Competition in the international niobium market: A residual demand approach

Resources Policy 65 (2020) 101564

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Competition in the international niobium market: A residual demand approach☆ �ilison W. Silveira a, Marcelo Resende b, * Ja a b

Secretaria de Política Econ^ omica, Minist�erio da Economia, Esplanada dos Minist�erios, Ed. Sede do Minist�erio da Economia, Bloco P, 70048-900, Brasilia, DF, Brazil Instituto de Economia, Universidade Federal do Rio de Janeiro, Av. Pasteur 250, Urca, 22290-240, Rio de Janeiro, RJ, Brazil

A R T I C L E I N F O

A B S T R A C T

Keywords: Niobium Residual demand Market power Pricing-to-market

Niobium is a highly strategic mineral, of which Brazil holds almost all the world’s reserves, followed by Canada. This study investigates the prevailing market power for niobium at the country level by using the residual de­ mand approach suggested by Goldberg and Knetter (1999). The empirical evidence on the US and European Union (EU) destination markets, indicates a significant and similar market power for Brazil and yet a nonnegligible intra-block trade that might play some role in the EU destination market. Moreover, some comple­ mentarity patterns are detected in connection to ferrovanadium alloys.

1. Introduction The intensity of exports competition among national markets is a recurring topic in the literature. Traditionally, the assessment of market power in selected industries builds on developments in the industrial organization literature, which devised methodologies for identifying the intensity of competition with unobserved marginal costs [see Bresnahan (1989) and Baker and Bresnahan (1992) for overviews of the so-called New Empirical Industrial Organization (NEIO)]. It is possible to high­ light two salient research strategies that pertain to the way firms and industries respond to changes in the elasticity of demand or in the marginal cost. The latter strategy involves a conceptual experiment with residual demand estimations that attempt to identify a firm’s ability to profitably implement cost pass-through following firm-specific cost shocks. This methodology is proposed by Baker and Bresnahan (1988) in the context of a firm-level analysis. Goldberg and Knetter (1999) adapt the aforementioned framework to address the intensity of competition in international markets by focusing on exports to selected destination markets, with a country-level analysis. Specifically, the authors estimate the elasticities of (inverse) residual demands by taking as references the US linerboard paper and the German beer export industries and obtain estimates that appear to be plausible, given the available information on market shares and the number of competitors. In the present study, we estimate the elasticities

of the inverse residual demand for Brazilian ferroniobium industry, using the methodology proposed by Goldberg and Knetter (1999). Niobium (Nb) is a chemical element with a high melting point, which is corrosion resistant and exhibits superconductivity properties at low temperatures and Brazil possesses the largest niobium reserves globally (80.2%), followed by Canada (17.6%) and is also the largest producer of niobium globally, being responsible for more than 88% of the world’s production.1 Ferroniobium is an iron niobium alloy used to add niobium to steel to obtain greater mechanical resistance, a lighter weight, and reduced cost and it is the main product made from niobium. The amount of niobium necessary to induce significant enhancements in mechanical properties is minimal. So-called high-strength low-alloy steel (HSLA) typically involves an addition of 400 g of niobium per ton of steel [see Socorro (2001)]. These steel alloys are used, for example, in oil and gas pipelines, in platforms for oil exploration in deep waters, in the shipping industry, and in the building sector. Moreover, these alloys have important applications in the manufacturing of truck and car frames and wheels. Another important application for niobium is in the production of superalloys (e.g. vacuum graded (VG) niobium) used in materials that can be subjected to long periods in an oxidizing and corrosive atmo­ sphere at temperatures above 650 � C. These superalloys are used, for example, in combustion equipment, nuclear reactor cores, rocket parts, and jet engine components. Finally, it is worth mentioning the use of niobium in stainless steel manufacturing. The incorporation of niobium

The authors acknowledge detailed comments from two anonymous referees, but the usual caveats apply. * Corresponding author. E-mail addresses: [email protected] (J.W. Silveira), [email protected] (M. Resende). 1 Data for 2017. USGS Mineral Commodity Summaries (2019).

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https://doi.org/10.1016/j.resourpol.2019.101564 Received 25 July 2019; Received in revised form 2 December 2019; Accepted 3 December 2019 Available online 20 January 2020 0301-4207/© 2019 Elsevier Ltd. All rights reserved.

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ensures better performance at high temperatures and contributes to neutralizing the effect of carbon and nitrogen, thus providing greater durability. This study assesses the market power in the US and EU destination markets of Brazilian exports of ferroniobium, based on quarterly data from 2003Q1 to 2016Q1. Those two destination markets respectively represent 14.8% and 31.2% of the total exports of ferroniobium from Brazil. The article contributes to the literature in at least two aspects:

international economics. Goldberg and Knetter (1997) overview the extensive literature on goods prices and exchange rates and highlight three main strands that comprise theoretical and empirical contributions with varied underlying concept assumptions, and yet diverse measure­ ment and estimation approaches. The authors provide the essential involved concepts. First, one can mention assessments of the law of one price (LOP) that aim at assessing the extent of market integration and posits that identical products sell for the same common-currency price in different countries. Under its stronger (absolute) version there are stringent assumptions pertaining to profit maximization and costless transportation, distribution, and resale that are somewhat relaxed in the so-called relative version. Second, there is a large branch of the litera­ ture that focuses on exchange rate pass-through (ERPT) that refers to percentage change in local currency import prices resulting from a one percent change in the exchange rate between the exporting and importing countries. The degree of completeness in such transmission depends on several market structure, demand and institutional features that tend to be country and sector-specific. For example, intra-industry trade and increasingly important non-tariff barriers can influence the magnitude of the ERPT. Additional details are discussed in the surveys by Menon (1995) and Aron et al. (2014). Third, and more recently, there has been a growing interest in the study of pricing-to-market (PTM) practices that refer to price discrimination across international markets that are prompted by changes in international relative costs (often related to exchange rates) and a more recent representative study is provided by Atkeson and Burstein (2008). Altogether, the aforemen­ tioned studies outline different aspects of imperfect competition in in­ ternational markets, for which the different specific effects may not be easily disentangled in empirical analyses. Nevertheless, the increasingly global character of economic transactions may render price discrimi­ nation across destination markets, as embodied in PTM practices, as particularly relevant. Such efforts do not preclude the quantification of market power and identification of the sources across distinct destina­ tion markets. Analogous applications to Goldberg and Knetter (1999) were un­ dertaken by Bragança (2005) in the case of the main coffee exporters to the US destination market and by Coronel et al. (2010) in terms of main exports of soymeal to the European Union (EU) market. In both cases, despite Brazil’s large market share, its indicated market power is particularly low. In Bragança’s (2005) study, this result is likely to reflect important product differentiation accruing from Colombian competition. Moreover, Pall et al. (2014) investigate the market power of Russian wheat exporters in eight low and middle-income destination markets in the case of lower quality wheat, with the market power prevailing only in a few markets. The study constitutes an exception in that segment of the literature in terms of the implementation of tests for non-stationarity. The literature on market power is somewhat small for metals in general and mostly focus on domestic markets with examples including Yang (2001) for the U.S aluminum industry, and Agostini (2006) for the U.S, copper industry. The former constitutes an exception in the litera­ ture by considering a residual demand approach at a domestic market. As for market power studies in international markets for metals, it is worth highlighting the studies by Zhu et al. (2019a) and Zhu et al. (2019b). The former investigated the change in market power for the main producer of tungsten, China, in terms of the 17 largest importers and identified a non-negligible increase in that outcome following export restriction policies. The latter, assesses market power in iron ore exports by Australia, Brazil, India and South Africa to the Chinese destination market and a salient result include the identification of significant and time-varying market power only for Australia and Brazil Both studies also rely on residual demand estimation, as in the present paper, however no preliminary investigations were implemented in terms of unit root and cointegration tests.

