John Christopher Beghin
email: beghin@iastate.edu

Anne-Célia Disdier
email: anne-celia.disdier@ens.fr

Stéphan Marette
email: stephan.marette@agroparistech.fr

Hiau Looi Kee

Alessandro Nicita

Rick Barichello

Antoine Bouët

Jean-Christophe Bureau

Guillaume Gruère

Michael Ferrantino

Julien Gourdon

Dermot Hayes

Daniel Sumner

Frank Van Tongeren

Trade Restrictiveness Indices in Presence of Externalities: An Application to Non-Tariff Measures 

Keywords: Non-tariff measures, externalities, ad valorem equivalents, trade restrictiveness indices JEL codes: F13, F18, Q56

) and derive ad valorem equivalents (AVEs) for NTMs. About 39% of the product lines affected by NTMs exhibit negative AVEs, indicating a trade-facilitating effect of these measures. Accounting for the trade-facilitating effect of NTMs significantly reduces previous measures of countries' trade policy restrictiveness obtained while constraining these NTMs to be trade reducing.

Introduction

Non-tariff measures (NTMs) cover all policies affecting trade other than tariff and other tax instruments at the border. When markets function well and do not exhibit imperfections such as externalities or information asymmetries, NTMs often reduce welfare and distort trade flows like tariff would. However, when imperfections are present NTMs differ fundamentally from more conventional trade barriers such as tariffs and quotas (see [START_REF] Van Tongeren | A Cost-Benefit Framework for the Assessment of Non-Tariff Measures in Agro-Food Trade[END_REF]. In particular, standard-like NTMs are playing an increasing role in international trade. Some of them may still have protectionist purposes, especially in a context of decreasing tariff barriers. However, some others are adopted by policymakers to address these market imperfections. In such cases, NTMs may be trade facilitating and welfare enhancing. Trade can expand when the perceived quality of imported goods is improved by the standard-like NTM such as imported certified organic coffee. Another case is when foreign suppliers satisfy the NTM at a lower cost than domestic suppliers do like for low-carbon biofuel imports from Brazil into OECD markets. The literature measuring the restrictiveness of trade policy, through the computation of various indices, has failed to consider these welfare and trade enhancing effects and the context of market imperfections. Our paper fills this gap.

With global sourcing, it becomes challenging to guarantee products' safety and quality and to mitigate negative externalities. Standards and regulations affecting quality help overcome asymmetric information issues. Occasional recalls by toy, pharmaceutical and food companies illustrate the importance of various safety concerns, such as lead paints in children toys [START_REF] Lipton | As More Toys Are Recalled, Trail Ends in China[END_REF].

Consumers may also care about global commons and avoid purchasing products obtained using unsustainable environmental practices. To preserve their reputation, large firms (e.g. Home Depot, IKEA, etc.) have shown strong support for forest certification [START_REF] Mcdermott | Forestry Driver, Mapping Project -Global and US Trade Report[END_REF]. Similarly, consumer welfare is improved by quality requirements limiting residues of dangerous pesticides and antibiotics in food products [START_REF] Disdier | The Combination of Gravity and Welfare Approaches for Evaluating Non-Tariff Measures[END_REF].

In this context, regulatory interventions have strong economic and political support, despite risks of inefficiency and distortions. For example, groundless precautionary measures could be an expedient way to address consumer concern. The effects of these regulatory instruments are indeed complex not only because instruments vary across countries and are imperfect but also because they impact costs of heterogeneous producers [START_REF] Carrère | Notes on Detecting the Effects of Non-Tariff Measures[END_REF]. Meeting the NTMs is costly for both domestic and foreign suppliers and often more so for the latter. In the context of North-South trade, these impacts have been contentious as they may hinder or enhance trade depending on the net effect of these standards [START_REF] Jaffee | Agro-food exports from developing countries: the challenges posed by standards[END_REF]. While a regulation may thwart a market failure and facilitate trade between countries, it may also reduce market access for foreign producers who cannot easily comply with this regulation. To illustrate, between October 2006 and 2007, the U.S. Consumer Product Safety Commission (CPSC) announced 473 products recalls of which 389 cases involved imported products [START_REF] Cpsc | The Impact of Regulations on Agricultural Trade: Evidence from the SPS and TBT Agreements[END_REF]. This last effect may outweigh the "legitimate action" to mitigate a market failure. Both trade and welfare impacts of regulation are ambiguous and in general hard to evaluate. A rigorous empirical measure of these impacts therefore requires a consistent framework, as proposed here.

We consider a small open economy, distorted, first, by arbitrary tariffs and other domestic price policy distortions, and second by market imperfections and existing NTMs allegedly addressing them. We pay particular attention to NTMs and their protective effects against import competing products, as well as their potential demand enhancing effects when NTMs reduce information asymmetries and trade cost. We then extend the trade restrictiveness index (TRI) approach of [START_REF] Anderson | Measuring the Restrictiveness of Trade Policy[END_REF] to this more general and realistic case encompassing market failures and the existing domestic regulations addressing them.

The TRI approach of [START_REF] Anderson | Trade reforms with quotas, partial rent retention and tariffs[END_REF][START_REF] Copeland | International Trade and the Environment: Policy Reform in a Polluted Small Open Economy[END_REF][START_REF] Anderson | A new approach to evaluating trade policy[END_REF], 2003, and 2005) provides a welfare-based consistent aggregation of various trade distortions into a scalar uniform surtax factor, equivalent to these distortions in terms of their welfare effects. The TRI approach is a concept applying to a whole economy because it relies on the balance of trade approach. Nevertheless, it has been applied successfully to partial equilibrium and multi-market situations for both developed and developing economies. [START_REF] Feenstra | Estimating the effects of trade policy[END_REF] has proposed some simplifying assumptions greatly fostering the applicability of the approach by reducing the number of price responses to estimate or calibrate in the implementation.

The TRI and its extensions such as the Mercantilist TRI (MTRI) of [START_REF] Anderson | The mercantilist index of trade policy[END_REF] have been used to derive the tariff equivalent of arbitrary tariff structures [START_REF] Anderson | Measuring the restrictiveness of trade policy[END_REF], tariffs and quotas (Anderson and[START_REF] Anderson | Trade reforms with quotas, partial rent retention and tariffs[END_REF]2005), tariffs and domestic production subsidies [START_REF] Anderson | Domestic Distortions and International Trade[END_REF][START_REF] Anderson | Measuring the Restrictiveness of Trade Policy[END_REF][START_REF] Beghin | Food Security and Agricultural Protection in South Korea[END_REF], and tariffs and AVEs of other NTMs [START_REF] Hoekman | Trade Policy, Trade Costs, and Developing Country Trade[END_REF][START_REF] Kee | Estimating Trade Restrictiveness Indices[END_REF][START_REF] Lloyd | An Estimated Trade Restrictiveness Index of the Level of Protection in Australian Manufacturing[END_REF][START_REF] Bratt | Estimating the Bilateral Impact of NTMs[END_REF], among others. All these applications abstract from external effects or informational asymmetries, which we allow for explicitly. As shown in these applications, the TRI approach provides a consistent aggregation of distortionary effects of various policy instruments into a single "total" AVE within a given sector. The latter property explains the recent success and popularity of the approach in empirical investigations of NTMs in presence of tariffs and other price policies at the sector level.

The novelty of the present paper is to allow for market imperfections and trade facilitating effects of NTMs in the TRI framework. Despite its inherent ability to capture second-best situations, the determination of the TRI under market failure has been overlooked in the trade literature. The only related effort in this direction is from [START_REF] Chau | Trade Restrictiveness and Pollution[END_REF] who develop a quantity-based distance function, a trade restrictiveness quantity index, in presence of environmental externalities but abstracting from existing policy interventions. Outside of the TRI literature, recent empirical investigations note that NTM regimes can facilitate trade (see [START_REF] Cadot | Assessing the price-raising effect of non-tariff measures in Africa[END_REF], for a review). Reputation and certification processes increase trust in exchange [START_REF] Blind | Mutual Recognition of Accreditation: Does it Matter to Trade? Evidence from the Food, Beverage, and Tobacco Industry[END_REF]; quality standards help reputation and reputation loss can be detrimental to trade [START_REF] Jouanjean | Standards, reputation, and trade: evidence from US horticultural import refusals[END_REF]; and transparency provisions in trade agreements can facilitate regulated trade flows [START_REF] Lejárraga | Transparency in Nontariff Measures: Effects on Agricultural Trade[END_REF].

We fill this gap in the TRI-related trade literature: we consider the TRI of arbitrary tariffs, domestic production subsidies, and NTMs in presence of possible external effects. 1 This undertaking is a 1 Several investigations using the standard gravity equation approach find some trade facilitating effects of NTMs but without a rationalization based on some demand increasing effect or market imperfection presumably mitigated by the NTMs being analyzed (see [START_REF] Li | A Meta-Analysis of Estimates of the Impact of Technical Barriers to Trade[END_REF]. substantive step forward for two reasons. First, trade policy reforms often occur in the context of market imperfections such as asymmetric information or negative externalities imposed on some agents.

Accounting for these imperfections is relevant and has been the central pillar of the trade and environment literature using the dual approach to trade [START_REF] Copeland | International Trade and the Environment: Policy Reform in a Polluted Small Open Economy[END_REF][START_REF] Beghin | Trade and Environment Linkages. Piecemeal Reform and Optimal Intervention[END_REF]. Surprisingly, this case has eluded the TRI literature. Second, numerous NTMs have been emerging in the last 15 years for several reasons, including potential protectionism, but also to address consumer and retailer concerns for health and the environment and associated external effects. A priori, excluding potential market imperfections when analyzing NTM policy reforms biases results and could lead to erroneous policy recommendations. Not surprisingly, sectoral AVEs and TRI estimates are likely to exhibit upward bias when they are econometrically constrained to treat all policies as trade-reducing. We depart from this restrictive premise and start from an agnostic prior on the impact of NTM policies on trade and welfare.

