UPPA, CATT Fabien Candau
email: fabien.candau@univ-pau.fr

Elisa Dienesch

Simone Bertoli

Antoine Bouët

Pierre-Philippe Combes

Matthieu Crozet

José De Sousa

Carl Gaigné

Michaël Goujon

Jacques Le Cacheux

Frédéric Serge Rey

François-Charles Wol

POLLUTION HAVEN AND CORRUPTION PARADISE

Keywords: Multinational rms, Environmental Regulation, Europe, Corruption, Market Access, Trade. JEL: F12, Q5, Q53

In this paper, we propose a new perspective to analyze the impact of institutions, environmental standards, and globalization on relocations of polluting rms in countries with lax environmental regulation (called pollution havens). Via a simple theoretical extension from the Economic Geography literature, we characterize the main features of pollution havens: a good market access to high-income countries and corruption opportunities. Using structural and reduced-form estimations, we analyse these determinants by exploiting a unique database on the number of European aliates located abroad. A 1% increase in access to the European market from a pollution haven fosters relocation there by 0.1%. We also nd that corruption in these countries lowers environmental standards, which strongly attract polluting rms: a 1% increase in this indirect eect of corruption fuels relocation by 0.28%. We test the economic signicance of these empirical ndings via simulations. The protection of the European market (e.g., a carbon tax on imports) to stop relocations to pollution havens must be high (a decrease of the European market for Morocco and Tunisia equivalent to 13%) not to say prohibitive (31% for China).

Introduction

Although existing studies suggest little or no evidence of industrial relocation, arguments over pollution havens persist. Why? [START_REF] Eskeland | Moving to greener pastures? Multinationals and the pollution haven hypothesis[END_REF] In the early 2000s, it was quite common to dismiss the existence of pollution havens by underscoring that there is no evidence of this hypothesis. Laxer environmental standards could not signicantly explain the location choice of polluting plants. However, failure to reject the null hypothesis does not ensure that the null hypothesis is always true, and a growing number of articles have successfully found this eect for inward, outward, and outbound foreign direct investments (FDI) in the context of the United States.

1 While the discussions on PHH have thus far relied to a great extent on data from the United States, a similar analysis for Europe has been neglected. This is surprising since the European environmental policy has been quite active over the past few years. Furthermore, Europe and its neighborhood have changed; post-communist economies (central and eastern European countries, Russia, and China) as well as partners in Maghreb (e.g., Tunisia and Morocco) have now reached an intermediate level of bad governance -good enough to conduct business (e.g., without risk of expropriations) but still poor enough to allow businesses to pollute with unspoken license. Lastly, access to the European market has been vastly improved thanks to multilateral, regional, and preferential trade agreements, which make relocation outside Europe and/or at its periphery less costly. As a result, Europe is the perfect eld to analyze the PHH and its interaction with governance and trade integration. 2

Regarding the literature on the PHH, this hypothesis has been viewed as a popular myth (Smarzynska [START_REF] Javorcik | Pollution Havens and Foreign Direct Investment: Dirty Secret or Popular Myth? The B[END_REF] for various reasons. The main two arguments are that environmental costs represent only a small part of the costs being considered when deciding where to locate factories, and that polluting industries are capital-intensive meaning that 1 Excellent considerations of the literature have been made by [START_REF] Brunel | Measuring Environmental Regulatory OECD Trade and Environment Working Papers[END_REF], who discuss measures of environmental regulatory stringency, and in [START_REF] Millimet | Empirical Tests of the Pollution Haven Hypothesis When Environmental Regulation is Endogenous[END_REF], who present empirical tests of the PHH, as well as the econometric biases and methodological issues for this type of analysis. Lastly, [START_REF] Rezza | A meta-analysis of FDI and environmental regulations[END_REF] presents a meta-analysis.

2 While data on the United States have been intensively used to analyze the PHH, studies on Europe have they are the comparative advantage of industrialized economies and are hard to relocate.

Countries with stringent environmental rules have advantages that overtake the environmental cost (e.g. better infrastructures, larger market size, better endowment in human capital etc) while countries with lax environmental standards have repulsive characteristics such as poor institutions and bad governance which represent a cost for multinational rms.

However, by improving market access to developed countries from pollution havens, globalization erodes the advantage of locating plants close to the point of consumption. Furthermore, a pollution haven can be a dirty secret in the sense that bribes may allow polluting rms to obtain laxer environmental rules in the law and/or in its enforcement.

Our model, which is based on [START_REF] Fujita | Globalization and the evolution of the supply chain: Who gains and who looses?[END_REF], displays these eects. When bad governance has no eect on environmental standards, corruption is only a cost for multinational rms and deters relocation to pollution havens. By contrast, the PHH is always veried under what we call the Corruption Paradise Hypothesis, hereafter CPH, which assumes that corruption allows lax environmental standards. Under this hypothesis, corruption may be an opportunity for a rm, through its indirect impact on regulation. The model also explains why the existence of PHH is hard to conrm since it depends very much on market access; explaining that no relocation is observed until a critical threshold of trade integration is reached.

We regress the number of European aliates on bilateral access from the destination towards the origin, and on market access from the origin country and from the destination. [START_REF] Bazillier | Is Reputation at Stake When Environmentally Responsible Multinationals Invest Abroad? An Empirical Investigation[END_REF] First, the results reveal a positive eect of the bilateral market access oered by each European member. We also nd a positive eect of market access from the destination (European partners) towards the rest of the world, and a negative eect of market access from European countries themselves. Thus, rms are motivated to reach new markets through the market potential oered by the partner while a good market access from Europe retains activities. This is a multilateral gain provided by the destination. Relocation is also clearly motivated by the market potential oered by European nations themselves in their own markets, matching the fragmentation process and the need to re-import cheaper inputs from abroad.

Various articles have contributed towards understanding the impact of trade liberalization on the specialization and economic behavior of multinational rms, 6 and many others have demonstrated the signicance of the market access towards explaining various economic variables.

7 However, our analysis on the impact of market access on relocation of polluting rms is novel to the literature.

The literature on corruption and relocation of dirty rms is more developed. Damania, Fredricksson and List (2003), in particular, nd evidence that corruption reduces the stringency of environmental policies. We go a step further by showing that this indirect eect attracts polluting rms. Our result is thus related to that of [START_REF] Cole | Institutionalized Pollution Havens[END_REF] who are the rst shed light on institutions with poor environmental governance, by observing that pollution havens are more likely to occur in countries having institutional structures with few legislative units (e.g., parliaments, congress, government parties, prime minister, and president). While these authors' main interest lies in assessing whether foreign rms that lobby and bribe host country governments succeed in obtaining laxer environmental regulations owing to the characteristics of the country's political institutions, we aim to capture the pros and cons of corruption for polluting rms. Instead of analyzing the net eect of bad governance, we estimate the direct and negative eect of corruption, as well as its indirect and protable eect.

Challenging the potential endogeneity of environmental regulation and the ambiguous role of corruption on location choices with an original two-step procedure, 8 we nd evidence of the existence of the CPH: corruption indirectly increases the number of relocations of polluting rms to pollution havens. 9

Finally, to evaluate the economic signicance of our results and to formulate policy implications, we simulate scenarios of environmental policy cooperation. The results show that the harmonization of environmental norms in Europe could reduce relocation by 6%. We also evaluate the equivalent variation of market access needed to get more stringent environmental standards for some partners without aecting their attractiveness. For instance, we nd that given the current level of relocation, a carbon tax reducing the European market by 13% for rms operating in the Maghreb erases the advantage provided by the gap in environmental regulation.

The paper proceeds as follows. Section 2 outlines the theoretical model and explores the relationship between trade, regulation and choices of location. Section 3 presents the overall empirical strategy, and Section 4 reports the results. In Section 5, we conduct simulations using previous estimates. Section 6 concludes the paper.

Theoretical model

To analyze the evolution of the supply chain in the case of pollution havens, our model is a slightly modied version of Fujita and Thisse's (2006) model 10 in order to improve the assessment of the complementarity between relocation of rms to pollution havens and trade integration. In this model of monopolistic competition and increasing returns, trade ows and capital ows are not always substitutes. This allows us to arrive to novel results concerning the location choices of polluting rms in comparison with neoclassical models (see [START_REF] Anderson | Trade and Environment. Book Review[END_REF], for a discussion). We start the description of this model by individual preferences.