a) Niobium plays an important role in steel alloys for the aerospace industry and has future potential for the industry’s superconductors. Although there are other descriptive studies reporting the main supply and demand characteristics of the niobium sector [see Lav­ �rio (1991), and Cunningham (2000)], there is erick (1988), Campana a significant gap in the literature in terms of quantitative economic analyses. The substantial dominance of Brazil in the world produc­ tion has raised concerns of niobium criticality. The many types of the risks associated with a regular supply of raw material has been the focus of a growing amount of studies in the last decades. There is a general consensus about the idea that criticality involves the supply risk and the impact of the vulnerability to that supply risk, although there is a huge divergence about how to quantify that impact [see Dewulf et al. (2016)]. However, niobium is considered as possessing a high level of criticality in different studies such as the US National Research Council (2008), which was the developer of the criticality matrix method, and included niobium as a critical metal for the US. The European Commission [EC (2017)] also conducted a study to identify the most critical metals for EU, and have listed 20 materials, including niobium. Hatayama and Tahara (2015) evaluated the criticality of 22 metals for Japan and found a high score for niobium as well. Erdmann and Graedel (2011), using a different method, distinguish three levels of criticality and put the niobium among the highest level. This singular situation of niobium production moti­ vates market power assessments, and the related interest is also reinforced given the unfulfilled potential of alloys that incorporate niobium. b) A second interest of our study is that the bulk of the residual demand literature do not perform (or mostly do not perform) the tests (unit root, cointegration) about non-stationarities. Exceptions, in terms of a dynamic specification, include Aiginger et al. (1995), Steen and Salvanes (1999) and Zeidan and Resende (2009), situated within the strand of literature that considers changes in the elasticity of demand for identifying market power.2 In contrast, previous works do not consider non-stationarity and cointegration issues in residual de­ mand estimation. In fact, Goldberg and Knetter (1999) advanced the latter type of estimation in methodological terms, by adapting firm-level residual demand estimation to national markets. However, a shortcoming of the empirical application was the very short annual time series used, which hindered addressing the aforementioned econometric issues. The paper is organized as follows. The second section reviews the literature. The third section provides a basic background on the niobium sector. The fourth section discusses the conceptual aspects of residual demand estimation at the country level. The fifth section discusses the construction of the database. The sixth section presents and discusses the econometric results. The seventh section provides some final comments. 2. Literature review It is possible to observe, over the last decades, a growing body of the literature that benefits from insights from industrial organization that are incorporated in both theoretical and empirical studies in 2

Aiginger et al. (1995) only obtain indirect evidence for cointegration. 2

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3. Brazilian niobium industry

corresponding market shares of 65% and 35% in 2017. In the EU destination market, in contrast, no import duties are charged for ferro­ niobium exports from Brazil and Canada and the corresponding market shares were 85% and 15% in 20179. A distinctive feature of the EU destination market pertains to the intense trade of ferroniobium within the EU area. Thus, although the Netherlands typically appears as the entry for niobium to the European Union, non-negligible intra-trade occurs within that economic block. In the next sub-section, we outline the data sources and definitions of the variables used in the estimated system for country-level residual demands for ferroniobium.

The ferroniobium alloys have a large potential of applications in the industry that ranges from infrastructure and automotive industry to nuclear reactor cores, rocket parts and jet engine components. Table 1 illustrates niobium’s main applications 3 and also indicates the major firms in operation, dominated by CBMM. Other firms include a multi­ national company headquartered in England (Anglo American), which also operates a mine in Brazil, and IAMGOLD/Niobec, extracting niobium in Canada.4 The major part of the Brazilian niobium production is directed to foreign markets. Among the destination markets for Brazilian niobium in 2016, one can highlight Netherlands (28.7%), China (25.9%), Singapore (14.9%), United States (11.9%), and Japan (9.6%). In the same year, less than 10% of the total production of standard ferro­ niobium alloy (66% of niobium content and 30% of iron) was directed to the domestic market.5 It is reasonable to say that the potential for applications of niobium has not yet been fulfilled and, despite the large supply potential and strong market dominance by a few firms in a few countries, the rele­ vance of substitute products cannot be underestimated. In fact, vana­ dium, molybdenum, manganese, titanium, and tantalum can be used as imperfect substitutes in some alloys, although the substitutions imply efficiency losses and increased costs.6 In the steel industry, titanium and vanadium can be also used to produce HSLA; however, the vanadium content in ferrovanadium alloys, for example, is twice the niobium content in ferroniobium alloys.7 Tantalum can also replace niobium in the manufacturing of superalloys used in the aircraft industry to manufacture special turbines and alloys resistant to corrosion and high temperatures. However, that metal has a higher price and a high density. Although those metals were used as imperfect substitutes in many cases, as we have described above, there are some applications where niobium can be to be used together with those metals, acting as complementary goods. That is the case for some special alloys that are used in space and nuclear industries, for example, because of the superconductivity char­ acteristic that the joint use gives to the alloys.8 It is important to mention the distinctive features of the two major producers and of the two destination markets considered in this study (the United States and the European Union). In Brazil, the cost condi­ tions for niobium extraction are more favorable, as it has open pit mines, in contrast to Canada, where underground mines prevail. This, in prin­ ciple, would favor the dominance of Brazil. However, other important aspects that could generate costs’ discrepancies between Brazil and Canada relate to the refining and conversion steps, since different techniques could be used but the associated data is not available to allow the assessment of the importance of such discrepancies. Some additional aspects of the niobium production process are summarized in Table 2. It may be also relevant to consider the prevailing tariff structure in the destination markets. During the sample period of our analysis no changes have occurred but some contrasts are worth mentioning. In the US destination market, import tariffs for ferroniobium are 5% and 0% for Brazilian and Canadian exports, respectively, with

