Dirk Veestraeten
email: dirk.veestraeten@uva.nl.

Roel Beetsma

Giuseppe Cavaliere

Paul De Grauwe

Casper De Vries

Hans Dewachter

Henk Jager

Eiko Kenjoh

Franc Klaassen

Piet Sercu

Marno Verbeek

The presence of target zone nonlinearities when narrower bands exist within official zones

Keywords: Brownian motion, Conditional probability density function, Nonlinearities, Exchange rate target zones Brownian motion, conditional probability density function, exchange rates, nonlinearities, target zones. JEL classification: F31, F33 Brownian motion, conditional probability density function, exchange rates, nonlinearities, target zones. JEL classification: F31, F33

The presence of target zone nonlinearities is generally refuted in empirical research. We argue that this may be due to estimation being performed vis-à-vis official limits when monetary authorities in fact are targeting a narrower band. Estimation results for the Belgian and French franc confirm that nonlinearities are present when narrower zones are accounted for.

Introduction

Empirical research on nonlinearities and implicit bands in exchange rate target zones has largely evolved independently. The literature on nonlinearities in the relationship between fundamentals and exchange rates found only sparse evidence of departures from linearity (see, for instance, Flood, Rose and Mathieson, 1991; de Jong, 1994; Lindberg and Söderlind, 1994;Garratt, Psaradakis and Sola, 2001). On the other hand, the empirical literature recorded strong evidence of narrower or implicit zones within official limits in the Exchange Rate Mechanism (see, amongst others, Bartolini and Prati, 1999;Anthony and MacDonald, 1999;Chung and Tauchen, 2001). However, connecting these two distinct lines of the literature could be a worthwhile exercise since the presence of narrower zones implies that expectation formation is to be evaluated with respect to these actual limits rather than vis-à-vis the wider, officially announced zone. This is of the utmost importance for the search for target zone nonlinearities since they are likely to be overlooked when inspection is performed with respect to limits that are too wide. Indeed, the Krugman (1991) model shows that nonlinearities primarily are to be found near the limits. Using official limits in estimation when actually a narrower zone is in place will therefore tend to underestimate nonlinearities. This paper estimates the Krugman (1991) target zone model in which asymmetric implicit fluctuation zones are allowed for. The sample consists of the Belgian and French francs and covers the period 1994-1997 in which official band limits were at ±15%. Using the analytical characterisation of the likelihood function for reflected Brownian motion, that was recently derived in [START_REF] Veestraeten | The conditional probability density function for a reflected Brownian motion[END_REF], we are able to employ maximum likelihood estimation. 2 The target zone model of Krugman (1991) The model of Krugman (1991) starts from the log-linear asset pricing equation that expresses the (log of the) exchange rate, s, as the sum of the (log of the) fundamental, f , and its own expected change:

s = f + α E[ds] dt , (1)
with α > 0 and E denoting the expectations operator. 1 Krugman (1991) specified the dynamics of f as a Brownian motion with drift between two limits.

The lower and upper band limits, f and f , result from the intervention obligations within the target zone arrangement. Monetary authorities thus decrease (increase) the control variable, i.e. the fundamental, whenever it reaches its upper (lower) band limit which gives rise to regulated or reflected Brownian motion:

df = µdt + σdz (t) + dL -dU, (2)
where dL and dU are the infinitesimal regulators.

Expressing the exchange rate as an explicit function of the fundamental, i.e. s = s (f ), applying Itô's lemma to the expectations term and solving Equation (1) then yields the well-known S-shaped exchange rate function, see Krugman (1991):

s (f ) = f + αµ + A 1 exp (λ 1 f ) + A 2 exp (λ 2 f ) . (3)
The two exponential terms cause the renowned bending of the exchange rate function and thus generate the target zone nonlinearities. The no-expected-arbitrage-profits condition then leads to the so-called smooth-pasting conditions that require the exchange rate function to be flat at the boundaries such that intervention does not lead to discontinuities in the exchange rate path. The resulting boundary conditions then allow for the determination of the two constants of integration A 1 and A 2 which completely solves the model. The exchange rate is a one-to-one transform of the fundamental since its first derivative to the fundamental can be shown to be always non-negative. Moreover, the first derivative of the inverted exchange rate function to the exchange rate is continuous. These monotone mapping and continuously differentiability properties allow us to express the conditional density of the exchange rate in terms of the conditional density of the fundamental through a simple change of variables:

p s (s, t; s 0 , t 0 ) = p f ¡ s -1 (s) , t; s -1 (s 0 ) , t 0 ¢ ¯ds -1 (s) df ¯, (4)
where p s (•) and p f (•) denote the conditional density functions of the exchange rate and the fundamental, respectively. The future and the present point of time are denoted by t and t 0 and the function

s -1 (s) is the inverse function of s (f ).
The conditional density function of the fundamental, i.e. of a reflected Brownian motion with drift, was recently derived in [START_REF] Veestraeten | The conditional probability density function for a reflected Brownian motion[END_REF] and in the present notation reads as follows:

p f (f, t; f 0 , t 0 ) = +∞ X n=-∞ ( 1 σ √ 2π(t-t 0 ) exp à 2µn ¡ f -f ¢ σ 2 ! exp à - ¡ f + 2n(f -f ) -f 0 -µ (t -t 0 ) ¢ 2 2σ 2 (t -t 0 ) !) + +∞ X n=-∞ ( 1 σ √ 2π(t-t 0 ) exp à - 2µ ¡ nf -(n + 1) f + f 0 ¢ σ 2 ! exp à - ¡ 2nf -2 (n + 1) f + f 0 + f -µ (t -t 0 ) ¢ 2 2σ 2 (t -t 0 ) !) - 2µ σ 2 +∞ X n=0 ( exp à 2µ ¡ nf -(n + 1) f + f ¢ σ 2 ! " 1 -Φ Ã µ (t -t 0 ) + 2nf -2 (n + 1) f + f 0 + f σ √ t -t 0 !#) + 2µ σ 2 +∞ X n=0 ( exp à 2µ ¡ nf -(n + 1) f + f ¢ σ 2 ! Φ Ã µ (t -t 0 ) -2 (n + 1) f + 2nf + f 0 + f σ √ t -t 0 !) , (5)
where The Markov property of the fundamental carries over to the exchange rate given their one-to-one relationship such that the joint distribution of s, P (s) , can be written as:

Φ (z) = 1 σ √ 2π Z z -∞ exp ¡ -
P (s) = T -1 Y t=0 p s (s t+1 , t + 1; s t , t) . (6)
The loglikelihood function for the observed exchange rate series s, in terms of the parameter vector

Θ = © α, µ, σ, f , f ª
, then emerges as:

LogLikF (s, Θ) = T -1 X t=0 ln (p s (s t+1 , t + 1; s t , t) | Θ) . (7)
The parameters in the maximisation process consist of the four parameters that fully specify the stochastic process of the fundamental, namely its instantaneous drift and standard deviation, µ and σ, and the upper and lower band limits, f and f . The fifth parameter is the sensitivity of the exchange rate to its own expected change, α, which governs the extent of nonlinearities, see Equations ( 1) and

(3). Earlier research, such as de Jong (1994) and Lindberg and Söderlind (1994), indicated that estimation of target zone models is rendered more complex by the flatness of the likelihood surface such that obtaining standard errors for all five coefficients is not sensible. We therefore calculate asymptotic standard errors for four parameters whilst keeping α at its estimated value. However, model specification and especially the significance of α will be formally tested via Likelihood Ratio Dewachter and Veestraeten (1999) argued that the start of EMU on 1 January 1999 had to be seen as a regime switch that already more than one year earlier drastically altered exchange rate dynamics.

