Spatial spillover effects of global forest product trade

Forest Policy and Economics 113 (2020) 102112

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Forest Policy and Economics journal homepage: www.elsevier.com/locate/forpol

Spatial spillover effects of global forest product trade a,⁎

a

b

Zhonghua Yin , Fang Wang , Jianbang Gan a b

T

School of Economics and Management, Beijing Forestry University, Beijing 100083, PR China Department of Ecosystem Science and Management, Texas A&M University, College Station, TX 77843, USA

ARTICLE INFO

ABSTRACT

Keywords: Forest product trade Spatial autoregressive model Effect decomposition Spatial spillover Core-periphery structure

Spatial spillover is widespread in forest product trade. We employ a spatial autoregressive (SAR) interaction model to analyze spatial spillover effects of global forest product trade in 2004 and 2014 and their underlying determinants. The SAR interaction model, specified with three types of spatial dependence (origin-to-origin, destination-to-destination and origin-to-destination) can account for the complex spatial interconnection of forest product trade, leading to unbiased and consistent estimates of effects of determinants on trade flows. Although all the spatial dependence parameters are statistically significant, the spatial interconnectivity was much stronger among destination countries than across origin countries. The identified spatial dependence reflects a core-periphery structure of the trade network. The effect decomposition shows that changes in the principal explanatory variables in the SAR interaction model had a greater total effect (TE) on trade flows in 2014 than in 2004. This attributes to a substantial increase of the origin effect (OE), destination effect (DE) and network effect (NE) from 2004 to 2014. Especially, NE rose by a big margin during this period and accounted for approximately 45% of TE in 2014 for each determinant. Based on the magnitude of NE that represents the spatial spillover effect, gross domestic product (GDP) ranked first, followed by gross national income (GNI) per capita, roundwood production per capita and tariffs. Besides the strong evidence of spatial spillover effects, we also find that countries tended to mimic their neighbors in forest product trade engagements. Our findings shed new light on the spatial interconnection of global forest product trade and offer implications for future trade and forest conservation policy design.

1. Introduction Spatial spillover, a source of externality, refers to the phenomenon that a seemingly unrelated event in one region can have an impact on other regions (Capello, 2009). Spatial spillover is common in international trade (Behrens et al., 2012; Kelejian et al., 2012; Ho et al., 2018), including forest product trade (Olsson et al., 2012; Suryanta, 2012; Faria and Almeida, 2016). However, measuring spatial spillover has been a challenging task due to the complexity of trade networks. The commonly-used gravity model of trade cannot adequately measure such effects because it assumes independence among trade flows, thus ignoring the spatial interconnections among trade activities and partners (LeSage and Pace, 2008; LeSage and Llano-Verduras, 2014). The spatial autoregressive (SAR) interaction model, on the other hand, has recently emerged as an effective tool to examine spatial interactions of trade activities. Based on the spatial weight matrix and spatially lagged terms, the SAR interaction model explicitly depicts the interconnectivity of trading countries and measures the spatial spillover effect (LeSage and Pace, 2008; LeSage and Llano, 2013; Krisztin and

⁎

Fischer, 2015). Yet, this unique modeling approach, to the best of our knowledge, has not been applied to assessing the spatial spillover effects of forest product trade. Uncovering the spatial connectivity and spillover effects will provide not only new insights into global forest product trade but also useful implications for trade and forest conservation policy design. This is especially important with the continued growth and spatial dynamics of global forest product trade. The global trade value of forest products (under the commodity classification headings 44, 47 and 48 in the Harmonized Trade System (HS)) steadily increased from US$193.77 billion in 2000 to US$354.25 billion by 2014 (UN Comtrade database, 2015). Alongside the growth of trade value, the spatial connectivity among the trading countries of forest products have intensified, leading to spatial spillover (Olsson et al., 2012; Suryanta, 2012; Faria and Almeida, 2016). Thus, changes in the determinants of forest product trade could be transmitted through the spatial trade network and amplify their influence on the markets connecting the origin and destination countries. Without taking spatial spillover into account, the estimates of the impact of determinants on forest product trade flows may

Corresponding author. E-mail address: [email protected] (Z. Yin).

https://doi.org/10.1016/j.forpol.2020.102112 Received 1 November 2019; Received in revised form 2 February 2020; Accepted 4 February 2020 1389-9341/ © 2020 Published by Elsevier B.V.