4. Residual demand estimation 4.1. – Basic concepts Bresnahan (1989) and Baker and Bresnahan (1992) highlight resid­ ual demand estimation as an appealing approach for assessing market power in oligopolistic markets. The basic conceptual experiment at­ tempts to isolate situations where firm-specific cost shocks are profitably transmitted to prices, thus allowing the firm to exercise market power. Baker and Bresnahan (1988) laid the foundation for determining firm-level market power through residual demand estimation with un­ observed marginal costs. Those authors generally define the residual demand function at the firm-level as the relationship between one firm’s price and quantity, taking into account the supply response of all other firms. The approach is simpler, as it does not require the estimation of the various cross price elasticities of demand, and the analysis relies on residual demand elasticity, which indicates the demand perceived by a specific firm after taking into account all supply adjustments of com­ petitors. The prevalence of market power is associated with a steep re­ sidual demand, and, thus, residual demand elasticity and its possible interpretation in terms of the mark-up play an important role. Goldberg and Knetter (1999) adapt the approach in the context of aggregate data related to exporters from different source countries that compete in the same destination market. Their approach assumes per­ fect substitution between the products within each of the source mar­ kets. However, goods produced by different exporting countries may not be perfect substitutes. As it will become clear later in the paper, in the development of the general case of the residual demand curve, the involved parameters summarize all information about competition is­ sues, including product differentiation. However, one needs to assume perfect substitution between the products within each of the source markets given the non-availability of firm-level data, Goldberg and Knetter (1999) propose to interpret the parameters as industry averages considering as weights the market shares of the firms in the source country. The approach advanced by Goldberg and Knetter (1999) can be summarized as follows: Consider a group of exporters selling to a particular foreign destination market, focusing on the estimation of the residual demand of a given exporting country (denoted by k). For a given source country A, let pex and Qex indicate the good’s price and exported quantity to the destination country, respectively. Moreover, let p1 ; …; pn indicate the good’s prices for the n competing exporting countries, where prices are expressed in terms of the destination market currency, and let Z represent a vector of demand shifters at the desti­ nation market. The set of demand equations will be: � (1) pex ¼ Dex Qex ; p1 ; …………::; pn ; Z

3

General discussions on niobium characteristics can also be found Linnen et al. (2014). 4 More recently, in 2014, IAMGOLD decided to concentrate its operation in the gold segment and sold the northern Quebec mine (Niobec) to an investor group led by Magris Resources Inc. 5 Source: http://www.anm.gov.br/dnpm/publicacoes/serie-estatisticas-e-eco nomia-mineral/sumario-mineral/sumario-brasileiro-mineral-2017/niobio_sm _2017. Accessed in 2019/10/02. 6 See Mineral Commodities Summaries (2019), United States Geological Survey (USGS). 7 Mineral Commodities Summaries (2019), USGS. 8 Examples of the joint use of those alloys can be found at: https://link. springer.com/article/10.1007/BF00660322. Accessed in 2019/09/14.

� pk ¼ Dk Qk ; pj ; pex ; Z ​ where ​ j ¼ 1; ……:; n and j 6¼ k

(2)

9 Relevant tariff information can be found at https://www.trademap.org/Ind ex.aspx.

3

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Table 1 Niobium: Main producers and applications. Product

Key Producers

% of Nb Market

Applications

Main markets

Standard grade ferroniobium (HSLA FeNb) ~60% Nb content

92.2%

Vacuum Grade Ferroniobium (VG FeNb)

� CBMM � Anglo American � Niobec � CBMM

3.0%

� high strength low alloy steel (HSLA) � stainless steel � heat resistant steels � superalloys

Niobium metals and alloys ~50–65% Nb content

� CBMM

3.4%

� superconductors

Niobium chemicals > 99% Nb content

� CBMM

3.4%

� Functional ceramics catalysts

� automotive industry � heavy engineering and infrastructure � petrochemical sector � aircraft engines � power generation � petrochemical sector � particle accelerators � magnetic resonance imaging � various small tonnage uses � optical � electronics

Source: Niobec. Accessed in 26/09/2017 at http://niobec.com/en/about/niobium/.

the competitors:

Table 2 Niobium: production process.

pk ¼ e⋅MCk

Step

Description

Mining

The primary sources of niobium are deposits of pyrochlore and tantalite-columbine. Pyrochlore deposits in Arax� a, Minas Gerais contain 2.3% niobium Mined pyrochlore undergoes a concentration process to increase niobium content to 50%. This is achieved through magnetic separation and flotation process that remove unwanted elements Pyrochlore concentrate, called niobium pentoxide, is refined in two stages: sulfur, chlorine, and water are removed first; then phosphorus and lead. The residual material is used to produce a range of niobium products. Most of the niobium produced is converted into a ferroniobium. First, aluminum is added to remove oxygen from the refined pyrochlore concentrate. Iron is added next.