Table 1 presents estimation results for the Krugman (1991) model. The first two rows present results when estimating with respect to the official target zone limits. The subsequent rows relate to the case in which implicit bands are allowed for. The estimation results and especially the loglikelihood values strongly suggest that implicit zones were present such that estimation with respect to official band limits could have obscured nonlinearities. This will be formally tested via the LR specification tests in Table 2.

Tables 1 and2: around here.

The first LR-test in Table 2 examines whether α significantly differs from zero, i.e. whether nonlinearities are present, when estimation is pursued with respect to the official zone. Constraining α to be zero causes s to equal f , see Equation [START_REF] Anthony | The width of the band and exchange rate mean-reversion: some further ERM-based results[END_REF], such that the exchange rate in the constrained model follows reflected Brownian motion. The restriction on α is non-standard since α is constrained to be nonnegative and, moreover, the restriction lies on the boundary of its domain. Chernoff (1954) showed that the resulting LR-statistic is distributed

1 2 χ 2 0 + 1 2 χ 2 1 .
We follow de Jong (1994) and approximate

this distribution as χ 2 1 . The second LR-test pursues the same strategy but now estimation allows for implicit bands. The findings reveal that estimation with respect to official bands obscures nonlinearities in the case of the Belgian franc since the constrained model cannot be rejected (LR-test 1) whereas it can safely be rejected when implicit limits are allowed for. For the French franc, nonlinearities are present in both cases but the p-value is far lower when analysing them vis-à-vis an implicit zone. The third LR test evaluates whether the implicit zones are significantly different from the official zones.

Hereto, the two models in

Conclusion

This paper examined exchange rate target zone nonlinearities by explicitly relating their presence to the potential pursuit of implicit, narrower fluctuation ranges within the official band. Since nonlinearities are typically to be detected near the band limits, estimation should devote careful attention to the precise location of the fluctuation limits. Estimation with respect to official limits when in fact monetary authorities are pursuing a narrower zone will inevitably bias results towards rejecting the presence of nonlinearities. The maximum likelihood estimation results confirmed the existence of an underestimation bias when official limits are used. In fact, the presence of nonlinearities for the Belgian franc between 1994 and 1997 had to be discarded when estimating with respect to official limits, whereas they were clearly present when gauged with respect to narrower, implicit zones. Or, nonlinearities can be overlooked if estimation fails to account for implicit limits. The underestimation bias was not present in the case of the French currency although rejection of the hypothesis of no nonlinearities was much stronger when examined with respect to implicit limits. Response to the referee report I would like to thank professor Mark P. Taylor for his positive appraisal of my paper and for giving me the opportunity to revise the paper under consideration. The discussion of his remark allowed me to better clarify the rationale of the paper as well as its positioning within the target zone literature.

In this "Response to the referee report", I will first include the relevant part of the referee report (section 1). In the second section, I will specify the answer to the question raised in the referee report.

Section 3 proceeds by listing the textual changes that have been undertaken in the revised version of the paper.

1 Referee report However, they take different routes in establishing this result. Personally, I see the two approaches as complementary. This is the focus of the changes to the original version of the paper.

Textual changes

I have included a reference to the papers of Iannizzotto and Taylor (1999) and [START_REF] Taylor | On the mean-reverting properties of target zone exchange rates: a cautionary note[END_REF] in the introduction to the revised paper. The last sentence of the first paragraph in the introduction will be extended by a footnote according to which the power argument can be thought of as a valid, additional reason for actually detecting few indications for the presence of nonlinearities. The reference list has been extended to include Iannizzotto and Taylor (1999) and Taylor and Iannizzotto (2001) and the acknowledgements have been changed in order to thank the editor.

Alteration #1: Original phrasing

The literature on nonlinearities in the relationship between fundamentals and exchange rates found

only

Alteration #2: Original phrasing

Using official limits in estimation when actually a narrower zone is in place will therefore tend to underestimate nonlinearities.

Alteration #2: Phrasing in the revision

Using official limits in estimation when actually a narrower zone is in place will therefore tend to underestimate nonlinearities. Chung and Tauchen, 2001). However, connecting these two distinct lines of the literature could be a worthwhile exercise since the presence of narrower zones implies that expectation formation is to be evaluated with respect to these actual limits rather than vis-à-vis the wider, officially announced zone. This is of the utmost importance for the search for target zone nonlinearities since they are likely to be overlooked when inspection is performed with respect to limits that are too wide. Indeed, the Krugman (1991) model shows that nonlinearities primarily are to be found near the limits. Using official limits in estimation when actually a narrower zone is in place will therefore tend to underestimate nonlinearities. 1 This paper estimates the Krugman (1991) target zone model in which asymmetric implicit fluctuation zones are allowed for. The sample consists of the Belgian and French francs and covers the period 1994-1997 in which official band limits were at ±15%. Using the analytical characterisation of the likelihood function for reflected Brownian motion, that was recently derived in [START_REF] Veestraeten | The conditional probability density function for a reflected Brownian motion[END_REF], we are able to employ maximum likelihood estimation. 1 Iannizzotto and Taylor (1999) and [START_REF] Taylor | On the mean-reverting properties of target zone exchange rates: a cautionary note[END_REF] argue that the low power of standard mean reversion tests such as the (Augmented) Dickey-Fuller and variance ratio tests can also obscure nonlinearities. Krugman (1991) The model of Krugman (1991) starts from the log-linear asset pricing equation that expresses the (log of the) exchange rate, s, as the sum of the (log of the) fundamental, f , and its own expected change:

s = f + α E[ds] dt , (1)
with α > 0 and E denoting the expectations operator. 2Krugman (1991) specified the dynamics of f as a Brownian motion with drift between two limits.

The lower and upper band limits, f and f , result from the intervention obligations within the target zone arrangement. Monetary authorities thus decrease (increase) the control variable, i.e. the fundamental, whenever it reaches its upper (lower) band limit which gives rise to regulated or reflected Brownian motion:

df = µdt + σdz (t) + dL -dU, (2)
where dL and dU are the infinitesimal regulators.

Expressing the exchange rate as an explicit function of the fundamental, i.e. s = s (f ), applying Itô's lemma to the expectations term and solving Equation (1) then yields the well-known S-shaped exchange rate function, see Krugman (1991):

s (f ) = f + αµ + A 1 exp (λ 1 f ) + A 2 exp (λ 2 f ) . (3)
The two exponential terms cause the renowned bending of the exchange rate function and thus generate the target zone nonlinearities. The no-expected-arbitrage-profits condition then leads to the so-called smooth-pasting conditions that require the exchange rate function to be flat at the boundaries such that intervention does not lead to discontinuities in the exchange rate path. The resulting boundary conditions then allow for the determination of the two constants of integration A 1 and A 2 which completely solves the model. The exchange rate is a one-to-one transform of the fundamental since its first derivative to the fundamental can be shown to be always non-negative. Moreover, the first derivative of the inverted exchange rate function to the exchange rate is continuous. These monotone mapping and continuously differentiability properties allow us to express the conditional density of the exchange rate in terms of the conditional density of the fundamental through a simple change of variables:

p s (s, t; s 0 , t 0 ) = p f ¡ s -1 (s) , t; s -1 (s 0 ) , t 0 ¢ ¯ds -1 (s) df ¯, (4)
where p s (•) and p f (•) denote the conditional density functions of the exchange rate and the fundamental, respectively. The future and the present point of time are denoted by t and t 0 and the function