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be invalid (biased and/or inconsistent). Previous studies have analyzed a variety of factors attributable to forest product trade. Some of these factors are also relevant to modeling spatial spillover effects. Here we provide a brief review of related previous work on forest product trade with an emphasis on the factors that are associated with forest product trade in general and spatial spillover effects in specific. One of these factors is timber resource endowments. Its influence on forest product trade is controversial. Some studies find that net exports of forest products are strongly and positively related to timber resource endowments (Bonnefoi and Buongiorno, 1990; Zhang and Li, 2009; Koebel et al., 2016). However, some other scholars argue that timber resource endowments have only a limited impact on net exports of forest products (Uusivuori, 2002). Another factor is tariffs and non-tariff trade barriers. The estimated impacts of tariffs vary across cases and modeling approaches. For instance, the simulation results of the Global Forest Sector Model suggest that the export tariffs on Russian roundwood would decrease timber harvest and roundwood prices in Russia and the welfare of its importers, but foster the development of the Russian sawnwood and pulp industry (Solberg et al., 2010). On the other hand, using a mathematical programming model, van Kooten and Johnston (2014) show that the export tariffs imposed by Russia would lead to a huge loss of its welfare while generating only small welfare gains for other countries. Non-tariff barriers can have larger aggregate impacts on global forest product trade than tariffs (Sun et al., 2010). The anti-dumping duty imposed on wooden bedroom furniture imports from China by the US is found to depress China's exports to the US and diverted the US imports from other countries (Luo et al., 2015). The influence of trade liberalization on forest product trade differs across countries. For instance, US forest product exports have been stimulated under the regime of the World Trade Organization (WTO), and the emerging markets in Asia and Latin America have been the major target of the US export expansion (Gan and Ganguli, 2003). China's accession to the WTO has drastically increased its imports of intermediate forest products, and promoted the re-exports of final forest products processed in China (Gan, 2004). Russian entry into the WTO has had very limited effects on its exports of main forest product – roundwood, because the new tariff-rate quota system implemented in Russia since August 2012 has restrictions on roundwood exports (Lin and Zhang, 2017). Among various timber trade policies, two have drawn tremendous attention: restrictions on roundwood exports implemented by exporting countries and legality assurance of timber trade initiated by importing countries. The roundwood export ban policies of Southeast Asian countries have increased their plywood exports and decreased their roundwood exports to Japan (Tachibana and Nagata, 1999). A similar policy in Ghana has augmented the proportion of its processed forest product exports (Amoah et al., 2009). Russian roundwood export taxes and the restriction on Canadian lumber exports to the US have also shown major impacts on the welfare of both the exporting and importing countries, although the overall impacts on the importing countries are mitigated to some extent as they increase supply from other sources (van Kooten and Johnston, 2014). With respect to timber legality assurance policies, by and large, the effects of the Lacey Act Amendment (LAA) of the US and the Voluntary Partnership Agreements (VPAs) of the EU are relatively small on reducing illegal logging (Bandara and Vlosky, 2012; Gan et al., 2013). The LAA has increased the prices and decreased the quantities of imports to the US from some tropical forest countries such as Brazil, Indonesia and Malaysia, but has insignificant effects on the exports of other countries to the US (Prestemon, 2014). Simulation results confirm that the effects of alternative measures to curb EU imports of illegally harvested wood are marginal and only for a short to medium term (Moiseyev et al., 2010). The EU Timber Regulation has no effective restrictions on illegal timber imports to the overall EU market, especially in the case of Eastern and Southern Europe such as Italy, Greece,

