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BY 4.0 license Open Access Published by De Gruyter January 29, 2020

The Determinants of the EU Import Demand for Soybean and Maize: What Role for GMOs?

Alessandro Varacca

Alessandro Varacca is Postdoctoral Researcher in Agricultural Economics at the Dipartimento di Economia Agroalimentare, Facoltà di Scienze Agrarie, Alimentari e Ambientali, Università Cattolica del Sacro Cuore, Piacenza, Italy. He holds a Bachelor Degree in Agriculture, a Master Degree in Agricultural and Food Economics and a PhD in Agricultural and Food Economics from Università Cattolica del Sacro Cuore. His research interests comprise food demand and consumers’ behaviour, price analysis and the economics of biotech crops.

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and Paolo Sckokai

Paolo Sckokai is Associate Professor in Agricultural Economics at the Dipartimento di Economia Agroalimentare, Facoltà di Scienze Agrarie, Alimentari e Ambientali, Università Cattolica del Sacro Cuore, Piacenza, Italy. He holds a Ph.D. in Agricultural Economics from the University of Padua and a Master’s Degree in Economics from Iowa State University. His research interests focus on food demand and price analysis, agricultural policy and the industrial organisation of the food sector.

Abstract

In this work, we analyse EU soybean and maize imports using a demand system borrowed from the differential approach to firm theory. Alongside providing own-price and cross-price (i. e. cross-country) elasticities for these two products, we test whether source-specific characteristics exert any influence on complementarity and substitution patterns between international exporters. Specifically, we look at country differences stemming from supply chain efficiency and the asynchronous approval of Genetically Modified (GM) varieties. We do so by introducing two measurements for such features into a linear demand model specified by Laitinen and Theil (1978). Estimation results suggest that the EU import structure is not affected by differences in supply chain efficiency between overseas suppliers while, depending on the product, asynchronous approval does seem to have an influence. We find that imports of maize are more sensitive than those of soybeans to differences in approval statuses between international exporters and the EU. Since soybean availability is a limiting factor for the EU feed industry, avoiding stock shortages may be a priority for European importers, hence the weaker effect of asynchronous approval. On the other hand, the substantial EU self-sufficiency for maize places more emphasis on product characteristics and prices.

JEL Classification: F14; Q17; Q18

1 Introduction

Soybean and maize are currently two of the most traded agricultural products on the international market, in terms of both volume and value. The high protein content and the reduced fat percentage of the most common soybean derivatives (particularly soybean meal) have made this crop the best available protein source for livestock breeding (Bertheau and Davison 2011). Soybean is, in addition, very flexible in feedstuff preparation and is a critical ingredient for poultry and pig dietary formulations: it is in fact a primary source of disposable lysine, a key growth promoter and a limiting factor in pigs, chickens, and turkeys (Tillie et al. 2012; de Visser, Schreuder, and Stoddard 2014). Maize is also largely used to feed livestock, at least within western economies. Whereas the diet of people in developing countries such as Southern and Eastern Africa, Central America and Mexico is typically based on maize, its main use in developed countries has long since shifted to feedstuff and energy production (Ranum, Peña-Rosas, and Garcia-Casal 2014). The European Union (EU), for example, uses approximately 65 % of its domestic supply (domestic production plus imports) to produce animal feed, while only 5.2 % is dedicated to human consumption (European Commission 2015).

Since the ban of high-protein meat and bone meals in early 2000, the absence of oilseed import duties (agreed in the Dillon round in 1962) together with the sharp rise in the EU’s domestic costs for feed grains have created the conditions for a widespread introduction of soybean into the EU livestock sector. However, given the EU’s limited supply,[1] this escalation in soybean demand has mainly been supported by imports. Soybean product imports (we hereafter refer to all soybean products as simply ‘soybean’) consist of both crushed grains (soybean meal) and whole grains (hereafter soybeans): data from Eurostat (2018) show that the EU imported approximately 13 million metric tons (MMT) of grains and 19 MMT of meal in 2017, while average imports for the period 2006–2014 were 14.5 MMT and 21 MMT, respectively. In contrast, the EU import of maize products[2] is not as compelling as for high-protein crops. In fact, most EU member states can easily cultivate maize, thanks to favourable weather conditions, a remarkable supply of hybrids suitable to most pedoclimatic situations and favourable trade regulations.[3] Nevertheless, the import of maize is just as relevant as the import of soybean: Eurostat (2018) figures indicate that the EU imported approximately 17 MMT of maize products in 2017. Indeed, these two products are the two most imported agricultural raw materials within the EU, followed by wheat and rice.

Although the literature often considers soybean and maize as undifferentiated commodities, within an importing area such as the EU imports from one source are likely to be perceived as imperfect substitutes for the same products from another exporter. If this were not the case, price ratios would be constant, and elasticities of substitution between these supplies would be infinite (Armington 1969). This imperfect substitution is typically due to a variety of source-specific features: when it comes to agricultural raw materials, countries may have differing reputations for product quality (as well as quality consistency), differing efficiency in managing the supply chain (Washington and Kilmer 2002) or differing regulations/approval statuses for genetically modified (GM) crops.

Regarding the latter, most exporting countries permit the commercialisation of GM agricultural products, and if the same permissions are not granted within the EU, export opportunities towards the European common market could be undermined (Stein and Rodríguez-Cerezo 2010; Henseler et al. 2013; Food and Agriculture Organization of the United Nations (FAO) 2014). In fact, the EU allows the marketing of Gentically Modified Organisms (GMOs) intended for use in food and feed only when these products have been approved under Regulations 1829/2003 and Directive 18/2001. The literature refers to this issue as asynchronous approval. On the other hand, products originating from countries with efficient supply chains are typically perceived as qualitatively superior (Nakamura, Sakakibara, and Schroeder 1998; Foster 2008) since storage, transportation and timely delivery have non-trivial impact on product quality (Mounts, List, and Heakin 1979; Narayan, Chauhan, and Verma 1988).

