Efficiency performance of the world's leading corporations in phosphate rock mining

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ARTICLE IN PRESS

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Resources, Conservation and Recycling xxx (2015) xxx–xxx

Contents lists available at ScienceDirect

Resources, Conservation and Recycling journal homepage: www.elsevier.com/locate/resconrec

Efﬁciency performance of the world’s leading corporations in phosphate rock mining Bernhard Geissler a,∗ , Michael C. Mew b , Olaf Weber c , Gerald Steiner a,d a

Danube-University Krems, Department of Knowledge and Communication Management, Dr. Karl-Dorrek-Straße 30, 3500 Krems, Austria CRU International, Chancery House, 53-64 Chancery Lane, London WC2A 1QS, United Kingdom University of Waterloo, Faculty of Environment, School of Environment, Enterprise and Development, Canada d Harvard University, Weatherhead Center for International Affairs (WCFIA), 1737 Cambridge Street, Cambridge, MA 02138, USA b c

a r t i c l e

i n f o

Article history: Received 4 September 2015 Received in revised form 5 October 2015 Accepted 6 October 2015 Available online xxx Keywords: Phosphate rock mining Data envelopment analysis (DEA) Efﬁciency Phosphate industry Resource efﬁciency Phosphate rock market Sustainable resource management

a b s t r a c t The prominence of phosphorus (P) is represented by three major aspects: ﬁrst and most important, P is essential for all life on Earth; second, no other element or substance can act as a substitute for P; and third, P is considered a non-renewable resource and thus ﬁnite. In regard to global food security and P as one of the three major macronutrients, the world faces an extensive challenge to utilize this ﬁnite and unsubstitutable commodity in the most effective as well as the most efﬁcient way. Efﬁciency in general has increasingly become of major importance over the last several decades, especially within competitive commodities. Practically all PR used for chemical fertilizers originates from exploitable deposits that are concentrated in a rather small number of countries and mined vastly by only a limited number of global enterprises. Whereas these enterprises differ in factors such as size, vertical and horizontal integration, legal form, or type of ownership, their overall goal as corporations remains the same-the optimization of their operations. Consequently, ﬁrms can strive to (a) minimize their inputs at constant output levels; (b) maximize their outputs at constant input levels; or (c) increase their efﬁciency ratio by adjusting inputs and outputs at the same time. In contrast to the oil industry, the PR market is demand driven, which means that not everything that could be produced is immediately consumed. This study attempts to measure, compare, and analyze the technical efﬁciency performance of the major global corporations involved in PR mining by using the BCC (Banker, Charnes, and Cooper) and CCR (Charnes, Cooper, and Rhodes) models of data envelopment analysis. The analysis includes total technical efﬁciency as well as the disaggregated pure technical and scale efﬁciency and a breakdown of the factors accounting for inefﬁciency. The 24 ﬁrms included in the analysis account for 67.3% of the global phosphate rock ore capacity and 61.4% of phosphate rock concentrate capacity. Based on the BCC and CCR modeling a higher percentage (36% vs. 20% – Model 1; 36% vs. 10% – Model 2) of publicly quoted companies (such as PotashCorp) are classiﬁed as efﬁcient compared to state-owned companies (such as OCP). However, the frequencies of efﬁciency performance do not differ in such a way that a Fisher Exact Test would suggest statistical signiﬁcance for these data. This indicates that general assumptions regarding the different strategies of state-owned and publicly quoted ﬁrms are not necessarily valid. © 2015 Elsevier B.V. All rights reserved.

Abbreviations: BCC, Banker, Charnes, and Cooper, DEA model with the assumption of variable returns to scale; CCR, Charnes, Cooper, and Rhodes, DEA model with the assumption of constant returns to scale; CRS, constant returns to scale, operation level of a company implying that a change of inputs leads to a proportional change of outputs; DAP, diammonium phosphate, group of phosphorus fertilizers; DEA, data envelopment analysis, method for efﬁciency benchmarking of multiple inputs and outputs; DMU, decision making unit, comparison unit used in our case for PR mining ﬁrms; DRS, decreasing returns to scale, operation level of a company implying that a change of inputs leads to an under-proportional change of outputs; EBIT, earnings before interest and tax, common performance indicator in accounting and ﬁnance; HHI, Herﬁndahl–Hirschman-Index, concentration indicator for markets; IRS, increasing returns to scale, operation level of a company implying that a change of inputs leads to an over-proportional change of outputs; P, phosphorus, chemical element; PR, phosphate rock, not further speciﬁed (PR-M or PR-Ore); PA, phosphoric acid, downstream product in the P supply chain; PR-M, phosphate rock concentrate, marketable concentrate; PR-Ore, phosphate rock ore; RTS, returns to scale, operation level of a company (CRS, DRS, IRS or VRS); SBM, slack-based model, further step in DEA analysis in order to recognize non-zero slacks; SE, scale efﬁciency; SFA, stochastic frontier analysis, alternative to DEA; VRS, variable returns to scale, operation level of a company implying that a change of inputs leads to an non-proportional change of outputs. ∗ Corresponding author. E-mail addresses: [email protected], [email protected] (B. Geissler). http://dx.doi.org/10.1016/j.resconrec.2015.10.008 0921-3449/© 2015 Elsevier B.V. All rights reserved.

