)	(1.641)
		0.786	0.153	3.328	6.563	0.669 0.988
	y=y HP ; = CPI	(0.106) (0.075) (1.710)	(5.222)
		0.779	0.148	0.190	5.379	0.562 0.934
	y=y ARMC ; = CPI	(0.082) (0.061) (0.946)	(2.806)
		0.831	0.084	1.980	8.500	0.508 1.047
		(0.111) (0.081) (0.920)	(5.261)
						(continued)

Table 1 :

 1 (concluded)

	Australia	1	2			j-test	see
	y=y DGAP ; = CORE
		1.031 -0.104	1.524	3.723	0.767 1.018
	y=y HP ; = CORE	(0.083) (0.080) (0.341) (1.018)
		1.062 -0.141	0.715	3.046	0.865 1.060
	y=y ARMC ; = CORE	(0.083) (0.081) (0.360) (1.454)
		1.118 -0.191	1.083	2.705	0.818 1.079
	y=y DGAP ; = CPI	(0.099) (0.090) (0.490) (1.772)
		1.012 -0.093	1.771	3.441	0.723 0.991
	y=y HP ; = CPI	(0.081) (0.079) (0.355) (0.970)
		1.048 -0.136	0.936	2.573	0.837 1.037
	y=y ARMC ; = CPI	(0.081) (0.080) (0.335) (1.461)
		1.094 -0.181	1.331	1.959	0.809 1.049
		(0.101) (0.089) (0.420) (1.647)
	New Zealand	1	2			j-test	see
	y=y DGAP ; = CORE
		0.746 -0.100	1.341 -0.520	0.680 1.388
	y=y HP ; = CORE	(0.120) (0.099) (0.066) (0.211)
		0.876 -0.196	1.349 -0.081	0.490 1.214
	y=y ARMC ; = CORE	(0.082) (0.069) (0.072) (0.267)
		0.860 -0.192	1.353 -0.117	0.479 1.230
	y=y DGAP ; = CPI	(0.089) (0.074) (0.095) (0.304)
		0.751 -0.107	1.328 -0.517	0.725 1.417
	y=y HP ; = CPI	(0.124) (0.104) (0.059) (0.210)
		0.864 -0.196	1.336 -0.078	0.540 1.228
		(0.

079) (0.067) (0.061) (

	y=y ARMC ; = CPI		0.273)
	0.873 -0.198	1.369	0.060	0.530 1.345
	(0.

088) (0.074) (0.098) (

	0.410)
	1/ The estimated parameters refer to equation (

Table 2 :

 2 Augmented Forward-Looking Taylor Rule

	Norway y=y ARMC ; = CORE	1 %	%	2	%	%		j-test	see
		0.608	0.061	1.760	0.643	0.006	0.917 1.051
	y=y HP ; = CORE	(0.120) (0.055) (0.205) (0.514) (0.119)
		0.571	0.051	1.718	0.198	0.060	0.874 1.028
	y=y DGAP ; = CORE	(0.147) (0.063) (0.266) (0.339) (0.109)
		0.577	0.040	1.716	0.100	0.050	0.848 1.018
	y=y ARMC ; = CPI	(0.122) (0.055) (0.214) (0.184) (0.098)
		0.658	0.080	1.714	0.895	0.067	0.715 1.101
	y=y HP ; = CPI	(0.116) (0.072) (0.250) (0.645) (0.114)
		0.667	0.050	1.435	0.919	0.219	0.517 1.048
	y=y DGAP ; = CPI	(0.139) (0.069) (0.301) (0.644) (0.125)
		0.655	0.031	1.533	0.228	0.173	0.574 1.035
		(0.119) (0.069) (0.202) (0.279) (0.084)
	Sweden y=y ARMC ; = CORE	1 %	%	2	%	%		j-test	see
		0.839 -0.067	1.668	0.076	0.075	0.538 0.952
	y=y HP ; = CORE	(0.067) (0.019) (0.267) (0.656) (0.070)
		0.923 -0.065	2.086	1.371 -0.063	0.638 0.937
	y=y DGAP ; = CORE	(0.094) (0.025) (0.564) (1.475) (0.171)
		0.847 -0.058	1.721	0.412	0.075	0.591 0.945
	y=y ARMC ; = CPI	(0.064) (0.020) (0.229) (0.391) (0.053)
		0.914 -0.097	1.202 -0.018	0.117	0.663 0.926
		(0.

050) (0.013) (0.226) (

	y=y HP ; = CPI		0.729) (0.076)
	0.962 -0.081	1.424	1.668 -0.009	0.705 0.914
	(0

.061) (0.025) (0.343) (

	F o r
		P
		e
		e r
		R e v
	y=y DGAP ; = CPI	1.417) (0.134) i e 0.313 0.109 (0.043) (0.015) (0.159) (0.357) (0.045) 0.892 -0.082 1.165 w	0.609 0.914 (continued)

Table 2 :

 2 (continued)