We then apply the proposed framework to the NTM global database of [START_REF] Kee | Estimating Trade Restrictiveness Indices[END_REF] consisting of a large cross section of products (at the 6-digit level of the Harmonized System -HSclassification) and importing countries. We derive ad valorem equivalents (AVEs) for so-called technical regulations in their NTM database. These measures are standard-like measures potentially addressing market imperfections, rather than other NTMs (e.g., quantity restrictions, price control and monopolistic measures) that impede trade. 2 We also compute AVEs for other policy distortions (tariffs and domestic production subsidies). 12% of HS 6-digit lines are affected by these NTMs and 39% of these (4.7% of the lines) exhibit negative AVEs of NTMs, indicating a net trade-facilitating effect of NTMs in those sectors.

These AVEs are then used to evaluate the restrictiveness of the trade policy defined by countries. TRIs computed with these AVEs reflect the frequent trade facilitating effect of NTMs. Accounting for these trade-facilitating effects significantly reduces previous measures of trade policy restrictiveness for most countries obtained while forcing standard-like NTMs to be trade impeding. These trade-facilitating effects 2 Earlier versions of our analysis were based on the full set of NTMs and reached similar qualitative results with these less appropriate NTM data. cast doubt on the predominant presumption that technical regulation NTMs are exclusively protectionist and cannot possibly boost trade, let alone welfare.

Our paper proceeds as follows. We present the framework in Section 2. We then describe the data and detail the econometric approach in Section 3. Section 4 presents the estimation results of AVEs and TRIs. We conclude in Section 5.

The TRI framework with market imperfection

We follow the standard TRI approach with the balance of trade function derived from the dual approach to trade for a small open distorted economy. We build on the usual framework with a negative externality affecting the representative consumer as in [START_REF] Copeland | International Trade and the Environment: Policy Reform in a Polluted Small Open Economy[END_REF]. The externality is assumed exogenous to the consumer but influenced by the policymaker via some NTM regulations such as standard-like regulations.

These regulations may not be set optimally and may be set at a protectionist level as in [START_REF] Fisher | Standards and protection[END_REF].

Market demand and supply, and balance of trade function

The utility of the representative consumer is u(x, H(NTM)) with non-negative market goods x and negative externality H influenced by a vector of standard-like NTM policies, NTM, and with the usual definitions and properties:

3 . 0 / with ) ( ; 0 / and 0 /             NTM H NTM H H H u u x u u H x 3
We could complicate the model by assuming that imports m influence the health externality or H(m(NTM), NTM). This would make health depends on all the arguments influencing imports and generate clutter with multiple feedback effects of all policies through health. The effect of NTM alone on health generates the possibility of trade enhancements which is what we are after. The direction of potential bias from ignoring these feedback effects is unclear to us.

All domestic consumer prices p are inclusive of the exogenous world price wp, a tariff τ, and the unit cost equivalent of the domestic NTM on foreign suppliers to sell in the domestic market, or p = wp + τ + t(NTM). 4 Given domestic prices p, the associated expenditure function is:

   

Expenditure function e exhibits all the usual homogeneity and curvature properties in prices, implying p'e pp =0, e H =p'e pH , e u =p'e pu ; e pNTM = e pH H NTM , and f'e pp f ≤ 0 for any arbitrary vector f of similar dimension as p. The marginal damage e H of the negative externality is positive for any given utility level.

To keep utility constant, expenditure has to increase when the negative externality increases. Partial derivative e u is the inverse of the marginal utility of income assumed positive. We eventually simplify preferences to follow [START_REF] Feenstra | Estimating the effects of trade policy[END_REF] in the empirical investigation section.

The impact of the NTM policy encompasses several possible cases. The demand enhancing case is e pNTM = e pH H NTM < 0. Protectionism of the NTM is implied by H NTM = 0 because the policy does not address an externality or is not based on science. Another special case could be that the NTM policy affects H (H ntm <0) but that H(NTM) does not affect a particular demand (particular good n) directly, or e p n H = 0. In this case, the policy is not protectionist per se but addressing the market imperfection has no bearing on that particular demand for good n. These last two cases show the difficulty to gauge revealed 4 Domestic and foreign firms have heterogeneous cost of meeting the NTM standard as explained later in the production component of the model and we assume that domestic firms are more efficient at meeting these NTMs.

protectionism. 5

For integrability of the Hicksian demands into the expenditure function, at least one of the demands represented by x has to be influenced by the external effect H. To illustrate, H could be the negative health effect of consuming products that are hazardous if minimum quality standards are not imposed on their production. The standard reduces the occurrence of sickness, which may affect the demand for these products, and possibly other demands via better health (reduced medical expenditure, more active leisure activities) or none other at all (all other demands independent of health status

Trade restrictiveness indices with externality

The TRI problem in our case is to find a scalar T equivalent (holding utility constant) to standard-like policies, tariffs, and production subsidies to apply as a tariff surcharge on world prices such that:

0 0 0 0 0 0 0 0 0 ( (1 ), (1 ), , , (0), ) ( ( ), ( ) , , , ( ), )
.

B wp T wp T wp z H u B wp t NTM wp t NTM s wp z H NTM u B

           (2)
The tariff surcharge accounts for several components: tariffs τ, domestic production subsidies s, the demand shift via H(NTM), and the protective effect from raising foreign cost to satisfy technical measure NTM, that is, t(NTM).

Next, as in [START_REF] Anderson | Domestic Distortions and International Trade[END_REF], while holding u constant, we differentiate equation (2) with respect to T, τ, s, and NTM to derive the relative change in T rather than T as it is customarily done in the TRI literature. This step yields: [START_REF] Fisher | Standards and protection[END_REF]. 4) shows that the TRI has three policy components corresponding to the tariff, subsidy, and NTM policies. The NTM component is the sum of a demand effect via reduced externality H, and a NTM protectionist effect relative to foreign goods (through a tariff equivalent t increasing in NTM). While the sign of this protectionist effect on imports is clear, the combined effect of NTM on m via the externality H and the protectionist effect t(NTM) is ambiguous as their relative magnitude is unknown analytically. For example, a pure protectionist NTM policy imposing useless labeling requirements would raise t(NTM) and have no effect on consumers' perception and would lead to a welfare loss and trade contraction. Conversely, standards requiring safe goods including imported ones are likely to lead to a net demand-enhancing effect lowering transaction costs for consumers. The latter NTM policy would be trade and welfare enhancing. The econometric investigation will sort the NTM regimes into trade reducing and trade facilitating since we do not impose any "protectionist" NTM prior.

Next, to further elucidate these effects and undertake our empirical investigation, we assume a simplified structure for the Hessian matrix of cross-price responses (e pp -gdp pp ) as in [START_REF] Feenstra | Estimating the effects of trade policy[END_REF], and others. The Hessians e pp and gdp pp are each assumed to be diagonal and constant, which leads to We recover TRI T from dT as in [START_REF] Feenstra | Estimating the effects of trade policy[END_REF] and [START_REF] Kee | Estimating Trade Restrictiveness Indices[END_REF], which is equivalent to the initial tariffs, subsidies, and NTMs relative to a world with all policies set to 0 by integrating both sides of (4) with respect to T going from zero to T and policies going from (0,0,0) to (τ, s, NTM). The latter approach works only if dT is non-negative. This step yields:

(

1 / '( ) ) ( ) , p p pp pp p p NTM p T wp gdp e wp B B B B TM s N           (5) with '
' ( ) / + p NTM p H NTM p B B B t NTM B H    
whose sign is undetermined. The original formula in [START_REF] Feenstra | Estimating the effects of trade policy[END_REF] contains the first positive element from tariffs abstracting from s and NTM. Here, two additional components originate from production subsidies (positive contribution to the TRI), as long as subsidies are positive, and from NTM policies (ambiguous sign). The formula in [START_REF] Kee | Estimating Trade Restrictiveness Indices[END_REF] has the protectionist effects of tariffs and subsidies and a protectionist effect of NTMs. No externality or demand enhancement appears in their equation. This additional effect included in our equation ( 5) can potentially facilitate trade and complicates the simple narrative of obstructive NTM policies and their tax equivalent.

Equation ( 5) is in essence the square root of a weighted sum of deadweight losses from tariff, production 7 This simplification reduces price effects to the own-price effect, and homogeneity holds implicitly by defining prices relative to a numéraire good. See also [START_REF] Lloyd | Partial-and General-Equilibrium Measures of Trade Restrictiveness[END_REF] for other structures of price responses.

subsidies, and the welfare effects of NTMs. If the latter is a pure protectionist policy, then B H H NTM is zero (no demand shift) and the dead weight loss from the tariff equivalent t(NTM) is added to the sum of deadweight losses. If the NTM policy facilitates trade, then the latter maps into a welfare gain. Removing the NTM decreases the TRI as welfare falls with its removal. If the latter effect dominates the distortionary effect of tariffs and subsidies, then dT is negative and T cannot be recovered using (5).

Instead, the change in TRI, dT, is the form of choice as in the early TRI investigations (e.g., [START_REF] Anderson | Domestic Distortions and International Trade[END_REF].

These effects are illustrated in partial equilibrium in Figure 1. Figure 1 shows the two effects of the NTM policies, that is, the demand enhancement shift (from x to x' with greater utility achieved with reduced health hazard), and the increase in border price (wp+t(NTM)+τ) reflecting the international cost of meeting the country's standard and the tariff, and their total effects on imports m. In previous investigations only the border price effect of NTM, t(NTM), was considered and the trade (and welfare) impact of NTM on imports was detrimental by assumption. Along with the TRI, we consider the MTRI, which holds aggregate imports (wp'm) constant. The MTRI yields the tariff equivalent to all distortions holding aggregate trade unchanged but allowing for welfare variation. The MTRI is derived in [START_REF] Anderson | The mercantilist index of trade policy[END_REF] and [START_REF] Kee | Estimating Trade Restrictiveness Indices[END_REF] who call it the overall TRI (OTRI). The derivation of the MTRI follows the spirit of the derivation of the TRI and we only present its final formula in equation ( 12). We refer readers to [START_REF] Anderson | The mercantilist index of trade policy[END_REF] for details.