In this paper, we investigate if and to what extent either of the two aforementioned sources of product differentiation influence the EU import allocation structure for maize and soybean. Given the limited number of import sources we will be examining, we choose to tackle this issue using a microeconomic approach instead of relying on trade models. In particular, we employ a differential factor allocation model (DFAM) to specify the EU demand for maize and soybean, and we control for the two sources of national-level heterogeneity through an index of supply chain efficiency and an index of asynchronous approval. In addition, by estimating the EU import demand distinguished by country of origin, we provide up-to-date own-price and cross-price (i. e. cross-country) elasticities that are currently missing in the literature and may be used for further trade policy analysis.

The remainder of this paper is organised as follows. Section 2 explores source differentiation under Armington’s hypothesis, and discusses in detail how different regulations regarding genetic engineering and the efficiency of the supply chain can affect trade. Section 3 suggests the choice of an import demand model based on producer’s theory and discusses why this is conceptually appropriate for the problem at hand. Sections 4 to 6 present data, estimation results, discussion and conclusions.

2 Source Differentiation

2.1 The Structure of EU Imports

The EU import structure for maize and soybeans[4] is presented in Table 1. The most important EU trading partners for soybeans are Brazil and the US, whose produce totals over 80 % of total EU imports. For maize, the most important import source is Ukraine, but Brazil, Argentina and the US also have relevant shares.

Table 1:

EU soybeans and maize import sources by volume and value, 2000–2014 average.

US Argentina Brazil Canada Paraguay Ukraine Total
Soybeans
Volume (MMT)* 3.86 0.25 7.37 0.61 1.07 13.16
Value (M€)* 1,132 75 2,116 207 350 3,880
Share (Volume) 30 % 2 % 56 % 4 % 8 % 100 %
Share (Value) 29 % 2 % 55 % 5 % 9 % 100 %
Maize
Volume (MMT)* 0.2 1.37 1.57 2.07 5.29
Value (M€)* 110 219 260 388 978
Share (Volume) 7 % 25 % 29 % 38 % 100 %
Share (Value) 11 % 22 % 26 % 39 % 100 %
  1. 1. Source: Own elaboration based on Eurostat (2018)

  2. 2. * MMT = Million metric tons; M€ = million euro

2.2 Nutritional Characteristics and Supply Chain Efficiency

Early works proposed by Rose (1988), Wolf et al. (1982) and Cure et al. (1982),[5] Grieshop and Fahey (2001), Karr-Lilienthal et al. (2004), and Park and Hurburgh Jr (2002) report significant quality differences in soybeans and soybean meal originating from South America, from North America and from the Far East. In particular, the analysis performed by Grieshop and Fahey (2001) on soybeans harvested in different Brazilian, Chinese and North American states, provinces or regions showed that North American and Chinese samples have higher levels of crude proteins and lysine. Similarly, Park and Hurburgh Jr (2002) indicate that soybean meal originating from the US has a higher feeding value (being more digestible, higher in protein, and of higher-quality protein) and a more consistent quality than meal from other geographic areas.

These differences may hinge on country-specific pedo-climatic conditions, but product characteristics are also highly dependent on transportation and storage conditions, and on management at the origination port (Mounts, List, and Heakin 1979; Narayan, Chauhan, and Verma 1988). For example, when farmers do not have access to on-farm storage, products are often stored in the open air, where spoilage and moulds may severely undermine their organoleptic characteristics (Koopman and Laney 2012). In addition, while some exporting countries may be able to guarantee consistent quality management along the entire supply chain, others may not. Brazil, for instance, has historically poor storage systems, both on-farm and off-farm, forcing the product into the marketing channel directly after harvest, depressing prices, generating congestion at the terminal elevators and compromising quality (Koopman and Laney 2012). It is well-established that correct conditions for maize storage, including controlled levels of moisture and light, are crucial to prevent the occurrence of mycotoxins in the final batches (Chulze 2010), while storing soybean in the open air may foster spoilage and moulds (Koopman and Laney 2012).

In accordance with the literature, we postulate that importing countries may attach more value to soybean and maize imported from geographic regions with efficient and well-managed supply chains. In order to consistently assess different supply chains and make comparisons between countries, we need to obtain a solid measure that includes information about agricultural systems, infrastructure quality and management. Since it is difficult directly to measure all these dimensions, we rely on the well-known Overall Infrastructure Quality (OIQ) index, produced by the World Economic Forum (WEF) in their annual publication, the Global Competitiveness Report (WEF 1999–2016). The OIQ is a survey-based index encompassing aspects of national infrastructures such as roads, ports, railways, air connectivity, shipping connectivity etc. Although not specific to the agricultural sector, we believe the OIQ captures almost all aspects of the supply chain relevant to this study. Figure 1 depicts the OIQ index for all the exporting countries relevant to the present analysis, and demonstrates consistently higher figures in North American countries, while Argentina and Brazil perform consistently better than Paraguay.

Figure 1: 
Overall Infrastructure Quality index between 2000 and 2015.
Source: Own elaboration.
Figure 1:

Overall Infrastructure Quality index between 2000 and 2015.

Source: Own elaboration.