and Spain (McDermott and Sotirov, 2018). Studies on the practice of VPAs in Cameroon and Indonesia also imply that the effects of such policies are far from their objectives (Carodenuto and Cerutti, 2014; Obidzinski et al., 2014). Furthermore, other determinants of forest product trade, which have been examined, include gross domestic product (GDP), population, and domestic wood production (Mohammadi Limaei et al., 2011); domestic income (Bonnefoi and Buongiorno, 1990); labor costs and total factor productivity (Koebel et al., 2016); exchange rates (Zhang and Li, 2009); and transportation costs (Zhang and Li, 2009; Parhizkar et al., 2010). All of these are found to have impacts on forest product trade although the magnitude of the impacts varies from case to case. Overall, many aspects of bilateral or multilateral forest product trade have been analyzed. Nevertheless, no studies, to our knowledge, have specifically examined spatial spillover effects of forest product trade or the marginal effects of individual determinants with the consideration of spatial spillover. Moreover, detailed decomposition of the total effect has not been undertaken. Hence, the major objective of this paper is to empirically analyze spatial spillover effects of the underlying factors in global forest product trade using the SAR interaction model. We employ the SAR interaction model with the data of 2004 and 2014 to examine spatial spillover effects of global forest product trade. Choosing these two years allows us to examine the change in the trade network and the spatial spillover effects over the 10-year period. The forest products included in this study are the commodities under the HS codes of 44, 47, and 48. The top 63 countries or regions in terms of trade value in 2014 are selected (Appendix A), and they collectively accounted for 86.8% and 95.3% of the gross global trade value of forest products in 2004 and 2014, respectively. The parameters of spatial lag variables are estimated to measure the complex and diverse spatial dependence in the trade network. The effect decomposition of determinants enables us to estimate their spillover and other effects on trade flows while allowing for spatial dependence. Besides demonstrating the applicability of the SAR interaction model to analyzing forest product trade, this study contributes to the literature in several aspects. The spatial spillover effects of global forest product trade are evaluated empirically. The identified spatial dependence uncovers a core-periphery structure of the trade network. Our findings provide new insights into global forest product trade and its principal determinants from the view of spatial autocorrelation, and suggest that allowing for spatial spillover effects, a change in a given factor in one country can exert an increasingly significant influence on trade flows as its neighboring and other countries react. The complex spatial interconnectivity and strong chain reactions call for strong regional or global cooperation in the policy pertinent to forest product trade and forest resource conservation. 2. Methodology 2.1. Model specification The SAR interaction model (LeSage and Pace, 2009) is employed to explore the spatial interconnectivity of forest product trade. Three different spatial lags of the dependent variable (trade flows) are specified to estimate the spatial dependence of trade network. The SAR interaction model can be expressed as follows: Y jk =

n2

+

o Wo Yjk

+

d Wd Y jk

+

w Ww Y jk

+ Xo

o

+ Xd

d

+ Xw

w

+

jk ,

(1) where the column vector of dependent variables Yjk is the log-transformation of origin-destination (O-D) trade flows between trading countries j and k; WoYjk, WdYjk, and WwYjk are the spatially-lagged terms reflecting the origin-to-origin (denoted by subscript o), destination-to-destination (denoted by subscript d), and origin-to-destination (denoted by subscript w) dependence, respectively, to capture the 2

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extent of the spatial interaction between trade flows; accordingly, ρo, ρd, and ρw quantify the intensities of these three types of spatial dependence; all the spatial-weight matrices Wo, Wd, and Ww are constructed using the Kronecker product: Wo = W ⨂ In,Wd = In ⨂ W,Ww = Wo Wd = W ⨂ W, where In is an n × n identity matrix with n being the number of importing or exporting countries, and W is an n × n rownormalized square matrix with the elements of distance weights, i.e.,

jk

1 j 2 djk

=

the impact of a change in a given explanatory variable of the origin region in the O-D dyad. DE reflects the impact resulting from a change of a factor in the destination region in the O-D dyad. IE represents the impact on intraregional trade flows. The formulas for computing OE, DE, and IE are as follows:

OEm = (In2

k

o Wo

d Wd

w Ww )

r (1) o Jo r (2) o Jo

1

r (n ) o Jo

0 j = k, with djk being the greatest circle distance between the capitals of trading nations j; α is the parameter of the intercept term ın2′ as an n2 × 1 vector of ones; Xo, Xd and Xw represent the determinants (independent variables) associated with the origin, destination and both the origin and destination countries in the O-D dyads, respectively; βo, βd,and βw are the parameter vectors used to measure the corresponding effects of the independent variables; εjk is the vector of error terms. In the SAR interaction model, the complex dependence among trade flows is tackled by analyzing the general spatial autoregressive specification. This is accomplished by the three spatial lags of the dependent variable, which is a distinct difference from the gravity model. In other words, the gravity model is a special case of the SAR interaction model with ρo = ρd = ρw= 0. The spatially lagged terms account for the complex spatial interconnectivity among the countries in the trade network. However, because of the inclusion of the lagged terms, the marginal effect of a change in an independent variable cannot be directly derived from Eq. (1). To calculate the marginal effects of determinants, we can transform Eq. (1) to:

OE = (1/ n2)

o Wo

d Wd

w Ww )