An important consequence from the potential presence of trade-enhancement effects and negative AVEs from NTMs is that our TRI and MTRI estimates will be equal or smaller than the TRI and MTRI where all policies are constrained to be trade reducing. We discuss this important point in the empirical section.

The import equation to estimate

Next, we derive the import equation to estimate and the AVEs of all policy instruments. Totally differentiation of m (holding u constant) for changes in exogenous variables leads to a change in imports of good n in any country equal to:

( / ) ( / ) [( / )( / ) ( / )( / )] ( / ) . n n n n n n n n n n n n n n n n n dm m dp d y p ds m dp t NTM x H H NTM dNTM y z dz                   (6)
Equation ( 6) and m provide a way to estimate the response of imports to tariffs, subsidies, and NTM policies, and other variables as in [START_REF] Feenstra | Estimating the effects of trade policy[END_REF]. We then derive the estimate of the AVE to the net effect of NTM policies on good n. Unfortunately we cannot separately identify the individual effects of NTM on m in ( 6), but we can estimate their net effect. Following a common practice we move the tariff effect on the left hand side of ( 6) and the general specification for the import demand of good n in country ) .

c (as indicated by superscript n,c) is: , , , , , , , , ln ln(1
n c n c z n c S n c NTM n c n c n k k n c n c k m z s N T M              (7)
Elasticity  n,c is the own-price response of import of good n in country c.

is the sum of two AVE components (the tariff equivalent of NTM on world prices, and the ambiguous import subsidy/tax effect of



if we further assume that there is no trade impediment effect of the NTM policy (t(NTM)=0) at the border. This is a limit case to establish the lowest non-negative prices faced by agents in the economy. 8 Equation ( 7) once estimated provides the basis for the total AVE of NTM policies on good n, , which is:

, , / ,with 1 .

total NTM NTM NTM n c n c total AVE AVE      (8)
An AVE is developed similarly for production subsidies, based on the fact that

c n c n S c n S AVE , , , / ) 1 (      , with (
). Unfortunately, parameter γ is not readily known as we only have estimates of import demand price elasticities and not the underlying output and demand price responses. Hence, we estimate a lower bound to the production subsidy AVE by abstracting from fraction

(1-γ). Alternatively, the production subsidy AVE estimate could be seen as a market price support subsidy, affecting both consumer and producer prices. This assumption is common although not fully accurate. 9

Next, we specify as a transformation of an exponential such that it satisfies a lower bound on the total AVE of the NTM effects as before and in addition allowing for fixed effects per commodity and interaction terms with country-specific exogenous shifters (endowments) z. For a continuous NTM variable, this leads to , with parameter a constrained such that the AVE of NTM is lower bounded at -1 or -100%. The corresponding value is a=||ε n,c ||. If NTM is 8 A small number of observations is concerned by this lower bound (1.4% of the AVEs). For simplicity, we decided to impose the constraint in all tariff lines. Alternatively, the constraint could have been imposed only in the tariff lines with a negative AVE. This alternative approach would however have slightly changed the density but not the results.

9 See also [START_REF] Lloyd | Partial-and General-Equilibrium Measures of Trade Restrictiveness[END_REF] footnote 4 for an elaboration of this point.

NTM total AVE / / = x p m p      , NTM n c  , , ( ) exp NTM NTM NTM n c n c n nk k k a z       
approximated by a dichotomous variable, then the various partial derivatives of m, and t with respect to NTM do not exist and are replaced by the first difference of m for NTM equal to one and zero. This leads to an alternative formula of the total NTM AVE ( dum NTM total

AVE

) following [START_REF] Halvorsen | The Interpretation of Dummy Variables in Semilogarithmic Equations[END_REF]:

(9)
The lower bound condition in ( 9) is slightly more cumbersome with a dichotomous NTM. The intuition is that cannot be too large of a positive number (not to blow up the magnitude of the AVE in (9) with its negative denominator) to keep producer and consumer prices non-negative (or that or ). Using the same specification as for the continuous variable case of , we specify the lower bound constraint for the dichotomous case using parameter a in with . For small values of , the dichotomous and continuous values of a are approximately equal.

A parallel formulation is used for

    k c n k nk S n S c n S z ) exp( , ,

  

. As production subsidy s is positive, presumably its AVE would not lead to negative producer price issues.

The total AVE of all distortions, that is, tariffs, NTMs, and subsidies for good n in country c is then (assuming the normalization wp=1):

. , , , , c n s c n NTM c n c n AVE AVE TOT     (10)
The TRI in equation ( 5) translates into:

1/ 2 2 , ( / ) ( / ) nc nc n c n c nc nc n m p TOT T m p                  . ( 11
)
Again, if (4) gives a negative dT, then (11) cannot be used and the change in TRI, dT, is kept to express the change in the index equivalent to the welfare impact of the policy interventions. Recall that dT , ,

[exp(

) 1] / , with 1 .

dum dum NTM NTM NTM total n c n c total AVE AVE       , exp( ) 1 NTM n c   , , exp( ) 1 NTM n c n c     , , ln (1 )
NTM n c n c     , NTM n c  , , ( ) exp NTM NTM NTM n c n c n nk k k a z        , ln (1 )
n c a    , n c
 is expressed as a sum of consumer welfare changes, and that T is the square root of a positive sum of deadweight losses.

As noted above, we use the same data and AVE estimates to compute the MTRI,

merc c T : , ( / ) ( / ) nc nc n c merc n c nc nc n m p TOT T m p                  . ( 12
)

Data and econometric specification

We use the UNCTAD 10 -Comtrade database of [START_REF] Kee | Estimating Trade Restrictiveness Indices[END_REF] 11 as well as their import demand estimates [START_REF] Kee | Import Demand Elasticities and Trade Distortions[END_REF] to estimate the import demand equation ( 7), recover AVEs (equations ( 9) and ( 10)) at the 6-digit level of the Harmonized System (HS), and compute the MTRI and TRI, (and dTRI) equivalents to the three types of distortions (tariffs, NTMs and subsidies) as in equations ( 11) and ( 12) (or (4) for negative dTRI) for each country.

Data

Trade data come from the Comtrade database. We use the average of imports at the HS 6-digit line by 11 As recently pointed by [START_REF] Breaux | Quality-Adjusted Handicraft Estimates of NTM Price Gaps[END_REF], the new NTM data collection effort under the interagency MAST project seems to be problematic and less promising than one could have hoped. The older TRAINS database appears more reliable than the new MAST dataset.

other NTMs (e.g., quantity restrictions, price control and monopolistic measures) included in the database as they are heterogeneous and different from standard-like policies in their potential effects on demand. A dummy is set to one if the importing country imposes at least one technical regulation on a given HS6 product. Regarding production subsidies, the global dataset of [START_REF] Kee | Estimating Trade Restrictiveness Indices[END_REF] covers agricultural domestic support. The source is the WTO domestic agricultural support notifications. This continuous variable is in dollars and its log form is used in the estimations.

Countries' characteristics are measured by the economic size (gross domestic product -GDP), and relative factor endowments (agricultural land over GDP, capital over GDP, and labor over GDP). Data are extracted from the World Development Indicators of the World Bank. Two geographical variables are also introduced: a dummy for islands and a measure of remoteness (average distance to world markets defined as the import-weighted distance to each trading partner). Our sample includes 93 importing countries and 4,934 products (HS6 lines).

Econometric specification

We run estimations HS 6-digit line by HS 6-digit line. To control for the potential endogeneity of NTMs and production subsidies, we instrument them using exports, GDP-weighted average of the NTM dummy variable at the HS 6-digit of the 5 closest neighbors (in terms of geographic distance) and the GDPweighted average of the agricultural domestic support at the HS 6-digit of the 5 closest neighboring economies [START_REF] Kee | Estimating Trade Restrictiveness Indices[END_REF]. The instrumented estimation is performed in two stages. We first estimate a probit where the dependent variable is the presence or the absence of a technical regulation NTM and the explanatory variables are the instruments. The Mills ratio derived from this first stage is then included in the second stage equation. If one (or more) country provides production subsidies, instruments for this variable (exports, GDP-weighted average of the agricultural domestic support of the 5 closest neighbors) are also included in the second stage equation.

The quantity impact of the standard-like NTMs and production subsidies is then transformed into price-equivalents (AVEs) using the provided import demand elasticities. AVEs are calculated for each importing country and HS6 line. We impose a positive cap AVEs at 50 for a few extreme values (0.01% of the observations). 12 To facilitate the interpretation of the results, we compute the mean over all importing countries at the HS6 and HS2 levels. HS2 is used in tables for ease of reporting summary tendencies. Following our estimation, 4.7% of AVEs for NTMs at the HS 6-digit level are negative, i.e., highlighting trade-facilitating NTMs. Without constraint on the sign of the AVEs, our procedure allows us to keep these negative values in our sample. AVEs of NTMs, tariffs and production subsidies are then aggregated at the country level to derive the trade restrictiveness indices corresponding to all three types of policy interventions.

Finally, we use bootstrapping to compute the standard deviations of the AVEs. The main advantage of this procedure is to account for sampling and estimation errors of the AVEs. We draw (with repetition) 200 random samples from our dataset and perform the AVEs estimation for each of these samples. Estimations are run HS6 line by HS6 line. We then compute the bootstrap standard errors as the standard deviations of these 200 AVEs.

Results

We first present the results on AVEs of NTMs in the presence of externalities. We also provide comparisons with the AVEs obtained when the latter are constrained to be trade reducing.

AVEs of NTMs

We focus the discussion on the results obtained for the first 20 HS sections. 13 Qualitative conclusions are unchanged if the discussion of results is performed at the HS 2-digit level (with 96 sectors, see 1 underlines the upward bias affecting the estimation of AVEs when NTM are constrained to be trade-reducing. As expected, the average AVE for each HS section is systematically higher than the average AVE obtained in column (2). Using averages, the bias is about 100% (from 0.088 to 0.044).