2.3 GMOs and Asynchronous Approval

Although globally traded, GM events’ approval for commercialisation and use in food and feed is still regulated by national schemes that include, among other elements, approval for import, cultivation, labelling policy and traceability (Vigani, Raimondi, and Olper 2012; Davison 2010; Berwald, Carter, and Gruère 2006). For example, Smart, Blum, and Wesseler (2017) discuss the differences between the US and the EU approval processes: while both legislative systems provide for scientific and political phases, the two schemes differ significantly. In each authorisation procedure an independent agency is involved (i. e. the USDA’s Animal and Plant Health Inspection Service (APHIS) in the US and the European Food Safety Authority (EFSA) in the EU), but, in the EU, the political component is much stronger. Thus, the political orientation of each EU member state (MS), which typically reflects different political views and orientations towards the use of biotechnologies in agriculture, may play a key role in determining the length of the process and the final decision (Smart, Blum, and Wesseler 2015).

As any newly introduced genetic trait is the result of a different modification process, a case-by-case regulatory oversight is responsible for every application for authorisation in the EU (Cararu 2009; Twardowski and Małyska 2015). The complexity of the authorisation process and the large number of applications has resulted in regulatory disharmony between the EU and some of the world’s major exporters of agricultural products, which have typically introduced GM crops rather more quickly. This temporal disparity has generated the well-known problem of asynchronous approval (hereafter AA). Coupled with the stringent EU rules on low-level presence[6] (LLP) (this is often referred to as ‘zero tolerance’ policy), AA has become a critical obstacle to international trade (Stein and Rodríguez-Cerezo 2010; FAO 2014; Viju, Yeung, and Kerr 2011) thereby fostering economic research on its role as a trade determinant (Henseler et al. 2013; Kalaitzandonakes 2011; Carter and Smith 2007; Pavleska and Kerr 2019).

Empirical evidence indicates reductions in bilateral trade between pairs of countries with asymmetrical authorisations (Vigani, Raimondi, and Olper 2012; de Faria and Wieck 2015). In some cases, this fragmentation and heterogeneity in regulations has led to strained relations that eventually escalated to formal international disputes (Punt and Wesseler 2015) and caused revenue losses to the feed industry (Brookes 2008). With reference to soybean, Henseler et al. (2013) suggest that trade disruption resulting from AA may compromise the performance of the EU livestock sector, thereby undermining the entire agricultural sector. Since traces of events not authorised by the EU cannot be present in imported batches, a costly segregation of the supply chains is required (Ghozzi et al. 2016; Varacca, Boccaletti, and Soregaroli 2014). Given that a complete segregation of approved and unapproved GM crops is hardly achievable (Smyth, Kerr, and Phillips 2017), one would expect that a large gap in the number of unauthorised varieties would lead to a greater likelihood of trade disruption. Consequently, trade should preferably occur between countries with comparable marketing statuses for GM events intended for export and food (feed) use. Since complete segregation is not feasible in practice, we expect efficient (upstream) product management to play a key role in minimising the risk of LLP. Reduced likelihood of LLP should then facilitate the exchange of products between trade partners with different authorisation procedures for GM events. As a result, we consider both AA and supply chain efficiency to be triggers of source differentiation under Armington’s hypothesis.

In recent years, the literature has proposed various methods of measuring AA, based on the number of GM events approved in the EU and in some of the most important international suppliers of maize and soybean (de Faria and Wieck 2014, 2015; Vigani, Raimondi, and Olper 2012). We adopt a slightly modified version of the Directional Heterogeneity Index of Trade (DHIT) defined by de Faria and Wieck (2015), which provides an effective way to describe the differences between country pairs in terms of authorisations, and also includes a time component, to enable tracking in changes in AA over time.

As we have already discussed, authorisations within the EU can be granted for either import and processing (including commercialisation for use in feed and food products) or cultivation. Since only one maize variety has received approval for cultivation (MON810, European Commission 2018), we restrict our attention to GM events approved for food and feed use. Moreover, since our data show that almost all events have received approval for both food and feed use, we make no distinction between them. Finally, since cross-contamination may occur if proper segregation is not achieved, we combine the DHIT for maize and the DHIT for soybean in order to obtain a global measure of AA between country i and country j .

Since our aim is to investigate the effect of AA on the allocation structure of European imports of maize and soybean, we consider country i to be the EU and we let j indicate one of the import sources (US, Brazil, Canada, Paraguay or Argentina).[7] We present details about the computation of the DHIT in Appendix A. Given its definition, this index is monotonic for the number of EU-unauthorised varieties: the higher the index, the more authorisations granted in country j but not in the EU. Figure 2 shows the AA index from 1996 to 2016: since the DHIT ranges between zero and one – with one indicating that 100 % of the commercialised varieties were approved for use in food and feed in country j , but none in the EU – the trend indicates an overall reduction in AA between the EU and both the US and Canada. On the other hand, AA between the EU and South American countries has remained steadily low for two entire decades, with a small increase between 2009 and 2016. The noticeable decrease in AA between North America and the EU is in line with the results in Smart, Blum, and Wesseler (2017): the authors show that, from 1996 to 2015, the overall time taken to gain approval in the EU decreased substantially, while the trend in the US stagnated with a much higher average approval time. In addition, de Faria and Wieck (2014) suggest that applicants may have strategically synchronised applications between Canada or the US and the EU in order to mitigate the impact of AA on trade and avoid costly disruptions.

Figure 2: 
Directional Heterogeneity Index of Trade (DHIT) for soybean and maize.
Source: own elaboration.
Figure 2:

Directional Heterogeneity Index of Trade (DHIT) for soybean and maize.