Please cite this article in press as: Geissler, B., et al., Efﬁciency performance of the world’s leading corporations in phosphate rock mining. Resour Conserv Recy (2015), http://dx.doi.org/10.1016/j.resconrec.2015.10.008

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1. Introduction The human-induced global demand for minerals and metals is rising, and this, in turn, is causing increasing concerns about long-term supply security (Wellmer and Scholz, 2015) and, consequently, concerns about efﬁciency. In particular, the case of phosphorus, which is neither substitutable nor inﬁnite, is of major importance for the global food supply because of its function in chemical fertilizers. While crop production in the pre-industrial era relied mainly on natural levels of soil phosphorus and locally available organic matter (Cordell et al., 2009), nowadays major portions of the world’s total crop yield are attributed directly to the application of chemical fertilizers. The use of fertilizers increased vastly over the twentieth century and is estimated to continue to grow in the future (Enger, 2010). P represents one of three macronutrients for which no substitute exists, while nitrogen (N) and potassium (K) are practically unlimited and exploitable resources through processing air and seawater, respectively. This is not the case for P (Wellmer and Scholz, 2015). Practically all of today’s inorganic P used for agricultural purposes (approximately 85% of total P) is produced from phosphate rock, a naturally occurring geological material that contains a relatively high concentration of P (approximately 85% from sedimentary deposits and the rest from igneous and guano deposits). The majority of PR mining market shares are controlled by a limited number of corporations. Although these ﬁrms differ vastly in categories such as size (e.g., Vale SA vs. Jordan Phosphate Mines Company – JPMC); vertical and horizontal integration (e.g., mining activities related to other commodities vs. solely PR raw material supplier); and legal form or type of ownership (e.g., publicly traded vs. privately held and state-owned), their overall goals remain the same, namely, successful business operations. The paper proceeds as follows: Sections 1.1 and 1.2 introduce the reader to basic economic efﬁciency considerations, followed by a brief overview of PR mining and the global phosphate industry. Section 2 provides insights on the DEA method and its applications in mining. It continues with the outlined sample data and the resulting DEA models. Section 3 proceeds with a discussion of the detailed results in regard to the research question. The paper concludes with Section 4, including an outlook on challenges for future research. In order to assist the reader with the used abbreviations we point to the included abbreviations above. 1.1. Basic economic efﬁciency considerations Efﬁciency is crucial for successful long-term business operations on all levels and in all operations. Although productivity is similar to efﬁciency and many authors do not differentiate between the two (Daraio and Simar, 2007), a distinction is necessary. Productivity can be deﬁned as the ratio between an output and the factor that made it possible (Vincent, 1968). The ratio itself is simple to calculate if only a single input and a single output are present; in the case of several inputs and outputs, they must be aggregated in order for productivity to remain a ratio of two scalars. In contrast to productivity, efﬁciency can be described as “. . . a valuation function that assesses how much we have to invest in order to receive a certain quantity and/or quality of a desired outcome or product” (Scholz and Wellmer, 2015c, Abstract). Technical efﬁciency was ﬁrst deﬁned by Koopmans (1951, p. 60) in the following way: “. . . an input–output vector is technically efﬁcient if, and only if, increasing any output or decreasing any input is possible only by decreasing some other output or increasing some other input.”. In the same year, Debreu (1951) provided the ﬁrst measure of productive efﬁciency by a coefﬁcient of resource utilization, which represents a radial measure (interpretable as the ratio of two distance measures according to Cooper et al., 2006) of technical efﬁciency. Farrell (1957) extended the

previous work of Koopmans and Debreu by raising the question of the selection of the “right” technically efﬁcient input–output vector under the consideration of given input and output prices, which is considered allocative efﬁciency. Therefore, Farrell (1957) deﬁned overall productive efﬁciency as the product of technical efﬁciency and allocative efﬁciency. In addition to the concept above, he introduced the concept of structural efﬁciency, which basically measures to what extent an overall industry (i.e., the “technical efﬁciency” of an industry) keeps up with the performances of its closest competitors (e.g., best ﬁrms within a sector). The original developments by Farrell (1957) assumed constant returns to scale, which was the foundation of the linear programming framework of Charnes, Cooper, and Rhodes in 1978, known as the CCR model, and led to the introduction of the BCC model assuming variable returns to scale (Daraio and Simar, 2007). Here, we want to emphasize that efﬁciency must not be confused with effectiveness or with efﬁcacy. A detailed discussion of the distinction, especially for P-mining, can be found at Scholz and Wellmer (2015b). Considering the concepts above, a company trying to increase its production efﬁciency can either adjust the input side, the output side, or both sides simultaneously. This leads to the conclusion that the input side (xi ) as well as the output side (yi ) separately allow for three different possibilities in terms of parameter change. Inputs (outputs) can either be decreased (x1 < x0 , y1 < y0 ), kept constant (x1 = x0 , y1 = y0 ), or increased (x1 > x0 , y1 > y0 ), which implies a total of nine possibilities (illustrated in Fig. 1). If the input (output) is kept constant while the output (input) is increased (decreased), then efﬁciency will increase, while for the opposite it will decrease. Additionally, if both parameters are adjusted in opposite directions, with increasing (decreasing) inputs and decreasing (increasing) outputs, efﬁciency will decrease (increase). The upper-left and lower-right corner ﬁelds are most crucial, since both parameters are adjusted at the same time in the same direction; in these cases, the efﬁciency development depends on the proportional increase (decrease) of the numerator (output) and the denominator (input), and can either increase or decrease. 1.2. Phosphate rock mining, industry, and phosphate reserve estimation The increasing human demand for minerals and metals raises questions about their long-term supply security, which holds especially true for the case of phosphorus mainly in the form of phosphate rock. A growing world population is combined with the fact that half of all food production relies on this mineral. The accuracy of current projections depends on the quality of the data we have today (Scholz and Wellmer, 2013, 2015a; Wellmer and Scholz, 2015). Phosphorus scarcity has been discussed repeatedly in history (Emsley, 2000). The present circle started with a workshop launched by Swiss environmental agencies and research groups of ETH in 2007, which included the world’s leading resource specialists (Wolfensberger et al., 2007). Global public attention was ﬁrst raised, unfortunately, through a scientiﬁcally incorrect application of the Hubbert Curve by a report of the Global Phosphorus Research Initiative, in which the members estimated peak phosphorus (the point in time at which world production will peak and slowly decrease regardless of the growing demand) for the year 2033, followed by a full depletion 50–100 years later (Cordell et al., 2009; GPRI, 2010). These ﬁgures were later adjusted based on new data, with a revised peak production around 2070 (De Ridder et al., 2012). In contrast to this static approach, Global TraPs emerged as a global transdisciplinary project involving experts from science and practice with the scope of sustainable phosphorus management (see www.globaltraps.ch for further details). Scholz and Wellmer (2013, 2015a), in addition to others, claim the inappropriate use of peak theory in the case of PR, which started the scarcity discussion,