As highlighted with the frequency ratio, the share of HS6 lines subject to at least one NTM greatly differs across sections and could therefore bias the average AVE calculated using all HS6 lines. To control 14 The sector of arms and ammunition is least likely to observed commercial trade and standard-like NTM policies.

for this bias, columns (4) and ( 5) of Table 1 report the average AVE computed only on HS6 lines on which at least one technical measure NTM is applied. Column (4) allows for the presence of market imperfections and trade-facilitating NTMs, while column (5) does not. As expected, the average AVE computed only on HS6 lines subject to a technical regulation NTM is always higher in absolute value than the one based on all HS6 lines (with or without a NTM). Table 1 also shows that forcing technical regulation NTMs to be trade impeding leads to an even larger bias when looking at sectors for which at least one technical regulation NTM is present. This bias is particular strong for agricultural and food sectors (sections 1 through 4). For example, for section 4 (Prepared foodstuff), the constrained AVE estimates are on average twelve-fold larger (0.608 and 0.049). NTM is lower to much lower. A strong exception is Pharmaceutical products (HS30) (frequency ratio of 37.9%). Interestingly, sectors included in Textiles and apparel (section XI) and Footwear and headgear (section XII) for which the competition between Northern and Southern countries has been historically contentious, are not subject to many standard-like NTMs.

To sum up, our results suggest the presence of both trade reducing and facilitating technical regulation NTMs, with substantial trade effects. Next, these AVEs of technical regulation NTMs are further used to calculate the TRI and MTRI. 15 The plot is truncated from above at AVE=1 for better clarity and excludes less than 0.18% of the AVE estimates. Columns ( 2) and ( 7) show the MTRI and TRI estimates including all distortions based on the AVEs from the estimation constraining NTMs to be trade reducing. As expected, MTRIs and TRIs exhibit larger values in columns (2) and ( 7) than those obtained using AVEs from the unconstrained estimation (see columns ( 4) and (9)). For example for the 93-country summary, the median and mean values of the MTRIs are respectively 0.117 and 0.158 with constrained estimates and only 0.048 and 0.057 with unconstrained estimates. Similarly, for the TRI the median and mean values are 0.293 and 0.325 (constrained estimation) versus 0.218 and 0.256 (unconstrained estimation). In other words, for all countries included in our sample, the MTRIs based on overall protection (tariffs, production subsidies, and NTMs) and allowing for negative AVEs are equal or smaller than the MTRIs based on overall protection computed with the constrained AVEs. This last result suggests that some technical regulation NTM regimes have trade facilitating effects for most countries. Finally, regardless of the estimation method, when comparing results using all AVE estimates or only the significant ones based on the bootstrap standard errors, one notes with the latter that ranges are reduced for most indices and Country groupings.

Trade restrictiveness indices

Countries' groupings also highlight interesting patterns. The OECD group exhibits negative MTRI values with a small mean (0.036). The LDC group shows all positive MTRIs. We also note that intuitively, many countries with low tariff-MTRIs exhibit negative total MTRIs because small tariffs do not counterbalance negative NTM AVEs. Lastly, using more disaggregated results by country (see Table A.3 of the Appendix), we note that only 28 over 93

countries have MTRI values for overall protection and based on unconstrained estimates are higher than the values based on tariffs only. In addition, as suggested by Figure 4, MTRI for tariffs only and MTRIs for all distortions show a strong downward pattern when income per capita increases. From Figure 4 it is clear that MRTIs inclusive of all distortions tend to be lower than the MTRI for tariffs only, strongly suggesting that standard-like NTMs facilitate can trade.

The analysis of the TRIs shows 24 countries with total TRIs based on unconstrained estimates smaller than the tariff-only TRI. These results show that positing protectionism of technical measure NTMs strongly biases the evaluation of the restrictiveness of these NTM trade policies. As previously mentioned, if equation (4) provides a negative dT (cf. supra), then the TRI level T cannot be computed using (5). The last columns of Tables 3 Several LDC countries also exhibit negative dTRIs and these can be rationalized by opportunities created with the agri-food trade integration and policy reforms. We have in mind the integration of LDCs in European trade following a sequence of structural adjustment policies that removed many protectionist NTMs, expanded preferential trade agreements and attempted to upgrade quality of goods. The latter induced upgrades of SPS regulations and improved food safety in countries like Côte d'Ivoire among others (FAO, 2003;[START_REF] Colen | Private Standards, Trade and Poverty: GlobalGAP and Horticultural Employment in Senegal[END_REF]and Maertens et al., 2012). Unconstrained estimation means that impact of NTMs on trade is not restricted in the econometric estimation. b: Constrained estimation means that NTMs are constrained to have a non positive impact on trade in the estimation. OECD: all OECD members included in our sample. BRICs: Brazil, Russia, India and China. LDCs: Burkina Faso, Bangladesh, Ethiopia, Madagascar, Mali, Malawi, Rwanda, Sudan, Senegal, Uganda, Zambia.

Conclusion

We extend the TRI approach to a small distorted open economy to account for market imperfections (externalities, asymmetric information) and NTM domestic regulations addressing them. Up to date, the presence of externalities and potential anti-protectionist effects of standardlike NTMs has been ignored in TRI application. Allowing for such occurrence, we derive the AVEs of NTMs, as well as the TRIs and MTRIs equivalent to all policy interventions (tariffs, NTMs and production subsidies). We show that in general the impact of NTMs on import demand is ambiguous depending on the relative strength of the import-facilitating effects of NTMs via a shift in import demand, and the protective effect of the same NTMs at the border. We then apply the approach to the UNCTAD-Comtrade database built by [START_REF] Kee | Estimating Trade Restrictiveness Indices[END_REF]. In our sample, 12% of HS6 lines are affected by technical regulation NTMs and about 39% of these (4.7% of all HS6 lines) show negative AVEs of technical regulation NTMs. The MTRI and TRI results show the sizeable changes in estimated aggregate trade and welfare effects of existing trade policies. Policy recommendations on the impacts of NTMs will be biased by overstating their trade reducing and welfare decreasing effects.

Although we show it is possible to rationalize and econometrically identify tradefacilitating effects of NTMs mitigating external effects and other market imperfections or having anti-protectionist effects on domestic suppliers, we do so using relatively simple NTM proxies and aggregate trade flows rather than bilateral ones. It would be interesting to refine these results and use more detailed NTM measures and bilateral trade data, and focus on a subset of sectors for which we identify negative NTM AVEs. Nevertheless our results corroborate the tradefacilitating effects found in the literature for some products and countries (e.g. Disdier et al., 2008;[START_REF] Moenius | Information versus Product Adaptation: The Role of Standards in Trade[END_REF]. The value added of our analysis is to formalize the possibility of antiprotectionist effects or external effects and their mitigation through regulations affecting quality of products and identify their effects on trade restrictiveness. Our analysis also extends the applicability of the TRI framework to more plausible market conditions and lets the data reveal unconstrained patterns.  Constrained estimation means that NTMs are constrained to have a non positive impact on trade in the estimation.

1 With an externality and some negative AVEs, the MTRI can be smaller or larger than the TRI and the two indices may not have similar signs.  : LDCs.  : BRIC countries.  : OECD countries.

  these conditions we derive an implementable framework to approximate the sector total AVE corresponding to all policy types τ, s and NTM as well as the implied TRI and the MTRI. In general, if the Hessian matrices of price responses of imports (or demand and supply responses) are not constrained to be diagonal, off-diagonal elements can be positive or negative and it is impossible a priori to sign elements the change in the TRI, dT. The computation of T is obviously cumbersome in the presence of cross-price effects and nonconstant slopes.

Figure

  Figure 1. The impact of NTMs on demand, supply and imports

  importing country between 2001 and 2003. Import demand elasticities are extracted from[START_REF] Kee | Import Demand Elasticities and Trade Distortions[END_REF]. Tariff data are taken from the UNCTAD and the World Trade Organization (WTO). Tariffs are for the most recent year for which data are available between 2000 and 2004. For specific tariffs, ad valorem equivalents are used. Data on NTMs are from the UNCTAD TRAINS (Trade Analysis and Information System) database with only technical regulations selected. As mentioned previously, we abstract from 10 United Nations Conference for Trade and Development.

  Figures 2 and 3 provide further insights on the technical regulation NTM AVES. Figure2shows the scattered plot of AVEs at HS6 level, average over all countries and sorted by HS2 line (x-axis

Figure 3

 3 Figure3shows the mean of the HS6 AVE averages by HS2 line when allowing for trade facilitating effects or not. All of the within-HS2 means are higher when constraining the estimation to exclude tradefacilitation effects. Some unconstrained means are negative in Lac, gums and resins (line 13), Preparations of meat and fish (line 16), Cocoa (line 18), Pharmaceutical products (line 30) and Fertilizers (line 31), suggesting again the presence of a number of trade-facilitating standard-like NTM regimes in sector like agri-food sectors and chemicals. To offset that, positive average AVEs also abound suggesting tradereducing effects in various sectors most visibly in Preparations of cereals (line 19). For some HS 2-digit sectors, such as Live animals, Dairy products, Live trees, and Edible fruit and nuts, more than one quarter of the HS6 lines are subject to at least one technical regulation NTM in one importing country. By contrast, for a number of manufactured products, the share of HS6 lines impacted by a technical regulation

Figure 2 .

 2 Figure 2. Scattered plot of HS6 level NTM AVES averaged over countries and shown by HS2 line

  report the change in TRI, dT, i.e., the change in the index equivalent to the welfare impact of the policy interventions. Countrylevel results indicate that for 17 over 93 countries, the change in TRI is negative (Table A.3 of the Appendix). Furthermore, for 25 over 93 countries, change in TRI values are smaller than the ones obtained when tariffs only are included in the computation (column (7) of Table A.3). These two last results highlight that some technical regulation NTMs can have positive welfare effects. Not surprisingly, many OECD countries exhibit negative dTRIs. This result is consistent with Disdier et al. (2008)'s results showing intra-OECD agri-food trade being enhanced by NTM regimes.

Figure 4 .