Source: own elaboration.

3 Model Specification

We model the European-derived demand for soybean grains and maize using a DFAM, following the differential approach to the theory of the firm initially proposed by Theil (1977) and Laitinen and Theil (1978). The production theory approach to international trade is preferred to traditional methodologies that consider imports as final goods entering the consumers’ utility functions directly. Since most traded products are not delivered directly to the final consumers, it is theoretically more appropriate to consider them as inputs of firms displaced at some point along the supply chain[8] and to exploit production theory to model import demand (Burgess 1974; Kohli 1978). The assumption that profit-maximising/cost-minimising firms make import decisions has several advantages. On the one hand, there is no need to specify a model for the final demand through a specification of consumers’ utility functional form and, on the other hand, aggregation between consumers is no longer necessary (Washington and Kilmer 2002; Kohli 1978). Moreover, when considering firms’ optimising behaviour, properties derived from individual producers’ behaviour are typically held in aggregate. Therefore, the aggregate profit obtained when each producer maximises separately is the same as would be obtained if the whole industry cooperated to take a joint profit-maximising decision at given prices[9] (Mas-Colell, Whinston, and Green 1995). Last, since the model employs quantities and prices in first-differences, the risk of spurious regression should be minimised.

Recent works proposed by Washington and Kilmer (2002),  Christou et al. (2005) and Muhammad and Kilmer (2008) employ the DFAM to specify the derived demand function of a variety of products in different countries. According to Davis and Jensen (1994), this methodology is consistent with a direct industry-level profit-maximisation procedure. Following Laitinen and Theil (1978), the multi-product firm’s differential demand for input i , conditional on the Divisia volume index Q , and assuming input-output separability[10] can be modelled as:

(1) h i d l o g q i = θ i d l o g Q ψ j = 1 n θ i j d l o g p j P ˜

where h i p i q i / C indicates the share of the i t h input (for all i , j 1 , , n ) as a proportion of total cost, p i is the price of the i t h input, d l o g Q i = 1 N h i d l o g q i defines the Divisia volume index which captures the change in (total) input quantities, θ i j are the conditional own/cross-input price coefficients, d indicates the differential operator and P ˜ is the Frisch’s price index. Conditional input prices and Divisia elasticities can be specified as:

(2) ε q i p j = l o g q i log p j = θ i j h i 1

(3) η q i Q = l o g q i log Q = θ i h i 1

where ε q i p j measures the impact of input j ’s price on the conditional demand for input i (holding total input decision Q constant) and η q i Q captures how a change in the total input decision affects the conditional demand for input i . Following Washington and Kilmer (2002), Christou et al. (2005), and Muhammad and Kilmer (2008), model (1) describes the input allocation decision of country i as a function of other countries’ relative prices and the input Divisia index. Assuming that soybeans and maize are exclusively imported through international trading companies, it is very likely that downstream enterprises will purchase the same product without any further transformation; if this is the case, the total input decision equals the total amount of output by the multi-product firm.

What international traders may convey with soybeans and maize is nothing but a set of services, such as storage, logistics, transportation, risk management and initial procurement. Therefore, the output of these upstream firms (in terms of volume) will be equal to the total quantity of the imported product ( Q ). Moreover, under (industry) profit aggregation and given prices, q i = k = 1 K q i k for any k 1 , , K , where q i k is the optimal quantity for the k t h international trader.

Model (1) must be properly specified for econometric estimation. We thereby express differentials as finite log changes and embed time in expression (1). Following Asci et al. (2016) and Valdez-Lafarga, Schmitz, and Englin (2019), we control for quarterly seasonality by translating differentials into four-period lags. In this way, differential prices and quantities are compared to the same quarter in the previous year. The resulting system of demand equations is:

(4) h ˉ i t Δ 4 q i t = θ i Δ 4 Q t + j = 1 n π i j Δ 4 p j t + u i t

Where h ˉ i = h i t + h i t 4 2 , h i t = V i t j = 1 n V j t with V i t being the import value from country i , Δ 4 q i t = l o g q i t l o g q i t 4 with q i t being the quantity imported from country i , Δ 4 Q t = Q t Q t 4 , Q t = i = 1 n h ˉ i Δ 4 q i t , Δ 4 p j t = l o g p j t l o g p j t 4 with p j t being the import price for soybeans or maize imported from country j . Brown and Lee (2002) show how to incorporate demand shifters in eq. (4) by including a vector of such exogenous regressors in the original production function. In our work, we would like to account for the effect of asynchronous approval (DHIT) and the efficiency of the supply chain (OIQ) in the exporting country. The resulting cost minimisation problem leads to a modified differential demand, whose empirical form is:

(5) h ˉ i t Δ 4 i t = θ i Δ 4 Q t + j = 1 n π i j Δ 4 p j t + j = 1 n γ i j Δ 4 D H I T j t + j = 1 n δ i j Δ 4 O I Q j t + u i t

where Δ 4 D H I T j t = l o g D H I T j t l o g D H I T j t 4 and Δ 4 O I Q j t = l o g O I Q j t l o g O I Q j t 4 . Estimated conditional elasticities ε ˆ q i p j and η ˆ q i Q are typically computed at the mean values of h i t , namely: h ˙ i = t = 1 T h i t T , for all i . We compute elasticities’ standard errors accordingly:

(6) s . e . ε ˆ q i p j = s . e . π ˆ i j h ˙ i

(7) s . e . η ˆ q i Q = s . e . θ ˆ i h ˙ i

Finally, model (5) requires that the following parametric restrictions are met in order to satisfy the theoretical demand properties of homogeneity, symmetry and adding up:

(8) j = 1 n π i j = 0

(9) π i j = π j i

(10) i = 1 n π i j = 0 i = 1 n γ i j = 0 i = 1 n δ i j = 0 i = 1 n θ i = 1

for all i , j 1 , , n .