	United Kingdom y=y ARMC ; = CORE	1 %	%	2	%	%		j-test	see
		0.778	0.031	1.760	0.470 -0.016	0.489 0.989
	y=y HP ; = CORE	(0.121) (0.083) (0.403)	(1.315) (0.072)
		0.615	0.123	1.829	1.502 -0.015	0.561 0.966
	y=y DGAP ; = CORE	(0.108) (0.067) (0.271)	(0.567) (0.035)
		0.801	0.008	1.740	0.689 -0.010	0.515 0.993
	y=y ARMC ; = CPI	(0.128) (0.083) (0.383)	(0.974) (0.062)
		0.608	0.165	1.657	-0.174 -0.033	0.630 0.874
	y=y HP ; = CPI	(0.094) (0.062) (0.217)	(0.682) (0.044)
		0.657	0.180	1.443	1.142 -0.032	0.791 0.908
	y=y DGAP ; = CPI	(0.097) (0.061) (0.346)	(1.118) (0.037)
		0.650	0.140	1.655	0.092 -0.022	0.634 0.886
		(0.117) (0.070) (0.213)	(0.737) (0.045)
	Canada y=y ARMC ; = CORE	1 %	%	2	%	%		j-test	see
		0.730	0.012	1.223	3.459	0.178	0.805 1.121
	y=y HP ; = CORE	(0.104) (0.074) (0.491)	(0.868) (0.071)
		0.640	0.133	0.681	2.141	0.180	0.845 1.018
	y=y DGAP ; = CORE	(0.087) (0.053) (0.373)	(0.502) (0.040)
		0.835	0.126	5.339	8.744	0.023	0.648 1.013
	y=y ARMC ; = CPI	(0.094) (0.074) (6.752) (13.724) (0.390)
		0.709	0.029	0.427	3.825	0.254	0.958 1.175
	y=y HP ; = CPI	(0.120) (0.100) (0.415)	(1.076) (0.065)
		0.666	0.118 -0.292	2.257	0.251	0.772 0.944
	y=y DGAP ; = CPI	(0.096) (0.063) (0.407)	(0.422) (0.047)
		0.785	0.145	2.835	5.621	0.049	0.579 0.993
		(0.106) (0.077) (1.600)	(4.212) (0.134)
								(continued)

Table 2 :

 2 (concluded)

	Australia y=y ARMC ; = CORE	1 %	%	2	%	%		j-test	see
		0.902 -0.149	1.054 -0.381	0.269	0.570 0.948
	y=y HP ; = CORE	(0.069) (0.066)	(0.135) (0.322) (0.035)
		0.911 -0.120	1.082	0.307	0.277	0.485 0.902
	y=y DGAP ; = CORE	(0.072) (0.072)	(0.158) (0.381) (0.039)
		0.996 -0.169	1.108	0.980	0.251	0.749 0.913
	y=y ARMC ; = CPI	(0.083) (0.075)	(0.182) (0.545) (0.043)
		0.827 -0.099	1.171 -0.596	0.242	0.517 0.923
	y=y HP ; = CPI	(0.072) (0.060)	(0.129) (0.369) (0.032)
		0.880 -0.104	1.231	0.305	0.261	0.454 0.865
	y=y DGAP ; = CPI	(0.069) (0.067)	(0.157) (0.372) (0.036)
		0.951 -0.142	1.287	0.786	0.245	0.607 0.868
		(0.080) (0.070)	(0.173) (0.465) (0.038)
	New Zealand y=y ARMC ; = CORE	1 %	%	2	%	%		j-test	see
		0.855 -0.214	1.434	0.552	0.150	0.468 1.301
	y=y HP ; = CORE	(0.077) (0.078)	(0.100) (0.336) (0.047)
		0.819 -0.219	1.341 -0.187	0.123	0.430 1.265
	y=y DGAP ; = CORE	(0.086) (0.078)	(0.058) (0.242) (0.044)
		0.748 -0.149	1.355 -0.369	0.102	0.581 1.359
	y=y ARMC ; = CPI	(0.106) (0.098)	(0.060) (0.215) (0.046)
		0.902 -0.224	1.475	0.929	0.158	0.600 1.474
	y=y HP ; = CPI	(0.084) (0.078)	(0.110) (0.500) (0.054)
		0.833 -0.225	1.343 -0.124	0.113	0.390 1.251
	y=y DGAP ; = CPI	(0.091) (0.082)	(0.062) (0.274) (0.044)
		0.743 -0.142	1.356 -0.375	0.097	0.545 1.335
		(0.121) (0.108)	(0.063) (0.212) (0.044)

Over the 1990's, Australia, Canada, New Zealand, Sweden, and the United Kingdom, all changed the institutional framework under which monetary policy was conducted, by shifting to an inflation targeting regime. Norway and Iceland followed suit in

The literature on the institutional aspects of inflation targeting in industrial countries is extremely vast. For a recent review, seeBernanke and Woodford (2005) and references therein.2 Svensson (2000), for example, analyzes the properties of a flexible inflation targeting regime in an open economy context.3 On recently introduced measures enhancing the transparency of Norway's monetary framework and related discussion, seeIMF (2005a). Analogous discussions for Sweden and the United Kingdom can be found inIMF (2004IMF ( , 2005b)), respectively.

[START_REF] Christiano | The Expectations Trap Hypothesis[END_REF] emphasize that a high inflation expectations trap may arise if policy accommodates inflation.

Sack and Wieland (2000) provide an in depth discussion of interest rate smoothing. On the issue of gradualism as optimal response to uncertainty, see[START_REF] Brainard | Uncertainty and the Effectiveness of Policy[END_REF] as canonical reference on the theory side,[START_REF] Woodford | Optimal Monetary Policy Inertia[END_REF] for a recent application, and[START_REF] Walsh | Implications of a Changing Economic Structure for the Strategy of Monetary Policy[END_REF] for an exhaustive review.

Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK Submitted Manuscript

< E -Macroeconomics and Monetary Economics, E52 -Monetary Policy (Targets, Instruments, and Effects) < E5 -Monetary Policy, Central Banking, and the Supply of Money and Credit < E -Macroeconomics and Monetary Economics, E58 -Central Banks and Their Policies < E5 -Monetary Policy, Central Banking, and the Supply of Money and Credit < E -Macroeconomics and Monetary


Inmaculada Martínez-Zarzoso

Eva María Perez-Garcia

Celestino Suárez-Burguet

§ Pérez-García

Eva María

DO TRANSPORT COSTS HAVE A DIFFERENTIAL EFFECT ON TRADE AT THE SECTORAL LEVEL?

Keywords: sectoral exports, transport costs, infrastructure, instrumental variables, Spain Exports, transport costs, infrastructure index, sectoral exports, Spain JEL classification: F14

d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

transport. Finally, to our knowledge, only one paper 1 has examined the differential impact of transport costs on sectoral trade using survey data. A few papers used sectoral trade data to estimate the elasticity of trade with respect to transport costs, but they did only calculate an average elasticity for all sectors (Martinez-Zarzoso and Suarez-Burguet, 2005) or used secondary data (Hummels, 1999a).

Section 2 presents a literature review. Data and sources are described in Section 3. In Section 4, a transport cost function is estimated by using data on Spanish exports by sector. Section 5 presents and estimates a variant of the standard gravity model of trade.

Section 6 comments on the results of the empirical application and concludes.

LITERATURE REVIEW

In the recent economic literature there have been several attempts to measure directly or indirectly transport costs. Some authors used cif/fob 2 ratios as a proxy for shipping costs [START_REF] Baier | The Growth of World Trade: Tariffs, Transport Costs and Income similarity[END_REF]Bergstrand, 2001, Limao andVenables, 2001;[START_REF] Radelet | Shipping Costs, Manufactured Exports and Economic Growth[END_REF]. Since most importing countries report trade flows inclusive of freight and insurance (cif) and exporting countries report trade flows exclusive of freight and insurance (fob), transport costs can be calculated as the difference of both flows for the same aggregate trade.

However, [START_REF] Hummels | Have International Transportation Costs Declined?[END_REF] showed that importer cif/fob ratios constructed from IMF sources are poor proxies for cross-sectional variation in transport costs and such a variable provides no information about changes over time or across sectors. Oguledo and Mcphee (1994) also doubted the usefulness of cif/fob ratios from IMF sources as a proxy of transportation costs. Hummels (1999a[START_REF] Hummels | Have International Transportation Costs Declined?[END_REF] used data on transport costs from various primary sources including shipping price indices obtained from shipping trade journals (Appendix 2 in [START_REF] Hummels | Have International Transportation Costs Declined?[END_REF]; air freight prices gathered from survey data; and freight rates (freight 1 [START_REF] Martínez-Zarzoso | The Impact of Transport Cost on International Trade: The Case of Spanish Ceramic Exports[END_REF] analysed the impact of transport cost on trade in the ceramic sector. 2 Cif stands for "cost, insurance and freight"; fob stands for "free on board." fob prices are obtained from the same reporting country. Since information is collected using identical methodology, the data are more reliable than the IMF rates. A second difference is that the data are disaggregated at 3 digit level SITC. However, the authors estimated a single equation for all the sectors and obtained a trade elasticity with respect to transport costs of -2.30.

THE DATA

The empirical application of this paper is based on extensive fieldwork based on personal interviews with import/export and logistics managers at export companies (160 interviews), and freight forwarding agents (78 interviews) 4 . Four sectors were selected for analysis: agro-industry (wine, cereals, canned food and vegetable oils), ceramic tiles, motor vehicle parts and accessories, and electrical and mechanical household appliances.

The selection was made attempting to find sectors with differentiated transport needs 5 . All four selected sectors are among the top 10 exporters, both in terms of weight and exported value, with the exception of household appliances (which only ranks among the top 10 exporters in terms of value). Agroindustrial products and ceramic tiles may be considered low value-added commodities -in comparison to motor vehicle parts and household appliances-, these two goods showing a large weight-to-value ratio. On the other hand, motor vehicle parts and household appliances may be seen as high value-added products, while presenting a large volume-to-weight ratio. The particular features of these four commodities will allow an evaluation of the influence of variables such as distance, weight, volume, number of shipments, transit time, among others, on transport costs.

Aiming at building a database that would permit the specification and estimation of a transport cost/trade model, 238 interviews were conducted in November 2001 among 4 See appendix 3 for additional details of how the data were collected and from whom. 5 Given the complexity of studying all Spanish export trade flows, the aim of selecting four sectors was to achieve a significant overview of transport cost and trade determinants by researching a representative sample of the Spanish production framework. From a statistical point of view, the collected sample is representative of the studied population and the results and conclusions should therefore be in line with those to be expected from the Spanish industrial structure. Detailed information concerning the regional distribution of interviews carried out and averages of the variables is shown in the Appendix in Tables A.1 and A.2 respectively.