Each individual consumes a basket M of dierent industrial varieties z and an agricultural good A. 11 The utility function is represented by a Cobb-Douglas Constant Elasticity of 9 All the results are robust to a number of checks, identication tests, and weak IV tests. 10 Fujita and Thisse (2006) analyze the evolution of the international fragmentation of supply chains due to wage dierentials in the context of trade and coordination integration between the North and the South. A simple change in the cost function of this model is a good t with the issue at hand. Instead of considering the wage dierential (weighting on the variable costs, as done by [START_REF] Fujita | Globalization and the evolution of the supply chain: Who gains and who looses?[END_REF], we analyze how rms slice up their supply chain in response to international gaps in environmental standards and bad governance. [START_REF] Bueno De Mesquita | Change and Revolutionary Entrepreneurs[END_REF] We do not introduce disutility from consuming the industrial goods, which are also the polluting goods, mainly because we are not interested in the welfare impact of pollution or changes in consumption. Such an introduction is easily accomplished with an additive term (as done by [START_REF] Markusen | Competition in regional environmental policies when plant locations are endogenous[END_REF], and it is interesting to analyze the welfare impact of the relocation of polluting rms in cases of global and/or local pollution, and when not in my backyard policies prevail.

Substitution (CES) utility function:

U = M µ A 1-µ , M = ˆn 0 q(z) σ-1 σ dz σ σ-1 µ ∈ [0, 1], σ > 1, (1)
where n is the number of varieties consumed, q(z) is the demand of a variety z and σ is the constant elasticity of substitution among these varieties. A share µ of nominal income, denoted as Y , is spent on manufacturing, and 1-µ is spent on the agricultural product. The budget constraint is then given by P M + p A A = Y , where p A is the price of the agricultural good and P the price index of varieties:

P = ˆn 0 p(z) 1-σ dz 1 1-σ , (2)
with p(z) being the price of a typical variety z. The total demand to a rm producing a variety z is then given by: q(z) = µY P σ-1 p(z) -σ .

(

Varieties are exchanged between countries under transaction costs which take the form of iceberg costs denoted by τ . The agricultural good is costlessly traded between countries, and its price is the same everywhere. This good is the numéraire and its price is set to unity.

Each rm has one headquarters (HQ) and one plant. Skilled workers are employed at the HQs and unskilled workers, at the plants. The aim of this paper is to analyze the location choice of these plants. The model is analyzed under a condition ensuring the agglomeration of HQs/skilled-workers in some countries (more details on this condition appear below).

This concentration of the demand represents an agglomeration force with regard to plants' locations, and goods are produced near the points of demand allowing savings on trade costs.

In opposition to this eect, we introduce a dispersion force by considering that environmental standards are stricter in these rich markets. Lastly, to take into account that pollution havens can be repulsive for rms because of inecient institutions, we consider corruption costs.

Formally, the total cost of producing q units of a typical manufactured goods varies according to the location of its HQ and plant in country i or j:

T C ij (z) = c j + f i w H i + v i e j w L j q(z), z ∈ [0, n], c j ∈ [0, 1], e j 1, (4)
with j = i for national rms, that is, rms that cluster HQs and plants in the same country, and i = j for multinational rms that spatially unpack factories and oces. f and v represent the requirement of unskilled and skilled labor determined by the HQ in i. Concerning environmental standards, e i , we assume that they aect the variable costs of production. This is a classical assumption considering that the expenditures on control and monitoring equipment necessary to bring a plant into environmental compliance varies with production.

In addition, we assume that the xed cost of production, denoted as c i , bears corruption costs that vary across countries. This xed cost can be viewed as the number of procedures, amount of time, and bribes that a rm must bear before it can operate legally. These costs of entry were rst documented by Djankov, La Porta, Lopez-de-Silanes and Shleifer (2002) and have been used in various studies as a proxy for the bureaucratic and corruption-related costs of starting a business. 12

Under the assumptions of Dixit-Stiglitz monopolistic competition, by maximizing prots, a domestic rm sets a single free-on-board (f.o.b) price, p * i (z) = w L i σv i e i /(σ -1), while a multinational rm with its HQ in i and its plants in j sets the factory to p * j (z) = w L j σv i e j /(σ-1). Under iceberg trade costs, τ ij , the sale prices in dierent countries are p * ij (z) = τ ij p * i (z).

Thus, prices dier spatially owing to reasons of geography (trade costs), production eciency, and environmental standards.

Using the demand expressed by (3), prices and trade costs, the equilibrium prot of a rm clustering its HQ and plant in i is given by:

π * ii = p 1-σ i M A i -c i -f i w H i , i = j (5)
with

M A i = M Ad i + M Af i , i = j (6)
where

M Ad i = Y i P 1-σ i τ 1-σ ii , M Af i = j M Ab ij , M Ab ij = Y j P 1-σ j τ 1-σ ij , i = j (7)
The term M A i is the sum of distance-weighted market capacities, and includes local sales, M Ad i which refers to domestic market access and M Af i which is the total demand of consumers located abroad. This foreign market access is the sum of bilateral market access

M Ab ij .
The prot at equilibrium of a multinational rm is:

π * ij = p 1-σ j M A j -c j -f i w H i (8)
Thus the fragmentation decision of a rm, based on a comparison of π * ii and π * ij , depends on market access from i and j as well as corruption. Since the factory price of each variety varies according to environmental norms, these standards are one of the most signicant determinants of the market potential of a country. The other obvious and crucial elements of MA are trade costs (including behind the border policies, and transportation, all other factors aecting τ ) and the size of each market ( Y /P in real terms).

So far, we have considered the HQ to be agglomerated in country i, while the plants are located in i or j depending on π ii and π ij , but the free-entry decision implies that the HQ can also be created in j. Thus, at equilibrium, we have to verify that:

max π * ii , π * ij , π * ji , π * jj = 0. (9)
While the empirical literature analyzing the impact of the market access on prots, wages and location choices 13 begins directly with these expressions (in particular ( 5), [START_REF] Ben Kheder | Environmental Regulation and French Firm Location Abroad[END_REF], and ( 7)), we go one step further by resolving the model in order to better understand how environmental norms, trade integration and bad governance aect location choices.

Results

We consider the model with two countries, North and South, designated with subscripts N and S respectively. Let us assume for now that all the HQs/skilled-workers are agglomerated in the North. We also consider that problems of bad governance are the worst in the South, that is, c S > c N , while environmental norms are stricter in the North, that is, e N > e S . There are skilled and unskilled workers in the North and only unskilled workers in the South. Then, incomes are given by:

Y N = Hw H N + w L N L N , Y S = w L S L S (10)
where L and H are the number of unskilled and skilled workers that earn w L and w H . Next, we normalize the total population of skilled and unskilled workers to one (H = 1,L N + L S = 1). The xed requirement of skilled worker to produce one variety is also set to one everywhere, f N = f S = 1.

The prot of a rm that keeps its plants in the North and those of a multinational rm that 13 See Head and Mayer (2011) for a survey.

slices up its supply chain are given by:

π N N = µ σn Y N s N + φs S ζ + φ Y S φs N + s S ζ -c N -w H N (11) π N S = µ σn ζ φ Y N s N + φs S ζ + Y S φs N + s S ζ -c S -w H N ( 12
)
with ζ ≡ (e N /e S ) σ-1 being the relative environmental norm for the north (when ζ = 1 there is no dierential in environmental standards), and φ ≡ τ 1-σ being the degree of trade openness (φ = 0 indicates a situation of autarky and, φ = 1, free trade). Lastly,

s N = n N /n
is the share of rms localized in the North. The demand of skilled labor in this economy is given by nf , and the supply of skilled workers by H, which gives when the labor market clears the mass of rms is n = H/f . Upon normalization, we get n = 1 which means that s S = 1 -s N .

Two eects drive these expressions. On the one hand, large markets generate more prots and attract plants because rms nd signicant outlets there (i.e., market-size eect). In this model, the North always has the largest market due to the presence of skilled workers, and consequently, it is intrinsically attractive. On the other hand, the concentration of plants exacerbates local competition and fosters a dispersion of activities (i.e., the market-crowding eect).

Resolving the equilibrium conditions π N N = π N S with c S = c N = 0 yields an explicit expression of the share of plants:

s * N = ζ ζ -φ 2ζφ -1 -φ 2 -µ σ (1 -φ 2 ) 2(ζφ -1) (13)
which leads us to the rst result.

Proposition 1 (Pollution Haven): Assume any given positive value of trade costs and no corruption. When the gap in environmental regulation is small enough, all plants are located in the North. When this gap increases, relocation of plants increases monotonically until full agglomeration is reached in the pollution haven.

Proof. Dierenting (13) yields:

∂s * N ∂ζ = - N U M 2(ζ -φ) 2 (ζφ -1) 2 (14)
as the denominator is always positive we focus on the numerator NUM, given by:

N U M = φ b(1 -ζ 2 )(1 -φ 2 ) + (1 -ζφ) 2 + φ(φ -2ζ) + ζ 2 with b = µ/σ (it is noteworthy that b < 1 because µ <
U M (ζ) = (1-b 2 )φ(φ 2 -1) 2 1-b+φ 2 (1+b) ) because b = µ/σ < 1.
A positive numerator entails that the derivative ∂s N /∂ζ be negative (see eq. ( 14)).