s -1 (s) is the inverse function of s (f ).
The conditional density function of the fundamental, i.e. of a reflected Brownian motion with drift, was recently derived in [START_REF] Veestraeten | The conditional probability density function for a reflected Brownian motion[END_REF] and in the present notation reads as follows:

p f (f, t; f 0 , t 0 ) = +∞ X n=-∞ ( 1 σ √ 2π(t-t 0 ) exp à 2µn ¡ f -f ¢ σ 2 ! exp à - ¡ f + 2n(f -f ) -f 0 -µ (t -t 0 ) ¢ 2 2σ 2 (t -t 0 ) !) + +∞ X n=-∞ ( 1 σ √ 2π(t-t 0 ) exp à - 2µ ¡ nf -(n + 1) f + f 0 ¢ σ 2 ! exp à - ¡ 2nf -2 (n + 1) f + f 0 + f -µ (t -t 0 ) ¢ 2 2σ 2 (t -t 0 ) !) - 2µ σ 2 +∞ X n=0 ( exp à 2µ ¡ nf -(n + 1) f + f ¢ σ 2 ! " 1 -Φ Ã µ (t -t 0 ) + 2nf -2 (n + 1) f + f 0 + f σ √ t -t 0 !#) + 2µ σ 2 +∞ X n=0 ( exp à 2µ ¡ nf -(n + 1) f + f ¢ σ 2 ! Φ Ã µ (t -t 0 ) -2 (n + 1) f + 2nf + f 0 + f σ √ t -t 0 !) , (5)
where The Markov property of the fundamental carries over to the exchange rate given their one-to-one relationship such that the joint distribution of s, P (s) , can be written as:

Φ (z) = 1 σ √ 2π Z z -∞ exp ¡ - 1
P (s) = T -1 Y t=0 p s (s t+1 , t + 1; s t , t) . (6)
The loglikelihood function for the observed exchange rate series s, in terms of the parameter vector

Θ = © α, µ, σ, f , f ª
, then emerges as:

LogLikF (s, Θ) = T -1 X t=0 ln (p s (s t+1 , t + 1; s t , t) | Θ) . (7)
The parameters in the maximisation process consist of the four parameters that fully specify the stochastic process of the fundamental, namely its instantaneous drift and standard deviation, µ and σ, and the upper and lower band limits, f and f . The fifth parameter is the sensitivity of the exchange rate to its own expected change, α, which governs the extent of nonlinearities, see Equations ( 1) and

(3). Earlier research, such as de Jong (1994) and Lindberg and Söderlind (1994), indicated that estimation of target zone models is rendered more complex by the flatness of the likelihood surface such that obtaining standard errors for all five coefficients is not sensible. We therefore calculate asymptotic standard errors for four parameters whilst keeping α at its estimated value. However, model specification and especially the significance of α will be formally tested via Likelihood Ratio Dewachter and Veestraeten (1999) argued that the start of EMU on 1 January 1999 had to be seen as a regime switch that already more than one year earlier drastically altered exchange rate dynamics.

Table 1 presents estimation results for the Krugman (1991) model. The first two rows present results when estimating with respect to the official target zone limits. The subsequent rows relate to the case in which implicit bands are allowed for. The estimation results and especially the loglikelihood values strongly suggest that implicit zones were present such that estimation with respect to official band limits could have obscured nonlinearities. This will be formally tested via the LR specification tests in Table 2.

Tables 1 and2: around here.

The first LR-test in Table 2 examines whether α significantly differs from zero, i.e. whether nonlinearities are present, when estimation is pursued with respect to the official zone. Constraining α to be zero causes s to equal f , see Equation [START_REF] Anthony | The width of the band and exchange rate mean-reversion: some further ERM-based results[END_REF], such that the exchange rate in the constrained model follows reflected Brownian motion. The restriction on α is non-standard since α is constrained to be nonnegative and, moreover, the restriction lies on the boundary of its domain. Chernoff (1954) showed that the resulting LR-statistic is distributed

1 2 χ 2 0 + 1 2 χ 2 1 .
We follow de Jong (1994) and approximate

this distribution as χ 2 1 . The second LR-test pursues the same strategy but now estimation allows for implicit bands. The findings reveal that estimation with respect to official bands obscures nonlinearities in the case of the Belgian franc since the constrained model cannot be rejected (LR-test 1) whereas it can safely be rejected when implicit limits are allowed for. For the French franc, nonlinearities are present in both cases but the p-value is far lower when analysing them vis-à-vis an implicit zone. The third LR test evaluates whether the implicit zones are significantly different from the official zones.

Hereto, the two models in

Conclusion

This paper examined exchange rate target zone nonlinearities by explicitly relating their presence to the potential pursuit of implicit, narrower fluctuation ranges within the official band. Since nonlinearities are typically to be detected near the band limits, estimation should devote careful attention to the precise location of the fluctuation limits. Estimation with respect to official limits when in fact monetary authorities are pursuing a narrower zone will inevitably bias results towards rejecting the presence of nonlinearities. The maximum likelihood estimation results confirmed the existence of an underestimation bias when official limits are used. In fact, the presence of nonlinearities for the Belgian franc between 1994 and 1997 had to be discarded when estimating with respect to official limits, whereas they were clearly present when gauged with respect to narrower, implicit zones. Or, nonlinearities can be overlooked if estimation fails to account for implicit limits. The underestimation bias was not present in the case of the French currency although rejection of the hypothesis of no nonlinearities was much stronger when examined with respect to implicit limits.

2

 2

3

  The conditional density function of the exchange rate

(

  LR) tests. The exchange rates of the Belgian and French currencies are Wednesday prices and are quoted against the German mark. The sample spans the period March 1994 until February 1997. The starting point was chosen in function of the theoretical prerequisite of credibility since the turmoil in the Exchange Rate Mechanism (ERM) of 1993 for these two currencies only faded away towards the end of that year. The endpoint reflects doubts on the continued appropriateness of the target zone model in view of the prospect of the European Monetary Union (EMU). In fact, De Grauwe,

3

  The conditional density function of the exchange rate

( 5 Page

 5 LR) tests. The exchange rates of the Belgian and French currencies are Wednesday prices and are quoted against the German mark. The sample spans the period March 1994 until February 1997. The starting point was chosen in function of the theoretical prerequisite of credibility since the turmoil in the Exchange Rate Mechanism (ERM) of 1993 for these two currencies only faded away towards the end of that year. The endpoint reflects doubts on the continued appropriateness of the target zone model in view of the prospect of the European Monetary Union (EMU). In fact, De Grauwe,

Table 1

 1

	Submitted Manuscript

are compared in which the official-zone model is the constrained model and the test statistic thus is distributed as χ 2 2 . Unsurprisingly, implicit zones significantly differ 6 Page 6 of 22 Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK from officially announced fluctuation ranges in line with the findings reported in Bartolini and Prati (1999), Anthony and MacDonald (1999) and Chung and Tauchen (2001).

Table 1 :

 1 Maximum likelihood estimates of theKrugman (1991) model with either official band limits or implicit band limits, sample period: 2 March 1994 -29 January 1997.

	7

Table 2 :

 2

	Belgian franc	0.03564	0.8503	75.61152 3.5E-18 75.57589 3.9E-17
	French franc	7.68802	0.0056	54.24664 1.8E-13 46.55863 7.8E-11
	1 Under χ 2 1 . 2 Under χ 2 2 .