4 Data and Estimation

The data we use to estimate model (5) are quarterly time series from 1999 to 2015. All the information comes from Eurostat’s trade statistics database (Eurostat, 2018). In ITGS (International Trade in Goods Statistics), the trade value corresponds to the amount which would be paid at the time and place the products cross the national border of the reporting MS For imported goods, this value is widely known as CIF (cost, insurance, freight). CIF values do not include import taxes, such as customs duties or VAT. In extra-EU trade, the statistical value is based on the value determined for customs purposes. Trade values are recorded in national currency. Eurostat expresses them in euros even for countries that do not belong to the eurozone and, in these cases, the currency conversion is performed using the monthly average exchange rate (Eurostat, 2018). Finally, imported quantities are expressed in 100 Kg.

We compute import prices by dividing the import value of soybeans or maize from county j by the corresponding quantity (these prices are called ‘unit values’ and are extensively used in trade analysis: see for example Muhammad and Kilmer 2008; Washington and Kilmer 2002). As discussed in the introduction, the exporting countries we consider in this analysis are the US, Brazil, Canada and Paraguay for soybean grains, and Argentina, Brazil, Ukraine and the US for maize. We estimate the conditional parameters in eq. (5) using a heteroscedasticity and autocorrelation robust (HAC) system IFGLS (Iterative Feasible Generalized Least Square), where the system variance-covariance matrix consists of a N T × N T generalisation of the Newey-West estimator (Newey and West 1986). This procedure employs an iterated fit of the system variance-covariance matrix to achieve maximum efficiency. As indicated by Kastens and Brester (1996) and Murphy, Norwood, and Wohlgenant (2004), the forecasting ability of demand systems improves when theoretical properties are imposed rather than tested (even when rejected by appropriate testing procedures). Therefore, after imposing homogeneity (8) and symmetry (9), we drop one equation and estimate the system defined in (5) using n 1 equations. Next, we use conditions (8)–(10) to recover the parameters of the excluded equation (Brown and Lee 2002) and calculate the corresponding standard errors through the Delta Method.

5 Results

5.1 Soybeans

Table 2 reports estimates of parameters for model (5) when the product is soybeans. With the exception of Brazil (column (ii)), most estimated coefficients for the DHIT turned out non-significant. This seems to indicate that AA only affects trade dynamics between the EU and its largest soybean supplier. Products imported from the US, Canada and Paraguay, however, appear largely unaffected by heterogeneous approval statuses of GM commodities. As expected, the higher the D H I T u s a value in eq. (5) (when j = Brazil), the more soybeans the EU imports from Brazil. This suggests a substitution pattern between the latter and the US when D H I T u s a is high. From this perspective, however, the negative sign of D H I T p r y is difficult to interpret, as it would essentially reverse the above suggestion. If anything, the fact that the absolute value of this parameter is significantly smaller than the coefficient for D H I T u s a , implies a milder effect of D H I T p r y on trade dynamics. The same reasoning would apply to D H I T b r a , whose coefficient is also difficult to interpret. The larger parameter associated with D H I T u s a may be explained by a steadily higher heterogeneity index in the US. This would translate into a more severe risk of LLP in US batches, thereby favouring the import of soybeans from Brazil. At the same time, however, the gradual synchronisation between the EU and the US might explain why the D H I T u s a parameter is not significant (see Table 2, column (ii)) . Results concerning the impact of supply chain efficiency on EU import dynamics are also complicated to disentangle. For instance, as one might expect, the estimated parameter for O I Q p r y is positive and significant in the equation for Paraguay, highlighting a positive relationship between efficient supply chains and exports. Yet, the index produces a negative coefficient in the case of Brazil: a counterintuitive result that conflicts with the literature. However, none of the remaining coefficients is significant, indicating that the overall effect of supply chain efficiency on the EU import structure is not very important.

Table 2:

Estimation results for model (5): soybean conditional own-price, cross-price and Divisia coefficients.

Equation
Variable (i) U.S.
(ii) Brazil
(iii) Canada
(iv) Paraguay
Parameter S.E. Parameter S.E. Parameter S.E. Parameter S.E.
Δ Q 0.373*** 0.075 0.158*** 0.06 0.029* 0.017 0.437*** 0.049
Δ p u s a −0.29** 0.13 0.112 0.106 −0.022 0.031 0.199 0.079
Δ p b r a 0.112 0.106 −0.261* 0.146 0.188*** 0.051 −0.040 0.098
Δ p c a n −0.022 0.031 0.188*** 0.051 −0.098** 0.043 −0.067* 0.038
Δ p p r y 0.199** 0.079 −0.04 0.098 −0.067 0.038* −0.091 0.0988
Δ D H I T u s a −0.126 0.139 0.271** 0.111 −0.076** 0.032 −0.068 0.092
Δ D H I T b r a −0.002 0.007 0.016*** 0.005 −0.002 0.001 −0.011 0.004
Δ D H I T c a n 0.005 0.039 −0.019 0.031 0.012 0.009 0.001 0.026
Δ D H I T p r y −0.551 0.372 −0.004* 0.002 0.000 0.001 0.002 0.002
Δ O I Q u s a 0.678 0.486 −0.448 0.391 0.143 0.114 −0.367 0.323
Δ O I Q b r a 0.443 0.441 −1.101*** 0.352 0.194 0.102 0.463 0.291
Δ O I Q c a n −0.004 0.925 0.02 0.739 0.240 0.217 −0.256 0.614
Δ O I Q p r y −0.551 0.372 −0.105 0.305 −0.113 0.091 0.771*** 0.257
  1. Source: Own elaboration. Note: *** for 1 %, ** for 5 % and * for 10 %.