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Fig. 1. Efﬁciency considerations for changing input and output volumes.

for reasons such as (a) the lack of knowledge about the amount of the ultimate recoverable resources and (b) the presence of a demand market (instead of a supply market). Alternatively, they propose the use of a dynamic perspective of reserves and resources. In regard to available data on reserves and accuracy, the most comprehensive discussion – to the best knowledge of the authors-began based on the paper “Recent revisions of phosphate rock reserves and resources: Reassuring or misleading? An in-depth literature review of global estimates of phosphate rock reserves and resources” (Edixhoven et al., 2013) and continued for almost two years, involving a broad set of experts in the ﬁeld (Cook, 2014; Hilton, 2014; Scholz and Wellmer, 2015a; Geissler and Steiner, 2015; Mew, 2015a; the full discussion is available online; see www.earth-syst-dynam-discuss.net/6/31/2015/esdd6-31-2015-discussion.html). To conclude, the available data for reserves, resources, and geopotential is neither complete (we simply cannot quantify a URR for the element phosphorus) nor precise (e.g., reports confuse or do not differentiate marketable concentrate from ore). Although, as shown in the discussion, the world does not face a potential phosphorus scarcity within the next few centuries – and most likely not even within the next 1000 years – the importance of material-resource efﬁciency in order to secure the long-term supply of this non-renewable resource is highly relevant. Within activities such as extraction, we have to distinguish at least two types of efﬁciency: economic efﬁciency and material-resource efﬁciency. Economic efﬁciency, from a return on investment perspective, basically describes the quantity of PR-Ore or PR-M that is returned for the amount of money invested. It is inﬂuenced by a large set of parameters during the mining and beneﬁciation processes. Changes in economic efﬁciency are either involuntary due to changing deposit characteristics and exogenous variables like price or competition, or voluntary through changes in the operational parameters. Material-resource efﬁciency, related to physical efﬁciency (Scholz and Wellmer, 2015b,c), aims to maximize the resource recovery. It may be of even greater interest for stateowned or state-controlled ﬁrms since they are not directly facing ﬁnancial pressure from shareholders while additionally bearing social responsibilities toward the national population. Finding a

balance between resource and economic efﬁciency is proposed to be of greater importance for all stakeholders (Steiner et al., 2015). The main focus of this paper is directed toward the economic efﬁciency of companies operating in the PR market. China, the US, Morocco, and Russia represent the major PR-M producing countries. This market concentration may harbors noteworthy geopolitical supply risks (De Ridder et al., 2012). Although the HHI for PR production (country concentration) lies just within the middle of other metals and minerals in terms of market risks (Scholz and Wellmer, 2013) these risks might appear greater if the distribution of exported PR or PR reserves is taken into account, where the HHI is signiﬁcantly higher (Scholz and Wellmer, 2013), since more than 75% of the world’s reserves are located in a single state, Morocco (Jasinski, 2015). Once again, a key element that can help to address mostly geopolitical supply challenges is efﬁciency. Awareness about processing this ﬁnite and limited resource under the concentration of economic stability for the ﬁrms involved in the operation will play a major role in securing its future supply. In supply chain considerations, the production of fertilizer is a downstream process following the extraction and beneﬁciation stages. It involves the production of phosphoric acid as an initial product in phosphorus fertilizers (Steiner et al., 2015), such as diammonium phosphate, mono-ammonium phosphate, and triple superphosphate, which represent the most common high phosphorus-content fertilizers (IBIS World, 2014). The extent of the vertical integration of downstream processes varies among the companies involved in PR mining, although it tends to increase within ﬁrms, such as the recent developments at the Saudi Arabian mining company Ma’aden, where the Wa’ad Al Shammal Phosphate Project includes all downstream plants in order to produce phosphate fertilizers (Ma’aden Phosphate Company, 2013). The companies considered in this research operate in various segments of the global phosphate industry. Following is a brief introduction to industry developments of note, as well as certain conditions and backgrounds from a practical perspective. The core information is derived from market studies and research by CRU International and the former Fertecon Research Centre. Several global phosphate industry-speciﬁc factors impact PR-M price levels. First, a decrease in underground mining (Jasinski et al.,

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2004) and an increase in mechanization have occurred over time (Fertecon Research Centre, 2004). Second, PR-M conversion into downstream chemicals now occurs more frequently near the site of production, previously exempliﬁed by Ma’aden, leading to a signiﬁcant decline in PR-M trade. While in 1970, trade in PR-M accounted for almost 50% of all RP-M use, by 2013, it had fallen to 23% of the total PR-M production (IFA, 2015). In addition, levels of consumption of P products vary across countries, with some of the largest producers of PR-M, PA, or phosphate fertilizer having only small domestic requirements for phosphate products, thus making them more likely to access the export market (IFA, 2015). Lastly, downstream plants need to be adjusted to particular types of rock – a laborious process reducing the likelihood of change between PR sources (Mew, 2000). This, in conjunction with the geographical distribution of large PR-M exporters and importers, has led to fairly stable trade patterns within the PR-M market (IFA, 2015). In sum, the PR-M export market is segmented by freight costs, product grade and/or quality, and the degree of competition, all of which vary over time and signiﬁcantly impact PR-M pricing (Weber et al., 2014). Mew (2015b) pointed out that, although generally ﬂat historically, PR-M prices have shown two distinct periods of sharp increases, one in the mid-1970s and another in 2008. In both cases, prices jumped a multiple of 6–8 times, before falling back to their baseline levels in subsequent years. Generally, pricing is governed by the balance between supply and demand in the PR-M market, but higher prices are also maintained by increasing the value of downstream products. In between, PR-M prices remained relatively subdued, providing existing producers with only a small return and therefore discouraging new entrants into the market (Mew, 2015b). All mining companies included in this research operate within the global phosphate industry; however, although they are all mining PR deposits to produce PR-M, their positions within the industry differ to some extent. A comprehensive overview of the ﬁrms included can be found in Section 2.3. In addition, an expert report providing valuable extensive background information on industry developments and country- speciﬁc characteristics can be found in Appendix.