1(

n2

+ Xo

o

+ Xd

d

+ Xw

w

+

n2

o Wo

(6)

d Wd

DE =

1

w Ww )

(1/n2)

n2

IEm = (In2

IE =

(1/n2)

o Wo

n2

In Eq. (5),

d Wd

w Ww )

1

TE =

n2

·TEm· n ,

(

r o

+

(n ) r d ) Ji

( ) Jo

jk ).

d Wd

w Ww )

1

+

r (n ) o Jo

+

r (n ) d Jd

(9) (10)

is the n × n matrix

( )

Jo(θ)

modified to have all zeros in

( )

elements in Eq. (9). The aim of these adjustments to J o ( ) Jo

( )

and J d

is to

= separate IE from OE and DE. That is, and . All other symbols in Eqs. (5)–(10) are the same as those described previously. NE, measuring the spatial spillover effect, is unique to the SAR interaction model, and quantifies the mean cumulated impact of a change in a determinant on trade flows across all countries throughout the trade network except for those on the O-D dyads. NE can be computed as follows:

NEm = (In2

r (1) d Jd r (2) d Jd

,

· IEm · n .

o Wo

d Wd

w Ww )

1

(n ) Y jk / X (n ), r

(1/ n2)

+

(1) r d ) Ji (2) r d ) Ji

the θth row and column. Likewise, J d is the n × n matrix Jd(θ) adjusted with all zeros in the θth row and column in Eq. (7). Ji(θ) is an n × n matrix with ones in the θth row and column and zeros for all other

Y (jk2) / X (2), r

o Wo

+

(

r o r o

NE =

(1/ n2) (θ)

n2

·NEm · n , Jo(θ)

Jo( )

Ji( ) ,

+

(

r o r o

+

r ( 1) d ) Jn r ( 2) d ) Jn

(

r o

+

r (n ) d ) Jn

(

+

(7) (8)

Y (jk1) / X (1), r

r (1) o Jo r (2) o Jo

,

· DEm · n ;

(

Obviously, the marginal effect arising from a change in a given independent variable can no longer be calculated using the estimated value of β alone, but also depends on the parameters ρo, ρd, and ρw and the spatial interconnectivity matrices Wo, Wd, and Ww, which measure the degree of spatial dependence. According to the algorithm proposed by LeSage and Thomas-Agnan (2015), the total effect (TE) can be calculated as follows:

= (I n 2

r (1) d Jd r (2) d Jd r (n ) d Jd

(2)

TEm =

(5)

· OEm· n ;

DEm = (In2

Yjk = (I n 2

,

, (11) (12)

(θ)

where Jn = J − − Jd , with J being an n × n matrix of ones. Also, NE = TE − OE − DE − IE.

, (3)

2.2. Variable selection and data sources

(4)

The trade flows of forest products, the dependent variable in this study, can be influenced by a variety of factors including those commonly associated with general international trade (e.g., prices, exchange rates, macroeconomic conditions) as well as those specifically pertinent to forest product markets and trade (e.g., timber resource endowment, timber trade legality assurance policies). We divide these factors into seven categories: economic factors, timber resource endowment, tariffs, regional economic integration, timber legality assurance trade policies, geographic attributes, and cultural factors. The descriptions and measurements of the dependent and independent variables are summarized in Table 1.

Jo(θ)

where is an n-dimensional square matrix of zeros with the θth column equal to ınβor; Jd(θ) is an n-dimensional square matrix of zeros with the θth row equal to ın′βdr; the n sets of n × n outcomes (one for each change in X(θ),r, θ = 1, …, n) are stacked vertically to form the n2 × n matrix TEm; and the superscript r denotes the rth factor shared by the origin and destination countries. According to Eq. (4), TE can be interpreted as the average cumulative impact caused by a change in an independent variable on trade flows over all countries. TE can be decomposed into the origin effect (OE), destination effect (DE), intraregional effect (IE), and network effect (NE). OE measures 3

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Table 1 Specific variables and data sources. Variable category

Variable definition

Unit

Symbol

Data source

Trade flows Economic factors

Log of trade value of forest products between two trading countries Log of gross national product (GDP) Log of gross national income (GNI) per capita Log of roundwood production per capita Weighted average import tariffs by trade value Dummy variable for landlocked origin or destination country Log of distance between the capitals of two countries Dummy variable for border between origin and destination countries Dummy variable for sharing a common official language between origin and destination countries Dummy variable for trade between EU member countries Dummy variable for trade between NAFTA countries The US Lacey Act: dummy variable for exports to the US The EU Timber Regulation: dummy variable for exports to the EU