Table 3 presents the estimated conditional elasticities for soybeans. All soybean Divisia (own-price) parameters have the expected positive (negative) sign and, with the exception of Paraguay own-price elasticity, they are all significant at 5 % or 10 % level. Our findings indicate that, if EU soybean import increases by 1 %, the export of these countries to the EU will increase by approximately 1.23 %, 0.29 %, 0.53 % and 4.98 %, respectively. Consequently, it appears that exports from Paraguay are particularly sensitive to EU demand for soybeans, followed by the US, and that Brazil and Canada appear to be less sensitive to variations in the EU demand for overseas product. Canada proves to be the most elastic soybean source, followed by Paraguay and the US. Brazil is the exporter showing the lowest own-price elasticity, yet this result is not surprising as it provides for the largest share of EU-imported soybeans. The opposite is true of Canada. The absolute magnitude of conditional own-price elasticities implies that Canada is the most price responsive soybean source while Brazil is much less sensitive.

Table 3:

Soybean Divisia and conditional price elasticities (standard errors in parentheses).

U.S.A. Brazil Canada Paraguay
Δ Q 1.239*** 0.296*** 0.537* 4.963***
(0.250) (0.112) (0.311) (0.562)
Δ p u s a −0.964** 0.211 −0.401 2.27**
(0.430) (0.199) (0.563) (0.896)
Δ p b r a 0.375 −0.488* 3.38*** −0.457
(0.353) (0.274) (0.915) (1.12)
Δ p c a n −0.075 0.352*** −1.77** −0.768*
(0.104) (0.095) (0.772) (0.438)
Δ p p r y 0.664** −0.075 −1.21* −1.04
(0.262) (0.185) (0.692) (1.12)
  1. Source: Own elaboration. Note: *** for 1 %, ** for 5 % and * for 10 %.

Interestingly, conditional cross-price elasticities highlight a substitution relationship between the US and Paraguay, while Canada emerges as a substitute for Brazil (see Table 3). Signs and p-values also indicate that Paraguay is complementary to Canada, and while this may be a result of the residual nature of these two product sources (see Table 1) and the fact that both countries function as substitutes for the two most important exporters (i. e. Brazil and the US), the substitution dynamics may have a more subtle interpretation. For example, South American and North American soybeans show contrasting country-specific features, such as distinct values of DHIT and OIQ index, as well as different nutritional values within the beans themselves. As illustrated in Figure 2, the levels of AA in Paraguay (Canada) have been constantly lower (higher) than those for the US (Brazil), indicating that the latter are less (more) synchronized to the EU then the former. The same argument holds for supply chain efficiency, as displayed in Figure 1. Although our model controls for aspects related to AA and supply chain efficiency in exporting countries, the variables we introduced may not fully capture the extent of these features, thereby leading to non-significant shifters’ parameters and leaving cross-price coefficients partially unaffected. Indeed, even using theoretically sound proxies, both AA and supply chain efficiency are rather complex phenomena to represent within a single variable. As a result, interactions, spill-over effects and uncontrolled heterogeneity might also be at work. Furthermore, reading these results simply from the perspective of product quality,[11] soybeans from the northern hemisphere typically show higher concentrations of protein and lysine, while grains grown in South America are usually less nutritious but substantially cheaper (Grieshop and Fahey 2001; Karr-Lilienthal et al. 2004). Therefore, it makes sense that a sharp rise in price in the US or Brazil would lead to higher imports from Paraguay or Canada, respectively.

5.2 Maize

Table 4 reports the estimates for model (5) when the product is maize. Once again, most coefficients associated with the two variables of interest, DHIT and OIQ, are not significant, although in this case signs and p-values seem to lean towards what intuition and theory would suggest. Let us first concentrate on the equation for Argentina, the second largest exporter of maize to the EU. Estimated parameters for the DHIT indicate that the larger the discrepancy between the EU and Argentina, the lower the product flow from the latter to the former. At the same time, when the asynchrony in commercialized GM varieties between the EU and its remaining trade partners increases, Argentina exports more maize to Europe. This trade pattern is however less clear in Ukraine (the US), although results in Table 4 suggest that a larger DHIT in Brazil (Argentina) corresponds to an increased flow of maize from Ukraine (the US) to the EU. The relationship between the US and Brazil, however, is harder to interpret, as a positive coefficient would be expected here too. Overall, controlling for AA indicates that the adoption of EU-unauthorized GMOs in overseas countries has a significant impact on EU imports, at least for maize. Results regarding the role of supply chain efficiency are once again less straightforward to interpret. For example, a higher infrastructure quality in Brazil (Argentina) translates to a growth in maize demand from Argentina (Brazil), just as higher OIQ levels in Brazil lead to lower imports from Ukraine. However, whereas an analogous dynamic can be observed between the US and Brazil, we observe a reversed effect when considering the US and Argentina. Notice, though, that the US accounts for only a small share of the EU maize import, so these numbers should be regarded with due caution. As may be expected, the arguments we made in the previous section hold for the maize model: unobserved heterogeneity and data issues could be also at work here, thereby making the interpretation of some coefficients ambiguous.