into the structure of a DEA. Stochastic frontier analysis, by contrast, is a parametric method that requires an a priori estimation of the production function. SFA is superior to DEA in that it allows for stochastic inﬂuence through the inclusion of a confounding variable, while every deviation from the frontier is regarded as inefﬁciency in DEA. The lack of justiﬁcation for the necessary a priori assumptions, especially the assumption of a production function, is a widespread point of criticism regarding stochastic frontier analysis (Coelli et al., 2005; Francksen and Latacz-Lohmann, 2006), which suggests that DEA seems to be a good ﬁt for this type of research. Starting with the assumption that PR mining ﬁrms (DMUs) operate under variable returns to scale, the dual problem is formulated as follows (Cooper et al., 2011): The objective function (1) provides the optimal solution * (the Greek letter theta, most commonly used in literature for the measure of efﬁciency) for a certain DMU j, implying that the linear program has to be solved for each DMU separately, whereby the index 0 always denotes the current DMU under consideration. The objective function, which aims to minimize inefﬁciencies on the input side while keeping the output side constant, is restricted to the constraints ((2)–(5)). On the left-hand side of the equation, the ﬁrst constraint (2) sums the weighted (j ) inputs (xij ) for all DMUs, which have to be smaller or equal to the current DMU0 ’s inputs multiplied by the efﬁciency (right-hand side), for all inputs (i = 1, . . ., m). Constraint 2 (3) deals similarly with the output side, which is kept constant for the input-oriented model. The difference in the CCR model is found in constraint 3 (4) by setting the sum of weights equal to 1. The ﬁnal constraint (5) represents the non-negativity constraint. The general question in regard to the objective may be formulated as: “Can and if so, by how much can DMU0 reduce its inputs compared to the other DMUs?” ∗ = min

(1)

subjected to: n

xij j ≤ xi0

(i = 1, . . ., m)

(2)

yrj j ≥ yr0

(r = 1, . . ., s)

(3)

j=1 n

2. Research question and methods j=1

Subsequent to the introduction of the DEA analysis we present its previous applications in the ﬁeld of mining, followed by the data set of PR mining ﬁrms as well as the resulting DEA models themselves and a statistical analysis.

n

j = 1

j=1

j ≥ 0 2.1. Data envelopment analysis Over more than three decades, data envelopment analysis has emerged as a powerful quantitative and analytic tool. It is used to measure and evaluate the performance of a set of comparable peers, the so-called decision-making units (DMUs, in this case PR mining ﬁrms) (Cooper et al., 2011). DEA has been widely used and successfully applied to a broad variety of ﬁelds such as banking, health care and hospital efﬁciencies (Emrouznejad et al., 2008); the military; education and universities; countries; (Cooper et al., 2011) and later to measure environmental performance, e.g., Färe et al. (1996). The literature includes extensive studies on the development and applications of DEA conducted by, e.g., Cook et al. (2010) and Emrouznejad et al. (2008). Cooper et al. (2006, p. 2) characterize DEA as an approach, that “. . . does not require the user to prescribe weights to be attached to each input and output, as in the usual index number approaches, and it also does not require prescribing the functional forms that are needed in statistical regression approaches to these topics”. Cook et al. (1993) even presented a way to incorporate an ordinal data factor

(4)

(5)

The model above (formula (1)–(5)) identiﬁes whether a DMU is located on the frontier (efﬁciency scores of 1) or not (efﬁciency scores below 1), whereby some of the efﬁcient points (i.e., efﬁciency value of 1) may account for non-zero slacks. Slacks indicate that, although these ﬁrms are on the frontier and therefore theoretically efﬁcient, they could still reduce their inputs without changing their efﬁciency score, and hence, without reducing their outputs. For example, in Fig. 2 we see the ﬁrms (DMUs) F and G on the frontier. DMU G has the same output level as DMU F but uses more inputs to achieve this level; hence, the distance xF xG represents the non-zero slack of DMU F and makes it, therefore, “weakly efﬁcient” (Cooper et al., 2011). In order to avoid unrecognized non-zero slacks, a second step, in the form of an SBM model, is performed with the objective (6) of maximizing the input- (si− ) and output (sr+ ) slacks. The optimum solution from the previous objective function (1) is included in constraint 2. The left-hand side for constraint 1 (7) includes the input slacks, which are added to the weighted inputs, while constraint 2 (8) subtracts them from the weighted outputs. While constraint 3 (9) remains identical to constraint 3 (4) of the model above, the

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Fig. 2. Comparison of a CCR and BCC model including non-zero slacks. Adapted from: (Odeck and Alkadi (2001, p. 214)).

non-negativity constraint (10) is extended by the slack variables (Cooper et al., 2011). max

m

si− +

i=1

s

sr+

(6)

r=1

scale; sums below 1 show operations at increasing returns to scale (Banker and Morey, 1986; Cooper et al., 2007; Wanke, 2012). To summarize the concept described above, the essence of an input-oriented DEA (BCC or CCR) is to identify whether a DMU (in our case, a PR mining ﬁrm) is able to reduce its inputs while keeping its outputs constant, compared to all other ﬁrms in the dataset.

subjected to: n

xij j + si− = ∗ xi0

(i = 1, . . ., m)

(7)

j=1 n

yij j − sr+ = yr0

(r = 1, . . ., s)

(8)

j=1 n

yj = 1

(9)

j=1

j , si− , sr+ ≥ 0∀i, j, r

(10)