Interestingly ζ is also the critical point after which full agglomeration becomes unstable. Indeed, by resolving the equation of agglomeration in the North s N = 1 with s N given by [START_REF] Candau | Spatial distribution of skills and regional trade integration[END_REF] we can also nd ζ. We have already shown that for ζ > ζ, there is a gradual relocation of rms from the North to the South. This relocation stops when s N = 0 and resolving this equation using [START_REF] Candau | Spatial distribution of skills and regional trade integration[END_REF], gives the critical level of ζ after which there is full agglomeration in the South. This critical value is:

ζ = 1-b+φ 2 (1+b)
2φ .

We can now consider the full model under dierent numerical cases (the analytical solutions and parametrization appear in Appendix A) to analyze the eect of trade integration on relocation for a given gap in environmental standards (and vice versa). An important result of this empirical investigation is that relocation responds very dierently to trade integration depending to the gap in environmental standards. To illustrate this, Figure 1 presents the Northern proportion of plants, s * N , given by ( 19) with respect to trade integration, φ, for dierent values of environmental standards 14 .

Three dierent results are obtained depending on the extent of the gap in regulations and trade integration.

When the gap in environmental standards is considerable, trade openness leads to relocation of rms to the pollution haven. Indeed with trade integration, the size advantage of the North is too weak to retain rms, which ee to the pollution haven owing to the large gap in environmental regulation.

In the reverse case, where the gap in environmental standard is negligible, more integration leads to the agglomeration of all rms in the North. The size of the market is the main determinant of location in that case. 14 The parameters are µ = 0.4, e N = 58.9, c N = 0.01, c S = 0.02, and σ = 4. The curve denoted the small gap in environmental regulation is plotted with e S = 57.25, the intermediate case (dashed line), with e S = 57.02, and the case denoting the large gap (black line), with e S = 55. These parameters are briey discussed in Appendix A.

Lastly, when the size of the gap is intermediate, relocation to the pollution haven is protable only when trade integration is high enough, i.e. when rms do not lose their market access to the North by relocating activities in the South. It is thus necessary to empirically determine whether high-income countries can retain their activities in countries having stringent policies with regards to globalization.

So far we have considered a general case in which bad governance represents a cost for multinational rms when they decide to produce in the South. Therefore, it is trivial to observe that an higher level of corruption in the South (compared to the North) inevitably reduces relocation to the pollution haven (see Eq. ( 8)) in our model. While several studies have found that corruption in a host country is indeed a signicant deterrent to inward FDI, there are arguments against this proposition concerning polluting rms (e.g. [START_REF] Cole | Institutionalized Pollution Havens[END_REF]. Corruption can grease the wheels of the fragmentation process by providing a way to negotiate less stringent environmental standards. We consider this mechanism by assuming that environmental costs can be manipulated by a dierent kind of corruption, c comp :

e i ≡ E i -c comp i ( 15
)
The gap in the level of corruption and stringency of environmental standards now comple-ment each other and attract rms to the South. As a corollary of the previous proposition, consider a pure corruption paradise, where there is no direct negative cost of corruption (c S = c N = 0) but where in contrast corruption allows reduction in the stringency of environmental standards (the indirect positive eects of c comp i ). Then, rms ee to the pollution haven. We summarize this result as follows.

Corollary 1 (Corruption Paradise Hypothesis): If corruption is not a sunk cost, but rather makes environmental standards in the South less stringent, then polluting plants are attracted by bad governance.

The proof of this result is obvious from Proposition 1. In contrast, the overall eect of corruption is less clear, i.e. when c S = c N = 0. Based on numerical simulations, we analyzed what happens when corruption has both a direct negative impact and an indirect positive one, i.e. c comp i = c i . 15 We nd a bell-shaped relationship between corruption and relocation.

A small gap with regards to corruption, leads to agglomeration in the North, but in the case of a bigger gap, the indirect eect dominates, and multinationals relocate their plants to the South.

3 Empirical strategy In our case, we choose zero-inated models as our core strategy. Although, both models deal with the high occurrence of zeros in the observed data, there is one important distinction in the interpretation of zero counts. In the case of the hurdle model, the process for zeros [START_REF] Cole | Institutionalized Pollution Havens[END_REF] The diversity in the nature of investments accounted in FDI implies greater heterogeneity in the deter- minants of location choices, which cannot be eciently captured by pair dummies and/or common variables of controls leading to over/under estimate the coecients of our main variables. For instance, rms that are very footlose will not be aected in the same way by corruption than other rms. A restriction on the ownership implies that rms have a more similar behavior in term of mobility.

is not constrained to be the same: a Bernoulli probability governs the binary outcome of zero/nonzero values and the positive realizations are governed by a truncated-at-zero model (Poisson or negative binomial). Conversely, with zero-inated models, the response variable is modeled as a mixture of a Bernoulli and Poisson (or negative binomial) distribution.

Positive values are determined similarly. This option seems the most relevant since location choices are not supposed to be sequential processes. Nevertheless, as there may be some issues, we report alternative estimations based on the hurdle and pseudo-maximum likelihood estimators in Table [START_REF] Brunel | Measuring Environmental Regulatory OECD Trade and Environment Working Papers[END_REF] of Appendix C.

Another distinctive feature of count data is overdispersion, which is observed when the variance is clearly higher than the mean value. This model is estimated by a the maximum likelihood method, consistent with the presence of heteroskedasticity. This estimator is also especially ecient in large samples [START_REF] Cameron | Econometric Models Based on Count Data: Comparisons and Applications of Some Estimators and Tests[END_REF]. As a more eective control for heteroskedasticity, we compute robust standard errors and confront with the bootstrapped standard errors to check the sensitivity of our results.

Estimation equation and data

Estimation equation

We estimate the following equation:

ν kt N S = exp (A + Φ + ξ + Ω + λ N S + λ t + λ k ) , (16)
where ν kt N S is the conditional mean of s kt N S , which refers to the number of foreign-controlled rms relocating from a European country N to the destination country S, in industry k at time t. The subscript N refers to 25 European countries, while S refers to the 145 recorded destinations, including the European ones.

Following the theory, three determinants are included in the regressors, through the following vectors Φ which refers to market capacities, ξ which isolates the eects of environmental policies and Ω which is associated with corruption eects. The sizes and contents of these three vectors are described in subsection (3.3.2).

Vector λ N S captures the country-pair heterogeneity. Two cases are considered. First, we control for unobserved heterogeneity with the use of a country-pair xed eect to capture all time-invariant unobservable bilateral factors. This xed eect may imply a multicollinearity issue and thus an important dummies elimination. To deal with this, we also estimate this equation by introducing a traditional vector of control variables, which is used to reect tax, economic social dierences. 18 Finally, λ t controls for time-specic elements that may aect the number of relocations and λ k is an industry xed eect systematically used to control for unobservable sectorial features. Countries xed eects may be also introduced but a large part of country heterogeneity is mostly captured by the vectorΦ of market capacities which already rely on country-specic xed eects. The following subsection presents this in details.

Core variables and data

Trade eects and the market access Focusing on market access, the vector Φ is given by:

Φ ≡ a 1 ln(M Ab SN ) + a 2 ln(M A N ) + a 3 ln(M A S )
where M Ab SN refers to the bilateral market access to N from S, while M A N and M A S denote multilateral market access towards N and S, in other words towards the origin and destination countries (including Europe) respectively. This index of market access can be decomposed into domestic and foreign shares, so that M A N = M Ad N + M Af N . In addition to match the theory, these distinctions among trade terms allow us to analyze the mechanism, by which market access aects the location choices of foreign-controlled rms.

More precisely, turning to the index used, one of the central tenets of the Economic Geography is the importance of proximity to consumers, namely, market access which is dened as the distance-weighted sum of the market capacity of surrounding locations, which is obviously aected by trade costs. Based on the theory, our empirical specication includes both domestic and foreign components of market access in the set of determinants of location 18 Are precisely introduced geographic features (distances), cultural and institutional proximity (a dummy for common language), and socio-economic dierences (tax incomes as a percentage of the GDP, GDP per capita, and unemployment rates).

choices. Thus, we need to contruct indexes that capture the domestic market access of a

country i with i = N, S M Ad i = Y i P 1-σ i τ 1-σ
ii and the foreign market access, with partners j

M Af i = j Y j P 1-σ j τ 1-σ ij , i = j.
To do so, we conduct a structural computation of market access, derived from our theoretical model. This structural computation proceeds in several stages. We rst estimate a bilateral trade gravity equation to obtain empirical estimates of bilateral trade costs (τ 1-σ ij ) and each country's market capacities (Y j /P 1-σ j ), which are captured by importer xed eects. 19 Then, using these predicted values of trade costs and importer xed eects, we construct empirical predictions of market access, denoted as M Ad i and M Af i following their theoretical specication and noting that M A i = M Ad i + M Af i .

To our knowledge, this current standard measure of market access has never been used in environmental economics. To measure its impact on relocation at the sectorial level, we conduct this two-step procedure on the disaggregated trade data from UN Comtrade, at the 2-digit level (SITC rev 2.). Particular attention is paid to the establishment of correspondence tables between sector classications, so as to match the SITC rev 2 classication to the Eurostat NACE rev 1. 20

Environmental policy The vector ξ allows us to isolate the eect of environmental policy on location choices and is given by ξ ≡ a 4 ln(E kt N ) + a 5 ln(E t N ) + a 6 ln(E t S ).