Concentration Refining

Conversion

As emphasized by the authors, expression (5) considers a set of n-1 partially reduced forms for the reference country’s competitors. Thus, the final step involves the substitution of eςςquation 5 into equation (1), and after eliminating redundancies, one can obtain the expression for the residual demand curve of the reference country: � � (6) pex ¼ Dex Qex ; p1* ð:Þ; ……:; pn* ð:Þ; Z ¼ Dres; ex Qex ; W N ; Z; ϚN

To obtain the residual demand for the reference country, it is necessary to consider the optimizing behavior of the remaining com­ petitors. Thus, from the first-order conditions follows the equality be­ tween the perceived marginal revenue and marginal cost. However, as firm-level data are not available, as in Baker and Bresnahan (1988), it is possible to consider market share weights (si) and obtain aggregate conditions by summation. Thus, one can obtain the following supply relations in the context of aggregate data, and interpret parameters as industry averages:

where: � � X ∂qex j θ¼ 1þ ∂qex i j6¼i φ¼

This expression comprises three classes of observables: the quantity produced by the exporter ðQex Þ – the reference source country, demand shifters ðZÞ that can include, for example, prices of substitute goods, industrial production of sectors that demand niobium, time trend, break dummy variable and real income among other demand-related variables at each destination market, and other firm cost shifters WN , that can include measures of input prices like wages, energy price and exchange rates among others. The estimation of such a class of models will clearly requires the consideration of instrumental variables methods given the endogeneity of the variable Qex accruing from the simultaneity between Qex and pex . The intuition for the econometric identification of residual demand lies in detecting the ability of profitably implementing cost pass-through following exporter-specific cost shifts. In other words, to make the re­ sidual demand curve econometrically identified, we need variables that are in the equation arising from the first order conditions, as indicated by (3), but it is not in the residual demand curve (6), we have it with the exporter group-specific cost shifters (Wex). In the empirical application by Goldberg and Knetter (1999), the sole exporter-specific cost shifter was the exchange rate faced by the exporter. In the empirical application undertaken below, we will consider a broader set of instruments.

(3)

Qex ⋅Dex 1 ⋅ θ ⋅ φ

(3a)

� � X ∂Dex ∂Dk 1þ ⋅ ∂pk ∂pex j6¼i

(4)

Expression (4) condenses the set of n-1 supply relationships of the reference country’s exporting rivals. The simultaneous solution of the demand equations given by (2) and the supply relationships given by (4) expresses the competitors’ prices as a function of costs (WN), the demand shifters of the n products (Z), and the quantity Qex exported by the reference country. Let ϚN stand for the union of all conduct parameters and then it follows that: � (5) pk ¼ pk* Qex ; W N ; Z; ϚN ; k ¼ 1; ……; n

Source: CBMM.

pex ¼ e⋅MCex

Qk ⋅Dk1 ⋅θk for k ¼ 1; :::; n

(3b)

where e indicates the exchange rate in the destination market currency; the weighted. P marginal cost in the source country is given by MCex ¼ si ⋅ MCex i , i

and in a similar vein. Qex denotes the (weighted) aggregate quantity for the export group. Dex 1 represents the partial derivative of the demand function with respect to the first argument, whereas θ and φ, respectively, refer to the (weighted) aggregates capturing the competitive behavior among ex­ porters within the source country and the competition between the source country and the other exporters. Similarly, the following expression represents the supply relations of

4.2. – Empirical model and interpretation The estimation of the residual demands and the related elasticities constitute the approach considered in the present study for assessing market power. Goldberg and Knetter (1999) adapted the firm-level approach advanced by Baker and Bresnahan (1988) to a country-level analysis to assess the intensity of the competition in different 4

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competition and Stackelberg leadership with a competitive fringe allow such a clear-cut interpretation for the (inverse) residual demand elas­ ticity. Our present application in the niobium market is associated with a homogeneous product with a dominant producer, and, thus, the residual demand elasticity is an appealing indicator for the intensity of competition. In the present study, we consider the estimation of a system of re­ sidual demands for niobium from Brazil as directed toward the US and EU destination markets. We consider a log-log functional form that, in generic terms, would lead to the following expression for a particular residual demand, where ε stands for a stochastic disturbance:

Table 3 Niobium: production, reserves and pricea. Brazil Canada World

Mine Production (tons)

Reserves (tons)

Price (US$/kg)

60,700 6980 69,100

7,300,000 1,600,000 9,100,000

21.1 22.5 21.5

Source: USGS and TRADEMAP. a The mine production and reserves are quantities of niobium content in 2017 from USGS. The price is the average world export price of ferroniobium in 2018 from TRADEMAP.

destination markets for firms located in different source markets [German beer and US linerboard paper]. As emphasized by the authors, the identification of the residual demand elasticity relies on exchange rate shocks that rotate the supply relation of the source-country export group relative to competitors located in other countries, as, in their application, the exchange rate is the sole country-specific cost shifter. The adaptation from a firm-level to a country-level analysis necessarily involves considering the average effects given the aggregation. In fact, a generic supply relation will comprise one component that captures the competitive behavior among the exporters within the source country and another component that reflects the competitive interaction be­ tween the source country’s firms and foreign producers. Therefore, we present a sharper empirical application, as we consider the intensity of competition of the two main niobium exporters to the US and EU destination markets, with Brazil being highly dominant compared to Canada, the second largest exporter. Moreover, the competition within the source markets is very limited,10 as, in Brazil, one firm (Companhia ~o - CBMM) dominates the supply Brasileira de Metalurgia e Mineraça compared to the second acting firm (Anglo American11), whereas a single producer (Niobec) operates in Canada.12 Table 3 reports nio­ bium’s production, reserves and price. This fact is important in our case given the data unavailability at the firm-level and led to the use of data at the country-level. Nevertheless, despite such data limitation, the strong dominance of a particular Bra­ zilian firm in that industry makes the aggregate behavior similar to the one by that referred firm. The interpretation of the (inverse) residual demand elasticity as properly portraying the intensity of competition relies on its association with the theoretical mark-up as given by the Lerner index L ¼

p

ex

MC pex

ex

’ ex ’ N ln pex mt ¼ λm þ ηm ⋅ln Qmt þ αm ⋅lnZmt þ βm ⋅ln W mt þ εmt

(7)