 4 Figure 4. MTRIs for tariffs and MTRIs for all distorsions against income par capida

  NTM via decreased externality). Note that the latter AVE component is bound to the left to -100% as prices are non-negative. This non-negative constraint provides a lower bound of -100% on

		NTM
		n	,	c
		, NTM n c

  Table A.1 of the Appendix). Table1first reports the simple frequency ratio of NTMs for each HS section, i.e., the share of HS6 lines within each HS section for which at least one importing country of our sample imposes at least one technical regulation NTM. The frequency ratio of NTMs should be interpreted as follows: for section I "live animals, animal products", the value 0.209 means that 20.9% of HS6 lines included in HS section I are affected by at least one technical regulation NTM in at least one importing country.Results suggest that agricultural and food products (sections I through IV) are more affected by NTMs than manufactured products. The frequency ratio is indeed larger for these products. These industries have high numbers of countries' notifications of sanitary and phytosanitary measures to the WTO.The next column of Table1reports the average AVE of these NTMs for each HS section allowing for the presence of externalities. The mean is computed over all importing countries and HS6 lines within

each section. The mean AVE on the whole sample is equal to 0.044, but strong differences can be observed across sections. First, the magnitude of the mean AVE varies significantly across sections from 0.002 to 0.083. All sections exhibit a non negative average AVE, indicating that technical regulation NTMs have, on average, a net negative impact on trade flows. For two sections (Pearls and precious metals and stones, and Arms and ammunition 14 ), the average AVE is near zero, suggesting that NTMs have on average no net impact. In our sample, 12.1% of HS6 lines are affected by NTMs and 38.8% of them exhibit negative AVEs of NTMs. These negative AVEs are spread over all HS sections (and HS2 sectors as shown in Appendix Table

A

.1). Column (3) of Table

Table 1 : Frequency ratios and AVEs of technical-regulation NTMs, by HS section

 1 The first column of Table2provides the share of technical regulation NTM-ridden observations with positive AVEs (trade-reducing NTMs). This share varies across sections, from 43.9% (Prepared The last 2 columns of Table2show the mean AVE for trade-reducing technical regulation NTMs and that of trade-facilitating technical regulation NTMs by HS section. We previously noticed that these NTMs were more numerous on agricultural products. According to the second column of Table2, the AVEs of trade-reducing NTMs on agricultural and food products are slightly higher than the ones

	HS section codes	HS section names	Simple of NTMs frequency ratio	AVE of NTMs all HS6 lines (mean)	AVE of NTMs if NTM=1 (mean)
				Unconstrained estimation a	Constrained estimation b	Unconstrained estimation a	Constrained estimation b
	I	Live animals, animal products	0.209	0.018	0.128	0.084	0.609
	II	Vegetable products	0.223	0.028	0.128	0.126	0.574
	III	Fats and oils	0.202	0.067	0.145	0.333	0.717
	IV	Prepared foodstuffs, beverages, spirits, tobacco	0.259	0.013	0.157	0.049	0.608
	V	Minerals	0.054	0.027	0.046	0.500	0.846
	VI	Chemicals, allied industries	0.134	0.033	0.088	0.244	0.657
	VII Plastics, rubber	0.121	0.052	0.094	0.432	0.774
	VIII Hides, leather, furskins	0.074	0.029	0.056	0.395	0.763
	IX	Wood and wood articles	0.105	0.051	0.077	0.486	0.732
	X	Pulp of wood, paper, printing	0.096	0.039	0.071	0.404	0.744
	XI	Textiles, apparel	0.097	0.033	0.068	0.339	0.695
	XII Footwear, headgear	0.103	0.025	0.064	0.241	0.622
	XIII	Stone, cement, ceramic articles, glass	0.081	0.055	0.074	0.681	0.917
	XIV Pearls, precious metals and stones	0.003	0.002	0.002	0.732	0.732
	XV Base metals and articles	0.085	0.044	0.067	0.516	0.796
	XVI	Machinery, electrical and video equipment	0.129	0.083	0.114	0.648	0.887
	XVII Vehicles, aircraft, vessels	0.109	0.035	0.080	0.317	0.730
	XVIII Optical, photo., medical instr.	0.096	0.042	0.074	0.441	0.775
	XIX Arms, ammunition	0.044	0.008	0.021	0.182	0.474
	XX	Miscellaneous (furniture, toys, others)	0.108	0.062	0.100	0.570	0.925
		All sections	0.121	0.044	0.088	0.362	0.729

a : Unconstrained estimation means that impact of technical regulation NTMs on trade is not restricted in the econometric estimation. b : Constrained estimation means that technical regulation NTMs are constrained to have a non positive impact on trade in the estimation.

Table

2

distinguishes between trade-reducing and trade facilitating NTM estimates using results from the unconstrained estimation (allowing for external effects). Again results are summarized by HS section.

obtained on most manufactured products. The average positive AVE for the whole sample is equal to 1.133. In the last column of Table

2

, AVEs of trade-facilitating NTMs are nonpositive of course, and because of the non-negative price constraint, they are included in the interval [-1;0]. Interestingly, we observe that the magnitude of these AVEs is high in absolute value. The minimum in absolute value per section is equal to -0.801 (section 19 Arms, ammunition) and the maximum (-0.912) is reached for section 18 (Optical, photographic, medical instruments). The mean over all sections is -0.856. Conclusions remain valid at the HS 2-digit level (see Table

A

.2 of the Appendix).

Table 2 . AVEs of trade-reducing and trade-facilitating technical regulation NTMs, by HS section

 2

	HS section codes	HS section names	Share of trade-reducing in NTM-ridden observations	Mean AVE trade-reducing NTMs (AVE>0)	Mean AVE trade-facilitating NTMs (AVE0)
	I	Live animals, animal products	0.464	1.213	-0.892
	II	Vegetable products	0.515	1.070	-0.876
	III	Fats and oils	0.554	1.269	-0.829
	IV	Prepared foodstuffs, beverages, spirits, tobacco	0.439	1.210	-0.860
	V	Minerals	0.646	1.259	-0.884
	VI	Chemicals, allied industries	0.551	1.130	-0.844
	VII Plastics, rubber	0.644	1.138	-0.846
	VIII Hides, leather, furskins	0.618	1.181	-0.873
	IX	Wood and wood articles	0.709	1.025	-0.826
	X	Pulp of wood, paper, printing	0.631	1.138	-0.853
	XI	Textiles, apparel	0.637	1.031	-0.875
	XII Footwear, headgear	0.594	1.014	-0.889
	XIII Stone, cement, ceramic articles, glass	0.748	1.190	-0.829
	XIV Pearls, precious metals and stones	1.000	0.732	--
	XV Base metals and articles	0.696	1.096	-0.816
	XVI	Machinery, electrical and video equipment	0.735	1.175	-0.817
	XVII Vehicles, aircraft, vessels	0.618	1.037	-0.847
	XVIII Optical, photo., medical instr.	0.655	1.155	-0.912
	XIX Arms, ammunition	0.672	0.663	-0.801
	XX Miscellaneous (furniture, toys, others)	0.653	1.341	-0.882
		All sections	0.612	1.133	-0.856
		Figures 2 and 3 provide further insights on the technical regulation NTM AVES.

AVE average per HS6 line shown by HS2 line

	6 0.8 NTM Average of HS6 averages by HS2 line (unconstrained) Live animals, animal products Vegetable products Animalss and vegetable fats Prepared foods, beverages, spirits, tobacco Mineral products Chemical, pharmaceuticals, cosmetics Rubber and plastics Hide and leather	Wood products	Wood pulp, paper, printed	Average of HS6 averages by HS2 line (constrained) Textiles, apparel and clothing Footwear, headgear Stone, cement, ceramic, glass Pearls Base metals and articles of base metal Machinery, electrical equipment Vehicles, aircraft, vessels, transport Optical, photographic, precision instr. Arms Miscellaneous
	0	2	4	6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50 52 54 56 58 60 62 64 66 68 70 72 74 76 78 80 82 84 86 88 90 92 94 96
					HS2 line

  Table3reports summary figures of the results for country-level MTRIs, TRIs and changes in TRIs. Three calculations are performed based on (i) tariffs only, (ii) overall protection using AVEs from the constrained estimation, and (iii) overall protection using unconstrained AVEs.The latter two sets of measures are also summarized for all AVE estimates and for the subset of significant AVE estimates based on the bootstrap standard errors. The summary statistics are presented for all 93 countries, OECD countries, Least Developed Countries (LDCs), and then BRIC (Brazil, Russia, India and China) countries.The tariff only MTRI and TRI (1 st and 6 th columns in Table3) represent the uniform tariff that would provide the same level of imports (MTRI) and welfare (TRI) as the initial tariff

structure. OECD countries where in most cases except Japan and South Korea tariffs have been significantly reduced, exhibit smaller tariff-MTRIs than the 93-country averages, LDCs' and especially the BRICs' averages. According to detailed country results reported in Table

A

.3 of the Appendix, India has the highest tariff-MTRI (0.257) among the 93 countries; Brunei and South Korea have the highest tariff-TRI at or above 0.5. Hong-Kong and Singapore have zero tariff indices as they do not impose border tariffs.