Fig. 2 visualizes the conceptual difference between a BCC model (VRS) and a CCR (CRS) model for a simpliﬁed model with one input and one output. The frontier in the CCR model (blue line) is a constant line starting from the point of origin that runs through the DMUs B, D, and E. All DMUs on the line can be considered technically efﬁcient for the assumption of the underlying CRS. From the mathematical perspective, the difference is found in constraints 4 and 9 where the sum of weights is bound to one. The VRS frontier (orange line) proceeds through the DMUs H, A, D, E, F, and G which can all be considered technically efﬁcient under the assumptions of variable returns to scale (Cooper et al., 2006, 2007,2011; Francksen & Latacz-Lohmann, 2006). Firms D and E can be considered technically efﬁcient on both scale levels, CRS and VRS, since both DMUs are on the combined frontier. The distance xA xC , for instance, shows input-saving technical efﬁciency (VRS), while xB xC shows the same for CRS. The distance xA xB represents the scale efﬁciency, computed as SE = CCR / BCC , whereby CCR and BCC represent the calculated efﬁciency scores for both models for DMU C. The current production level (RTS level) on which a DMU is operating is determined by inspecting the sum of weights within the CCR model speciﬁcations. A sum equal to 1 represents a scale efﬁciency of 1 (indicating that the ﬁrm is operating at the most-productive scale size); sums above 1 indicate that the ﬁrm is operating at decreasing returns to

2.2. DEA in mining and research question The main point in question for this research was whether state-owned ﬁrms operate less efﬁciently in economic terms than do publicly quoted ones. A subsequent question is whether state-owned enterprises might be more strongly focused on the long-term availability of reserves and resources and, therefore, willing to mine lower ore grades that typically account for higher costs per ton of product. By contrast, publicly traded companies may be more focused on short-term success, which often results in rising stock prices and, ultimately, in bonus payments for decision makers. The ﬁeld of DEA applications is wide, and this form of analysis has already been applied to various industries, although only a very limited number of publications in regard to mining are available. For example, Kulshreshtha and Parikh (2002) used a DEA analysis to assess productivity growth in opencast and underground coal mining in India between 1985 and 1997, and the results indicated a decline in productivity. Comparative studies are also found in the coal mining industry; Fang et al. (2009) compared public companies in China and the US, and the results indicated much lower efﬁciency in Chinese ﬁrms compared to US ﬁrms. Further research was conducted by Wang (2011), who analyzed the relationship of input safety to output safety in coal mining. Analyzing the performance of 15 coal mines in Illinois (US), Tsolas (2011) applied an extended classical DEA approach in the form of bootstrapping through a data-generating process. A research question similar to the one presented in this paper has been raised by Das (2012), who determined whether public or private companies extract minerals more efﬁciently in the Indian mining industry. The results, in this case, a total factor productivity analysis, indicated a signiﬁcantly higher performance for private ﬁrms. Most studies have been conducted in relation to coal mining industries; in terms of PR mining and DEA, no empirical studies could be identiﬁed. Fundamental efﬁciency studies in PR mining

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Table 1 PR mining ﬁrms included in the analysis sorted by country (based on ORBIS data); ﬁrst group (Kailin – Vinachem) represents state-owned or state-controlled companies; second group (Incitec – Monsanto) represents non-state-owned companies. Name Group I – state-owned/state-controlled Guizhou Kailin Group Co., Ltd. Wengfu (Group) Co., Ltd. Yunnan Yuntianhua International Chemical Co., Ltd. Deyang Haohua Qingping Phosphate Mine Co., Ltd. Hubei Provincial Huangmailing Phosphate Chemical Co., Ltd. Rajasthan State Mines & Minerals Ltd. Jordan Phosphate Mines Company PLC. OCP S.A. Ma’aden Phosphate Company DAP Vinachem Co. Ltd. Group II – publicly quoted Incitec Pivot Ltd. Vale Fertilizantes S.A. Anglo American Fosfatos Brasil Ltda. Potash Corporation of Sasketchewan China Bluechemical Ltd. Israel Chemicals Ltd. Kazphosphate LLC Yara International ASA Open Joint Stock Company Mineral and Chemical Company Eurochem Open Joint Stock Company PhosAgro Joint Stock Company Acron Agrium Inc. Mosaic Company (THE) Monsanto Co.

have been presented (for a comprehensive overview see Scholz and Wellmer, 2015b, Table 1). However, the current state of the art does not allow us to answer the following proposed question: “Is there a statistically signiﬁcant coherence between a ﬁrm’s type of ownership and its efﬁciency?” Signiﬁcantly, therefore, an extensive analysis is presented in the following section as well as the used dataset of PR mining ﬁrms. 2.3. Sample dataset The two main models (i.e., Model 1 and Model 2) include a total of 24 ﬁrms operating in phosphate rock mining for which sufﬁcient ﬁnancial data were available. From the overall global capacities of 661 Mt PR-Ore and 258 Mt PR-M, this study covers 67.3% and 61.4%, respectively (CRU International, 2013). Financial data were obtained from the ORBIS ﬁnancial database for the year 2012 wherever possible; in single cases, e.g., numbers of employees for some ﬁrms, missing data were abstracted from publicly available company reports. The global PR market includes a host of additional companies that vary in parameters, such as size, ownership, legal form, or company structure, and for which no information or insufﬁcient information was available. In the determination of the sample data, the authors selected the respective subsidiary involved in PR mining for which independent reporting was obtainable. For example, we included Vale Fertilizantes S.A. instead of the parent company Vale S.A. The same applies for Anglo American Fosfatos as a subsidiary of Anglo American. Yara International, as a major global player in fertilizer production, operates only one PR mine in Finland; therefore, PR is just a very small portion of its overall business (similar to Agrium and Monsanto). The question of whether to include Yara, Agrium, and Monsanto led to the introduction of two additional models (Models 1b and 2b, in which these three ﬁrms were excluded; the rest of the model remained identical) in order to determine their inﬂuence on the results of the other ﬁrms. Table 1 lists each ﬁrm with its ofﬁcially stated name, country of origin, and a shortened name that is used in the following. Information about each ﬁrm’s legal form is also provided.