It is worth emphasizing that the conceptual experiment considered by residual demand estimation aims to isolate in the data, situations where firm-specific (or country-specific) cost shocks can be transmitted to prices. This requires some form of instrumental variable estimation, where firm-specific cost shocks must be instrumented for. The coeffi­ cient pertaining to the (inverse) residual demand elasticity (ηm) refers to the key-parameter for the assessment of market power and the remaining coefficients not always are easily interpreted. Previous studies, such as Baker and Bresnahan (1988) and Yang (2001), imple­ ment the residual demand approach for firm-level studies. However, Goldberg and Knetter (1999), Bragança (2005), and Coronel et al. (2010) did not explore the non-stationarity of the data. In contrast with Knetter and Goldberg (1999), we undertake a pre­ liminary analysis of the stationarity of the individual series in terms of unit root tests, and, in the case of I(1) variables, it is the possible to implement the usual cointegration tests. Such preliminary tests, prior to the residual demand estimations, are crucial so as to avoid the possibility of spurious regressions. As we shall see later in the application, the evidence suggests the prevalence of cointegration among the variables. In Section 3.2, we outline the variables of our empirical model. 5. Data set construction The data set was constructed using different sources. Table 4 de­ scribes the variables, taking as a reference the log-log specification that will be implemented below, and Table 5 provides summary statistics for the untransformed variables. The different indexes used were put in terms of a common base period, and the basic categories, as in any NEIO study, include prices, quantities, demand shifters, and cost shifters. The construction of the database aimed to match disaggregated information in connection to the niobium as closely as possible, although in some cases, some series are more aggregated. In the present application, exports from Brazil and Canada were considered in terms of two destination markets, namely, the United States and the European Union in terms of its main entrance market represented by the Netherlands. We consider quarterly data over the period from 2003Q1 to 2016Q1. The choice of this frequency reflects the data availability. In fact, electricity prices for Brazil are only avail­ able after 2003, whereas GDP data are only available on a quarterly basis. Furthermore, niobium exports of Brazil to the two selected destination markets (the United States and European Union) involved some missing monthly data. The construction of quarterly data involved using the averages of the existing values within each quarter. The two markets received around of 50% of the global ferroniobium exports along the whole studied period, whereas the two aforementioned pro­ ducing countries are responsible for 90% of the global production.13 As regards the exchange rates, sample averages were calculated from daily data. Finally, all the series, unless already seasonally adjusted,

. In

theoretical terms, the lower bound of such index occurs when p ¼ MC and therefore L ¼ 0 corresponds to a perfect competition scenario, whereas the strongest case with a monopolistic exercise of market power would prevail when L ¼ 1/|εdp| and thus refers to the reciprocal of the absolute value of the price elasticity of demand which is not always known. Values of the (inverse) residual demand elasticity that are close to 1 would indicate oligopolistic market power. The theoretical upper bound of 1 for a monopolist only would prevail in the case of a unit elasticity and thus a more common upper bound for the referred index would occur in the case of elastic demands and would be less than 1. It is important to stress that the later reported negative coefficients for the (inverse) residual demand elasticities are negative. However, one is indeed focusing on their absolute values for interpretation in terms of Lerner indexes. As contended by Baker and Bresnahan (1988) and Goldberg and Knetter (1999), there are some salient cases where the residual demand elasticity has an exact correspondence with the theoretical Lerner index. In fact, in the cases of consistent conjecture equilibrium, perfect

10 Brazilian producers were responsible for 92% of the world supply in 2008, while the CBMM alone supplied 91.4% of all ferroniobium produced in Brazil, besides of this the CBMM owned 75% of the Brazilian niobium reserves in 2008. 11 The Anglo American (currently NIOBRAS) was bought by a Molybdenum China group in 2016. 12 See Section 3 for additional background on the sector.

13

5

See USGS Mineral Commodity Summaries (2019).

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Table 4 Data description. Description

Source

pnbbr

Logarithm of ferroniobium alloy price exported by Brazil to the U.S (US$ per kg)

US TRADE - Census Bureau

qnbbr

Logarithm of quantity of ferroniobium alloy exported by Brazil to the U.S. in kg

US TRADE - Census Bureau

pnbeu

Logarithm of ferroniobium alloy price exported by Brazil to the EU (Euro per kg)

EUROSTAT

qnbeu

Logarithm of quantity of ferroniobium alloy exported by Brazil to the EU in kg

EUROSTAT

ecan=us

Logarithm of the nominal exchange rate (C$/US$)

Federal Reserve

pelecbr ebr=us

Logarithm of the industrial energy distribution tariff by the main firm in the state of Minas Gerais, Brazil (CEMIG) (R$ per MW) Logarithm of the nominal exchange rate (R$/US$)

Brazilian Regulatory Agency for Energy (ANEEL) Brazilian Central Bank(BACEN)

gdpus

Logarithm of the index of Gross domestic product (GDP)

FEDERAL RESERVE

gdpeu

Logarithm of the index of Gross domestic product (GDP)

EUROSTAT

pvausa

Logarithm of the price of vanadium (US$ per kg)

US TRADE - Census Bureau

pvaeu

Logarithm of the price of vanadium (Euro per lglo)

EUROSTAT

eus=br

Logarithm of the nominal exchange rate (US$/R$)

Brazilian Central Bank(BACEN)

Logarithm of the nominal exchange rate (€/R$)

Brazilian Central Bank(BACEN)

Dummy variable that assumes value 1 within the 2007Q1-2009Q4 period and 0 otherwise Dummy variable that assumes value 1 when Netherlands exports more niobium to the rest of the EU than Brazil (in all quarters if 2013) and 0 otherwise

– EUROSTAT

Variables

e

br=eu

DSB DSHARE

Note: all variables that are index numbers were put in the common base period of 2017:12.

were subject to the usual method using the X-13 ARIMA-SEATS module available in the R platform. Furthermore, two dummy variables were created. First, it was important to acknowledge the sudden niobium price increase related to the abrupt demand increase from China. Thus, a dummy variable, DSB, was considered, which assumes the value 1 during the 2007–2009 period and the value 0 otherwise. Second, non-negligible intra-trade within the EU block was considered, using the dummy variable DSHARE, which assumes the value 1 during the period from 2013Q1 to 2013Q4 and the value 0 otherwise. In fact, the Netherlands typically acts as the entry port for the EU destination market, but over the years its role has shifted between that of an importer and an exporter: between 2000 and 2012, it acted more as an importer of Brazilian niobium than as an exporter to the remaining EU countries (except in 2007). However, a reversal in that pattern was observed in 2013, with Netherland sup­ plying 77% of the niobium imported by the European Union, whereas Brazil provided only 6%. Thus, the aforementioned dummy variable aims to control for the effect of the Dutch market in the EU destination market. Some industry demand shifters associated with the aerospace, automotive, and infrastructure industries were considered at first. However, as we will implement the Johnsen’s cointegration test, all variables have to be integrated of order 1 [I(1)], but not all were diag­ nosed as I(1) series, and even in favorable cases, no satisfactory results were obtained in terms of the instruments’ validity. Thus, we have opted

to consider GDP as a demand shifter. 6. Empirical results 6.1. – Unit root tests The results are presented in Table A1 and A2 in appendix. A neces­ sary first step in the empirical analysis is assessing the degree of inte­ gration of the involved series. The evidence indicates that the different series possess a unit root. 6.2. – Cointegration tests Since the involved series are I(1), we can proceed with the cointe­ gration test by Johansen (1988). First, we have to choose the correct number of the lags in the model, we did it through of VAR estimation14 and it was select 1 lag by HQI and SBIC information criteria. After this, we can implement the cointegration test that was performed with a constant and 1 lag.15 The evidence displayed in Table A3 and A4, at the appendix, suggests the prevalence of cointegration. Thus, we gain additional confidence in the estimated residual demands for Brazil in the two destination markets, as presented and discussed in the next sub-section. 6.3. – Residual demand estimation results

Table 5 Summary statistics [No.of observations: 53]. Variables

Mean

Std. Dev.