Table 3 . Trade restrictiveness indices, summary statistics

 3

	Indices	MTRI (T merc )	T merc	T mccr	T merc	T merc	(T) TRI	T	T	T	T	(dT) TRI change	dT	dT	dT
	Protection tariffs Estimation	overall protection constrained b unconstrained a	tariffs	overall protection constrained b unconstrained a	overall protection constrained b unconstrained a
	Estimates	all	all	signif.	all	signif.	all	All	signif.	all	signif.	all	signif.	all	signif.
		(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)	(10)	(11)	(12)	(13)	(14)
								All 93 countries
	Minimum 0.000 0.004	0.002	-0.360 -0.074 0.000 0.046	0.043	0.046	0.045	0.002	0.002	-0.266 -0.077
	Maximum 0.257 0.642	0.553	0.279	0.257 0.585 0.894	0.855	0.842	0.595	0.800	0.731	0.708	0.354
	Mean	0.081 0.158	0.140	0.057	0.073 0.142 0.325	0.279	0.256	0.185	0.144	0.114	0.064	0.040
	Median	0.072 0.117	0.112	0.048	0.066 0.121 0.293	0.221	0.218	0.139	0.086	0.049	0.039	0.016
	Std. dev	0.056 0.151	0.131	0.083	0.064 0.098 0.198	0.191	0.157	0.134	0.181	0.153	0.127	0.077
								OECD countries
	Minimum 0.008 0.009	0.009	-0.106 -0.063 0.042 0.050	0.048	0.050	0.045	0.002	0.002	-0.103 -0.077
	Maximum 0.153 0.387	0.350	0.214	0.133 0.510 0.595	0.566	0.515	0.509	0.354	0.321	0.265	0.259
	Mean	0.041 0.070	0.061	0.036	0.035 0.111 0.270	0.191	0.234	0.127	0.088	0.056	0.054	0.019
	Median	0.028 0.041	0.035	0.030	0.022 0.069 0.272	0.161	0.216	0.093	0.074	0.026	0.039	0.005
	Std. dev	0.036 0.079	0.072	0.050	0.037 0.102 0.125	0.140	0.120	0.101	0.077	0.077	0.078	0.051
									LDCs
	Minimum 0.029 0.029	0.029	0.007	0.029 0.046 0.046	0.046	0.046	0.046	0.002	0.002	0.002	-0.003
	Maximum 0.174 0.598	0.508	0.172	0.236 0.220 0.773	0.713	0.520	0.443	0.598	0.508	0.270	0.196
	Mean	0.103 0.193	0.177	0.100	0.108 0.131 0.276	0.262	0.195	0.172	0.126	0.111	0.053	0.038
	Median	0.092 0.136	0.136	0.092	0.094 0.116 0.208	0.205	0.181	0.127	0.043	0.042	0.033	0.016
	Std. dev	0.043 0.180	0.150	0.052	0.056 0.054 0.234	0.216	0.130	0.117	0.202	0.175	0.076	0.057
									BRICs
	Minimum 0.103 0.233	0.216	0.033	0.071 0.126 0.298	0.298	0.180	0.165	0.089	0.089	0.033	0.027
	Maximum 0.257 0.292	0.257	0.257	0.257 0.298 0.499	0.471	0.298	0.298	0.249	0.222	0.089	0.089
	Mean	0.151 0.261	0.242	0.107	0.142 0.190 0.417	0.399	0.249	0.231	0.179	0.163	0.064	0.057
	Median	0.123 0.260	0.247	0.069	0.120 0.168 0.435	0.413	0.260	0.231	0.190	0.171	0.068	0.056
	Std. dev	0.072 0.024	0.019	0.102	0.081 0.081 0.088	0.076	0.054	0.067	0.069	0.058	0.026	0.031
	a:

  Unconstrained estimation means that impact of NTMs on trade is not restricted in the econometric estimation.  Constrained estimation means that NTMs are constrained to have a non positive impact on trade in the estimation.

				Cork and articles	0.074	0.135	0.141	1.831	1.915
			46	Straw	0.081	-0.014	0.020	-0.177	0.246
	X		47	Pulp of wood	0.058	0.061	0.084	1.062	1.457
			48	Paper	0.106	0.028	0.066	0.262	0.627
			49	Printed books, newspapers	0.075	0.080	0.088	1.065	1.166
	XI		50	Silk	0.041	0.015	0.029	0.366	0.702
			51	Wool	0.079	0.053	0.081	0.665	1.025
			52	Cotton	0.096	0.017	0.061	0.182	0.635
			53	Other vegetable textile fibres	0.071	0.030	0.047	0.423	0.665
			54	Man-made filaments	0.118	0.013	0.060	0.107	0.506
			55	Man-made staple fibres	0.105	0.017	0.062	0.160	0.593
			56	Wadding	0.115	0.051	0.082	0.447	0.711
	II XII III IV XIII XIV XV V VI XVI XVII XVIII VII XIX VIII XX IX	*	57 6 58 7 59 8 60 9 61 10 62 11 63 12 64 13 65 14 66 15 67 16 68 17 69 18 70 19 71 20 72 21 73 22 74 23 75 24 76 25 78 26 79 27 80 28 81 29 82 30 83 31 84 32 85 33 86 34 87 35 88 36 89 37 90 38 91 39 92 40 93 41 94 42 95 43 96 44	Products of animal origin Carpets Live trees & other plans, bulbs, roots Special woven fabrics Edible vegetables Impregnated, coated, covered, lamin. textile fabrics 0.095 0.112 0.096 0.304 0.081 0.234 Edible fruit and nuts 0.265 Knitted or crocheted fabrics 0.087 Coffee, tea, maté 0.224 Apparel & clothing accessories, knitted/ crocheted 0.090 Cereals 0.223 Apparel & clothing access., not knitted/ crocheted 0.109 Products of the milling industry 0.191 Other made-up textile articles 0.094 Oil seeds & oleaginous fruits 0.199 Footwear 0.149 Lac, gums & resins 0.206 Headgear 0.055 Vegetable plaiting materials 0.108 Umbrellas 0.052 Animal or vegetable fats and oils 0.202 Feathers 0.045 Preparations of meat, of fish 0.309 Stone articles 0.058 Sugars 0.278 Ceramic products 0.099 Cocoa 0.250 Glass articles 0.092 Preparations of cereals, flour, starch or milk 0.385 Pearls, precious stones and metals 0.003 Preparations of vegetables, fruit, nuts 0.274 Iron & steel 0.084 Miscellaneous edible preparations 0.317 Articles of iron or steel 0.110 Beverages, spirits and vinegar 0.204 Copper 0.064 Residues and waste from the food industries 0.120 Nickel 0.026 Tobacco 0.193 Aluminum 0.099 Salt 0.053 Lead 0.031 Ores, slag and ash 0.023 Zinc 0.057 Mineral fuels, mineral oils 0.085 Tin 0.041 Inorganic chemicals 0.093 Other base metals 0.022 Organic chemicals 0.130 Tools 0.110 Pharmaceutical products 0.379 Miscellaneous articles of base metal 0.095 Fertilizers 0.199 Nuclear reactors 0.118 Tanning or dyeing extracts 0.126 Electrical machinery & equipment 0.150 Essential oils and resinoids 0.229 Railway 0.057 Soaps 0.171 Vehicles 0.150 Albuminoidal substances 0.149 Aircraft 0.045 Explosives 0.078 Ships, boats 0.052 Photographic or cinematographic goods 0.075 Optical, photog., measuring, prec., medical instr. 0.136 Miscellaneous chemical products 0.119 Clocks and watches 0.000 Plastics and articles 0.122 Musical instruments 0.045 Rubber and articles 0.119 Arms and ammunitions 0.044 Raw hides and skins 0.077 Furniture 0.114 Leather 0.097 Toys 0.127 Fur skins and artificial fur 0.036 Miscellaneous manufactured articles 0.088 Wood and articles of wood 0.110	0.024 0.050 0.019 0.032 0.037 0.058 0.043 0.046 -0.001 0.029 0.087 0.051 0.072 0.058 0.008 0.026 -0.086 0.022 0.014 0.015 0.067 0.031 -0.090 0.051 -0.027 0.024 -0.077 0.074 0.310 0.002 0.018 0.034 0.018 0.048 -0.023 0.025 0.003 0.034 0.026 0.026 0.027 0.050 0.007 0.015 0.045 0.031 0.043 0.052 0.044 0.079 -0.056 0.056 -0.036 0.087 0.076 0.077 -0.062 0.057 0.008 0.034 0.002 0.030 0.036 0.008 0.050 0.060 0.040 0.000 0.048 0.018 0.061 0.008 0.051 0.081 0.012 0.053 0.010 0.054 0.048	0.066 0.075 0.153 0.054 0.138 0.087 0.151 0.076 0.117 0.061 0.190 0.082 0.121 0.084 0.110 0.086 0.050 0.048 0.080 0.028 0.145 0.036 0.105 0.059 0.150 0.058 0.092 0.095 0.427 0.002 0.170 0.062 0.208 0.075 0.105 0.047 0.082 0.038 0.098 0.060 0.045 0.057 0.013 0.041 0.076 0.032 0.076 0.055 0.091 0.106 0.184 0.077 0.076 0.111 0.102 0.121 0.087 0.060 0.087 0.105 0.062 0.036 0.048 0.032 0.069 0.106 0.082 0.000 0.088 0.033 0.106 0.021 0.078 0.129 0.053 0.100 0.017 0.078 0.075	0.218 0.518 0.061 0.395 0.157 0.607 0.161 0.529 -0.003 0.327 0.392 0.468 0.376 0.611 0.042 0.176 -0.418 0.412 0.129 0.286 0.333 0.697 -0.290 0.875 -0.097 0.247 -0.309 0.804 0.805 0.732 0.065 0.408 0.056 0.441 -0.111 0.386 0.026 1.300 0.137 0.259 0.511 1.605 0.307 0.269 0.531 0.737 0.465 2.327 0.339 0.720 -0.147 0.592 -0.181 0.736 0.602 0.516 -0.271 0.994 0.047 0.230 0.012 0.667 0.464 0.149 0.666 0.443 0.340 0.000 0.392 0.400 0.511 0.182 0.668 0.712 0.119 0.419 0.270 0.618 0.433	0.782 0.592 0.666 0.503 0.918 0.590 0.878 0.572 0.681 0.521 0.755 0.852 0.887 0.633 0.579 0.552 0.876 0.244 0.535 0.741 0.805 0.717 1.018 0.339 0.586 0.540 1.037 0.366 0.732 1.110 0.746 0.619 0.679 0.658 0.739 0.514 1.443 0.681 0.605 0.508 1.841 0.855 0.711 0.585 0.785 0.896 2.461 0.814 0.962 0.705 0.810 0.485 0.940 0.383 0.806 0.813 1.053 0.382 0.697 0.510 0.799 0.417 0.613 0.613 0.777 0.920 0.000 0.692 0.737 0.715 0.474 0.890 1.136 1.017 0.788 0.549 0.890 0.480 0.679