With respect to sectoral exports, the interviewees did not directly report the value or quantity exported. They only indicated the percentage of exports directed to each foreign market. Therefore export data (quantity and value) were obtained from the database "Spanish Foreign Trade Statistics" published by the Spanish Custom Agency for the different sectors under analysis. A careful matching of the export data and the transport costs data were made at a high disaggregation level (8 digits). For illustration, Export codes at 4 digits and product descriptions are listed in the Appendix A.5.

DETERMINANTS OF TRANSPORT COSTS

A number of authors have recently investigated the determinants of international transport costs. Estimates are given in [START_REF] Hummels | Towards A Geography of Trade Costs[END_REF], [START_REF] Limao | Infrastructure, Geographical Disadvantage and Transport Costs[END_REF], [START_REF] Radelet | Shipping Costs, Manufactured Exports and Economic Growth[END_REF], [START_REF] Micco | Maritime Transport Costs and Port Efficiency[END_REF] Fink et al. (2001), [START_REF] Sánchez | Port Efficiency And International Trade[END_REF] [START_REF] Kumar | Globalization -The Maritime Nexus[END_REF]. The explanatory variables used in their analysis are basically related to distance and connectivity, such as if countries are land-locked, or if trading partners are neighbours, and to country characteristics such as GDP per capita. Some of them focus on the impact of specific factors on transport costs, for example [START_REF] Micco | Maritime Transport Costs and Port Efficiency[END_REF] and [START_REF] Sánchez | Port Efficiency And International Trade[END_REF] analyse the impact of port reform on transport costs, and study the possible effects of port reform in Latin America. Fink et al. (2001) investigate how liberalisation in trade and transport services leads to further reductions in transport costs, which in turn leads to a further promotion of trade in goods. [START_REF] Kumar | Globalization -The Maritime Nexus[END_REF] consider the mutual relationship between trade volumes, transport costs, and the quality of transport services. They find that the market for maritime transport services is growing and observe increased concentration in the maritime industry and, at the same time, more competition. Although transport unit costs decline, the incidence of the maritime transport costs in the final value of the good increases since many components are purchased internationally. The authors state that the strong relationship between trade and transport costs detected by [START_REF] Limao | Infrastructure, Geographical Disadvantage and Transport Costs[END_REF] does not only reflect the elasticity of trade towards transport costs, but might be also reflecting the economies of scale through which higher volumes lead to lower costs of transport.

More evidence is needed at sector level and using primary sources, as most of the research has used aggregated data and secondary sources. In this line, we estimate a linear equation where transportation costs are specified as a function of distance, mode of transport, infrastructure, port efficiency, transit time, number of shipments, average size of shipments and various dummies. Distance has been widely used in gravity equations as a proxy for transport costs since a higher distance implies a longer journey and a higher associated cost, and it is very difficult to collect transport costs data of good quality. A F o r P e e r R e v i e w 8 differential relationship is observed in our data between transport cost and distance for road and sea transport, indicating that as distance grows road transport costs always increase but sea transport costs only increase for shorter distances and then, slightly decrease. This feature will be considered in the transport costs equation by adding interaction variables (distance*mode) and (distance square*mode). Infrastructure in the exporting country and in the transit countries has also proved to be an important determinant of transport costs [START_REF] Limao | Infrastructure, Geographical Disadvantage and Transport Costs[END_REF]. Infrastructure measures are related to the quality of communications and transport infrastructure that countries possess. Transit time, average number of shipments per year and average size of the shipments in each sector are also taken into account as explanatory variables. Transit time may be a proxy for the quality of the service, whereas average number of shipments (frequency) and average size of shipments could be indicating high volumes of exports going through a particular route, pointing towards the existence of economies of scale.

The costs of the journey between countries are influenced by other geographic characteristics such as adjacency, being an island or being landlocked. Countries sharing a common border usually have better communication network connections and more possibilities for back-hauling, due to the fact that they trade more extensively, allowing for fixed costs to be shared over two trips and thereby reducing total costs. Some cultural similarities, such as a common language, could also be considered as determinants of transport costs, assuming that this facilitates trade transactions. Furthermore, being landlocked normally adds extra costs, because it means that the commodities being traded must be transported on a relatively more expensive and on average longer leg by road and need to face customs formalities twice, at the landlocked country and at the country were the port of loading/unloading is located. We added a dummy according to the mode of transport. The basic specification is given by: where TC ijk denotes transport costs incurred when transporting product k from province i in Spain to country j, D ij denotes distance from the city of origin in Spain (i) to destination j, Mode is a dummy that takes the value one when products are transported by sea and zero when goods are transported by road, INF ij denotes the quality of roads that connect i and j, PE j denotes port efficiency in country j, TT ij is transit time from door to door, NS ijk is the average number of shipments per year for a specific company to a particular destination and for sector k, AS ijk is the average size of the shipments for a given route and for sector k. All the variables except dummies are in natural logs. µ ijk denotes the error term that is assumed to be independently normally distributed.