Country

Shortened name

Legal form

China China China China China India Jordan Morocco Saudi Arabia Vietnam

Kailin Wengfu Yuntianhua Qingping Huangmailing RSMM JPMC OCP Ma’aden Vinachem

Private company Private company Private company Private limited liability company Limited liability company Limited company Publicly quoted company Limited company Limited liability company Limited company

Australia Brazil Brazil Canada China Israel Kazakhstan Norway Russia Russia Russia USA USA USA

Incitec Vale Fertilizantes Fosfatos PCS China Bluechemical ICL Kazphosphate Yara EuroChem PhosAgro Acron Agrium Mosaic Monsanto

Publicly quoted company Public limited company Limited liability company Publicly quoted company Publicly quoted company Publicly quoted company Private limited company Publicly quoted company Private Company Publicly quoted company Publicly quoted company Public limited company Public limited company Public limited company

The question of whether a company can be considered stateowned/state-controlled or not is always unequivocally answerable. Although JPMC is publicly quoted, major shares are still in the control of the state or state departments; this is similarly true for OCP. Eurochem, by contrast, is controlled by the Russian oligarch Andrey Melnichenko and therefore not considered state owned. Although, as stated above, not all ﬁrms from the PR mining market could be included in the analysis due to a lack of obtainable data, all major ﬁrms (the seven largest, excluding those in China), according to a study by CRU International (Phosagro and CRU International, 2013), are included, and illustrated in Fig. 3. 2.4. DEA modeling for the case of PR mining The authors decided to choose the widely applied BCC and CCR models, followed by an SBM in the second step. The results for the constant returns to scale (CRS, CCR model) and the variable returns to scale (VRS, BCC model) allowed the calculation of scale efﬁciency as well as the determination of the current operation status (IRS or DRS) and the identiﬁcation of potential non-zero slacks. Modeling also included the decision on the direction of the analysis. The PR market is demand driven; therefore, an input-oriented model is superior to an output-oriented model. According to the approximate rule of thumb, n ≥ max {m × s, 3(m + s)} where n represents the number of DMUs (in our case, the number of PR mining ﬁrms), m is the number of inputs, and s is the number of outputs; thus, the selection of three inputs and two outputs is justiﬁed (Cooper et al., 2011). This is comparable to the general premise of a linear regression model, where the number of model parameters is not allowed to exceed the number of observations minus 1. The selection of useful inputs and outputs is based on whether the indicators are measureable, comparable, and consistent (Jamasb et al., 2005; Vaninsky, 2006). In addition, factors such as the reliability of sources, costs, and the necessary effort of obtaining the data have to be considered (Fang et al., 2009). In our case (illustrated in Fig. 4a), we selected the operating costs as input and turnover and EBIT (i.e., earnings before interest and taxes) as outputs from the proﬁt and loss account; the total assets as input from the balance sheet; and the number of employees as input

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Fig. 3. Leading global phosphate rock producers by production volume of PR-M in 2013, excluding Chinese producers. Adapted from Phosagro and CRU International (2013).

Fig. 4. (a) and (b) DEA Model 1 (left) and DEA Model 2 (right), showing input variables (left) and output variables (right) (For interpretation of the references to color in this ﬁgure legend, the reader is referred to the web version of this article.).

from the memo lines within the ORBIS reports. The inﬂuence of exogenous bias (i.e., confounder variables) on the input and output variables is discussed in Section 1.2, from a practical perspective, as well as in Section 2.5 from the mathematical perspective. The selection of inputs aligns with Fang et al. (2009), who applied the same rationale. In terms of outputs, they chose earnings per share (this is not possible in our case since we also consider private ﬁrms); operating revenue, which equals turnover minus non-operating proﬁt; and net proﬁt before tax, which already includes interest compared to EBIT. Fang et al. (2009) also introduced two further studies (both available only in Chinese) by Wei and Wang (2005) and Ran and Hui (2006). The former used total assets, net assets, and operating costs as inputs, and earnings and operating proﬁt as outputs (all indicators are per share). The latter used total capital, number of employees, and operating costs as inputs, and net proﬁt and operating proﬁt as outputs. In order to investigate the sensitivity of our results, we formulated an additional model (Model 2). Practice shows that DMUs often feature no inefﬁciency or close to none (i.e., efﬁciency values close to 1), which is mostly a consequence of a small number of DMUs (the number of reference DMUs is strongly limited) and/or too many inputs and/or outputs (it is easier for a DMU to become efﬁcient if a fewer number of inputs and/or outputs is included). By increasing the number of DMUs and/or decreasing the number of inputs or outputs, discrimination is improved (Cooper et al., 2011). In our case, only the latter was an option since we could not obtain additional data for other ﬁrms. Therefore, we kept the base of ﬁrms constant as well as the inputs, and we reduced the output side to only one variable, EBIT, which is illustrated in Fig. 4b.

In addition to the DEA analysis, further statistical analysis was performed to underpin our results, which are discussed in Section 2.5. 2.5. Statistical analysis To improve the normality of our data, we log-transformed the inputs (i.e., operating costs, total assets, and number of employees) as well as the outputs (i.e., turnover and EBIT) and used linear regression models to test whether the selected inputs signiﬁcantly explain the selected outputs. To test for statistically signiﬁcant coherence between the type of ownership and the level of efﬁciency, we used Fisher’s Exact Test at the signiﬁcance level of 0.05. Additionally, to control for the impact of a ﬁrm’s size on efﬁciency, a two-factor ANOVA was conducted. 3. Results and discussion In the following section, the results of the DEA as well as the regression analysis are presented and discussed. The procedure follows the methods introduced in the previous section. For the DEA analysis the publicly available software DEAP Version 2.1 was used while all statistical computations were performed with the statistical software suite SPSS v22. 3.1. DEA results and discussion The focus is mainly on Model 1 and Model 2. In order to verify the robustness of our results, we introduced two additional models