US$ million US$ billion US$ thousand m3 % – km – –

Y GDP GNIpc RWpc Tar Llk Dis Bod Lan

UN Comtrade World Bank World Bank FAO World Bank CEPII CEPII CEPII CIA's website

– – – –

EU NAF ULA ETR

EU's website USTR's website USFWS's website European Commission's website

Resource endowment Tariffs Geographical factors Cultural factors Regional economic integration Timber legality-assurance trade policies

Notes: Log refers to natural logarithm. CEPII refers to Centre d'Etudes Prospectives et d'Informations Internationales'. CIA refers to the Central Intelligence Agency. USTR refers to the Office of the United States Trade Representative. USFWS refers to the US Fish and Wildlife Service.

2.2.1. Economic factors GDP (gross domestic product), an important indicator for measuring the size of an economy, is considered as an “attractive force” for international trade (Egger, 2000). Countries with larger GDPs are expected to export more and/or absorb more imports. Hence, GDP is used to analyze the impact of the economic size of the 63 main countries engaged in forest product trade. Furthermore, GNI (gross national income) per capita is used as an indicator for the average income of producers in the exporting country and of consumers in the importing country. The exporter's income per capita tends to have a positive (negative) impact on exports if the commodity is capital (labor) intensive (Bergstrand, 1989). Meanwhile, the importer's income per capita tends to have a positive (negative) impact on imports if it is a luxury good (necessity) (Bergstrand, 1989).

Shared borders have an effect on cross-border trade (Anderson and Van Wincoop, 2003). Cross-border trade has an exceptional advantage over other categories of trade due to the shorter distances and more efficient transport.

2.2.2. Timber resource endowment According to the Heckscher-Ohlin-Vanek framework, resource endowments are an important determinant of trade flows (Vanek, 1968), particularly in the case of trade in forest products, which are typically resource-intensive products. Ceteris paribus, countries with abundant timber resources tend to export more forest products, and countries with scarce timber resources are inclined to importing more forest products, and vice versa (Koebel et al., 2016). Because of the unavailability of forest inventory data on commercial timber volumes, we use the roundwood production volume as a proxy for timber resource endowments in each country/region.

2.2.6. Regional economic integration Regional economic integration can have both trade creation and diversion effects (Bergstrand et al., 2015). The impacts of the EU membership and the North American Free Trade Agreement (NAFTA) on international forest product trade are the most prominent of all the existing regional economic integration initiatives (Thalassinos, 2007; Sbragia, 2010). We use two dummy variables to represent the membership in the EU and the NAFTA countries, respectively.

2.2.5. Cultural factors The use of a common language between trade partners, a proxy for cultural similarity, can affect international trade. Language barriers have found to be negatively correlated with international trade, while a common (official) language can increase trade flows (Egger and Lassmann, 2015). We use a dummy variable to indicate whether the trading partners share a common language. The dummy is set to 1 when the trading partners share an official language; otherwise, it is equal to 0.

2.2.7. Timber legality-assurance trade policies Timber legality-assurance trade policies have recently been adopted by several major importing countries to mitigate illegal logging and related timber trade. Among these policies are the US Lacey Act Amendment of 2008 and the EU Timber Regulation of 2010 (Overdevest and Zeitlin, 2014). We also use dummy variables to separate the countries adopting these legality-assurance policies from others.

2.2.3. Tariffs Tariffs are a main means used to restrict imports and protect domestic markets (Egger, 2000). We use the weighted average tariffs with the weight scheme based on the trade value of different forest products. 2.2.4. Geographical factors The transportation distance between two trading countries is taken as a proxy for transportation costs as it represents a “repulsive force” of international trade (Brun et al., 2005). The transportation distance is measured as the great-circle distance between the capitals of two countries. Landlocked countries have a disadvantage for international shipping, affecting both exporters and importers (Raballand, 2003). Forest products are relatively low in bulk density and value, except for a few high-grade timber products. Sea transportation is a cost-effective means for long-distance shipment of forest products. Thus, lack of ocean access can increase transportation costs.