The variable INF ij is constructed 6 for road transport. We consider the quality of roads in the countries that have to be crossed scaled by the area of the countries and weighted by the number of borders:

( ) ij j j j t t t i i i ij NB A PR m A PR m A PR m INF + + = (2)
where NB ij depends on the number of borders that have to be crossed to reach the final destination. It takes the value 1 for transport inside the EU, the value increases by 0.10 when a border is crossed. A i, A t and A j are the areas of the countries which infrastructure is considered. PR i , PR t and PR j are kilometres of paved road in countries i, t and j, t denotes transit countries. m takes a value between zero and one according to the quality of roads in a given country (equals 0.75 for paved roads and 1 for motorways). 6 The variable INF ij has been initially constructed as an index (by taking information on roads, paved roads, railroads and number of telephone lines) differentiating between importer and transit countries' infrastructure as explanatory variables of transport costs. This index is comparable to that of [START_REF] Limao | Infrastructure, Geographical Disadvantage and Transport Costs[END_REF] highest levels of infrastructure quality. Additionally, the port efficiency variable is only significant and negative signed for agro-industry and in some cases for vehicle parts.

The estimated coefficient for the variable transit time shows that for agro-industry, ceramics and household appliances a 1% increase in the time of transit increases the cost by 0.15%. The number of shipments and the average size of shipments are also shown to be significant and negative signed almost always (apart from the number of shipments for household appliances). This result may be indicating the existence of economies of scale, as a higher frequency or a greater size of shipment indicates that more trade goes through a particular route. However, the first variable may also be (indirectly) showing a better quality of the service offered by a particular route.

The inclusion of additional variables improves the fit of the regression since the adjusted R 2 considerably increases corroborating the importance of infrastructure, transit time, number of shipments and average size of the shipments in determining transport costs for these sectors.

The adjacency dummy presents a negative and significant coefficient for three out of four sectors, showing that being neighbours reduces transport cost by 0.25%. The dummy island is only significant for agro-industry and negative signed, and the landlocked dummy is significant and positive signed for high value-added sectors. Dummy variable coefficients are not significant for the adjacency, language, island and landlocked dummies for the ceramic sector. This result validates earlier findings obtained in Martinez-Zarzoso et al. ( 2003) with a different data set for the same sector.

Finally, since not only the levels of freight rates might be affected by the mode of transport, but also the distance elasticities, we introduce interaction variables (Mode*Distance). The (Mode*Distance) coefficient is significant for all the sectors apart from ceramics. For the agroindustry sectors a second interaction variable (Distance square*Mode) is found to be statistically significant and negative signed, whereas the (Mode*Distance) coefficient is significant and positive signed. In this particular case, the results indicate that transport costs are increasing with distance for road transport, however for sea transport costs are increasing only for shorter distances and decreasing for longer distances. Finally, for high value-added sectors the (Mode*Distance) coefficient presents a negative sign indicating that unit cost is decreasing with distance when the mode of transport is sea.

Summing up, we find that distance is a significant determinant of transport costs; it has a higher impact for road transport than for sea transport and higher sea distances reduce transport costs in the agroindustry sector. Infrastructure variables are only significant for low-value added sectors (agroindustry and ceramics) and economies of scale in transport are present in all the sectors (proxied by average size and number of shipments).

Concerning geographical dummies, interior countries face higher transport costs, whereas neighbour countries have lower transport costs. These two dummies are non-significant only for the ceramic sector.

TRADE VOLUMES

In order to assess the relative importance of transport costs on trade we need an appropriate theoretical framework. In recent years, the gravity model of trade has become the workhorse of international trade 8 . From the large empirical literature in this field (see [START_REF] Oguledo | Gravity Models: A Reformulation and an Application to Discriminatory Trade Arrangements[END_REF] for a survey of the literature), it is commonly accepted that gravity models explain well bilateral trade patterns.

We estimate a demand model for sectoral exports, based on a log-linear form of a gravity equation augmented with infrastructure variables. The model is specified as, where ln denotes natural logarithms, Y j is the income in the destination market, D ij is distance from the province of origin to the destination market, INF ij is the infrastructure variable defined previously, Ldl is a dummy for landlocked countries, Lang is a dummy for countries sharing the same language, Isl takes the value 1 when countries are an island and zero otherwise and Adj takes the value 1 when countries share the same border, zero otherwise.

The model is jointly estimated for the four sectors with 2001 data. Pooled estimation with fixed effects was the best option since most of the explanatory variables are common across sectors and we only found statistical differences for the distance variable. We perform OLS estimation on the double log specification as given by Equation 3.

Table 2. Determinants of sectoral exports

Table 2 shows our results. Model 1 presents the OLS results for the baseline case, which excludes infrastructure variables and dummies. The standard regressors are income and distance variables. The coefficient on income is positive, as expected, and the income elasticity is 0.64. The coefficient on distance is negative signed and highly significant.

In Model 2 the mode variable is added, showing a negative and significant coefficient, variable coefficients have the same sign and similar magnitude as before, apart from the distance coefficient that decreases in magnitude. In Model 4 the infrastructure variable is added showing a positive and significant coefficient and high elasticity (3.46). We can see how the distance coefficient is not significant, as it shows the correct sign but a smaller magnitude (-0.63) when compared to Model 3. The fit of the equation is also better (R 2 increases a 0.05). In Model 5 we estimate different distance coefficients 9 for each sector to allow for more flexibility in the model. We find that the distance coefficient is significant and with the expected negative sign for the agriculture and food sector and for vehicle parts, whereas it is lower in magnitude and insignificant for ceramics and household appliances. The sectoral dummies are not significant in Model 5.

indicating
Finally, Model 6 was estimated in order to check whether there were problems of reverse causation between exports and income. The model was estimated using the Two Stages Least Square estimator. Two additional variables are selected as instruments for the income variable: the area of the country and the distance to the Equator. In this model the distance specific coefficients are significant and above unity for high value-added sectors, whereas the coefficient is smaller and less significant for low value-added sectors 10 .