Likelihood Ratio tests, sample period: 2 March 1994 -29 January 1997. LR-test 1 p-value 1 LR-test 2 p-value 1 LR-test 3 p-value 2

  [START_REF] Iannizzotto | The target zone model, non-linearity and mean reversion: is the honeymoon really over?[END_REF] and[START_REF] Taylor | On the mean-reverting properties of target zone exchange rates: a cautionary note[END_REF] estimate theKrugman (1991) model via the method of simulated moments and detect evidence of small but significant degrees of target zone nonlinearities. Subsequently, they examine standard mean reversion tests that in the existing literature are frequently employed as a testing procedure for the presence of target zone nonlinearities. tests possess low power and fail to reject the absence of nonlinearities even when the data generating process in fact is that of a credible target zone arrangement. Phrased differently, standard mean reversion tests tend to overlook target zone nonlinearities.In my paper, the conditional density function of regulated Brownian motion between two reflecting boundaries for the fundamental is directly embedded into theKrugman (1991) exchange rate target zone model. This specification is subsequently estimated within a maximum likelihood framework.Likelihood ratio tests then indicate that target zone nonlinearities are present when estimation is pursued vis-à-vis implicit, narrower zones. In other words, target zone nonlinearities can be overlooked when estimation is pursued with respect to official limits when in fact an implicit zone is present.

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

In particular, I would like you to think about how your work relates to two papers that I wrote on exactly this topic (listed below). In it, I demonstrate that the failure to find nonlinearity is not surprising if the target zone model is parameterised properly. Does the fact that you can detect nonlinearity therefore mean (as I suspect) that your results are all the more impressive since they reject linearity even when the test has low power? Please have a look at the papers and let me know how you square your results. They are: "On the Mean-Reverting Properties of Target Zone Exchange Rates: A Cautionary Note" Authors: Taylor, Mark P. and Matteo Iannizzotto, Economics Letters v71, n1 (April 2001): pp. 117-29 "The Target Zone Model, Non-linearity and Mean Reversion: Is the Honeymoon Really Over?" Authors: Matteo Iannizzotto and Mark P. Taylor, Economic Journal v109, n454 (March 1999): C96-110 2 Reply to comments Their Monte Carlo simulations convincingly show that (Augmented) Dickey-Fuller and variance ratio 1 Page 10 of 22 Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK Both approaches have in common that they point to the potential of target zone nonlinearities being overlooked in existing research. Iannizzotto and Taylor (1999) and Taylor and Iannizzotto (2001) build their argument upon the power properties of mean reversion tests, whereas my paper addresses the question as to which the nonlinearities may be obscured by narrower zones within official zones. The two approaches thus focus on different reasonings that can account for overlooking of nonlinearities.

  The literature on nonlinearities in the relationship between fundamentals and exchange rates found only sparse evidence of departures from linearity (see, for instance,Flood, Rose and Mathieson, 1991;

	1.2. Alteration #1: Phrasing in the revision
	de Jong, 1994; Lindberg and Söderlind, 1994; Iannizzotto and Taylor, 1999; Taylor and Iannizzotto,
	2001; Garratt, Psaradakis and Sola, 2001).
	F o
	r
	P e
	e
	r
	R e
	v i
	e w
	2

sparse evidence of departures from linearity (see, for instance, Flood, Rose and Mathieson, 1991; de Jong, 1994; Lindberg and Söderlind, 1994; Garratt, Psaradakis and Sola, 2001).

  1 Taylor and Iannizzotto, 2001; Garratt, Psaradakis and Sola, 2001). On the other hand, the empirical literature recorded strong evidence of narrower or implicit zones within official limits in the Exchange Rate Mechanism (see, amongst others,Bartolini and Prati, 1999; Anthony and MacDonald, 1999;

	Submitted Manuscript
	1 Introduction
	The presence of target zone nonlinearities when narrower bands Empirical research on nonlinearities and implicit bands in exchange rate target zones has largely
	exist within official zones evolved independently. The literature on nonlinearities in the relationship between fundamentals and
	exchange rates found only sparse evidence of departures from linearity (see, for instance, Flood, Rose
	and Mathieson, 1991; de Jong, 1994; Lindberg and Söderlind, 1994; Iannizzotto and Taylor, 1999;
	F o F o
	r r
	P e P e
	e e
	r r
	R e R e
	v i v i
	e w e w

1

Iannizzotto and

[START_REF] Iannizzotto | The target zone model, non-linearity and mean reversion: is the honeymoon really over?[END_REF]

and

[START_REF] Taylor | On the mean-reverting properties of target zone exchange rates: a cautionary note[END_REF]

argue that the low power of standard mean reversion tests such as the (Augmented) Dickey-Fuller and variance ratio tests can also obscure nonlinearities. 3 Page 12 of 22 Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK

2

  Page 14 of 22 Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK

	Submitted Manuscript
	2 The target zone model of
	F o
	r
	P e
	e
	r
	R e
	v i
	e w

Table 1

 1 are compared in which the official-zone model is the constrained model and the test statistic thus is distributed as χ 2 from officially announced fluctuation ranges in line with the findings reported inBartolini and Prati (1999),Anthony and MacDonald (1999) andChung and Tauchen (2001).

	Submitted Manuscript

2 . Unsurprisingly, implicit zones significantly differ 6 Page 18 of 22 Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK

Table 1 :

 1 [13] Veestraeten, D., 2004, The conditional probability density function for a reflected Brownian motion, Computational Economics 24, 185-207. Maximum likelihood estimates of the Krugman (1991) model with either official band limits or implicit band limits, sample period: 2 March 1994 -29 January 1997. Note: Asymptotic standard errors are given within brackets.

	7

9 Page 21 of 22 Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK

Table 2 :

 2 Likelihood Ratio tests, sample period: 2 March 1994 -29 January 1997. LR-test 1 p-value 1 LR-test 2 p-value 1 LR-test 3 p-value 2

	Belgian franc	0.03564	0.8503	75.61152 3.5E-18 75.57589 3.9E-17
	French franc	7.68802	0.0056	54.24664 1.8E-13 46.55863 7.8E-11
	1 Under χ 2 1 . 2 Under χ 2 2 .

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

Submitted Manuscript

Time subscripts are omitted for notational brevity.

F31 -Foreign Exchange < F3 -International Finance < F -


Colin Kirkpatrick

Kenichi Shimamoto

and Environment < , F21 -International Investment|Long-Term Capital Movements < , Q56 -Environment and Development|Environment

Keywords: environmental regulation, foreign direct investment, Japanese multinationals JEL classification: Q56, F21 Keywords, Environmental regulation, foreign direct investment, Japanese multinationals

This paper assesses the impact of environmental regulation in host countries on Japanese foreign direct investment (FDI) decision-making. It tests the pollution haven hypothesis using data on national environmental regulation standards and Japanese inward FDI in five dirty industries (iron and steel industry, non-ferrous metals industry, chemicals industry, paper and pulp industry, non-metallic products industry). The results do not support the pollution hypothesis. On the contrary, inward Japanese FDI appears to be attracted to countries which have committed themselves to a transparent and stable environment regulatory environment, suggesting that the quality of the regulatory framework in terms of its certainty and transparency has a greater influence on foreign investors' choice of location than the level of environmental regulatory measures.

Introduction

Foreign direct investment (FDI) has risen dramatically in recent years. In 2003, global FDI flows amounted to $559,576 million, representing 23 per cent of world GDP (UN, 2004). FDI has in turn been a key driver of the growth in international trade and the growing integration of the global economy. This increased integration of the world economy has been accompanied by a growing interest in the relationship between international investment and the environment. In part, this debate has reflected the concerns of environmentalists that the global trend towards trade and investment liberalization will intensify environmental pressures as countries compete for an increased share of foreign investment by engaging in a 'race to the bottom' on environmental regulations. Similar concerns have been raised by economists who have argued that the adoption of more stringent national environmental standards could reduce a country's competitive advantage and encourage pollution intensive industries to relocate to countries with lower standards. On the other hand, some commentators have argued that foreign investment may be attracted to locations where environmental regulations are more stringent, on the grounds that tighter regulation reduces the risks of environmental liabilities and allows foreign firms to exploit their competitive advantage based on technological innovation.