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Table 2 Technical efﬁciency and scale efﬁciency results (CRS, technical efﬁciency under the assumptions of constant returns to scale; VRS: technical efﬁciency under the assumption of variable returns to scale; SE, scale efﬁciency as the ratio of VRS and CRS; RTS, current scale level of operation; IRS, ﬁrm is operating at IRS; DRS: ﬁrm is operating at DRS). Results Model 1 CRS Group I – state-owned/state-controlled Kailin 0.806 Wengfu 0.742 0.894 Yuntianhua 0.829 Qingping 0.753 Huangmailing 0.918 RSMM 0.970 JPMC OCP 1.000 Ma’aden 0.933 0.931 Vinachem 0.878 Mean group I Group II – publicly quoted 0.917 Incitec 0.609 Vale Fertilizantes 1.000 Fosfatos 1.000 PCS 0.952 China Bluechemical ICL 1.000 0.726 Kazphosphate Yara 1.000 0.951 EuroChem 1.000 PhosAgro 0.890 Acron 1.000 Agrium Mosaic 0.964 0.956 Monsanto Mean group II 0.926 0.906 Overall mean

Results Model 2 VRS

SE

RTS

CRS

VRS

SE

RTS

0.808 0.762 0.953 1.000 0.820 0.961 0.983 1.000 0.937 1.000 0.922

0.997 0.973 0.938 0.829 0.919 0.955 0.988 1.000 0.995 0.931 0.953

irs irs drs irs irs irs irs – irs irs

0.806 0.742 0.968 0.823 0.750 0.913 0.970 0.938 0.930 0.987 0.883

0.808 0.762 0.969 1.000 0.816 0.958 0.983 0.954 0.935 1.000 0.919

0.997 0.973 0.999 0.823 0.919 0.953 0.988 0.983 0.995 0.987 0.962

irs irs irs irs irs irs irs drs irs irs

0.921 0.612 1.000 1.000 0.959 1.000 0.757 1.000 0.960 1.000 0.893 1.000 0.970 1.000 0.934 0.929

0.997 0.996 1.000 1.000 0.993 1.000 0.959 1.000 0.990 1.000 0.997 1.000 0.994 0.956 0.992 0.975

irs irs – – irs – irs – drs – irs – drs drs

0.917 0.609 1.000 1.000 0.952 1.000 0.720 1.000 0.951 1.000 0.887 1.000 0.964 0.956 0.925 0.908

0.921 0.612 1.000 1.000 0.959 1.000 0.752 1.000 0.960 1.000 0.890 1.000 0.970 1.000 0.933 0.927

0.997 0.996 1.000 1.000 0.993 1.000 0.958 1.000 0.991 1.000 0.997 1.000 0.994 0.956 0.992 0.979

irs irs – – irs – irs – drs – irs – drs drs

(Model 1b and 2b which contain only 21 ﬁrms; Agrium, Monsanto, and Yara are excluded). These models were used to counter the argument that ﬁrms with major operations outside phosphate rock mining might distort the overall results since they could represent inappropriate reference peers. In general, it is noteworthy in terms of inefﬁciency interpretations to consider the heterogeneity/homogeneity of the DMUs (e.g. ﬁrms) within the operating industry. Input factors in certain countries may be inﬂuenced externally, for example, by environmental, legislative, or social regulations (see Sections 1.2 and Appendix). 3.1.1. Total technical efﬁciency of the CCR and BCC models Table 2, which follows, lists the results of the DEA analysis for Model 1 (left group) and Model 2 (right group) by keeping the same sequence as Table 1. Based on the concept of the DEA (see Section 2.1), a company that is efﬁcient under the assumption of constant returns to scale is also efﬁcient under variable returns to scale. Hence, the opposite is impossible (see Fig. 2). For these companies, the scale efﬁciency is also 1 (the ratio of VRS/CRS efﬁciencies), and therefore, no improvements can be made since they are already at the optimum level in terms of returns to scale (indicated by “–” in Table 2). In the current study, only six companies show a scale efﬁciency of 1: Fosfatos, PCS, ICL, YARA, PhosAgro, and Agrium, all of which are publicly quoted ﬁrms. A ﬁrm that is efﬁcient under the VRS assumption is not necessarily efﬁcient under CRS. This holds true for Qingping, Monsanto, and Vinachem. While Qingping and Vinachem are operating under increasing returns of scale, suggesting that an increase of inputs will result in an overproportional increase of outputs until the point where it reaches its optimum, the opposite holds true for Monsanto with decreasing returns to scale. Comparable low-efﬁciency values below 0.8 are present at only three ﬁrms – Brazilian Vale Fertilizantes, Chinese Wengfu, and

Kazakh Kazphosphate, with all of them operating at increasing returns to scale, whereby Vale Fertilizantes and Kazphosphate are publicly quoted and Wengfu is state owned. The results of Models 1 and 2 seem robust; the average value of CRS, VRS, and SE distinguish themselves only at the third decimal place. The only noteworthy difference can be found at Moroccan PR giant OCP; this privately owned ﬁrm accounts for no inefﬁciencies in Model 1 but it does in Model 2. Since the output expressed as overall turnover is excluded in Model 2, this can be interpreted as the turnover being high in comparison to its peers, while the EBIT is not. The results show PCS, ICL, and Monsanto as selected peers in this case. 3.1.2. Target values and sources of inefﬁciency Table 3 shows the reduction targets for the inputs in order for the ﬁrms to become technically efﬁcient for Model 1 and Model 2. Whereas, the input targets include radial movement, in order to reach the frontier, as well as slack movement for ﬁrms already on the frontier in an aggregated form, output slacks and relevant peers are not presented in Table 3 since the study focuses on possible input reductions, due to the characteristics of a demand market. In this analysis, Model 1 and Model 2 distinguish themselves mainly at the reduction targets for input 3, the number of employees, which is considerably higher for Model 1 at 19.8% compared to 9.5% in Model 2. The reduction targets are calculated based on the results of the VRS model. Therefore, in addition to the six leading ﬁrms (i.e., Fosfatos, PCS, ICL, Yara, PhosAgro, and Agrium), three additional ﬁrms do not have any reduction targets, namely, those that account for a technical efﬁciency of 1 under VRS assumptions (i.e., Qingping, Monsanto, and Vinachem). In terms of operating costs, we ﬁnd the highest reduction target for Vale Fertilizantes with more than 38%, followed by Kazphosphate (approximately 24%) and Wengfu (approximately 23%), with only the latter being considered state owned.