We exploit both sectorial and country-specic dimensions, by introducing a measure of environmental regulation at the industry level, available for European countries and a global index of environmental policy stringency in the origin and destination countries. In order to 19 Basically, we estimate the following bilateral trade equation for each cross-section:

X ij = ln(τ ij ) + F X i + F M j , with τ ij = dist ij e border and F M j = ln(Y j /P 1-σ j ) and F X i = ln(Y i /P 1-σ i ).
With regard to the internal transport cost, we follow the existing literature and use τ ii = 2/3 area/π. Consequently, for E t N and E t S we suggest to use a direct measure of the regulation on Persis- tent Organic Pollutants (POPs). These twelve toxic products are chemical substances that persist in the environment. They include pesticides, industrial chemicals and unintentional by-products of industrial processes (such as dioxins and furans). This indicator consists in scoring information on whether countries allow, restrict, or ban the POPs. Several advantages can be underlined: 1-low sectoral dependance, since all industries are concerned, 2very good data availability among the 145 countries, 3-an important cross-sectional variance in the data. We can also consider that POPs regulation is a de jure measure, since its directly concerns the legislation and not its application. In comparison with de facto indicators often related to performance, the endogeneity bias between environmental standards and levels of productivity is limited.

23 21 Alternatively we also built measures of the gap in environmental policies, noted

ζ t N S = E t N E t S
, to check our main assumption. A positive sign is then expected.

Eects of Corruption The last vector Ω takes into account the direct eect of corruption, as suggested by the theory:

Ω ≡ a 7 c t S
where c t S is a measure of corruption costs. Following the literature on FDI and our theoretical prediction regarding the direct cost of corruption, we expect a negative sign. To answer our question, Is a pollution haven also a corruption paradise? we also check the interaction between the gap in environmental regulation and corruption levels. Following our theoretical results, we expect a negative coecient for corruption but a positive one for its interactive eect with the gap in environmental regulation, particularly for intermediate-levels of bad governance. An extra set of regressions is performed to rene the results of the interactive eects of corruption, notably by separating countries into dierent groups.

Good governance is complex to grasp, especially in the case of a panel study. As a result, we are conservative, and to construct an indicator of corruption, we use Kaufmann, Kraay and Mastruzzi's (2010) classical database on institutional quality, available for 209 countries. 24

This data set contains notations, the lowest one representing poor governance. To directly capture the estimated coecient of corruption costs, we rescale these notations to obtain that the highest values represent poor governance, while negative values proportionally refer to a low level of corruption.

All the data sources are reported in Table [START_REF] Broda | Globalization and the Gains from Variety[END_REF] in Appendix B. We also report the descriptive statistics of all the explanatory variables, in Table [START_REF] Brunel | Pollution Oshoring and Emission Reductions in European and US Manufacturing[END_REF].

Our empirical identication mainly relies on cross-national and cross-sectorial variability, since data for only three years are fully available. All market access indexes vary across countries (x2), sectors and time. Similarly, environmental expenditures also vary over all the dimensions. Concerning regulation on POPs and corruption, we observe an important variation across countries but a small variation across years. However, the environmental performance index presents higher time-variability than the regulation on POPs. Thus, we prefer bilateral variables of controls, to xed eects even if the parameter estimates are actually not sensitive to the introduction of pair dummies.

24 [START_REF] Kaufmann | Governance matters VIII : aggregate and individual governance indicators 1996-2008[END_REF] process data in order to make cross-country comparisons. All country scores are accompanied by standard errors, which reect the multiplicity of available data sources (used to build the corruption scores) and the potential dierences of opinion among those sources. In order to capture the corruption eect highlighted in the economic model, we use the index of corruption but also conduct regressions after including other governance indicators, in particular, the rule of law. The results are very similar and not reported here.

Results

Pollution haven and market access

The rst set of results is reported in Table [START_REF] Amiti | Trade Costs and Location of Foreign Firms in China[END_REF]. Our two main objectives are to stress the role of market access, and to show evidence of a pollution haven eect. The following regressions dier in the variable used to capture environmental policy (regulation on POPs or performance), in the multilateral market access (total or foreign market access), in the way to control for the pair heterogeneity (by observable variables or pair dummies) and nally the nature of the dependent variable (number of aliates or turnover). ab denote signicance at the 1 and 5 level respectively; 1 denotes the rejection of Null hypothesis at the 1% level. Robust standard errors are reported under each coecient. They have also been confronted with bootstrapped standard errors.

In colums (1) and (2), environmental reg. is captured by POPs regulation, while EPI is used in the other regressions. When λ N S is captured by observable variables, are actually introduced bilateral distances (-), a dummy for common language (+), the dierential of tax income (+), GDP per capita (+) and unemployment rates (-).

Concerning trade eects, market access plays a signicant role in the location choice of multinational rms. All the coecients are dierent from zero, regardless of bilateral or multilateral trade openness. As observed from each column, the number of aliates is signicantly inuenced by the market access from abroad (ln(M A kt S )). This market access is multilaterally dened which means that European rms easily export to each partner of country S, it is thus the market potential of country S that created the agglomeration force. This result suggests that rms escape the European regulation but not its market.

This argument is also eective for intra-European relocations (i.e. when S concerns european countries). The same eect is obtained for bilateral market access (ln(M Ab kt SN )), a 1% increase in bilateral market access from S to N , fosters relocation of 0.09% from N to S (e.g. Column 1). Interestingly, this variable does not include third multilateral) eects but directly captures relocation due to the re-importation strategy.

On the contrary, better market access from each European country (ln(M A kt N )) reduces relocation. This illustrates the common tradeo between relocation and trade, wherein a rm with good market access obtains no benet by relocating its activities abroad. This result is veried for all specications (see Columns 1-3).

Column (4) presents the results estimated with a more restrictive denition of market access. In addition, we systematically introduce environmental expenditures, at the sectorial level to complete our analysis with a monetary index of environmental policy. The positive relationship between environmental expenditures and relocation is constant among the alternative specications. Regulation appears as an inducement to leave, showing that the PHH is not a myth. Column 6 reports the results of an estimation in which the turnover of Europeancontrolled aliates, instead of the stock of aliates, is regressed. The parameter estimates are consistent with the ndings from the rst round of regressions.

verify that these conclusions are not misleading, we deal with several econometric issues, such as heterogeneity, omitted bias and measurement error. First, the vector λ N S aims to control for the sample heterogeneity by capturing specic features of pair countries. In columns 1 to 4, we systematically use bilateral controls, including geographic features (distances), cultural and institutional proximity (a dummy for common language), and socio-economic dierences (tax incomes, GDP per capita and unemployment rates). In column 5, we conduct an estimation based on pair dummies instead of observable features. Even if we experience the elimination of important dummies owing to multicollinearity, the main coecients are maintained, and the conclusions about trade and regulation eects are robust. However, we observe a marked change in the corruption eect. Indeed, all the regressions include an index of corruption abroad. As expected, corruption negatively impacts the location choices, since it represents a signicant cost. Once pair dummies are included, the estimated coecient increases signicantly, revealing a potential multicollinearity issue. Based on the theoretical model (Corollary 1) and following the CPH, we consider that, corruption indeed aects both location choices and environmental regulation, especially for intermediate levels of bad governance.

Before testing Corollary 1 and to deepen the prior analysis, we focus on Proposition 1 by analyzing the joint eect of the gap in environmental policy and trade integration. We complete the econometric specication in Equation ( 16) with an interaction term between bilateral market access M Ab N S and the gap in environmental regulation ζ N S . A positive coecient will conrm the results seen in Figure 1; the wider the gap in regulations, the greater the impetus for relocation due to trade openness. [START_REF] Eskeland | Moving to greener pastures? Multinationals and the pollution haven hypothesis[END_REF] These dummy variables are denoted as corrupt S and we assess their interaction with the gap ζ N S . All the other previously used independent variables are also included in the regression but, as the ndings are fairly identical, we only report the results of the interaction term in Table (3).

Columns 1 and 3 reveal that the gap in environmental regulation is not attractive at the extreme of the spectrum of governance. In short, the pollution haven hypothesis is neither veried in countries with good governance nor in countries with very bad governance. This certainly explains why many studies consider the PHH to be a myth. But the myth turns out to be true for an intermediate level of governance. Indeed the eect is signicantly positive for moderately bad governance. We obtain similar results for turnovers, an additional interesting nding being that good governance is now signicantly attractive, which is a well known fact in the literature on tax havens [START_REF] Dharmapala | Which countries become tax havens?[END_REF]. This analysis serves as a preamble to a closer examination of the eects of bad governance on the location choice.