Minimum

Maximum

pnbus

19.60

8.26

7.65

28.25

qnbus

743,442

226,453

178,001

1,202,913

pnbeu

14.18

5.04

6.39

20.06

qnbeu

1,115,567

435,808

86,742

1,807,302

ecan=us

1.14

0.13

0.96

1.50

ecan=euro

1.46

0.10

1.24

1.64

gdpus

1.14

5.07

75.38

95.86

gdpeu

87.76

3.55

79.83

94.14

pelecbr

219.30

111.23

49.15

528.44

eus=br

2.30

0.58

1.59

3.91

ebr=eu

2.93

0.51

2.26

4.30

pvaus

26.10

11.29

7.30

56.50

pva

17.55

6.65

6.8

36.44

eu

The logic of residual demand estimation involves identifying situa­ tions where there are incentives for cost pass-through following specific shocks, and the ability to identify such situations indicates the preva­ lence of market power and is summarized by the residual demand elasticity. The oligopolistic feature of the interaction between compet­ itors would suggest an endogeneity issue that must be addressed using a method of instrumental variables. In the specific applications of the present study, we consider a three-stage least squares (3SLS) estimator. This system estimator allows obtaining more efficient estimates by ac­ counting for possible correlation between the errors of the equations of the two destination markets, which could reflect common shocks. All the estimations were implemented in the Stata 12.0 software. The results for 14

It was used the command of Stata ‘varsoc’. We have used the Stata commands ‘vecrank’ and ‘VEC” to obtain the (normalized) cointegration vector. 15

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when average annual imports measured 5.5 tons, and in to 2015, they reached 15 tons. Fig. 1 is suggestive: The interpretations of the estimated coefficients in a residual de­ mand model are not always straightforward, with the exception of the main parameter of interest, given by the residual demand elasticity [see Baker and Bresnahan (1988)]. It is worth mentioning the significant coefficients associated with the cost shifter of the competitor [eca­ n/usa (-1)] and with the demand shifters pva. A detailed analysis of the dynamic effect of exchange rates cannot be accomplished in the present analysis. However, the lagged effect is suggestive of the complex dy­ namics that can emerge and possible J patterns associated with export revenue responses to exchange rates. Bahmani-Oskooee et al. (2014) find evidence of such patterns for different Brazilian exporting sectors, including the mineral sector, which comprises ferroniobium. A general survey on the J curve can be found in Bahmani-Oskooee and Ratha (2004). The negative sign of the exchange rate is intuitive, as the apprecia­ tion of the local currency would raise the cost of the competitor in the destination market and thus allow Brazilian exporters to raise their prices. However, in the case of the price of vanadium, the negative and significant coefficient plays a complementary role as regards ferro­ niobium in high performance alloys. Finally, the main coefficient of interest is the elasticity of the residual demand, which under reasonable assumptions can be interpreted as the Lerner index. The 3SLS results imply a statistically significant residual demand elasticity of 0.725 (whose p-value is 0.000), which indicates significant market power for Brazil in the US destination market, considering the restrictions imposed by the main competitor (Canada), as it was already expected due the characteristic of this market mentioned before.

Table 6 Residual demand estimation results by three stage least squares (3SLS) – U.S. and E.U. destination markets, Brazilian exports of ferroniobium alloy [2003Q2/ 2016Q1]. No. of observations: 52. U.S. destination market Dependent variable: pnbus regressor constant qnbus gdpus pvaus ecan/us(-1) DSB F(11,40) ¼ 39.93 p-value ¼ 0.000

coefficient 14.340 0.725 6.300 0.256 2.632 0.203

p-value 0.000 0.000 0.000 0.001 0.000 0.030

Dependent variable: pnbeu regressor coefficient constant 40.373 qnbeu 0.814 eu 12.326 gdp eu 0.190 pva ecan/eu(-1) 0.723 DSB 0.061 DSHARE 1.934 F(11,40) ¼ 174.86 p-value ¼ 0.000

p-value 0.000 0.002 0.000 0.056 0.234 0.534 0.007

E.U. destination market

Instrumented: qnbus, qnbeu. Included instruments:ecan/us(-1),ecan/eu(-1),gdpus, gdpeu, pvaus, pvaeu,DSB, DSHARE. Excluded instruments: eus=br , eeu=br , pelecbr . Sargan’s overidentification test statistic: 44.094. Under H0 distributed as χ2(11), p-value ¼ 0.000.

the US and EU destination markets are presented in Table 6. We have experimented with a lag structure of variables with a maximum order of 3. The main criteria for model selection emphasized the validity of the chosen instruments and the significance of the coefficients. Based on this logic, the less parameterized specification was selected, according to the Akaike Information Criteria, while respecting the validity of the chosen instruments. Inspecting the results reported in Table 4 first requires conducting the usual Sargan’s test16 for validity of the instruments to assess the orthogonality between the residuals of the estimated model and the selected instruments. If one cannot reject the null hypothesis of orthogonality the instruments appear satisfactory. Specifically, upon the residuals from an instrumental variables estimation, one can consider an auxiliary regression of that series on the selected instruments and assess the individual or joint significance of the associated coefficients. In the case that there are more instruments beyond the necessary minimum for identification, the joint test can also be interpreted as a test for overidentifying restrictions. In the present application, we have 3 excluded instruments for 1 endogenous variable to be instrumented, and, therefore, one can also interpret the test as an over-identification test.