Table 4:

Estimation results for model (5): maize conditional own-price, cross-price and Divisia coefficients.

Equation
Variable (i) Argentina
(ii) Brazil
(iii) Ukraine
(iv) U.S.A.
Parameter S.E. Parameter S.E. Parameter S.E. Parameter S.E.
Δ Q 0.138*** 0.025 0.295*** 0.039 0.075** 0.027 0.490*** 0.042
Δ p a r g −0.339*** 0.114 0.119** 0.051 0.123 0.088 0.097* 0.051
Δ p b r a 0.119** 0.051 −0.236*** 0.072 0.147*** 0.052 −0.030 0.062
Δ p u k r 0.123 0.088 0.147*** 0.052 −0.285*** 0.093 0.014 0.049
Δ p u s a 0.097* 0.051 −0.030 0.062 0.014 0.049 −0.082 0.081
Δ D H I T a r g −0.034*** 0.001 0.004 0.017 −0.017 0.010 0.047** 0.019
Δ D H I T b r a 0.038** 0.015 −0.012 0.027 0.039** 0.016 −0.065** 0.030
Δ D H I T u s a 0.373* 0.219 0.0541 0.382 −0.006 0.224 −0.421 0.428
Δ O I Q a r g 0.785 1.287 −6.627*** 2.125 0.850 1.296 4.991** 2.391
Δ O I Q b r a −2.839* 1.205 2.655 2.069 −3.048** 1.221 3.232 2.324
Δ O I Q u k r 0.430 0.483 −0.671 0.830 0.507 0.489 −0.266 0.937
Δ O I Q u s a 1.764 1.151 −1.020 1.858 0.429 1.171 −1.174 2.108
  1. Source: Own elaboration. Note: *** for 1 %, ** for 5 % and * for 10 %.

From an inspection of Table 5, Divisia elasticities suggest that a 1 % increase in EU maize imports would drive exports from Argentina, Brazil, Ukraine and the US up by approximately 0.4 %, 1.3 %, 0.29 % and 2.7 % respectively. As expected, all conditional own-price elasticities are negative and significant at the 1 % level, except for the US. Conditional cross-price elasticities are quite different from those we observed for soybeans: we only find substitution effects among maize sources and no complementary relationships. Specifically, Argentina is substituted by both Brazil and the US, Ukraine is a substitute for Brazil (and, of course, vice versa) and the US exports more maize to the EU when prices in Argentina are higher. As in the case of soybeans, it is not easy to provide an explanation for such trade dynamics, especially because (in this case) substitution occurs both between North and South American countries as well as overseas countries and Ukraine. Contrary to what we have seen for soybeans, however, these results suggest that the imperfect substitution among exporters does not follow any clear pattern attributable to country-specific characteristics. Since the supply of maize is not a limiting factor for the European livestock industry, this would give way to a pure price competition in which the cheapest product is typically preferred.

Table 5:

Maize Divisia and conditional price elasticities (standard error in parenthesis).

Argentina Brazil Ukraine U.S.A.
Δ Q

0.421***

(0.076)
1.299***

(0.173)
0.289***

(0.103)
2.689***

(0.234)
Δ p a r g

−1.034***

(0.348)
0.524**

(0.224)
0.470

(0.336)
0.535*

(0.283)
Δ p b r a

0.362**

(0.154)
−1.039***

(0.319)
0.563***

(0.200)
−0.166

(0.343)
Δ p u k r

0.375

(0.267)
0.649***

(0.231)
−1.090***

(0.359)
0.081

(0.271)
Δ p u s a 0.297*

(0.157)
−0.133

(0.275)
0.056

(0.189)
−0.450

(0.447)
  1. Source: Own elaboration. Note: *** for 1 %, ** for 5 % and * for 10 %.

6 Discussion and Conclusion

In this work, we analyse the European soybean and maize import structure using a demand system derived from the popular differential approach to the firm theory (Laitinen and Theil 1978). This particular method of modelling derived demand allows a relaxation of the assumption that intermediate goods enter the consumers’ utility function (a popular approach in the trade literature), while eliminating non-stationarity in integrated variables through first-differences.

Estimated coefficients indicate that AA and differences in supply chain efficiency play a mixed role in affecting EU import demand dynamics for either maize or soybeans. In particular, we find that AA only exerts a significant effect on soybean trade when considering the product flow from Brazil to Europe. This implies that the problem of unauthorised GM varieties concerns almost exclusively the largest soybean supplier, with no clear effect observed on imports from the US, Canada and Paraguay. Specifically, our estimates suggest that a higher discrepancy in authorised GMOs between the EU and the US has a significant cross-effect on imports from Brazil. However, we find no direct impact of AA on soybeans originating directly from North America. An analogous pattern emerges for maize, although we note a significant effect of AA on the EU’s second largest supplier, Argentina, and a partial effect on the US, the country with the lowest share. In the first case, our results follow the literature precisely, while conclusions about the US are harder to draw.

With regard to the role of AA, previous literature, based (mostly) on gravity models, indicates that the higher the discrepancy between trade patterns, the lower the bilateral trade. Our results are therefore only partially in line with previous findings. However, our observations may be examined in light of those presented by de Faria and Wieck (2015) and Vigani, Raimondi, and Olper (2012). Although both these works conclude that AA exerts a significant negative effect on bilateral trade flows,[12] de Faria and Wieck (2015) indicate that trade disruptions are weaker for soybean. Given the key role of this crop as a source of cheap protein for the EU livestock sector, this may explain why the AA shifters in our demand model prove to be non-significant in three equations out of four. Next, as discussed earlier in this paper, the DHIT indicates a clear reduction in AA between the EU and North American countries, while the index remains steadily low in Brazil, Argentina and Paraguay. As some authors have suggested (de Faria and Wieck 2015; Smart, Blum, and Wesseler 2017), this could be the result of strategic behaviour in terms of a gradual harmonisation of approval statuses between country pairs. In view of our results, however, this assumption may hold for soybean but not for maize.