In order to compare our results with those obtained by [START_REF] Limao | Infrastructure, Geographical Disadvantage and Transport Costs[END_REF], using estimates from Model 6 we will be able to link trade volumes to transport costs by calculating parameter , the elasticity of trade volumes with respect to transport costs.

We use the coefficients of significant variables (at least for some sectors) included in both the transport cost and the import demand equations. We focus on the distance variable.

Table 3 presents the parameter estimates for this variable and the ratio of the trade 9 We used a Wald test to test for the equality of slopes in the sectoral-distance elasticities. The test is included at the end of Table 2 and the result indicates rejection of the null hypothesis (equality of slopes). 10 Sectoral dummies are excluded since they were non-significant in Model 5.

Table 3. Estimates of export elasticity with respect to transport costs

For comparative purposes, we estimated in the same way as [START_REF] Limao | Infrastructure, Geographical Disadvantage and Transport Costs[END_REF]. They calculated implied elasticities of -2.95 on the basis of distance. However, as aggregated data was used as opposed to the sector data that we use, our results are more specific and not directly comparable to [START_REF] Limao | Infrastructure, Geographical Disadvantage and Transport Costs[END_REF]. We obtain export elasticities with respect to transport costs implied by the point estimates on the basis of distance of -1.52 and -1.20 for low value-added sectors and -2.82 and -3.93 for high value-added sectors. According to our calculations low value-added sectors seem to have considerably lower export elasticity with respect to transport cost than high value-added sectors, pointing towards a lower incidence of transport costs in trade for the former.

Vehicle parts present the highest elasticity, indicating that a decrease of 1% in transport costs would increase exports by 3.93%. An explanation of the sectoral differences in trade-transport-cost elasticities could be related to searching costs and consumer risk aversion as determinants of exports. These two factors are relatively more important for differentiated products than for homogeneous products. [START_REF] Huang | Distance and Trade[END_REF] shows that distance deters trade to a higher extent for differentiated commodities than for homogeneous goods. In fact, Table 2 in the Appendix show that average distances travelled for high value-added products are lower than for low value-added products. A second explanation could be based on Yeats' (1977: p.469) findings. He noted that some processed products have a tendency to be more difficult to handle, more fragile or even subject to higher insurance costs and these factors contribute to increased transport costs for differentiated products. A higher unitary transport cost and a lower distance travelled for differentiated products than for more homogeneous products could give rise to a higher trade-transport- cost elasticities for the former. Finally, the elasticity of trade with respect to transport costs is also related to the elasticity of substitution among products. Since the elasticity of substitution is higher for processed products, that could also explain that trade is more elastic with respect to transport costs for this type of goods, that have more closer substitutes than homogeneous products (Hummels 1999a).

CONCLUSIONS

The objective of this paper was to investigate the determinants of sectoral transport costs and the role they play in deterring international trade. We estimated a transport cost equation using data on transportation costs for four sectors obtained from interviews held with Spanish exporters and logistics operators. We also studied the relationship between transport costs and trade and we estimated an export supply (import demand) model. Our results from the first estimation show that the distance variable behaves differently according to the mode of transport. The infrastructure variable is only significant for lowvalue added sectors, poor infrastructure leads to a notable increase in transport costs.

Inclusion of infrastructure measures improves the fit of the regression in low-value added sectors, corroborating the importance of infrastructure in determining transport costs.

Additionally, higher frequency or larger size of the shipments lowers transport costs in all four sectors, indicating the presence of economies of scale.

Our results from the second estimation show that importer income, as expected, has a positive influence on bilateral trade flows. The distance variable loses significance when infrastructure variables are considered and it is only significant for half of the sectors. The inclusion of distance specific coefficients improves the fit of the trade equation and once the endogeneity of the income variable is considered, distance is clearly significant for high value-added sectors and less significant and with a lower magnitude for low valueadded sectors.

A.3.2. Interview process

The interviews were carried out by 4 research assistants who worked for the Institute of International Economics (University of Valencia). Most interviews were personal interviews arranged previously with logistics managers and/or import/export managers in exporting companies as well as logistic operators and freight forwarders. The sample was selected according to a previously designed segmentation of the market. The objective of this sampling procedure was to undertake the fieldwork with a highly representative sample of the Spanish exporting industry, in terms of geographical location, company size and exported commodities. A structured questionnaire was prepared in advance and detailed questions were asked concerning the commodity exported, the most commonly covered transport routes, the transport mode selected for each route, the specific characteristics (cost, transit time, frequency) of the transport mode chosen and its alternative mode. Currently, the Institute of

  makers in the following 11 autonomous regions in Spain: Andalucía, Aragón, Cantabria, Castilla La Mancha, Cataluña, Comunidad Valenciana, La Rioja, Madrid, Murcia, Navarra and País Vasco, -which are the most industrialised Spanish regions-. Fieldwork conducted was based on personal interviews with import/export and logistics managers at export companies (160 interviews), and freight forwarding agents (78 interviews). 1,251 observations were compiled as a result of these 238 interviews, of which, 1,028 were valid observations for the regressions. The distribution of interviews across sectors is shown in

  freight elasticities indicates the elasticity of trade with respect to transport costs.

  trade elasticities with respect to transport costs indicates that transport cost have a greater effect on trade flows for high value-added sectors, whereas its influence is significantly lower in the case of low value-added sectors. However, future estimations for sectors and products with different logistic processes will be of interest in order to improve the knowledge of the effects of transportation costs on trade flows under diverse conditions of international transport.