3. Empirical results 3.1. The estimated SAR interaction model Most of the regression parameters estimated in the SAR interaction model are highly statistically significant and their signs are consistent with the expected signs (Table 2). The variance inflation factors (VIFs) of all the variables are below 3, confirming that multicollinearity is not present in the model. We test the distributions of residuals in the models for both 2004 and 2014 using Jarque-Bera and D'Agostino tests, and do not reject the null hypothesis of a normal distribution in both 4

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destination country. Therefore, major trading countries gradually become the hubs of forest product trade, while their neighboring countries diminish their role in the trade network. Consequently, a coreperiphery structure of global forest product trade is formed (Figs. 1 and 2). The trade network consists of several core countries and many peripheral countries (Rossa et al., 2013). Each of the core countries is highly interconnected with other countries and major trade flows pass through the core countries. The peripheral set of countries loosely links to their trading partners with minor trade flows. Based on the strength weighted by the ratio of trade flows to total value of global forest product trade, USA (0.274), Germany (0.194), Canada (0.182), and France (0.104) ranked among the top four in 2004. The order of ranking changed to USA (0.214), China (0.189), Germany (0.188), and Canada (0.116) in 2014. These countries represented the trade hubs (cores) of forest products in Asia, America, and Europe in 2004 and 2014, respectively.

Table 2 Estimated parameters associated with independent variables in the SAR interaction model. Independent Variable

Intercept (α) WoY (ρo) WdY (ρd) WwY (ρw) OGDP OGNIpc ORWpc OLlk DGDP DGNIpc DRWpc DLlk Tar Dis Bod Lan EU NAF ULA ETR Number of observations Log likelihood Wald test χ2 Likelihood ratio test χ2

2004

2014

Estimate

Std. Error

Estimate

Std. Error

−0.283⁎ 0.226⁎⁎⁎ 0.594⁎⁎⁎ −0.312⁎⁎⁎ 0.293⁎⁎⁎ −0.082⁎⁎ 0.092⁎⁎⁎ −0.092 0.451⁎⁎⁎ −0.089⁎⁎ −0.090⁎⁎⁎ −0.655⁎⁎⁎ −0.017⁎⁎⁎ −0.330⁎⁎⁎ 1.849⁎⁎⁎ 0.866⁎⁎⁎ 0.970⁎⁎⁎ 1.767⁎⁎ – – 3969 −7733 856⁎⁎⁎ 778⁎⁎⁎

0.168 0.030 0.021 0.037 0.025 0.037 0.020 0.092 0.026 0.045 0.020 0.090 0.005 0.021 0.142 0.109 0.095 0.705 – –

−0.470⁎⁎ 0.126⁎⁎⁎ 0.507⁎⁎⁎ −0.197⁎⁎⁎ 0.414⁎⁎⁎ −0.070 0.141⁎⁎⁎ −0.170⁎ 0.593⁎⁎⁎ −0.210⁎⁎⁎ −0.101⁎⁎⁎ −0.907⁎⁎⁎ −0.047⁎⁎⁎ −0.465⁎⁎⁎ 2.228⁎⁎⁎ 0.614⁎⁎⁎ 1.079⁎⁎⁎ 1.370⁎ −0.008 −0.078 3969 −7932 617⁎⁎⁎ 576⁎⁎⁎

0.188 0.029 0.021 0.038 0.027 0.045 0.022 0.097 0.031 0.051 0.021 0.096 0.008 0.023 0.151 0.115 0.100 0.744 0.250 0.090

3.2. Effect decomposition and comparison The TE of major explanatory variables in the SAR interaction model rose significantly in magnitude from 2004 to 2014, suggesting that spatial interconnectivity of global forest product trade increased during this period (Table 3). The growth of TE reflects the cumulative effect of OE, DE, and NE. The increase of NE was particularly outstanding. IE also grew from 2004 to 2014, but it had smaller influence on TE than OE, DE, or NE. Based on TE, GDP was the largest attractive force for forest product trade, followed by roundwood production. In contrast, GNI per capita was the most repulsive force of forest product trade, followed by tariffs. NE represents the cumulated average impact of a change in a certain characteristic of a trading country on the trade flows across all countries not associated with the O-D dyads. It measures an indirect effect arising from spatial interconnectivity of forest product trade, as opposed to the direct effects of OE and DE involved in the O-D dyads. The ratio of NE to TE for most factors increased considerably from 2004 to 2014 (e.g., GDP, from 11.72% to 45.87%; GNI per capita, from 12.33% to 45.85%; tariffs, from 9.09% to 46.48%) except for roundwood production per capita (from 44.68% to 44.74%). The expansion of NE verifies that spatial spillover effects are prominent in global forest product trade. In terms of spatial spillover effects, GDP is the highest, followed by GNI per capita, roundwood production per capita, and tariffs. According to NE, on average a 1% increase in GDP of a trading country indirectly spurred 0.187% growth of trade flows throughout the trade network in 2004, and the corresponding elasticity increased to 1.449% in 2014. On the contrary, a 1% increase of GNI per capita in a typical country caused a 0.046% reduction in trade flows over all other countries not associated with the O-D dyads in 2004, and this negative cumulative impact expanded to 0.398% in 2014. A 1% increase in roundwood production per capita in a typical country yielded a 0.021% increase in trade flows cumulated over all countries other than those in the O-D dyads in 2004, and this effect rose to 0.068% in 2014. And, a 1% increase in import tariffs in a typical country led to a 0.003% cumulative shrinkage in trade flows across countries except those in the O-D dyads in 2004, and this negative effect grew to 0.066% in 2014.