Growing international concern for the environmental impact of international trade and investment flows has been reflected in an increasing level of international cooperation on environmental regulation. The number of multilateral environmental agreements (MEAs) currently exceeds 200, with more than 20 of these incorporating trade measures [START_REF] Brack | Multinational Environmental Agreements and the WTO[END_REF]. In addition, the World Trade Organisation (WTO) is committed to the goal of sustainable development, and negotiations on the relationship between WTO trade rules and environment regulation are a key component of the Doha Development Agenda. The objective of this paper is to provide an empirical investigation of the impact of environmental regulation on the pattern of Japanese outward investment during the 1990s. This introduction is followed by Section 2 which provides a short literature review. Section 3 presents the methodology, and the data used in the study are discussed in section 4. The fifth section contains the results. The final section gives a summary of the paper's main findings.

Review of the Literature

Variations in the scope and effectiveness of environmental regulation have given rise to concerns about the impact of environmental regulation on international investment flows. If the costs of compliance with environmental regulations differ across national boundaries, then we might expect to see the relocation of pollution intensive industries to locations where the costs of compliance are lower. These shifts may in turn have a 'chilling' effect on the introduction of new environmental regulation as countries become more reluctant to increase environmental control measures or deliberately try to attract FDI by offering lower environmental standards, leading to a competitive 'race to the bottom'. Although this 'pollution haven' effect has been the subject of extensive empirical investigation, the literature has failed so far to produce conclusive evidence confirming that differences in environmental regulations across countries are a significant determinant of trade and investment flows [START_REF] Smarzynska | Pollution Havens and Foreign Direct Investment: Dirty Secret or Popular Myth?[END_REF] 2 . A comprehensive review of the earlier literature concluded that 'fears of a "race to the bottom" in environmental standards, based on the idea of "pollution havens" may be generally unfounded' (OECD, 1997). The majority of more recent studies of the pollution haven hypothesis have confirmed this conclusion (see Jaffe et al., 1995;[START_REF] Levinson | Environmental Regulations and Manufacturers' Location Choices: Evidence from the Census of Manufactures[END_REF]Adam,1997;[START_REF] Busse | Trade, Environmental Regulations and the World Trade Organization: New Empirical Evidence[END_REF]. A number of studies, however, have found (weak) evidence that differences in environmental regulations can affect FDI flows (Mani and Wheeler, 1997;[START_REF] List | The Effect of Environmental Regulation on Foreign Direct Investment[END_REF][START_REF] Eskeland | Moving to Greener Pastures? Multinationals and the Pollution Haven[END_REF].

The existing empirical literature has a number of limitations, which may go some way in explaining the ambiguity in the results obtained. These limitations include differences in 2 [START_REF] Copeland | Trade, Growth and the Environment[END_REF] distinguish between the pollution haven effect and the pollution haven hypothesis. In the former case, a tightening of environmental regulations will, at the margin, have an effect on trade and investment flows. In the latter case, the effect of environment regulation dominates the influence of all other factors that affect trade and investment flows, and leads to a shift in pollution intensive industry from countries with more stringent regulations to countries with weaker environmental regulation. econometric methodologies, data sources and proxies, as well as alternative conceptual frameworks [START_REF] Letchumanan | Reconciling the Conflict between the Pollution-Havens Hypothesis and An Emerging Trajectory of International Technology Transfer[END_REF]. A major limitation of empirical studies that have examined the linkage between trade and investment flows and environmental regulation has been the almost complete lack of comparable data on environmental regulation across countries. In attempting to overcome this lacuna in the data, most studies have tested the pollution haven hypothesis indirectly, by examining the international changes in the emissions output of 'dirty' industries on the assumption that stricter environmental regulations results in better environmental conditions, and vice versa [START_REF] Hoffmann R Lee | FDI and Pollution: A Granger Causality Test Using Panel Data[END_REF].

Typically, US data on either emission intensity or the level of pollution-abatement costs as a fraction of value added are used in estimating output levels. Assuming that environmental regulation and compliance costs are increasing more rapidly in the developed economies (the 'North') than in the lower income economies (the 'South'), evidence of a rising share of pollution intensive output or investment in the South is taken as confirmation of the pollution haven hypothesis. An additional limitation has been that due to a paucity of data on international investment flows over time, most empirical studies have relied on FDI flows by US transnational corporations.

The methodology and data used in this study are intended to address a number of these difficulties. In particular, the model that is used allows for the effect of other determinants of FDI flows, in addition to the impact of environmental regulation and in this way tests for the separate impact of the pollution haven effect. Second, we use a direct measure of environmental regulation which is comparable across countries. Third, we focus on Japanese outward FDI, rather than US data, in recognition of the importance of Japan as one of the world's largest outward investors 3 .

3 Although there has been a large number of empirical studies on the determinants of Japanese FDI (e.g. [START_REF] Cassidy | Spatial determinants of Japanese FDI in China, Japan and the World Economy[END_REF][START_REF] Farrell | Determinants of Japan's foreign direct investment: An industry and country panel study, 1984-1998[END_REF]In-Mee and Ozawa 2001;[START_REF] Co | Japanese FDI into the U.S. automobile industry: An empirical investigation[END_REF], very few have examined the relationship with environmental regulations. An exception is the study by [START_REF] Friedman | What Attracts Foreign Multinational Corporations? Evidence from Branch Plant Location in the United State[END_REF] who find that Japanese FDI in the U.S. choose to locate in regions with relatively lax environmental regulations. However, this study is restricted to Japanese FDI inflows to the U.S., and covers the earlier period 1977 to 1988.

Methodology

Following the approach used in the recent literature (see, for example, [START_REF] Bartik | The Effects of Environmental Regulation on Business Location on the United States[END_REF][START_REF] Levinson | Environmental Regulations and Manufacturers' Location Choices: Evidence from the Census of Manufactures[END_REF][START_REF] List | The Effect of Environmental Regulation on Foreign Direct Investment[END_REF][START_REF] Mcconnell | The Impact of Environmental Regulation on Industry Location Decisions: The Motor Vehicle Industry[END_REF], an industry is assumed to have a latent (unobserved) profit function that is dependent on the characteristics of the country in which it locates:

) , (

j j ij o e F =
(1) ij represents the latent profit that could be earned by firm i in country j , and e j is a measure of the stringency of the country j 's environmental regulations. Other observable country characteristics that affect the location decision are represented by o j . A conditional logit model can be used to represent the firm's location choice if the firm selects the country location at which its profit would be maximized4 . Profits for firms i at location j are given by:

ij j ij X µ + = ' (2)
where X j = (e j , o j ) is a vector of country characteristics that affect the firm's costs and accrued revenues from product sales. is a vector of estimated parameters and u ij is the random error component. It is generally acknowledged that if the u ij in equation ( 2) follow a Weibull distribution and are independently and identically distributed, the probability that country j maximizes profits for firm i can be represented by equation (3).