Table A .2. AVEs of trade-reducing and trade-facilitating NTMs, by HS 2-digit sector

 A

	HS2 codes	HS2 names	Share of trade-reducing in NTM-ridden observations	Mean AVE trade-reducing NTMs (AVE>0)	Mean AVE trade-facilitating NTMs (AVE0)
	01	Live animals	0.408	1.495	-0.919
	02	Meat & edible meat offal	0.446	1.394	-0.899
	03	Fish and crustaceans	0.504	1.071	-0.893
	04	Dairy products, eggs	0.408	1.267	-0.875
	05	Products of animal origin	0.616	0.888	-0.858
	06	Live trees & other plans, bulbs, roots	0.525	0.903	-0.868
	07	Edible vegetables	0.537	1.034	-0.862
	08	Edible fruit and nuts	0.529	1.084	-0.878
	09	Coffee, tea, maté	0.473	0.994	-0.899
	10	Cereals	0.542	1.533	-0.956
	11	Products of the milling industry	0.648	1.035	-0.834
	12	Oil seeds & oleaginous fruits	0.467	1.119	-0.900
	13	Lac, gums & resins	0.267	0.597	-0.788
	14	Vegetable plaiting materials	0.487	1.263	-0.948
	15	Animal or vegetable fats and oils	0.554	1.269	-0.829
	16	Preparations of meat, of fish	0.329	0.866	-0.858
	17	Sugars	0.344	1.470	-0.921
	18	Cocoa	0.322	1.012	-0.936
	19	Preparations of cereals, flour, starch or milk	0.641	1.557	-0.539
	20	Preparations of vegetables, fruit, nuts	0.454	1.172	-0.856
	21	Miscellaneous edible preparations	0.424	1.384	-0.923
	22	Beverages, spirits and vinegar	0.414	1.079	-0.952
	23	Residues and waste from the food industries	0.518	0.878	-0.892
	24	Tobacco	0.503	1.055	-0.795
	25	Salt	0.674	1.188	-0.890
	26	Ores, slag and ash	0.712	0.793	-0.893
	27	Mineral fuels, mineral oils	0.596	1.483	-0.878
	28	Inorganic chemicals	0.629	1.226	-0.827
	29	Organic chemicals	0.594	1.156	-0.859
	30	Pharmaceutical products	0.344	1.211	-0.858
	31	Fertilizers	0.379	0.937	-0.862
	32	Tanning or dyeing extracts	0.732	1.100	-0.757
	33	Essential oils and resinoids	0.299	1.032	-0.828
	34	Soaps	0.492	0.933	-0.809
	35	Albuminoidal substances	0.577	0.666	-0.882
	36	Explosives	0.768	0.861	-0.849
	37	Photographic or cinematographic goods	0.738	1.197	-0.826
	38	Miscellaneous chemical products	0.626	1.055	-0.856
	39	Plastics and articles	0.659	1.025	-0.833
	40	Rubber and articles	0.616	1.372	-0.869
	41	Raw hides and skins	0.656	1.483	-0.885
	42	Leather	0.529	0.983	-0.851
	43	Fur skins and artificial fur	0.784	0.609	-0.964
	44	Wood and articles of wood	0.725	0.924	-0.863
	45	Cork and articles	0.795	2.507	-0.799
	46	Straw	0.381	0.514	-0.603
	47	Pulp of wood	0.600	2.381	-0.917
	48	Paper	0.603	0.989	-0.843

Table A .3. Trade restrictiveness indices, by country

 A Unconstrained estimation means that impact of NTMs on trade is not restricted in the econometric estimation.

	T merc 0.117 South Korea  0.107 Kenya Lebanon 0.057 Country Tariffs Sri Lanka 0.074 Lithuania 0.021 Latvia 0.028 Albania 0.118 Morocco 0.228 Argentina 0.130 Australia  0.058 Austria  0.016 Belgium  0.021 Moldova 0.048 Madagascar  0.029 Mexico  0.153 Mali  0.095 Burkina Faso  0.106 Bangladesh  0.172 Mauritius 0.122 Malawi  0.091 Belarus 0.085 Malaysia 0.063 Bolivia 0.080 Nigeria 0.219 Brazil  0.109 Brunei 0.148 Canada  0.028 Switzerland  0.041 Nicaragua 0.048 Netherlands  0.014 Norway  0.046 New Zealand  0.028 Chile  0.069 Oman 0.117 China  0.137 Peru 0.126 Ivory Coast 0.093 Philippines 0.037 Cameroon 0.139 Colombia 0.112 Costa Rica 0.041 Pap. N. Guinea 0.035 Poland  0.104 Portugal  0.037 Czech Rep.  0.041 Paraguay 0.119 Germany  0.015 Denmark  0.018 Algeria 0.131 Romania 0.120 Russia  0.103 Rwanda  0.092 Egypt 0.128 Spain  0.015 Estonia  0.009 Saudi Arabia 0.142 Sudan  0.174 Senegal  0.086 Ethiopia  0.136 Singapore 0.000 Finland  0.011 France  0.014 Gabon 0.153 El Salvador 0.066 Slovenia  0.102 Sweden  0.015 Great Britain  0.019 Thailand 0.110 Ghana 0.145 Trinidad & T. 0.072 Greece  0.013 Guatemala 0.068 Tunisia 0.229 Turkey  0.044 Hong Kong 0.000 Honduras 0.053 Tanzania 0.137 Uganda  0.066 Hungary  0.061 Ukraine 0.064 Indonesia 0.045 India  0.257 Uruguay 0.097 United States  0.025 Ireland  0.008 Venezuela 0.136 Iceland  0.032 Italy  0.017 South Africa 0.070 Zambia  0.086	T merc 0.117 0.108 0.116 Overall protection T merc 0.117 0.108 -0.029 0.074 0.074 Constrained estimation  Unconstr. estimation* ,1 0.053 -0.045 0.099 -0.046 0.127 0.114 0.594 0.083 0.178 0.121 0.149 0.056 0.026 0.023 0.033 0.031 0.048 0.048 0.029 0.029 0.387 0.214 0.112 0.091 0.163 0.143 0.172 0.172 0.159 0.106 0.148 0.138 0.168 0.084 0.302 0.155 0.122 0.020 0.475 -0.065 0.262 0.033 0.196 0.158 0.041 -0.052 0.041 0.041 0.130 0.002 0.025 0.024 0.060 0.023 0.176 0.094 0.103 0.052 0.157 0.078 0.233 0.083 0.243 0.083 0.323 -0.360 0.642 0.279 0.162 0.139 0.235 -0.009 0.041 0.040 0.035 0.034 0.118 0.080 0.050 0.047 0.047 0.030 0.219 0.029 0.022 0.020 0.027 0.034 0.478 0.006 0.143 0.065 0.292 0.055 0.092 0.092 0.571 0.152 0.032 0.030 0.009 0.009 0.158 0.152 0.598 0.145 0.488 0.007 0.136 0.136 0.108 0.046 0.020 0.017 0.020 0.019 0.155 0.157 0.066 0.066 0.230 0.052 0.019 0.018 0.028 0.026 0.135 0.088 0.190 0.128 0.072 0.072 0.027 0.026 0.086 0.001 0.400 0.157 0.046 0.046 0.004 -0.009 0.053 0.053 0.618 0.249 0.067 0.065 0.069 0.039 0.134 -0.108 0.073 0.025 0.257 0.257 0.220 -0.075 0.091 -0.106 0.012 0.011 0.234 0.044 0.032 0.032 0.027 0.026 0.075 0.067 0.115 0.083	T 0.183 0.510 0.098 Tariffs 0.138 0.064 0.073 0.135 0.275 0.142 0.097 0.055 0.068 0.205 0.046 0.213 0.110 0.121 0.220 0.232 0.122 0.106 0.269 0.087 0.307 0.131 0.585 0.077 0.195 0.080 0.061 0.258 0.045 0.069 0.258 0.206 0.129 0.117 0.068 0.159 0.131 0.072 0.166 0.151 0.176 0.063 0.130 0.049 0.047 0.160 0.157 0.126 0.116 0.197 0.056 0.050 0.350 0.215 0.108 0.181 0.000 0.043 0.046 0.175 0.101 0.121 0.053 0.092 0.169 0.247 0.298 0.052 0.098 0.300 0.096 0.000 0.080 0.160 0.083 0.088 0.154 0.083 0.298 0.117 0.050 0.042 0.159 0.128 0.073 0.133 0.113	T 0.183 0.515 0.265 Overall protection T 0.183 0.515 0.138 0.138 Constrained estimation  Unconstr. estimation* 0.177 0.261 0.161 0.122 0.894 0.677 0.324 0.265 0.314 0.150 0.407 0.236 0.328 0.272 0.205 0.205 0.046 0.046 0.595 0.485 0.176 0.113 0.292 0.267 0.220 0.220 0.321 0.250 0.263 0.247 0.323 0.211 0.559 0.429 0.228 0.677 0.342 0.463 0.180 0.863 0.842 0.117 0.198 0.198 0.304 0.322 0.298 0.293 0.182 0.426 0.332 0.207 0.070 0.340 0.207 0.407 0.233 0.426 0.250 0.543 0.788 0.592 0.221 0.175 0.460 0.258 0.082 0.073 0.173 0.169 0.212 0.137 0.304 0.252 0.087 0.351 0.293 0.219 0.258 0.487 0.704 0.366 0.225 0.499 0.287 0.116 0.116 0.837 0.617 0.341 0.313 0.050 0.050 0.374 0.367 0.773 0.520 0.676 0.227 0.181 0.181 0.312 0.214 0.263 0.188 0.226 0.199 0.180 0.178 0.101 0.101 0.398 0.207 0.180 0.152 0.271 0.216 0.252 0.173 0.381 0.308 0.298 0.298 0.354 0.347 0.158 0.576 0.395 0.107 0.106 0.053 0.080 0.080 0.880 0.713 0.085 0.078 0.125 0.383 0.333 0.259 0.298 0.298 0.460 0.273 0.134 0.112 0.396 0.141 0.131 0.131 0.272 0.248 0.149 0.128 0.208 0.128	dT 0.033 0.260 0.010 Tariffs 0.019 0.004 0.005 0.018 0.076 0.020 0.009 0.003 0.005 0.042 0.002 0.045 0.012 0.015 0.049 0.054 0.015 0.011 0.072 0.008 0.094 0.017 0.343 0.006 0.038 0.006 0.004 0.066 0.002 0.005 0.067 0.042 0.017 0.014 0.005 0.025 0.017 0.005 0.028 0.023 0.031 0.004 0.017 0.002 0.002 0.026 0.025 0.016 0.013 0.039 0.003 0.002 0.123 0.046 0.012 0.033 0.000 0.002 0.002 0.031 0.010 0.015 0.003 0.008 0.028 0.061 0.089 0.003 0.010 0.090 0.009 0.000 0.006 0.026 0.007 0.008 0.024 0.007 0.089 0.014 0.003 0.002 0.025 0.016 0.005 0.018 0.013	dT 0.033 0.265 0.070 0.019 Overall protection 0.033 0.265 dT -0.037 0.019 Constrained estimation  0.031 -0.054 Unconstr. 0.068 -0.050 estimation* 0.026 0.800 0.458 0.015 0.105 0.070 0.098 0.022 0.166 0.055 0.108 0.042 0.042 0.002 0.002 0.354 0.235 0.031 0.013 0.074 0.085 0.071 0.049 0.103 0.062 0.069 0.061 0.049 0.105 0.312 0.184 0.045 0.052 0.458 0.117 -0.026 0.214 0.033 0.745 0.708 0.014 -0.063 0.039 0.092 -0.003 0.104 0.089 0.086 0.033 0.182 0.110 0.039 0.043 0.116 0.043 0.005 0.166 0.182 0.063 0.054 0.294 0.621 0.350 -0.266 0.049 0.031 0.212 0.067 0.007 0.030 0.029 0.045 0.019 0.092 0.063 0.005 0.008 0.123 -0.026 -0.003 0.086 0.048 0.067 0.237 0.496 0.050 -0.009 0.249 0.082 0.013 0.013 0.134 0.700 0.380 0.116 0.098 0.002 0.140 0.135 0.598 0.270 0.457 0.051 0.002 0.033 0.097 0.046 0.033 0.069 0.035 0.051 0.039 0.032 0.010 0.010 0.158 0.043 0.032 0.023 0.032 0.073 0.064 0.030 0.047 0.145 0.089 0.089 0.095 0.125 0.120 0.025 0.331 0.156 0.011 0.011 -0.037 0.003 -0.009 0.006 0.774 0.509 0.007 0.006 0.006 0.016 0.147 -0.085 -0.007 0.111 0.067 0.089 0.212 -0.035 0.075 -0.103 0.089 0.018 0.157 0.020 0.013 0.017 0.017 0.074 0.062 0.022 0.016 0.043 0.016
	Jordan Japan 	0.119 0.082	0.261 0.088	0.092 0.054	0.162 0.330	0.439 0.364	0.279 0.216	0.026 0.109	0.193 0.133	0.078 0.047
	Kazakhstan	0.043	0.146	-0.006	0.075	0.320		0.006	0.103	-0.031