6.3.2. Discussion: EU destination market As regards the EU destination market, the statistical fit is also satis­ factory in terms of the significance of the individual coefficients, although with two exceptions. The effect of the abrupt demand change in China, as captured by DSB, is not relevant in the case of the EU destination market. In contrast with the US destination market, the cost shifter of the competitor, as given by the exchange rate ecan/eu(-1), does not suggest a relevant role. However, the demand shifter represented by vanadium, as represented by pvaeu, exhibits a qualitatively similar effect on Brazilian ferroniobium exports, albeit with a significance value slightly greater than 5%. Again, the main coefficient of interest is the elasticity of the residual demand, which also reveals important market power for Brazil in EU destination market. The 3SLS results imply a statistically significant residual demand elasticity of 0.814 (whose p-value is 0.002). It is worth noting that, although there is a slight numeric difference between the coefficients of elasticity of the residual demand from USA and EU destination markets, this difference does not hold under a statistical point of view, as seen by testing of the following linear restriction: ηus ηeu ¼ 0.18. In this market the role played for Canada is already smaller than in USA destination, as we can see for observing the cost shifter (exchange rate). It could be due the characteristic mentioned above such as import tariffs and geographic proximity. However, it is desirable to refrain from a definite conclusion on how close one is to a full exercise of monopoly power. In fact, as explained before, the theoretical upper bound of the Lerner index would need the knowledge of the standard demand elasticity for niobium, but such information is not available in the context of that market. Nevertheless, given that it is largely recog­ nized that the full potential of niobium-based high-performance alloys has not been fully explored, the demand is expected to be elastic. The results of this paper appear as coherent with an oligopoly with a

6.3.1. Discussion: US destination market In the US destination market, the individual significance of the estimated coefficients is generally satisfactory, and their signs were as expected. Furthermore, the overall results displayed robustness across different lag structures. Examining the specific results, it is first worth highlighting the significant coefficients pertaining to the dummy vari­ able for structural break (DSB).17 The positive significant effect reflects the strong demand growth from China. In fact, important shifts in the demand from China appear to have impacted the international market for niobium, as it has experienced an intensive growth starting in 2007,

16

See Sargan (1958). Assumes the value 1 for periods within 2007–2009 and the value 0 otherwise. 17

18 The results of the test (X2(1)) ¼ 0.12 and p-value ¼ 0.731) does not allow to reject the null hypothesis that the values are statistically equals.

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Resources Policy 65 (2020) 101564

Fig. 1. Share of Brazilian ferroniobium exports for selected destination markets. Source: TRADEMAP.

dominant supplier.

criticality. Although it was not possible to detect a statistically significant dif­ ference between the market power of Brazilian exports in the US and the EU destination markets, a non-negligible intra-block trade was detected and might play some role in the EU destination market. Additionally, it was possible to detect a complementary role of ferrovanadium alloys in relation to ferroniobium. In fact, the price of vanadium exerts a negative effect on ferroniobium quantities in both destinations markets and therefore the referred intermediate goods can be seen as complemen­ tary. As mentioned before, there in exist fact production processes that make simultaneous use of both ferroniobium and vanadium alloys. A tentative note may raise the case for export promotion in that particular case, as the potential for the use of niobium in highperformance alloys does not seem to have been fully explored. Perhaps, incentives in terms of funding for R&D projects associated with niobium could be desirable in Brazil to some extent. In fact, the domi­ nant focus on exporting mostly primary metals, as for example occurs with iron ore, seems less desirable than aiming at exports of goods with greater value added. Future research may progress in different directions, as for example:

7. Final comments The niobium is a strategic mineral with important applications in space, aircrafts and military industries and with great possibilities of use in civil construction and gas and oil industries. The Brazilian near mo­ nopoly situation causes a big concern in developed countries as can be seen in the criticality list elaborated by specialized agencies from the US and EU,19 for example, and is an important source of foreign currency for Brazil. In 2010, the export of niobium was the third biggest among metals (about US$ 1.5 billion), behind only iron and gold, in monetary terms. This study assessed the intensity of competition in the international market for niobium in the US and EU destination markets. The adopted approach built on country-level residual demand estimation as advanced by Goldberg and Knetter (1999) and considered the source markets of Brazil and Canada. However, in contrast with the related empirical literature, we explicitly addressed the non-stationarities of the data. Statistically, the results for the residual demand of Brazil in both destination markets were generally satisfactory. As regards the main parameter of interest, the residual demand elasticity, the evidence suggested a significant market power as would be expected due to the � vis Canada in supply of ferro­ very strong dominance of Brazil vis a niobium. Theses results confirm supply risks assessments found in crit­ icality studies mentioned in introduction section and could make this method to be a relevant sub indicator complementary to the other subindicators about criticality. Indeed, as it simultaneously takes into ac­ count the supply and demand aspects (cost and demand shifters) and can be estimated not only on the upstream market (raw material market, here niobium), but also on the downstream market (where the material is used by importers in their production and international trade of product, here steel, superalloys, etc.), this can be seen as an expanded

i) Further exploring the role of alternative minerals used in highperformance alloys, such as tantalum and molybdenum, should the data be available. Other products derived from niobium could be investigated, in particular, niobium oxide. ii) Given the growing importance of the Chinese destination market, it should be considered in future studies should the necessary data be available. iii) The analysis could be also enhanced by considering possible dy­ namic specifications for residual demand. Such an extension has not yet been considered in the context of residual demand estimation.

19 See U.S. National Research Council (2008) and European Commission [EC (2017)].

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Declaration of competing interest

CRediT authorship contribution statement

No funding was received for this paper and there are no conflicts of interest.

�ilison W. Silveira: Software, Formal analysis, Data curation, Ja Writing - original draft, Writing - review & editing. Marcelo Resende: Supervision, Conceptualization, Formal analysis, Writing - original draft, Writing - review & editing.