) ' exp( / ) ' exp( ) ( 1 = = K k k j X X ij P (3)
where K represents the total number of possible countries. In the empirical work that follows, the maximum likelihood is used to estimate parameter . However, while equation ( 3) has been widely used in the literature, the 'independence of irrelevant alternatives (IIA)' restriction may apply to the predicted probabilities under the assumption that the error term in equation ( 2) is independently and identically distributed Weibull 5 . This becomes a problem since it assumes that, for example, a foreign firm's decision not to locate in Germany is independent of its decision to reject the UK and the Netherlands. This paper mitigates this problem by including region dummies, as in the studies by [START_REF] Bartik | The Effects of Environmental Regulation on Business Location on the United States[END_REF], [START_REF] Levinson | Environmental Regulations and Manufacturers' Location Choices: Evidence from the Census of Manufactures[END_REF] and [START_REF] List | The Effect of Environmental Regulation on Foreign Direct Investment[END_REF]. If the error terms are collated within regions and not across regions, the region dummies will capture this correlation and reduce the IIA problem.

Data Description

Foreign Direct Investment (FDI)

Data on Japanese FDI are taken from the Kaigai shinsyutsu kigyo soran (Foreign Investing

Companies Profiles) by [START_REF] Keizai | Changing Patterns of Trade in World Industry: An Empirical Study on Revealed Comparative Advantage[END_REF]. These data are based on a questionnaire survey, distributed to all listed and non-listed companies at the end of October 1997. The 5 An alternative assumption would be that FDI first selects a region and then a country within the region. This would require the use of a nested logit model. We are grateful to the referee for drawing out attention to this point. criterion for the inclusion of firms in the dataset is if the firm has two or more companies with more than 20 per cent of the shares abroad. The criteria for FDI cover newly established and merges and acquisition. In other words, if a firm has more than 20 per cent of the shares in two or more companies and has FDI through either newly established or merges and acquisition, it then gets a 1. In any other case, it gets a 0. The period covered is from 1992 to 1997. The industries observed are iron and steel, non-ferrous metals, industrial chemicals, paper and pulp, and non-metallic mineral products. Based on US emissions data for air, water and metal discharges, these industries are among the top ten industries in terms of actual emission intensity for overall pollutants and are commonly classified as dirty industries [START_REF] Copeland | Trade, Growth and the Environment[END_REF]Mani and Wheeler, 1997). It is expected that they will be sensitive to changes in environmental regulation, and they are therefore widely used in cross country studies as a proxy for pollution data.

Environmental Regulations (ER)

In an attempt to overcome the problems of obtaining reliable cross country data on the extent and stringency of environmental regulation, this study uses a measure of environmental regulation based on participation in international environmental treaties 6 . The measure has the advantage of permitting cross national comparisons of environmental regulations in a systematic and quantitative fashion. The measure uses participation information on five international treaties: the Framework Convention on Climate Change; the Vienna Convention for the Protection of the Ozone Layer; the Montreal Protocol for CFC Control; the United Nations Convention on the Law of Sea; and the Convention on Biodiversity, over the period 1982 to 1997 7 . We assume that the level of compliance and enforcement will increase over time [START_REF] Chung | Host koku no zeisei ga nichibei no taigai chokusetsu toshi ni oyobosu koka, [The Impact of Host Countries' Taxation on Japanese and American Foreign Direct Investment[END_REF]). If, for example, a country has participated in a particular treaty for five years prior to 1992, it will be given a score of six for 1992. These annual scores are then aggregated for each international treaty the country belongs to, and the total is taken as a measure of the stringency of that country's environmental regulations 8 .

Other Determinants of FDI

6 Participation in international environmental agreements is also used as a measure of environmental stringency in [START_REF] Busse | Trade, Environmental Regulations and the World Trade Organization: New Empirical Evidence[END_REF] and [START_REF] Smarzynska | Pollution Havens and Foreign Direct Investment: Dirty Secret or Popular Myth?[END_REF].

7 National participation information for these five treaties is provided in World Bank (2000), World Development Indicators. 8 We accept the referee's comment that the stringency of environmental control may vary between different treaties. However, we do not have the information that would allow us to make this differentiation in the variable. A wide variety of variables have been used in the literature as possible determinants of inward FDI flows, although as noted by [START_REF] Globerman | Global Foreign Investment Flows: The Role of Governance Infrastructure[END_REF], surprisingly few are consistently significant across the broad spectrum of studies that have been reported in the literature9 .

Market Size

One variable that has been found consistently to be a significant determinant is a measure of the size of the host country, confirming that market size as an important determinant of FDI [START_REF] Cheng | What are the Determinants of the Location of Foreign Direct Investment? The Chinese Experience[END_REF][START_REF] Culem | The Locational Determinants of Direct Investments among Industrialised Countries[END_REF][START_REF] Cushman | The Effect of Real Wages and Labour Productivity on Foreign Direct Investment[END_REF][START_REF] Loree | Policy and Non-Policy Determinants of U.S. Equity Foreign Direct Investment[END_REF][START_REF] Moore | Determinants of German Manufacturing Direct Investment: 1980-1988[END_REF][START_REF] Schneider | Economic and Political Determinants of Foreign Direct Investment[END_REF][START_REF] Smarzynska | Pollution Havens and Foreign Direct Investment: Dirty Secret or Popular Myth?[END_REF][START_REF] Wheeler | International Investment Location Decisions: The Case of U.S. Firms[END_REF] 10 . In this study, we use real GDP (in constant 1995 U.S. dollars) as a measure of the level of income and demand in the economy. The data are from the World Bank (2001).

Labour Costs

Labour force characteristics have been widely used as explanatory variables in empirical studies of FDI, with a range of different measures being used in the literature, including, wage rates, skills level, and educational achievement. The hypotheses tested have varied, and on occasion, been competing. In the earlier literature, low wage, unskilled labour was seen as being attractive to FDI, particularly to export-oriented, labour intensive assembly activities. More recent literature has stressed the quality of human capital, as measured by education attainment or health status. The empirical evidence on the influence of the labour force variable is not clearcut, and in a number of studies it has been found to be either statistically insignificant or appears with the 'wrong' sign in regression equations [START_REF] Altomonte | Economic Determinants and Institutional Frameworks: FDI in the Economies in Transition[END_REF]Stein and Daude, 2003). In this study, we follow [START_REF] Smarzynska | Pollution Havens and Foreign Direct Investment: Dirty Secret or Popular Myth?[END_REF] and use GDP per capita as a proxy for unit labour costs. Other things being equal, we expect to find that higher wage costs will discourage foreign direct investment. The data are provided in World Bank (2001).

Distance

According to [START_REF] Chung | Nihon no chokusetsu toshisakikoku no ukeire seisaku kettei yoin nitsuiteseizogyo no sogyo kyoka joken nikansuru jissho bunseki, [The Determining Factors of Host Country's Policy for Japanese Foreign Direct Investment[END_REF], the further a host country is from the parent company, the higher the cost of shipping and communications. Other things being equal, we therefore expect that distance will have a negative impact on the locational choice for FDI. This has been confirmed in a number of recent empirical studies, for example, the [START_REF] Smarzynska | Pollution Havens and Foreign Direct Investment: Dirty Secret or Popular Myth?[END_REF] FDI and MITI (1993) for Japanese FDI flows. This study uses the distance from Tokyo to the capital of each country 11 .

Regional Dummy

In order to overcome the IIA issue raised in section 2, regional dummy variables were included in the estimation equation. The regions are: Asia Pacific (AP); Europe and Central Asia (EC);

Latin America and Caribbean (LA); Middle-East and North Africa (MA); North America (NA); and Sub-Sahara Africa (SA).

The descriptive statistics for each dirty industry's independent variables are summarised in Appendix A. The correlation matrices for the independent variables of each dirty industry are presented in Appendix B. The results of the correlation matrix do not show a significant degree of correlation between any of the independent variables.