A two-step analysis The rst set of regressions emphasizes that while corruption has a negative eect on the whole sample, it can have a positive impact for rms attracted by the gap in environmental regulation in countries with a moderately bad level of governance.

This means that in this group of countries, environmental regulation can be endogenously inuenced by corruption. Here, we consider this potential endogeneity bias and assess the indirect eect of bad governance on location choices.

25 Each group is classied based on a threshold of the corruption index. If it is less than -1, the country is considered to be very corrupt (denoted as bad). If it is between -1 and 0, the country is moderately corrupt, and otherwise, it is not corrupt (denoted as good).

this second empirical analysis comprises a two-step procedure involving an instrumentation strategy. To resolve the identication problem, we must nd suitable instruments for environmental regulation. The rst step consists of decomposing and estimating the environmental regulation prevalent for all partners with moderately bad governance, namely, countries to which rms relocate a part of their activities, with an instrument measuring environmental regulation and the indirect eect of corruption on these standards:

E t S = ϕE 1971 S + c t S + ι t S . (17)
where as noted previously c t S is the measure of corruption while E 1971 S is the historical level of the norm and ι t S an error term. We use the level of regulation on POPs in 1971 as an instrument considering that past levels of this regulation, preceding the 1973-1974 oil crisis, are a good proxy of green vanguard activism preceding and explaining future political mobilization and current environmental laws. [START_REF] Esty | Environmental Sustainability Index: Benchmarking National Environmental Stewardship[END_REF] We employ the level of regulation in alternative periods for the robustness checks.

The instrument is relevant if the coecientϕ is signicantly dierent from zero. The second econometric condition requires that this variable has no eect on the number of established rms forty years later. Once again, the year 1971 is meaningful for many countries that are now open to FDI, for instance in China, which ushered in the open door policy with the creation of Special Economic Zones only in 1978. In later years, the opening up of the economy has been gradual, but it has served as a major turning point, spurring strong investment in infrastructures, tax holidays to attract FDI, and eventually resulting in a fully export-oriented economy dating back to the 1990s. In addition, even if we nd that the current level of environmental regulation, E t S , is linked to institutions, and precisely, to the level of corruption ( < 0), this is not true with past levels of regulation, E 1971 S , which are not correlated with current levels of corruption (cov(E 1971 S ; c t S ) = 0). In short, the exogeneity/exclusion condition, which assumes that cov(E 1971 S ; ε kt N S ) = 0, is well veried. 27The introduction of c t S in the rst step, is our technical trick to extract the indirect eect of corruption out of the environmental standard when constructing the predicted value of this last variable. Indeed, the predicted value we consider is only ϕE 1971

S

. The data built from c st can thus be used to isolate and directly measure the positive eect of corruption with regard to buying laxer environmental laws in a soft kleptocracy interested in attracting dirty rms. More precisely, we perform the second step to explain relocation to a moderately bad governance environment. The new conditional mean, kt N S , is expressed as

ν kt N S = exp(a 1 ( ϕE 1971 S ) + a 2 ( c st ) + X tk N S + λ t + λ k ), (18)
where a 1 and a 2 are the coecients we aim to estimate, and where X tk N S considers the variables used in the previous estimation and nominally reported in Table [START_REF] Baldwin | Gravity for Dummies and Dummies for Gravity Equations[END_REF]. Because xed eects are used in prior regressions (in particular, for market access), we computes the bootstrapped standard errors in order to obtain unbiased condence intervals.

Table 4 reports the results of the two step-estimation. ab denote signicance at the 1 and 5 level respectively; 1 denotes the rejection of Null hypothesis at the 1% level. Boostrapped standard errors are reported under each coecient. They have also been confronted with bootstrapped standard errors.

λ N S is captured by observable variables, are actually introduced bilateral distances (-), a dummy for common language (+), the dierential of tax income (+), GDP per capita (+) and unemployment rates (-).

The results of the rst step, verify the good performance of the instrumentation strategy.

The coecient of determination for the rst step is strong, reaching 0.45, and the estimated coecient of our instrumental variable is highly signicant, enabling us to reject the null hypothesis of the weak instrument test. The level of regulations for POPs in 1971 positively explains the current regulation. Still from this rst step, costs signicantly reduce the level of regulation, which is in keeping with one of the main assumptions of the theoretical framework, namely, that corruption is viewed as a means to lower environmental standards.

Turning to the second step, the most interesting result concerns the impact of corruption, which (as seen previously) is always negative when taking into account the direct measure, c t S (see Columns 1 and 2). However, it becomes positive when considering the indirect eect of bad governance on green regulation c t S (see Columns 3 and 4).

Thus, the result obtained in the previous section using interaction terms is conrmed here for the second time with a very dierent method: bad governance, in this group of countries, is attractive owing to its impact on environmental regulation.

Sector-based analysis

The last robustness check is a response to concerns about potentially spurious results

specically the possibility that sectors may experience some other exogenous disturbance aecting location choices. To conrm the PHH and the CPH, it is important to consider whether the hypothesis is only observed in the context of polluting industries. To do so, we evaluate whether being a polluting or a non-polluting sector aects the impact of environmental norms on location choices. This is a sort of placebo test. We cannot reject the null hypothesis that the coecients on environmental policy and its interaction with a dummy for polluting sectors are equal to zero. This provides some assurance that the eect of green regulation is signicantly linked to the polluting nature of the sectors, and our results are not driven by some other spurious third cause.

Technically,we consider two groups. A reference group is dened as the most highly polluting industries based on their nitrous oxide emissions, and a control group, by less polluting industries. A closer look at the coecients of less polluting industries only (Column 2), indicates the similitude with the main results regarding market access, which are still signicant. The most striking dierence concern the impact of the environmental norms in the destination country, which is no longer signicant. Conversely, the impact of environmental policy in the origin country is still surprisingly signicant. 29 However, this last check allows us to conrmthe CPH. Indeed,the indirect eect of corruption is not signicant when the less polluting industries are only considered. A comparison of the results for the chemicals and transport equipment industries leads to similarndings: bad governance and laxer environmental norms are only attractive for the chemical sector.

Economic signicance and policy implications

Our results are statistically signicant, but do they also matter in the economic sense? Using the previous parameters estimates, it may be interesting to shock the data in order to quantify how more stringent environmental regulations in partner countries may aect relocation choices. We rst use the results obtained by estimating equation ( 16) and reported in Table [START_REF] Baldwin | Gravity for Dummies and Dummies for Gravity Equations[END_REF] Column 3), to simulate the impact of an eective policy of environmental harmonization in the European Union. We enforce stricter environmental regulation, E t S , in European 29 We cannot consider this test as a strict placebo one, since we would need an higher level of disagregation to formally distinguish which are the polluting rms.

countries S (among the EU-27) to raise this regulation to the level of their partners N E t N . In short, we cancel out the gap in bilateral environmental in Europe. We consider the parameter estimates after controlling for the indirect eect of corruption, since it captures the two hypotheses: pollution haven and corruption paradise. The predicted number of relocated rms after this shock is far from negligible. The number of rms interested in relocating their activities to these countries after harmonization decreases by 6%. This gure is quite important as it shows that third-country eects have been captured by market access from the origin and from the destination. Taming the PHH in the European context can thus be economically important. Now, we turn to the second scenario. We previously emphasized that market access is an important determinant of relocation. Here, we study how trade negotiations between Europe and its partners can be useful to negotiate environmental regulations. We present our result for a a carbon tax scenario, that is, we assess the reduction in market access required to compensate the gap in environmental regulation. Although, this is typically a policy of environmental regulations for trade, it is evident that the manner of computation for this policy is strictly equivalent in our framework to a policy of trade for environmental regulations, where, for instance, preferential agreements can be enforced with countries willing to abandon their laxer environmental policies. We select key partners, namely, the Maghreb region (Tunisia and Morocco, countries for which the data are available), and China which attract a signicant number of European aliates. This time, we suppose that the removal of the gap in environmental regulation will not aect the number of relocations but will be compensated by a variation in market access to the single European market. We evaluate the equivalent variation in market access needed to obtain more stringent environmental standards from these partners without aecting their attractiveness. Given the current level of relocation, a carbon tax reducing access to the European market by 13% for rms operating in the Maghreb erases the advantage of the gap in environmental regulations. This protection needs to be considerably higher for China (31%). Although, analyzing these numbers is outside the scope of this paper, such a high level of tax could imply substantial monetary loss for European consumers.