Notes: The dependent variable is the trade flows of forest products. The symbols “O” and “D” prefixed to the variable names denote exporting (origin) and importing (designation) countries, respectively; symbols inside parentheses represent the parameters associated with the independent variables as explained in Eq. (1); all other symbols are described in Table 1. ⁎ Denotes statistical significance at the 10% level. ⁎⁎ Denotes statistical significance at the 5% level. ⁎⁎⁎ Denotes statistical significance at the 1% level.

tests. This verifies the normal distributions of the residuals. The inclusion of spatially-lagged terms allows for the SAR interaction model to disclose spatial interconnection among trade flows. All three types of spatial lag terms show highly significant and sizeable effects on global forest product trade flows. This reveals the evidence of strong and complex spatial dependence in global forest product trade. The estimated origin-to-origin dependence parameter (ρo), which measures the spatial dependence among origin countries, was significant at the 1% level and valued at 0.226 in 2004 and 0.126 in 2014, respectively. Likewise, the estimated destination-to-destination dependence parameter (ρd), representing spatial dependence among destination countries, was significant and amounted to 0.594 in 2004 and 0.507 in 2014. This implies that spatial interconnection is much stronger among destination countries than across origin countries. The positive parameter values suggest that a country exporting or importing forest products also influences its neighboring countries to do the same. The estimated parameter of the origin-to-destination dependence (ρw), was also statistically significant at the 1% level but negative. Unlike ρo and ρd, ρw has a negative spatial effect on the trade flows from the neighbors of an origin country to the neighbors of a destination country. Hence, the overall spatial spillover is partially offset by the origin-to-destination dependence, which operates in the opposite direction of ρo and ρd. Under the influence of spatial dependence, when an origin country increases the exports of forest products to a destination country, trade flows of the neighbors are partially absorbed by the spatial spillover effects of the origin and destination countries. Thus, the neighbors of the origin country reduce their exports to the neighbors of the

4. Summary and discussion We analyze spatial spillover effects of global forest product trade using the SAR interaction model and the data of 2004 and 2014. The model incorporates three types of spatial dependence – origin-to-origin, destination-to-destination, and origin-to-destination, which are all found to be highly statistically significant. Our results reveal the strong evidence of complex spatial interconnectivity in global forest product

5

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Fig. 1. The network of global forest product trade in 2004. Notes: The size of a node denotes the total trade value of the corresponding country. Nodes with larger trade flows are closer to each other, reflecting the hierarchy of the trade network. The thickness of edges represents the magnitude of trade flows between the trading countries. The length of edge is not related to geographic distances. The acronyms of country names are shown in Appendix A.

trade, and indicate that the spatial dependence is greater among destination countries than across origin countries. The identified spatial dependence helps explain the core-periphery structure of the trade network. The TE of main determinants in the SAR interaction model grew significantly from 2004 to 2014, as a result of the increases in OE, DE and NE. In particular, NE expanded rapidly over this period, and had reached around 45% of TE for major independent variables by 2014. This indicates that spatial spillover plays an increasingly important role in the trade network. Hence, unless spatial spillover is appropriately accounted for, we would be left with biased and inconsistent results concerning the marginal effects of independent variables on trade flows (LeSage and Pace, 2008; LeSage and Llano, 2013).