  Table A.3 in the appendix and in Table A.4 the destination countries for exports are listed.

Table 1 . Determinants of transport costs When

 1 but opposite signed. the estimation results is shown in Table1. Different regressions were run for each sector since we could not accept equality of slopes for the independent variables in a pooled regression. The number of cross-sections is higher for agroindustry (668) and

	F o r
	P
	e
	e r
	R e v
	i e
	w

sectors. This magnitude is slightly lower than those found in other studies for different commodities.

[START_REF] Hummels | Towards A Geography of Trade Costs[END_REF]

finds commodity specific distance coefficients clustered between 0.2 and 0.3 and

[START_REF] Kumar | Globalization -The Maritime Nexus[END_REF]

found a distance elasticity of 0.24 for the case of Intra-Latin American trade. Secondly, the mode dummy has a negative and significant coefficient, showing that transport costs for a given distance are lower for sea transport. infrastructure variables are added in the model, they show a statistically significant coefficient with the expected negative sign for agro-industry and ceramics (low valueadded sectors). A 1% improvement in the infrastructure of the destination country lowers transport costs by 0.20% on average. However, we find that infrastructure variables are in most cases not significant at conventional levels for high value-added sectors: household appliances and vehicle parts. A plausible reason could be that these products are generally sold to the most developed European countries and these countries already have the 7 Results are available upon request.

Page 10 of 28 Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK

  that exports are higher if the goods are transported by road. In Model 3 we add the list of dummies that might influence exports. The landlocked dummy presents the

	F o r
	P
	e
	e r
	R e v
	i e
	w
	expected negative sign showing that when a country has no sea-shore, exports to this
	country are 282% [exp(1.34)-1] lower than for a coastal country. The adjacency dummy
	presents a slightly significant positive signed coefficient, showing that neighbour
	countries trade 249% [exp(1.25)-1] more than non-neighbour countries. The island
	dummy presents a positive sign and the coefficient is also significant. The remaining
	14

Table 1 . Determinants of transport costs

 1 Note: All variables are for the year 2001. ***, **, * indicate significance at 1%, 5% and 10% respectively. T-statistics, based on White Heteroskedasticity-Consistent Standard Errors, are in brackets. The dependent variable is the natural log of transport costs measured in € per tonne. All the variables except dummies are in natural logs. Mode is a dummy variable that takes the value one when the good is transported by sea and zero otherwise. Distance*mode is an interaction variable that takes a positive value (distance in Km between trading cities) when the good is transported by sea and zero otherwise. Port efficiency*mode is another interaction variable that takes a positive value when the good is transported by sea and zero otherwise. *, * indicate significance at 1%, 5% and 10% respectively. T-statistics are in brackets. The dependent variable is the natural log of exports in volume. Mode is a dummy variable that takes the value one when the good is transported by sea and zero otherwise. All the variables except dummies are in natural logs.

	Variable	Agroindustry Ceramics Household Appliances	Vehicle Parts
	Constant term	-2.56***	-0.88	1.52**	3.58***
		(-3.42)	(-1.25)	(2.07)	(3.24)
	Distance	0.61***	0.69***	0.46***	0.31**
		(9.33)	(8.13)	(3.37)	(1.93)
	(Distance*mode)	1.94*	-0.86	-0.55***	-0.36***
		(1.22)	(-1.31)	(4.54)	(-2.47)
	(Distance square*mode)	-0.18**	0.01	-	-
		(-2.29)	(0.24)
	Mode	-4.23	5.38**	3.49***	2.68**
		(-0.88)	(2.02)	(3.26)	(2.30)
	Infrastructure	-0.17***	-0.23***	0.02	-0.09
		(-2.58)	(-3.78)	(0.15)	(0.88)
	(Port efficiency*mode)	-0.33***	-0.008	0.10	-0.21
		(-2.98)	(-0.07)	(0.44)	(-1.14)
	Transit time	0.10***	0.12***	0.18*	-0.05
		(2.45)	(2.87)	(1.45)	(-0.65)
	Number of shipments	-0.05***	-0.02*	-0025**	-0.08***
		(-4.85)	(-1.77)	(-0.84)	(-3.83)
	Average size of shipments	-0.15***	-0.08***	-0.23***	-0.15***
		(-12.55)	(-7.61)	(-7.76)	(-4.63)
	Cereals	0.26**	-	-	-
		(2.08)
	Wine	0.19**	-	-	-
		(2.08)
	Canned food	-0.02	-	-	-
		(-0.19)
	Oil	0.25***	-	-	-
		(2.93)
	Adjacency	-0.18***	0.03	-0.18*	-0.26***
		(-4.13)	(0.73)	(-1.77)	(-2.88)
	Island	-0.10***	0.02	-0.12	-0.02
		(-2.74)	(0.81)	(-1.05)	(-0.18)
	Landlocked	0.02	0.03	0.29**	0.35**
		(0.23)	(0.25)	(2.49)	(2.48)
	Number of observations	668	548	318	450
	R-squared	0.66	0.55	0.47	0.34
	Adjusted R-squared	0.65	0.54	0.450	0.32
	S.E. of regression	0.330	0.316	0.613	0.626

Note: White Heteroskedasticity-Consistent t-values are in brackets. All variables are for the year 2001. ***, *

Table 3 . Estimates of export elasticity with respect to transport costs

 3 Note: The point estimates for distance in the transport cost equation, are from Table1. The point estimates for distance in the export equation are from Model 6 in Table2.