Conclusions

The world is not at; however, countries with heterogeneous institutions have rarely been so integrated. How do polluting rms evolve in this political and economic environment? Do pollution havens exist, and if so, are they also corruption paradises? To tackle these issues,

Appendices

Appendix A: Theoretical model

Conditions for the general case

We present the conditions for the full model presented in equation ( 5), ( 8), ( 9) and [START_REF] Brunel | Measuring Environmental Regulatory OECD Trade and Environment Working Papers[END_REF] ensuring that the HQ remain agglomerated in the North. Resolving the equilibrium condition π N N = π N S yields an explicit expression of the share of plants :

s * N = - t 1 -Γ [b(1 + b -2c S ) + 2(bg 1 -gζφ) -(b -1)bφ 2 ) + t 2 1 ] Γ (19) withg = c N -c S , g 1 = (c N -1)ζφ, t = b(ζ -φ)(1 -c S + g 1 ), t 1 = t -gζ(-1 + 2ζφ -φ 2 ), t 2 = (ζ -φ)(ζφ -1), Γ = 2gt 2 .
Lastly, the wage paid to the skilled workers at the HQ cis sourced from the operating prots earned by the plants. To close the model, we analyze π N N = 0 using the number of rms at equilibrium [START_REF] Dam | The curse of the haven: The impact of multinational enterprise on environmental regulation[END_REF] in order to determine the wage of the skilled workers:

w H N = b/2 s * N + φs * S ζ + φb/2 φs * N + s * S ζ + c N b s * N + ζφ -ζs * N φ -1 -1 (20)
The fragmentation of the rm, therefore gives rise to an international transfer of prots from the plant to the HQ. While in [START_REF] Fujita | Globalization and the evolution of the supply chain: Who gains and who looses?[END_REF] wage does not depend on trade integration, but owing to the dierential in the corruption costs accrued to build a plant in the North and in the South, the wage of skilled workers varies with trade costs in our study. 30

Lastly, we determine under what conditions the HQ/skilled workers are always agglomerated in the North. Skilled workers decide to stay in the North if their indirect utility is higher there than in the South; that is at spatial equilibrium we have w H N /P µ N = w H S /P µ S which is equivalent to w H S = w N (P S /P N ) µ . From π * SS = 0 and π * N S = 0 we compute the following ratio ν

1-σ N /ν 1-σ S = c N + w H N / c N + w H S .
Using these two expressions, the condition implying that the relocation of the HQ to the South is not protable, π * SS ≤ 0, gives (ν S /ν N ) σ-1 > c N + w H N / c N + w H N (P S /P N ) µ with w H N given in [START_REF] Damania | Trade liberalization, corruption, and environmental policy formation: theory and evidence[END_REF] and P S /P N = τ 1/(1-σ) at s * N = 1.

Thus, if the ineciency of the HQ in the South is high enough agglomeration in the North is always protable. π * SN ≤ 0 also holds under this condition.

Parametrization

To distinguish between the North and the South, we use the index of corruption used by Kaufmann, Kraay and Mastruzzi (2010).

31 All countries with a positive index are considered as southern countries.

32 We use this classication to compute the average measure of the Environmental Performance Index of Esty, Levy, Srebotnjak and de Sherbinin (2010). [START_REF] Jug | Environmental Regulations in Gravity Equations: Evidence from Europe[END_REF] We obtain e N = 58.9 and e S = 49.3 on which our simulations are based. Regarding the share of income spent on industrial goods, using OECD data, Bosker et al. nd that µ = 0.335.

We follow this estimation and the literature in Economic Geography in general by setting µ = 0.4. We also need the elasticity of substitution between varieties, for instance to evaluate E i = e σ-1 i . We set σ = 4 following Broda and Weinstein (2010) who show that the elasticity of substitution between varieties decreased from 6.8 between 1972 and 1988 to 4.0 (with a standard error of 0.5) over the period 1990-2001.

30 Numerical simulations show that with trade integration, the wage of skilled workers rst increases and then decreases.

31 For more details, see our empirical analysis, which describes this index. 32 In order to capture corruption cost, we rescale the notation of Kauman et al. (2010) so that the highest value corresponds to the highest level of corruption while the lowest one refers to good governance. [START_REF] Jug | Environmental Regulations in Gravity Equations: Evidence from Europe[END_REF] For more details, see our empirical analysis, which describes this index.

1

 1 and σ > 1 by denition). Observing that NUM can be upward-concave with respect to ζ and resolving ∂N U M/∂ζ = 0 for ζ gives a unique solution: ζ = 2φ 1-b+φ 2 (1+b) NUM attains its minimum at ζ and this minimum is positive (N

Figure 1 :

 1 Figure 1: Northern share of plants, openness and environmental regulation

  Instead of using ln(M A kt N ) and ln(M A kt S ) which include domestic as well as foreign market access, we only use the foreign one ln(M Af kt N ) and ln(M Af kt S ). The signs and signicance of the parameters are consistent with the rst set of results. The location choice of multinational rms is thus motivated by the market potential of the new location. Now, turning to the impact of environmental regulation, we rst conduct the estimation based on the regulation for POPs, at country levels (E N , E S ) in column (1) and on the dierential ζ N S = E N E S in column (2). As expected, we nd signicant and positive coecients for the regulation in the origin country E N and for the dierential between the regulation levels of the origin and destination ζ N S . The same instrument in the destination country clearly restricts location choices. This opposition in sign between environmental policy at the origin and destination is robust to the use of an alternative index of stringency, namely, the composite indicator of performance. Column (3) presents the results of this alternative estimations and conrms our conclusions.

  our study proposed a theoretical model and an empirical analysis of the PHH regarding the locations of European aliates over the period 2007-2009. W found evidence of the positive inuence of trade integration magnied by the gap in environmental on the ight of polluting rms to pollution havens have been found. In the current context of globalization, some countries, especially developing and emerging countries with an intermediate level of bad governance, attract dirty rms. Pollution haven and corruption paradise are not theoretical curiosities; they are attractive for some European rms. The European Union and its large market has the potential to bring about some improvements; however, only coordinated policies at the global level for environmental standards, trade integration, and governance can really change the current situation. Owing to the failures of past global collective actions on these subjects, the future looks less bright with validations of the PHH than without.

Finland

  biggest contributor in all variables and its plants are present at almost all destinations. The United States' aliates play a very important role in Germany (41%), as well as the Netherlands, Italy, Sweden, Finland, and Belgium. Italy has a strong presence in Africa, and the Netherlands ,in South-East Asia. Twenty-one percent of the Netherlands' turnover is recorded by aliates located in oshore nancial centers. Overdispersion To conrm the choice of the Negative binomial distribution instead of Poisson, we report the summary statistics of the number of aliates in Table (7) and we plot its distribution in Figure (2):

Figure 2 :

 2 Figure 2: Distribution of the number of aliates from Europe, 2007-2010

  Classications for governance In sub-section 4.2 countries are split into three groups, ordered by their respective corruption scores, with bad, moderately bad or good governance.Given below is each group of countries sorted by their score) Bad governance (score <-1): Somalia, Myanmar, Afghanistan, Equatorial Guinea, Turkmenistan, Angola, Iraq, Congo (Democratic Republic of.), Korea (Democratic People's Republic), Papua New Guinea, Chad, Zimbabwe, Uzbekistan, Kyrgyzstan, Congo, Sudan, Venezuela, Libya, Jamaica, Haiti, Cambodia, Tajikistan, Azerbaijan, Russia, Laos, Guinea-Bissau, Côte d'Ivoire, Pakistan, Burundi, Kenya, Yemen, Guinea, Togo, Ukraine, and East Timor.Moderately bad governance (score between -1 and 0.5): Bangladesh, Nigeria, Syria, Sierra Leone, Gabon, Cameroon, Ecuador, Central African Rep., Kazakhstan, Honduras, Uganda, Paraguay, Indonesia, Lebanon, Philippines, Comoros, Iran, Mongolia, Tonga, Dominican Rep., Nicaragua, Ethiopia, Benin, Maldives, Moldova, Nepal, Bolivia, Belarus, Niger, Fiji, Mauritania, Cook islands, Niue, Armenia, Gambia, Guyana, Algeria, Senegal, Zambia, China, Guatemala, Liberia, Argentina, India, Albania, Tanzania, Vietnam, Jamaica, Egypt, Eritrea, Mozambique, Sri Lanka, Sao Tome, Malawi, Nauru, Palau, Burkina Faso, Bosnia, Suriname, Mexico, Peru, Thailand, Morocco, Panama, Colombia, Djibouti, Romania, Serbia, Georgia, Bulgaria, Trinidad, Madagascar, El Salvador, Swaziland, Tuvalu, Kiribati, Brazil, Tunisia, Croatia, Belize, Malaysia, Italy, Greece, Ghana, Saudi Arabia, Turkey, South Africa, Samoa, Rwanda, Latvia, Macau, Lithuania, Lesotho, Jordan, Namibia, Bahrain, Slovakia, Seychelles, Kuwait, Grenada, Vanuatu, Hungary, Oman, Czech republic, Poland, and Cuba.

  bias.[START_REF] Cole | Institutionalized Pollution Havens[END_REF] The other advantage of this data set is that it ts our theoretical analysis, as it presents the stock of aliates instead of ows.The data are recorded at the sector-level (the Statistical Classication of Economic Activities in the European Community, abbreviated as NACE). Notably, 30% of European aliates are located in another European union member state, underlying the importance of the single market. The United States is the principal destination of rms controlled by Germany and the United Kingdom. Emerging markets, such as China, India, Brazil, Mexico, Turkey, Argentina and Thailand, are the selected destinations of 16% of rms located abroad. For instance, Germany is the biggest investor in China. Developing countries are also well represented (expected emerging and eastern countries), accounting for 23.62% of total aliates. This mainly includes African countries, which comprise 35 destinations among the 145 avail-