Our findings also have implications for policies relating to forest product trade and forest conservation. Because of spatial spillover, when designing a trade policy or other policies that have trade implications or consequences such as forest conservation policy, a country needs to take into full consideration the possible indirect impact of the policy on other countries, as well as the reactions by other countries throughout the trade network. The neglect of spatial spillover could lead to unanticipated outcomes, which may undermine the policy objectives, as demonstrated in studies on trade-induced transfer effects (Gan and McCarl, 2007; Lewison et al., 2019). Thus, the strong spatial spillover effects call for enhancing cooperation in trade and forest conservation policy design and implementation at the regional and

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Fig. 2. The network of global forest product trade in 2014. Notes: The size of a node denotes the total trade value of the corresponding country. Nodes with larger trade flows are closer to each other, reflecting the hierarchy of the trade network. The thickness of edges represents the magnitude of trade flows between the trading countries. The length of edge is not related to geographic distances. The acronyms of country names are shown in Appendix A.

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Future studies can develop different types of spatial regression models and examine each of specific forest product categories. Such efforts would further broaden and deepen our knowledge on the structure and magnitude of spillover effects of forest product trade. Moreover, while the cross-sectional data at two time points of 2004 and 2014 enables us to examine the spatial autocorrelation of global forest product trade, thus achieving our objective, it does not allow us to probe time series autocorrelation, which has been extensively discussed in the study of international trade previously (Brun et al., 2005; Ho et al., 2018). Both spatial and serial autocorrelation of global forest product trade can be explored in further research.

Table 3 Decomposition of effects of key determinants on forest product trade. Variable

Effect

2004

2014

GDP

OE DE IE NE TE OE DE IE NE TE OE DE IE NE TE OE DE IE NE TE

0.588 0.799 0.021 0.187 1.595 −0.164 −0.158 −0.005 −0.046 −0.373 0.185 −0.159 0.000 0.021 0.047 – −0.030 0.000 −0.003 −0.033

0.723 0.961 0.026 1.449 3.159 −0.123 −0.340 −0.007 −0.398 −0.868 0.246 −0.163 0.001 0.068 0.152 – −0.075 −0.001 −0.066 −0.142

GNIpc

RWpc

Tar

Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgements The work by ZY and FW leading to this paper was financially supported by the Fundamental Research Funds for the Central Universities (Grant No. 2018RW15, Grant No. 2019YC20), the Ministry of Education Humanities and Social Sciences Project (Grant No. 18YJA790096), and China Scholarship Council Fund. The work by JG was supported by the US Department of Agriculture (USDA) McIntireStennis Program. However, opinions expressed here do not reflect the funding agencies' view. The authors thank the Editor and anonymous reviewers for their helpful comments and suggestions.

Notes: The dependent variable is trade flows of forest products. The symbols of variables are shown in Table 1. OE denotes the origin effect, DE denotes the destination effect, IE denotes the intraregional effect, NE denotes the network effect, and TE denotes the total effect. Tar refers to import duties, so it has no OE.

global levels. Such cooperation can help maximize positive spatial spillover effects while avoiding undesired spillover. Appendix A Table A.1

Countries or regions and their acronyms. Acronym

Country or region name

Acronym

Country or region name

DZA ARG AUS AUT BLR BEL BRA BGR CAN CHL CHN HKG COL HRV CZE DNK EGY EST FIN FRA DEU GRC HUN IND IDN IRL ISR ITA JPN KAZ LVA LTU

Algeria Argentina Australia Austria Belarus Belgium Brazil Bulgaria Canada Chile China China Hong Kong SAR Colombia Croatia Czech Rep. Denmark Egypt Estonia Finland France Germany Greece Hungary India Indonesia Ireland Israel Italy Japan Kazakhstan Latvia Lithuania

LUX MYS MEX MAR NLD NZL NGA NOR PER PHL POL PRT KOR ROU RUS SAU SRB SGP SVK SVN ZAF ESP SWE CHE THA TUR UKR ARE GBR USA VNM

Luxembourg Malaysia Mexico Morocco Netherlands New Zealand Nigeria Norway Peru Philippines Poland Portugal Rep. of Korea Romania Russian Federation Saudi Arabia Serbia Singapore Slovakia Slovenia South Africa Spain Sweden Switzerland Thailand Turkey Ukraine United Arab Emirates United Kingdom United States of America Viet Nam

Notes: The top 63 countries or regions are listed in this appendix, ranked by trade value of forest products in 2014. Acronyms are three-letter country codes from the ISO 3166 standard for countries and dependent territories (International Organization for Standardization, 2019). 8

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