		Transport cost	Trade equation	Export Elasticities
		equation
		1	2	=	2	1
		Dist.	Dist.
	Agro-industry	0.61	-0.94	-1.52
	Ceramics	0.69	-0.84	-1.20
	Household App. 0.46	-1.31	-2.82
	Vehicle Parts	0.31	-1.22	-3.93

24

APPENDIX

Table A1 . Fieldwork: Regional distribution of interviews

 A1

	Sectors:	Andalucía	Aragón / La Rioja	Cataluña	Comunidad Valenciana	Madrid / Mancha Castilla La	Murcia	País Vasco / Cantabria Navarra /	Total
	Agro-industry (Xc)	10	3	9	11	15	14	2	64
	Ceramic Tiles (Xc)	0	0	2	31	0	0	0	33
	Vehicle Parts (Xc)	0	6	13	1	4	0	8	32
	Hous. Appliances (Xc)	0	1	23	0	0	0	7	31
	Total Xc	10	10	47	43	19	14	17	160
	Agro-industry (Ff)	3	1	4	8	4	2	0	22
	Ceramic Tiles (Ff)	1	1	1	16	2	1	0	22
	Vehicle Parts(Ff)	3	4	5	4	8	0	0	24
	Hous. Appliances (Ff)	2	1	3	0	4	0	0	10
	Total Ff	9	7	13	28	18	3	0	78
	Total	19	17	60	71	37	17	17	238
	Note:

Xc Denotes Export Companies And Ff Denotes Freight Forwarders. Page 24 of 28 Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK

	Page 25 of 28		Submitted Manuscript Submitted Manuscript
	Table A2. Variable Averages A.3 Data collection
	Variables A.3.1 Fieldwork: Number of interviews	Sector 1:Agro-industry	Sector 2: Ceramic Tiles	Sector 3: Vehicle Parts	Sector 4: Household Appliances.
	F o r P e e r R e v i e w Road Sea Road Sea Road Exporters Logistic Operators Frequency of Shipments (No. of Shipments Sector per Annum) 109.60 125.30 962.90 205.83 141.52 Average Size of Shipments (m 3 ) 124.17 358.21 942.02 129.76 64.90 Frequency of Shipments (No. of Shipments per Annum) Export Companies 101.92 68.51 1,005.40 124.22 79.04 Average Size of Shipments (m 3 ) Export Companies 51.03 312.55 83.89 74.42 30.54 Distance (Km) 1,759.72 3,074.15 1,640.50 3,433.68 1,527.41 Transport Cost (Euro/Tm) 109.84 66.83 82.16 53.89 285.22 Transit Time (Hours) 77.05 181.70 59.77 188.30 65.67 % of Delayed Shipments 0.47 4.18 0.95 1.13 2.76 Average Delay (Hours) 1.20 9.26 3.24 14.33 6.16 % of Shipments Damaged or Lost 0.98 0.28 0.29 0.44 0.10 Average Damage (% of Total Value of Shipment) 0.23 3.07 0.16 0.62 0.40 % of Consolidated Shipments 31.03 12.18 58.82 20.25 77.14 Transport Restrictions (No. of Days per Year) 107.39 0.00 104.07 0.00 110.93 Agroindustry 33 22 Ceramic Tiles 64 22 Vehicle Parts 32 24 Household Appliances 31 10 F o r P e e r R e v i e w Total 160 78	Sea 47.42 247.45 42.23 330.72 1,538.83 77.21 118.11 9.35 8.66 2.35 3.71 43.26 0.00	Road 184.92 61.09 173.98 55.05 1,389.43 238.27 56.25 2.93 4.71 0.98 3.59 46.21 106.29	Sea 78.75 51.80 69.64 53.58 2,379.66 113.06 153.20 5.60 16.29 0.00 0.00 31.67 0.00
	% of Shipments Delayed due to Restrictions	0.04	0.00	0.00	0.00	0.00	0.00	0.00	0.00
	Average Delay due to Restrictions (Hours)	0.43	0.00	0.00	0.00	0.00	0.00	0.00	0.00
				25 26

Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK

Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK Submitted Manuscript

ACKNOWLEDGEMENTS

Financial support from Fundación Caja Castellón-Bancaja, Generalitat Valenciana and the Spanish Ministry of Education is grateful acknowledged (P1-1B2005-33, Grupos 03-151, INTECO; Research Projects GV04B-030, SEJ 2005-01163 and ACOMP06/047). We also would like to thank two anonymous referees and the participants in the 6 th INFER Workshop on Economic Policy in Reus (Spain) and in the I International Transport Congress hold in Castellón (Spain) for their comments and suggestions. We thank Elena Sanjuan-Lucas for excellent research assistance.