3.1 European outward Foreign AliaTes Statistics (o-FATS)

We base our empirical analysis on an original database that reports all European-controlled enterprises, located abroad. We use the outward Foreign AliaTes Statistics (FATS) from Eurostat, covering the period 2007-2010. This unique data set contains the number of aliates, associated turnover and corresponding number of employees. It is quite exhaustive; the French statistical institute, INSEE

[START_REF] Cole | Endogenous Pollution Havens: Does FDI Inuence Environmental Regulations[END_REF]

for instance, acknowledges this data set as a complete census of French multinational rms located abroad. Furthermore, while the literature uses data for FDI ows, which by themselves are quite heterogeneous (including mergers, acquisitions, new facilities, reinvested prots, etc.), we focus on foreign-controlled rms that are majority-owned by a single investor or by a group of associated investors acting in concert and owning more than 50% of ordinary shares or voting rights. As a result, the sample is more homogeneous, especially in terms of capital mobility, which helps us reduce the esti-15 Not reported here, but available upon request. mation able in the data set. Italy is the most active country in Africa, and the Netherlands in South-East Asia. These data are potentially responsible for many zero counts. This issue may induce a severe estimation bias if an appropriate econometric model is not adopted. For this reason, sub-section (3.2) is dedicated to a discussion about the choice of the relevant estimator, to address these data issues.

3.2 Econometric strategy: choosing the count data model

As mentioned previously, the dimension of our data set, by origin, destination and industry, implies the presence of null values which need to be treated with the appropriate count data model. Figure (2, Appendix B) illustrates the distribution of the number of European aliates in foreign markets for the period 2007-2010. This histogram clearly displays a high proportion of zero values, which justies the use of derived Poisson models. In reality, a basic Poisson model underestimates the probability of zero counts. This excess of zero is a signicant and relatively common issue, requiring specic econometric processing, that is, zero-inated and hurdle models. Such models address the high occurrence of zeros, by assuming that the zeros and positive values do not stem from the same data-generating process.

  To check the presence of overdispersion, we rst observe Figure[START_REF] Anderson | Trade and Environment. Book Review[END_REF] Appendix B), that illustrates intrinsic heteroskedasticity, with variance increasing with the mean value. We report summary statistics in Table[START_REF] Bosker | Adding geography to the new economic geography: bridging the gap between theory and empirics[END_REF] Appendix

B), showing that the mean number of European aliates is lower than the variance. Thus, the data are clearly over-dispersed. Following this statement, the econometric treatment needs to be adjusted by replacing the Poisson distribution by the negative binomial distribution, which is a better t for over-dispersed data. This concern leads us to choose a zero-inated negative binomial (ZINB) model as the core of our econometric strategy and to compare results with other count data models. Overdispersion tests, such as the likelihood-ratio test are conducted for each regression, and their results add weight to the idea that the Poisson distribution is not appropriate.

  evidence of the PHH, both E tk N and E t N should be positive and, E t S , negative. 21 Measuring the environmental policy stringency is really challenging and the choice of indicators among several options is usually driven by data availability. A growing number of indicators has been used in the literature, among which we nd monetary variables, composite and performance indexes or more direct measures of environmental standards. In order to test the robustness of our results, we employ several measures, starting with a monetary indicator, namely environmental expenditures, measured at the sectorial level in each origin country (E kt N ). 22 The main advantage of using expenditures is to deal with the sectorial diversity in environmental policy, unfortunately, this index is not available for countries outside the E.U.. To measure the environmental regulation of all countries, we have the choice to use a composite index, like the Environmental Performance Index (EPI) data set of Yale University, or a more direct measure of environmental standards. Regarding our empirical strategy, composite indicators (e.g. EPI) have many drawbacks. The main downside is the diculty to identify the direct and isolated eect of the environmental policy. Such perfor-

See Appendix (B) for more technical details about this gravity equation estimation.

[START_REF] Damania | Trade liberalization, corruption, and environmental policy formation: theory and evidence[END_REF]

All the data sources are presented in Table

[START_REF] Broda | Globalization and the Gains from Variety[END_REF]

of Appendix B.

Correspondence tables are available upon request. nd mance indicators are based on outcomes which are actually the product of several policies (in agriculture, energy, infrastructures, etc,.) and not only the result of environmental regulation. This is clearly an identication issue which leads us to only consider this index for a robustness check.

Table 2

 2

only reports the coecient of this joint eect, all other results are similar.

Table 2 :

 2 Joint eect of market access and gap in environmental policy on the model. It likely that countries with the worst institutions are not attractive even if they impose very lax environmental regulations. We thus re-estimate the previous model thrice by considering dierent thresholds of corruption. In concrete terms, we dene three separate dummy variables, each of which takes the value 1 for bad governance, moderately bad governance, and good governance.

	Dep variable	S kt N S	T kt N S
	Bilateral market access*env gap ln M Ab kt N S ζ t N S	0.103	0.133
	RSE	0.011 a	0.011 a
	a denotes signicance at the 1 level; The econometric specication is an augmented
	version of Equation 16 and other coecients are closed to those
	reported in columns 3 and 6 of Table 1.
	4.2 Direct and indirect eects of corruption

Interaction eects As discussed previously, bad governance in the context of a pollution haven is attractive only for intermediate levels of corruption, as suggested by the numerical simulations based

Table 3 :

 3 Interaction eects

	Dep variable		s t N S			T t N S
	Country group	Bad	Middle	Good	Bad	Middle	Good
	ζ N S corrupt S	-1.342	0.110	-0.012	-1.803	0.590	0.534
	RSE	0.124 a	0.008 a	0.100	0.167 a	0.161 a	0.146 a

a denotes signicance at the 1 level; The econometric specication is an augmented version of Equation

16

. s

Table 4 :

 4 The Pollution Haven Hypothesis and the role of corruption

	(1)	(3)	(4)

Table 5

 5

	: Sector-based analysis
	(1)	(2)	(3)	(4)

Table ( 5

 ( ) presents the last set of results. Column 1 reports the results for the whole sample in which the environmental policy variables interact the dummy of polluting rms. The model is then re-estimated by considering only the less-polluting sectors, and the estimations are reported in Column 2. At this stage, the PHH is veried since the coecients of regulation in POPs are strongly signicant as far as polluting sectors are concerned, but they become insignicant when the less polluting ones are isolated. The CPH is also well veried since the coecient for the indirect eect of corruption is not signicant for the less polluting sectors. Now, we proceed to a sector-based analysis, by working only with chemicals rms, which are usually considered as polluting rms (e.g.,[START_REF] Millimet | Empirical Tests of the Pollution Haven Hypothesis When Environmental Regulation is Endogenous[END_REF]. Lastly, we focus on transport equipment, which is produced without generating too much pollution. The results from the last group can be interesting because these industries are related to economic growth, thus allowing us to analyze if our environmental variables incorrectly capture other eects (linked to development), which are not our primary interest. Comparisons of the results show that all variables have the same sign when analyzing polluting rms only. The results for the whole sample displayed in the previous section are thus primarily driven by polluting rms.

Table 7 :

 7 Summary statistics

		Mean	Std deviation	Min	Max
	Nb of European aliates	11.77	84.23	0	4217
	Turnover	488.52	4946.79	0	324116

Table 8 :

 8 Data sources

	First-step	Construction of market access	Source
	Bilateral trade	Imports and Exports at the 2-digit SITC rev2 UN Comtrade database
	Gravity variables	Distances, contiguity, borders, language from Geodist and Gravity (CEPII)
	GDP	in current dollars	WDI database
	Second-step	Outsourcing equation
	Nb of rms	Firms relocated from European countries	FATS (Eurostat)
		at the industry level Nace rev1 and rev2
	Environment regulation Env expenditures by industry (Nace rev 1)	Eurostat
		Env taxes as share of total taxes	Eurostat
	Env Performance Index Global index of env performance	EPI 2013
	POPs regulation	Global notation	UNEP-Chemicals
	Corruption costs	Kauman, Mastruzzi Kraay index	ITU-ITC

Table 9 :

 9 Descriptive statistics of explanatory variables

		Mean	Std deviation	Min	Max
	MA from the origin	28.652	11.158	16.032	55.026
	MA from the destination	28.118	16.435	7.589	77.461
	foreign MA from the origin	17.722	6.607	14.747	40.455
	foreign MA from the dest.	17.762	7.679	7.310	40.455
	Env expenditures	1410.238	3515.732	0.14	28149
	POPs origin	18.307	4.852	0	21
	POPs destination	15.097	7.932	0	21
	POPs origin 1971	4.401	5.862	0	18
	POPs destination 1971	2.328	4.128	0	18
	EPI origin	62.842	5.401	48.256	70.374
	EPI destination	57.427	10.194	32.543	77.993
	Corruption index	0.654	1.032	-1.801	2.162