*

If we set the cap at 100 (symmetric cap as the one imposed to negative AVEs), then the share of concerned observations is 0.006% instead 0.01% and our results remain unchanged. We chose a lower bound (50) to limit distortions.

Section XXI (objects of art and antiques) has very few HS6 lines with NTMs and is not reported.

Beghin acknowledges support from the Marlin Cole fund at Iowa State University and Disdier acknowledges financial support from the Agence Nationale de la Recherche (grant ANR-12-JSH1-0002-01) and from the European Commission (FP7-SSH-2013-2. Grant Agreement N°613504).

Appendix


Marc Hässig

Yann Rolland

Gültekin Topuz

Ömer Faruk Çelik

Marc Sosson

Western

Kenet Sevan-Akera

Arasinda Zonlari

Yapi Köken

Tektonik Ve

İlişkin Gelişime

Verilerin Yeni

Marc Karşilaştirilmasi

Hassig
email: hassig@geoazur.unice.fr

WESTERN EXTENSION OF THE SEVAN-AKERA SUTURE ZONE (ERZINCAN-ERZURUM REGION, TURKEY): COMPARISION OF NEW DATA CONCERNING ORIGIN, STRUCTURE AND TECTONIC EVOLUTION BETWEEN SEVAN-AKERA AND ERZINCAN SUTURE ZONES

HAL is

ÖZ

Sevan-Akere kenetinin tektonik evrimini, özellikle batıya doğru İzmir-Ankara-Erzincan keneti ile bağlantısını, daha iyi anlamak için, Erzincan civarında İzmir-Ankara-Erzincan kenetinde gözlemler ve örnekleme yapıldı. Bu çalışmanın hedefi, her iki yörede gözlenen ofiyolitler arasındaki bağlantının belirlenmseidir. Ermenistan'da olduğu gibi Türkiye'deki yapılar karmaşık, kıtasal çarpışma ve sonrasında gerçekleşen olaylar sonucunda yeniden işlenmiş ve hareketlenmiştir. Şu ana kadar yapılan arazi çalışmaları sonucunda, (1) Hınıs civarında ofiyolitlerin güney doğuya doğru Toridler üzerine itilmiş olduğu, (2) ofiyolitlerin Erzincan yöresinde volkanit, volkanoklastit ve resifal kireçtaşlarından oluşan düşük dereceli metamorfitler üzerine bindirmiş olduğu, ve (3) Pontidlerin güney kıta kenarının ofiyolitler üzerine itilmiş olduğunu belirlenmiştir. Geleneksel olarak kenet zonu okyanusal kabuk parçaları ve yersel olarak eklojit dilimleri içeren tektonik melanj ile Toridler arasına yerleştirilmektedir. Ancak üç nolu gözlem Erzincan-Aşkale hattının kuzeyinde eklem zonu varlığını gerektirmektedir. Biz düşük dereceli metamorfik birimin okyanusal bir ada yayından türemiş olduğunu tahmin ediyoruz.

Bu gözlemler, Ermenistan'da Sevan-Akera Zonu'nda olana oldukça benzer bir bağlam arz etmektedir: (1) Kuzeydeki Pontid kıta kenarının altına dalan ofiyolit dilimlerinin varlığı saptanmıştır. (2) Olasılıkla bir okyanus içi ada yayından türemiş metamorfitler üzerine ofiyolit dilimi bindirmektedir, ve (3) ofiyolitler güneydeki pasif kıtasal kenarı üzerine bindirmekteler. Burada yanıtlanması gereken en önemli soru, bu kadar değişik jeolojik ilişkiler içinde görülen ofiyolitlerin tek bir ofiyolit olup olmadığıdır. Ayrıca, Erzincan kenedinin çalışılması obduksiyona uğramış ofiyolitin altında beklenen okyanus içi bir ada yayının varlığı olasılığını doğurmaktadır.

Anahtar Kelimeler: Ofiyolit, yerleşme; düşük-dereceli metamorfik birim; Erzincan Kenedi; Sevan-Akera Kenedi

ABSTRACT

In order to better understand the tectonic evolution of the Sevan-Akera suture zone, particularly its connection westward into the Ankara-Erzincan suture, field observations and sampling were carried out on the Erzincan suture zone (near Erzincan). The goal of this study is to solve the problem of linking both odbucted ophiolitic domains. As in Armenia, the structures in Turkey are complex and have been reworked and reactivated because of post-obduction collision stages. Still, field investigations have been able to evidence that (1) ophiolites are thursted above the margin of the Taurides to the SE, near Hınıs. Futhermore, (2) these ophiolites overthrust a lowgrade metamorphic unit characterized by volcanites, volcanoclastics and reef limestones, in the Erzincan area. In addition, (3) these ophiolites are overthrusted by the Pontides margin which imply a suture zone farther north of the Erzincan and Akale areas. Ordinarily, the Erzincan suture zone is placed between the metamorphic unit and the Taurides platform, characterized by a tectonic mélange containing blocks of oceanic crust which may include some eclogite slices (observed by G. Topuz). We believe that the metamorphic unit could originate from an island arc setting.

These observations illustrate a similar context to that which has been observed in Armenia limited to the North by the Sevan-Akera suture, specifically from north to south: (1) The ophiolites were underthrusted below the Pontides margin, (2) the ophiolite body is found overthrusting a metamorphic unit potentially derived from an intra-oceanic arc and (3) the ophiolites were thrusted to the south upon the Taurides continental domain. The question of having a single obducted ophiolite body in the three places is posed. In addition, the study of the Erzincan suture brings new insights on the possibility of the presence of an expected intra-oceanic island arc under the obducted ophiolite domain.

Keywords: Ophiolite, obduction, lowgrade metamorphic unit, Erzincan Suture, Sevan-Akera Suture

THE SEVAN-AKERA SUTURE ZONE (ERZINCAN-ERZURUM REGION, TURKEY): COMPARISION OF NEW DATA CONCERNING ORIGIN, STRUCTURE AND TECTONIC EVOLUTION BETWEEN SEVAN-AKERA AND ERZINCAN SUTURE ZONES Marc Hassig 1 , Yann Rolland 1 , Gültekin Topuz 2 , Ömer Faruk Çelik 3 , Marc Sosson 1

	65.Türkiye Jeoloji Kurultayı	2-6 Nisan/April 2012	65 th Geological Congress of Turkey
	WESTERN EXTENSION OF 1 Université de Nice-Sophia Antipolis, Observatoire de la Côte d'Azur, Geoazur, Faculté
	des Sciences, Parc Valrose, 06108 Nice cedex 2, France.
	2 İstanbul Teknik Üniversitesi, Avrasya Yerbilimleri Enstitüsü,
		TR-34469 Maslak, İstanbul, Turkey
	3 Kocaeli Üniversitesi, Jeoloji Mühendisliği Bölümü, 41150 Kocaeli, Turkey
		(hassig@geoazur.unice.fr)
		113