From this perspective, Vigani, Raimondi, and Olper (2012), also discuss the endogeneity of GMO regulations in trade models, suggesting that marketing GM events may be the result of a trade-off between agronomic advantages and market access. As a result, prices and quantities in the demand model would already retain such information, thus explaining why AA does not create any additional effect when included in some of the equations. Nevertheless, we ran a robustness check by splitting the sample into two periods: 2000–2007 (i. e. a time window with higher DHIT for the US and Canada) and 2008–2017 (i. e. a time window with lower DHIT for the US and Canada), obtaining similar results.

The role of supply chain efficiency does not fully emerge from our model. Indeed, our results are rather inconsistent across equations and products, with most estimates yielding non-significant sets of parameters that are hard to interpret when analysed ceteris paribus. The reason might be found in the objective difficulty of choosing an accurate measure for such a complex phenomenon. In other words, although OIQ should provide a solid way to distinguish efficient countries from less efficient ones, this variable might not fully capture the extent of the issue, possibly because of the relatively scarce variability of the index, or because some information was missing regarding both upstream and downstream product handling. We ran a Chi-Square test to verify whether these sets of variables should be omitted from our model: in the case of both maize and soybeans, we rejected the restricted specification, and decided to keep the OIQ controls in the model. Moreover, as Valdez-Lafarga, Schmitz, and Englin (2019) point out, incomplete exchange-rate pass-through may also have a non-trivial impact on both Divisia and price elasticities. Since our model does not include exchange rates, we are aware that values in Table 3 and Table 5 may have been overestimated.

Finally, as the main EU import sources are large producers and developers of GM products (Pavleska and Kerr 2019), and since genetic improvement takes place predominantly in GM varieties, it is very likely that the relative price of GM products will decrease, thus creating the conditions for an increase in the share of GM products in EU feed. Combining this trend with the recent sharp reduction in EU maize acreage (−15 % from 2012 to 2017), which will increase dependence on imports, we are likely to see a relevant reduction in AA, as a result of the pressure to maintain a sufficient flow of imports of maize and soybean at affordable prices.

About the authors

Alessandro Varacca

Alessandro Varacca is Postdoctoral Researcher in Agricultural Economics at the Dipartimento di Economia Agroalimentare, Facoltà di Scienze Agrarie, Alimentari e Ambientali, Università Cattolica del Sacro Cuore, Piacenza, Italy. He holds a Bachelor Degree in Agriculture, a Master Degree in Agricultural and Food Economics and a PhD in Agricultural and Food Economics from Università Cattolica del Sacro Cuore. His research interests comprise food demand and consumers’ behaviour, price analysis and the economics of biotech crops.

Paolo Sckokai

Paolo Sckokai is Associate Professor in Agricultural Economics at the Dipartimento di Economia Agroalimentare, Facoltà di Scienze Agrarie, Alimentari e Ambientali, Università Cattolica del Sacro Cuore, Piacenza, Italy. He holds a Ph.D. in Agricultural Economics from the University of Padua and a Master’s Degree in Economics from Iowa State University. His research interests focus on food demand and price analysis, agricultural policy and the industrial organisation of the food sector.

Appendix

A

The D H I T index

Let x i m k t be the approval status for the GM event m in country i at time t and let k be the product of interest (i. e. soybean); then if the event m is authorised in country i at time t , x i m k t = 1 and 0 otherwise. We define a dissimilarity measure between two countries i and j at time t as:

D i j m k t = x m k t x j m k t

D i j m k t can take on values 0 , 1 or 1 . If we let j be the importer, then D i j m k t = 1 implies that the exporter has approved event m for use in food and feed products, while the importer has not. On the contrary, when D i j m k t = 1 the importer has approved product m for use in food and feed products, but the exporter has not. Last, D i j m k t = 0 when neither (both) country i or (and) the importer has (have) approved event m at time t . Let now M k t indicate the total number of marketed[13] GM events for product k at time t , then a measure of asynchronous approval between country i and j is:

(11) D H I T i j t = ( k K M k t ) 1 [ k K m k t = 1 M k t ( D i j m k t | D i j m k t 0 ) ]

where, K = soybean ,  maize , i { USA ,  Brazil ,  Argentina , Ukraine  |  k = maize } , i { USA ,  Brazil ,  Canada ,  Paraguay |  k = soybean } , j = EU and t goes from 1996 to 2016. Conditioning on D i j m k t > 0 implies selecting all GM events for which approval is granted by the exporting countries but not by the EU. Therefore, eq. (11) measures the share of GM events marketed in year t (both for maize and for soybean) that has received approval for food and feed use by country i but not by the EU. As such, D H I T i j t is indeed a proxy of Asynchronous Approval between the EU and some of the major international exporters of maize and soybean. We collected information about x i m k t and M k t from the ISAAA GM approval database (ISAAA 2017) and the CERA GM Crop Database (CERA 2016).

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Published Online: 2020-01-29

© 2020 Alessandro Varacca and Paolo Sckokai, published by De Gruyter, Berlin/Boston

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