Appendix We have implemented 3 different tests of stationarity to be able to evaluate the order of integration of all variables in the model. Augmented Dickey and Fuller (ADF) test, which is the most widely used test, Phillips-Perron (PP) and Kwiatkowski–Phillips–Schmidt–Shin (KPSS).20 The two first are evaluated under the null hypothesis that the variable possesses a unit root. In contrast, the last one is evaluated under the null hypothesis that the variable is stationary at level. Shin and Schmidt (1992) made a comparative assessment of the referred tests and concluded that the KPSS statistic does not constitute a powerful unit root test if compared to the Dickey-Fuller test. The tests were implemented in the R plataform with the urca package. The results, for PP, ADF and KPSS tests, have indicated the all variables are � and Fedorova � (2016) calculated and integrated of order 1 but qnbusa , qnbeu ​ and ​ ecan=eu , are appointed as stationary in level by the KPSS test. Arltova compared power functions for those tests. According to the authors, the KPSS test could be a suitable complement in the cases that the autoregressive coefficient is smaller than 0.7 and the sample was not bigger than 50 observations. The length of our sample is 53 and the autoregressive coefficient of the variables pnbusa ; ​ ​ qnbeu ​ ; ​ ​ and ​ ​ ecan=usa , in the underlying regressions for the unit root rests, are bigger than 0.7 and therefore the KPSS test must not be taken into account. In the case of qnbeu , in spite of KPSS it has been tested as a stationary variable, the ADF and PP tests indicate the presence of a unit root. Table A1 Unit Root Tests for variables in level Phillips-Perron Test1 Coef. usa

KPSS Test3

Augmented Dickey and Fuller Test

Test statistic

lag

ADF Statistic

p-value

lag

Test statistic

lag

ecan=usa

1.011 0.620 0.968 0.729 0.956

0.35 3.47 1.24 3.07 0.93

3 3 3 3 3

1.22 3.01 1,91 2,79 1.37

0.894 0.143 0.635 0.209 0.854

2 0 3 5 1

0.27(a) 0.06 0.24(a) 0.06 0.27(a)

3 3 3 3 3

eusa=br

0.991

0.23

3

0.94

0.940

1

0.32(a)

3

pwbr pibusa pibeu pvusa pveu

0.883

pnb qnbusa pnbeu qnbeu ecan=eu

0.854

ebr=eu

0.996 0.915 0.921 0.841 0.888

2.18 0.30 2.59 2.19 2.10 2.69 2.60

3 3 3 3 3 3 3

2.45

0.348

0.32

0.987

1.87

0.654

1.53 2.84 2.82 2.25

0.803 0.192 0.197 0.449

1

0.10

0

0.30(a)

1

0.19(b)

1 1 4 1

0.15(a) 0.17(b) 0.19(a) 0.17(b)

3 3 3 3 3 3 3

Note: To ADF, PP and KPSS tests were considered the specification was with constant and trend. In ADF test a maximum lag of 10 was allowed. In PP and KPSS tests was used the automatic selection presented in package urca from R. Observe that (a), (b) and (c) mean that the rejection of the null hypothesis at level of 1%, 5% and 10%, respectively. 1 The critical values are 0.216, 0.146 and 0.119 at level of 1%, 5% and 10%. 2 The critical values are 4.145, 3.498, and-3.178 at level of 1%, 5% and 10%. Table A2 Unit Root Tests for variables in first difference

ecan=usa

Phillip-Perron Test1 Coef PP statistic 0.286 5.47(a) 0.148 8.17(a) 0.505 3.90(a) 0.079 6.46(a) 0.198 5.23(a)

lag 3 3 3 3 3

eusa=br

0.189

3

pwbr pibusa pibeu pvusa pveu

0.441

pnbusa qnbusa pnbeu qnbeu ecan=eu

0.280

5.06(a)

ebr=eu

0.189

4.45(a)

20

0.455 0.590 0.181 0.509

5.22(a) 4.24(a)

4.24(a) 3.55(b) 5.52(a) 3.81(a)

3 3 3 3 3 3 3

Augmented Dickey and Fuller Test ADF Statistic p-value 1.96 0.049 7.10 0.000 2.58 0.010 5.05 0.000 4.49 0.000 4.99

0.000

5.27

0.000

4.52

0.000

3.89

0.000

3.52 3.06 4.20 6.02

0.000 0.003 0.000 0.000

See Phillips and Perron (1988) and Kwiatkowski et al. (1992). 9

lag 1 0 3 3 0

KPSS Test2 Test statistic 0.34 0.04 0.16 0.03 0.55(b)

lag 3 3 3 3 3

0

0.62(b)

3

0 0 0 0 0 3 0

0.11

0.53(b) 0.24 0.18 0.13 0.29 0.30

3 3 3 3 3 3 3

J.W. Silveira and M. Resende

Resources Policy 65 (2020) 101564

Note: To ADF, PP and KPSS tests were considered the specification without constant and trend were considered. In ADF test a maximum lag of 10 was allowed. In PP and KPSS tests was used the automatic selection presented in package urca from R. Observe that (a), (b) and (c) mean that the rejection of the null hypothesis at level of 1%, 5% and 10%, respectively. 1 The critical values are 0.739, 0.463 and 0.347 at level of 1%, 5% and 10%. 2 The critical values are 3.562, 2.919, and 2.597 at level of 1%, 5% and 10%.

Consider a levels VAR(p) model:

(A1)

ex ex ln pex t ¼ αDt þ ​ φ1 ln pt 1 þ … þ ​ φp ln pt p þ εt

table A3 reports the result of Johansen’s test performed on the following equation:

ex Δ ln pex t ¼ Γ0 Dt þ Πln pt 1 ​ þ

p 1 X

(A2)

Γj Δ ln pex t j þ εt

j¼1

Where: Dt is vector of deterministic variables (constant, qnb, gdp, pva, e(-1)); Γj ¼ I þ ​ φ1 þ … þ ​ φj ; j ¼ 1; …; p l are m x m matrices; Π ¼ ϕA0 is the long term-run impact matrix, A and ϕ are m x k matrices, and; P εt are i.i.d, N(0, ) errors Table A3 Johansen tests for cointegration U.S. destination market rank 0 1 2 3 4

Trace Statistic 100.53 56.20 29.61* 13.08 0.685

5% Critical 68.52 47.21 29.68 15.41 3.76

E.U. destination market 0 1 2 3 4

83.15 49.35 26.91* 11.89 0.66

68.52 47.21 29.68 15.41 3.76

* It indicates that this estimator has selected the number of. Cointegrating equations corresponding to this row of table. Table A4 Normalized Cointegration Vectors U.S. destination market Regressor pnbus constant qnbus gdpus pvaus ecan/us(-1) X2 ¼ 198.09 p-value ¼ 0.000

coefficient 1 8.990 0.834 5.618 0.486 3.163

p-value . . 0.000 0.000 0.000 0.000

coefficient 1 77.206 0.842 19.995 0.043 4.942

p-value . . 0.000 0.000 0.910 0.042

E.U. destination market regressor pnbeu constant qnbeu gdpeu pvaeu ecan/eu(-1) X2 ¼ 29.365 p-value ¼ 0.000

Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi.org/10.1016/j.resourpol.2019.101564.

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