Results

The estimated results for the conditional logit model are summarised in Tables 1 and2. We first report the results for all countries, and then consider the developing country results separately 12 . First, analyzing the results for the environmental regulation variable, we find the coefficients for each of the five industries are positive and highly significant in both the all country and developing country samples. This is contrary to the environmental haven hypothesis and suggests that, other things being equal, Japanese firms in dirty industries are choosing regions with more stringent environmental regulations as opposed to regions with lax environmental regulations as a location for their FDI. This supports the results reported in the studies by [START_REF] Mcconnell | The Impact of Environmental Regulation on Industry Location Decisions: The Motor Vehicle Industry[END_REF] and [START_REF] Smarzynska | Pollution Havens and Foreign Direct Investment: Dirty Secret or Popular Myth?[END_REF], which suggest that firms do not deliberately choose regions with relatively lax environmental regulations in order to reduce environmental compliance costs. Our results are also consistent with the argument that dirty industries will prefer to choose regions with more stringent environmental regulations, since these regions will have a high quality of environment in place and therefore will not require firms to invest in improvements to the general environmental infrastructure (Adam,1997;OECD, 1997). The results may also imply that firms have become more aware of environmental issues on a global scale during the post-Earth Summit period between 1992 and 1997, a view that has been confirmed by numerous surveys (see, for example, [START_REF] Amuro | Empirical Study Concerning the Environmental Management/ Audit in Japanese Multinational Corporations[END_REF][START_REF] Ando | Chance ga ippai Ecobusiness[END_REF][START_REF] Letchumanan | Reconciling the Conflict between the Pollution-Havens Hypothesis and An Emerging Trajectory of International Technology Transfer[END_REF].

F

Concerning the other FDI determining factors, the market size represented by GDP is positively signed as predicted, and is statistically significant in all cases other than the paper and pulp industry. This suggests that, other things being equal, FDI in these industries is attracted to host countries that have a large market. This supports the studies by Chen (1992), MITI (1993MITI ( , 1994) ) and [START_REF] Mito | India eno chokusetsu toshi doko, [Foreign Direct Investment and India[END_REF] on Japanese FDI, as well as studies of FDI in general, which have identified market size as a determinant of FDI (see [START_REF] Cheng | What are the Determinants of the Location of Foreign Direct Investment? The Chinese Experience[END_REF][START_REF] Loree | Policy and Non-Policy Determinants of U.S. Equity Foreign Direct Investment[END_REF][START_REF] Moore | Determinants of German Manufacturing Direct Investment: 1980-1988[END_REF][START_REF] Smarzynska | Pollution Havens and Foreign Direct Investment: Dirty Secret or Popular Myth?[END_REF][START_REF] Wheeler | International Investment Location Decisions: The Case of U.S. Firms[END_REF].

Concerning unit labour costs, as proxied by GDP per capita, the coefficient is not statistically significant ( except for non metallic products), suggesting that wage costs wage costs are not a significant determinant of FDI by Japanese firms in these dirty industries. Finally, distance to the host country is shown to have a statistically significant (at the 1 per cent level), negative effect. As [START_REF] Chung | Nihon no chokusetsu toshisakikoku no ukeire seisaku kettei yoin nitsuiteseizogyo no sogyo kyoka joken nikansuru jissho bunseki, [The Determining Factors of Host Country's Policy for Japanese Foreign Direct Investment[END_REF] points out, this may reflect the impact that distance from the parent (head) company has on the cost of shipping and communications. Similar results are reported for Japanese firms in the study by MITI (1993), and in [START_REF] Smarzynska | Pollution Havens and Foreign Direct Investment: Dirty Secret or Popular Myth?[END_REF] for global FDI flows.

We also examined the flow of Japanese FDI in dirty industries, where the sample of host countries is restricted to developing countries. The purpose is to examine whether Japanese FDI to the South behaves similarly to global flows, particularly with respect to the environmental policy regime of the host country. Table 2 shows the results for Japanese FDI only to developing countries. The results show that stringency of environmental regulations has a significant and positive impact on locational decision-making of Japanese FDI within developing countries for each of the five dirty industries. This finding is contrary to the 'race to the bottom' hypothesis which is frequently advanced in the context of developing countries.

The results for the other determinants are in general consistent with those reported in Table 1 for the all countries sample. Market size has a statistically significant and positive effect for all dirty industries, and distance has a negative impact in each industry (although the coefficient is statistically significant only in the case of the non-ferrous metals). The wage variable is not a significant determinant (except for chemicals) and fails to display a consistent sign pattern across the industries. Equation (3) explained the predicted probability of a firm choosing a region under the conditional logit model specification. We can use equation ( 4) 13 to interpret the size of the coefficient.

)

1 ( / ) ( k k k p P X ij P = (4)
This represents a coefficient as dependent on the characteristics of the region being analyzed.

To understand these coefficients in context, Table 3 shows the percentage change in the probability of any one firm locating in a country with average characteristics, resulting from an increase in each of the listed parameters by one standard deviation 14 .

13 See [START_REF] Greene | Econometric Analysis[END_REF] for details. [START_REF] Mcconnell | The Impact of Environmental Regulation on Industry Location Decisions: The Motor Vehicle Industry[END_REF] use the same approach in their empirical study.

14 For example, the Iron and Steel results suggest that, increasing the value of the ER Index from 9 to16, while holding all of the other parameters at their averages, would mean a 0.946% increase in the probability that a firm would choose to invest in the hypothetical average country. 1) is significant at 1% ** Underlying coefficient (Table 1) is significant at 5% * Underlying coefficient (Table 1) is significant at 10%

The predicted percentage change in the probability of locating in a country with average characteristics as a result of standard deviation increase in each independent variable: all countries When examining the cross industries sampled here, this result suggests that the paper and pulp, chemicals and non-ferrous industries, which are all resource based industries 15 , are more inclined to undertake FDI in regions with more stringent environmental regulations compared to the non-resource based industries 16 . Regions with more stringent environmental regulations are likely to have developed an environmental infrastructure, which provide FDI firms with certain benefits. Some examples are: less risk of environmental scandals by complying with regulations, less risk of the liability of cleaning up for past environmental damages by previous businesses, and a higher quality environment for living and health for its workers as well as for the local people (Adams, 1997;OECD,1997). Compliance with environmental standards may also induce technological change which improves the competitiveness of firms (Porter and van der Linde, 1995).The nature of the resource based industries lead to little product differentiation and therefore is likely to suffer from the difference of environmental costs.

Therefore, the cost savings in environmental costs and lower risks in stringently regulated countries may be more attractive to these industries. Environmental costs such as those identified above may form a large part of the total potential environmental costs incurred and there may therefore be a cost saving in undertaking FDI in host countries with more stringent environmental regulations. The iron and steel industry and non-metallic products industry 15 For further details on the distinction between the categories in resource-and non resource based industries, refer to UNIDO (1982). [START_REF] Van Beers | An Empirical Multi-Country Analysis of the Impact of Environmental Policy on Foreign Trade Flows[END_REF] also make this distinction. 16 We tested for the non-linearity of the relationship by adding ER squared as an additional variable. The results were less significant and confirmed the superiority of the linear specification. which are non-resource based industries, were found to be more likely to undertake FDI in regions with larger market size.

When examining cross independent variables in Table3, the dirty industries examined here are more inclined to be influenced by environmental regulations and distance rather than market size and wage. This may simply be that environmental regulations are more influential factors for dirty industries because they will be more affected by environmental costs than non-dirty industries. Concerning distance, since pollution intensity is positively related to capital intensity (e.g. [START_REF] Antweiler | Is Free Trade Good for the Environment?[END_REF][START_REF] Cole | FDI and Capital Intensity of 'Dirty' Sectors: A Missing Piece of the Pollution Haven Puzzle[END_REF][START_REF] Cole | Characteristics, Environmental Regulations and Air Pollution: An Analysis of the UK Manufacturing Sector[END_REF], we can draw the conclusion that for dirty industries, the transportation costs of capital products are an important factor affecting FDI.