Table 10 :

 10 Alternative estimators -Baseline Equation

		(1)	(2)	(3)	(4)
			s kt N S
			Pooled (λ N S = obs)
		ZINB	Hurdle	ZIP	PPML
	Bilateral MA	0.094	0.101	0.202	0.221
	ln(M AB kt SN )	0.008 a	0.006 a	0.013 a	0.013 a
	MA from the origin	-0.468	-0.456	-0.103	-0.103
	ln(M A kt N )	0.063 a	0.058 a	0.093	0.093
	MA from the destination	0.830	0.901	1.018	1.068
	ln(M A kt S )	0.058 a	0.051 a	0.088 a	0.088 a
	Environmental regulation
	Env expenditures	0.542	0.568	0.596	0.678
	at the industry-level ln(E kt N )	0.014 a	0.014 a	0.019 a	0.017 a
	Env. regulation in the origin	0.108	0.102	0.096	0.128
	at the country-level E N	0.007 a	0.007 a	0.009 a	0.008 a
	Env regulation in the dest.	-0.010	-0.010	-0.031	-0.027
	at the country-level E S	0.002 a	0.002 a	0.003 a	0.003 a
	Corruption eects
	Corruption index c t S	-0.047	-0.055	-0.065	-0.081
		0.023 b	0.020 b	0.029 b	0.030 a
	Ination model	Logit	Logit	Logit	Logit
	Country-pair λ N S	obs	obs	obs	obs
	Pseudo-R²	0.82	0.82	0.82	0.82
	Log Likelihood	-20525	-20241	-207899	-245350
	Vuong test	95.20 1
	Overdispersion test (alpha)	1.185 1
	Zero observations	6277	6277	6277	6277
	Observations	10507	10507	10507	10507

abc denote signicance at the 1, 5 and 10% level respectively; Robust standard errors are reported under each coecient.

In order to operate in the largest markets, rms agree to pay the highest environmental costs, but if these costs are excessive, they move out of their green fortress, particularly if they can secure access to consumers from a peripheral location and follow laxer environmental standards.

Market access is thus a central variable to understand the reason behind the change in locations of rms. To conduct the empirical analysis, we use our theoretical model to measure this concept. In theory, market access is the sum of demands weighted by trade costs.Empirically, a structural trade gravity equation is estimated, that is bilateral trade ows are regressed on distances, exporter and importer dummies to capture bilateral trade costs and market characteristics. This gravity equation is estimated for each industry and each year, and the parameter estimates are used to calculate multilateral market access for each country in each industry. Then, this variable is used to explain location choice of foreign-controlled enterprises, originating from European countries and located in 148 destination countries in2007, 2008, and 2009. 3 This result is supported byWagner and Timmings (2009) who nd that once agglomeration economies are taken into account, robust evidence of a pollution haven eect for the outward FDI ows of the German chemical industry can be detected. See alsoEderington, Levinson and Minier (2003).[START_REF] Baldwin | Gravity for Dummies and Dummies for Gravity Equations[END_REF] To our knowledge, only Zeng and Zhao (2009) provide a similar result with a dierent model, in which the manufacturing sector generates cross-border pollution that reduces the productivity of the agricultural sector.

The estimator used is a zero-inated negative binomial model, which is particularly adapted to count data, and deals with the excess of zeros and overdispersion, characterizing our data set.

See, for example,[START_REF] Copeland | Trade and the Environment: Theory and Evidence[END_REF], and Ederington,[START_REF] Ederington | Footloose and pollution free[END_REF]

[START_REF] Redding | Economic geography and international inequality[END_REF] published a seminal paper analyzing the impact of market access on income per capita. Other studies have followed analyzing wages[START_REF] Hering | Market Access and Individual Wages: Evidence from China, The Review of Economics and Statistics[END_REF], location choices of rms[START_REF] Amiti | Trade Costs and Location of Foreign Firms in China[END_REF] and individuals[START_REF] Candau | Spatial distribution of skills and regional trade integration[END_REF].

The environmental standard is rst regressed by historical levels of regulation, considered as our instru- ment and by corruption.

In a previous version of this paper, we conducted the analysis assuming that environmental standards aect the xed cost while corruption was assumed to represent a variable cost. All the results were identical.

L'Institut National de la Statistique et des Etudes Economiques, http://www.insee.fr/fr/themes/document.asp?ref_id=ip1439

This measure is preferred to the widely used private abatement costs[START_REF] Levinson | An Industry-Adjusted Index of State Environmental Compliance Costs, Arik Levinson[END_REF][START_REF] Greenstone | The impacts of environmental regulations on industrial activity: Evidence from the 1970 and 1977 Clean Air Act amendments and Census of Manufactures[END_REF][START_REF] Keller | Pollution Abatement Costs and Foreign Direct Investment Inows to U.S. States[END_REF][START_REF] Per | Bureaucratic corruption, environmental policy and inbound US FDI: theory and evidence[END_REF][START_REF] Eskeland | Moving to greener pastures? Multinationals and the pollution haven hypothesis[END_REF][START_REF] Henderson | Pollution Abatement Costs and Foreign Direct Investment Inows to U.S. States: A Nonparametric Reassessment[END_REF] because they are based on surveys at plant-level data, which are obviously not available for our sample.See[START_REF] Kellenberg | An empirical investigation of the pollution haven eect with strategic environment and trade policy[END_REF] for an interesting discussion on environmental policy data.

see Bazillier, Hatte and Vauday (2016) for a detailed discussion on de jure and de facto measures of environmental regulation.

The role of a vanguard is particularly important in explaining political change (e.g., Bueno de Mesquita, 2010)

We also use more recent data, namely, the levels of regulation for POPs in 1975 and 1985, in the instrumentation strategy. The results are very similar.

This classication is very similar when sulfur dioxide or carbon dioxide emissions are used as criteria. Then, we generate a dummy variable (polluting sector=1). We proceed in three ways, rst by putting into eect the interaction with the dummy variable identifying polluting industries (column 1 of table 5), by isolating the less polluting industries, and later by conducting sector decomposition (columns 3 and 4). We select two specic sectors: transport equipment, identied as a less polluting sector, and chemicals, which is recognized as a polluting sector.[START_REF] Per | Bureaucratic corruption, environmental policy and inbound US FDI: theory and evidence[END_REF] The coking, extraction, energy, chemicals, metallurgy, food processing, and rubber industries are con- sidered as polluting indutries.The less polluting industries are manufacturing, textile and apparel, transport and equipment, wood, communication and computing.

Appendix B: Data Some gures on Foreign Aliates Statistics (o-FATS) To complete the descriptive statistics of sub-section (3.3), we report in Table [START_REF] Ben Kheder | Environmental Regulation and French Firm Location Abroad[END_REF] some gures concerning E.U. countries which record more than 1000 aliates located abroad. Three main variables are reported, the number of rms, the associated turnover and employment.

where F X i and F M j are xed eects to control for a country's market and supply capacity. Note that we use the indices i and j instead of N and S because we use trade data for all trading partners and not only countries recorded in the FATS database. The following is the 34 Following the work of [START_REF] Silva | The Log of Gravity[END_REF], the use of pseudo-maximum likelihood estimators (Poisson and derivative econometric models) is justied for treating heteroskedasticity and dealing with the presence of zero trade values. 35 Indeed, the particular normalization of Anderson and van Wincoop (Ω i = λP i , where λ = 1) is incorrect for panel data applications, as [START_REF] Baldwin | Gravity for Dummies and Dummies for Gravity Equations[END_REF] have explained and proved with a simple application for three countries and a time dimension.

bilateral expression of market access is following

in which a i , a j , and λ are the parameter estimates of the PPML regression for each cross section. It appears from Equation ( 22) that domestic market access can be distinguished from foreign market access. In this equation, bilateral trade costs τ ij take the following common specication

where Z ij is a vector of dummies characterizing bilateral trade barriers, such as contiguity. Turning to internal trade costs τ ii , we follow Redding and Venables (2004) by expressing internal trade costs as a function of internal distances, computed for a circular country, as it follows:

where area i is the surface in km².

Appendix C Alternative estimators

The following table reports the parameter estimates obtained with alternative estimators.

Our core strategy is to use a zero-inated model based on a negative binomial distribution (ZINB), to deal with the excess of zeros and overdispersion. However, we test here how our coecients are sensitive to other models. Three alternatives are used: an hurdle model, a zero-inated one based on a Poisson distribution (ZIP) and nally, a pseudo-maximum likelihood strategy (PPML), commonly used for count data, suering from heteroskedasticity.

If results are quite similar, coecients are all slightly overestimated compared to ZINB.

Overdispersion tests lead us to choose the negative binomial distribution instead of Poisson in our core strategy. Then, the dierence between ZINB or hurdle models is not very signicant.