With regard to Japanese FDI decision-making within developing countries, in terms of magnitude of the coefficient, Table 4 shows that the stringency of environmental regulations are the most important factor for dirty industries sampled here, similar to the all countries case.

When examining non-ferrous metals industry, where environmental regulations, market size and distance are statistically significant, distance is the second most influential factor. Since dirty industries are more inclined to be capital intensive, indicating that transaction costs for capital goods will be expensive and that environmental costs will tend to burden more heavily on the dirty industries, the stringency of the environmental regulations and distance are important determinants factors for Japanese FDI decision. When examining the results cross industries, Table 4 shows that environmental regulations have an impact on Japanese FDI decision-making in developing countries for resource based industry rather than non-resource based industry. Market size has a weaker influence on FDI decision-making for non-resource based industry than resource based industry. 2) is significant at 1% ** Underlying coefficient (Table 2) is significant at 5% * Underlying coefficient (Table 2) is significant at 10%

The predicted percentage change in the probability of locating in a state with average characteristics as a result of standard deviation increase in each independent variable: developing countries

Conclusion

The pollution haven hypothesis, which predicts that dirty industries will relocate their production activities to regions where environmental compliance costs are lower, has received considerable attention in recent years. Empirical testing has failed however, to produce robust evidence in support of the 'flight to the bottom' hypothesis. A number of alternative explanations of the ambiguous and sometimes contradictory nature of the empirical evidence have been proposed. It may be that the impact of different levels of compliance costs on the FDI location decision is out-weighted by the effect of the other determinants of FDI flows.

Furthermore, there is the argument that the quality of the general environmental conditions is likely to be higher in regions with stringent environmental regulations. In so far as this may reduce the investment that firms will have to make in environmental improvements and lower the risk of having to clean up for past environmental damages, dirty industries would prefer to locate to such regions.

This paper has analysed the pattern of FDI by Japanese dirty industries in the 1990s. The methodology used in this analysis is the conditional logit model which can be applied to the non-ordered dataset which represents the choice-specific attributes of the location choices of Japanese firms. The results showed that for each of the five dirty industries examined, firms were found to be undertaking FDI in regions with more, rather than less, stringent environmental regulations. Very similar results were found for the case of Japanese FDI in In addition to the host countries' environmental regulations, this paper has found that Japanese FDI is dependent on various locational factors. . The host countries' market size and the distance between Japan and the host countries were both statistically significant determinants of firms' choice of location for dirty industry DFI. In contrast, host country wage costs did not appear to have a significant effect on Japanese FDI.

Environmental regulations (and distance) were shown to have more impact on Japanese FDI decision-making than market size and wage costs. This can be due to the industries observed being dirty industries which are strongly affected by environmental regulations. Also, since pollution intensity is positively related to capital intensity, transaction costs such as import cost of capital products are crucial factors effecting FDI. The environmental regulations generally had larger impact on Japanese FDI decision for resource based industries compared to non-resource based industries, which may be explained by the limited product differentiation in resource based industries which limits the option of responding to differential environmental regulations by a change in technology.

In conclusion, the pattern of Japanese FDI in dirty industries during the 1990s, did not conform with the pollution haven hypothesis, whereby weak environmental regulation in a host country may attract inward FDI by firms seeking to circumvent regulatory compliance. On the contrary, inward Japanese FDI appears to have been attracted to countries which have committed themselves to a transparent and stable environment regulatory framework, as demonstrated by through their participation in international environmental agreements. This is consistent with the general literature on FDI which shows that regulatory stability, consistency and transparency are at least as important as the level of the regulatory measures, in influencing an investor's choice of location for foreign investment. Policy makers' fears of a race to the bottom can be allayed therefore, and need not act as a deterrent to the progressive strengthening of environmental standards.

Table 1 .

 1 Environmental Regulation and Japanese FDI Location Choice: All Countries

						Non-
			Non-ferrous		Paper and	metallic
		Iron and steel	metals	Chemicals	Pulp	products
	ER	0.173 ***	0.187 ***	0.212 ***	0.363 ***	0.178 ***
		[0.387]	[0.028]	[0.023]	[0.074]	[0.030]
	Market size	0.104 ***	0.081 ***	0.054 ***	0.012	0.110 ***
		[0.036]	[0.021]	[0.011]	[0.014]	[0.029]
	Wage	-0.287	-0.008	0.005	-0.018	-0.030 **
		[0.208]	[0.124]	[0.094]	[0.025]	[0.015]
	Distance	-0.248 ***	-0.361 ***	-0.043 ***	-0.058 ***	-0.035 ***
		[0.083]	[0.057]	[0.004]	[0.012]	[0.006]
	Log-likelihood	-219.503	-414.663	-623.776	-92.264	-310.855
	Pseudo R^2 No.of obs.	0.388 o r 0.397 9964 19120	0.384 28110	0.448 4653	0.409 14644
	Standard errors in parentheses *** Statistically significant at 1%	P
	** Statistically significant at 5% * Statistically significant at 10%	e r e
				R e v
				i e
					w

Table 2 .

 2 Environmental Regulation and Japanese FDI Location Choice: Developing Countries

						Non-
			Non-ferrous			metallic
		Iron and steel	metals	Chemicals	Paper and Pulp	products
	ER	0.173 ***	0.161 ***	0.173 ***	0.233 ***	0.1366 ***
		[0.044]	[0.032]	[0.028]	[0.086]	[0.037]
	Market size	0.219 ***	0.190 ***	0.286 ***	0.281 **	0.328 ***
		[0.075]	[0.059]	[0.053]	[0.014]	[0.069]
	Wage	-0.044	0.122	0.307 ***	-0.387	0.030
		[0.234]	[0.147]	[0.011]	[0.485]	[0.189]
	Distance	-0.082	-0.229 **	-0.116	-0.308	-0.051
		[0.121]	[0.098]	[0.085]	[0.00025]	[0.109]
	Log-likelihood	-138.080	-237.533	-330.134	-44.556	-179.836
	Pseudo R^2	0.415	0.497	0.525	0.607	0.536
	No.of obs.	5629	11299	16628	2721	9274
	Standard errors in parentheses
	*** Statistically significant at 1%
	** Statistically significant at 5%
	* Statistically significant at 10%

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

The conditional logit model is appropriate when the data consist of choice-specific attributes. This model is widely used when three or more dependent variables are not consecutively ordered(Green, 2000;[START_REF] Mcfadden | Conditional Logit Analysis of Qualitative Choice Behaviour[END_REF]

[START_REF] Dunning | Multinational Enterprises and the Global Economy[END_REF] discusses these various factors and discusses the empirical evidence regarding their impact on FDI flows.

This also holds for studies of Japanese FDI[START_REF] Chen | Determinants of Taiwan's Direct Foreign Investment: The Case of a Newly Industrialising Country[END_REF] MITI, 1993 MITI, , 1994;;[START_REF] Mito | India eno chokusetsu toshi doko, [Foreign Direct Investment and India[END_REF] Economic Planning Agency, Japan, 1993[START_REF] Dunning | Multinational Enterprises and the Global Economy[END_REF].

The main

data used were taken from CASIO

World Bank (2000), World Bank Indicators, Washington, D. C., World Bank. World Bank (2001), World Bank Indicators on CD-ROM, Washington, D. C., World Bank.