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Table 3 Input reduction targets’ deviation in order to become efﬁcient [%]. Results Model 1 Operation costs (%) Group I – state-owned/state-controlled 19.2 Kailin 23.8 Wengfu 8.7 Yuntianhua Qingping 0.0 Huangmailing 18.0 3.9 RSMM 1.7 JPMC 0.0 OCP 6.3 Ma’aden Vinachem 0.0 Mean group I 8.2 Group II – publicly quoted 7.9 Incitec 38.8 Vale Fertilizantes 0.0 Fosfatos Brazil 0.0 PCS 4.1 China Bluechemical 0.0 ICL Kazphosphate 24.3 0.0 Yara Eurochem 4.0 0.0 PhosAgro 10.7 Acron 0.0 Agrium 3.0 Mosaic Monsanto 0.0 6.6 Mean group II Overall mean 7.3

Results Model 2 Total assets (%)

Employees (%)

Operation costs (%)

Total assets (%)

Employees (%)

19.2 23.8 4.7 0.0 18.0 3.9 1.7 0.0 76.4 0.0 14.8

19.2 23.8 44.5 0.0 49.8 44.1 1.7 0.0 72.0 0.0 25.5

19.2 23.8 8.6 0.0 18.4 4.2 1.7 4.6 6.5 0.0 8.7

19.2 23.8 3.1 0.0 18.4 4.2 1.7 4.6 76.5 0.0 15.2

19.2 23.8 3.1 0.0 18.4 4.2 1.7 56.2 6.5 0.0 13.3

7.9 68.1 0.0 0.0 4.1 0.0 24.3 0.0 4.0 0.0 10.7 0.0 3.0 0.0 8.7 11.3

7.9 53.8 0.0 0.0 4.1 0.0 62.4 0.0 27.6 0.0 60.2 0.0 3.1 0.0 15.7 19.8

7.9 38.8 0.0 0.0 4.1 0.0 24.8 0.0 4.0 0.0 11.1 0.0 3.0 0.0 6.7 7.5

7.9 68.1 0.0 0.0 4.1 0.0 24.8 0.0 4.0 0.0 11.1 0.0 3.0 0.0 8.8 11.4

7.9 38.8 0.0 0.0 4.1 0.0 24.8 0.0 4.0 0.0 11.1 0.0 3.1 0.0 6.7 9.5

Input factor 2, total assets, identiﬁes again Vale Fertilizantes and additionally Ma’aden as the biggest spendthrifts, whereas the values of approximately 68% for Vale Fertilizantes and more than 76% for Ma’aden (state-owned) with both numbers seem alarmingly high. The highest average reduction targets are found for the number of employees. In this case, we ﬁnd ﬁrms with values above 50% (both in Model 1 and Model 2). In addition to Vale Fertilizantes, Kazphosphate, and Ma’aden, which did not score very highly on the other inputs either, Russian Acron and OCP are among the worst counterparts. In absolute terms, for the latter two, this seems remarkable with more than 15,000 employees (Acron) and around 23,000 employees (OCP) at those ﬁrms. CRU International (2015) provides employment data on staff directly involved in the mining and upgrading processes. The ratio of employees per ton of capacity shows different results for several of the previously mentioned ﬁrms: 0.26 (Ma’aden), 0.35 (OCP), 0.43 (Vale Fertilizantes), 0.57 (Kazphosphate), and 0.86 (Acron). Mosaic and PCS represent the industry leaders with 0.10 and 0.11, respectively. The highest ratios can be found at the Chinese producers Huangmailing (1.39) and China Bluechemical (1.20). The results for Models 1b and 2b (see Section 3.1) show that the results for Models 1 and 2 are robust. The efﬁcient ﬁrms are still efﬁcient after the reduction of the peer set. Additionally, Yuntianhua and Mosaic are efﬁcient in Models 1b and 2b, which could be due to the reduced set of large counterparts.

3.2. Statistical results and discussion The summarized model of the regression analysis shows an adjusted R2 of 0.994 for output 1 (turnover) and 0.803 for output 2 (EBIT), which both indicate that the variances of the dependent variables (i.e., y1 , y2 ) are well explained by the predictors (i.e., x1 , x2 , x3 ). The regression residuals total was signiﬁcant for both models (p = 0.000), which supports that the regression model signiﬁcantly

predicts the outcome variables (i.e., y1 , y2 ) and that, therefore, the models are a good ﬁt for the data. Outputs 1 and 2 are mathematically represented through the regression equations as follows: ln(y1 ) = −0.062 + 0.918 × ln(x1 ) + 0.070 × ln(x2 ) + 0.046 × ln(x3 ) ln(y2 ) = −4.590 + 0.381 × ln(x1 ) + 0.686 × ln(x2 ) + 0.173 × ln(x3 ) The efﬁciency results of the DEA analysis were used to create crosstabs to ﬁnd an answer to the research question. Table 4 shows the crosstabs for Model 1 (left-hand side) and Model 2 (right-hand side). The classiﬁcation of efﬁciency was set to Yes (i.e., efﬁcient) when both the CRS and VRS efﬁciency values were 1; otherwise, it was set to No (i.e., inefﬁcient). In order to get a feeling for how the observed differences would be judged from an inferential statistics perspective (if we were to assume that the companies would be sampled as a population of companies), we applied Fisher’s Exact Test. The test value for Fisher’s Exact Test (some expected values were below 5 due to the small sample size) was 2 = 0.697 with an exact two-sided p-value of p = 0.653 for Model 1 and 2 = 2.057 and p = 0.341 for Model 2, respectively. If the present data were the outcome of an experiment, then Fischer’s Exact Test would indicate that there is no statistical difference. Although efﬁciency scores and the proportion of efﬁcient ﬁrms differed between state-owned or state-controlled enterprises, technical efﬁciency was not statistically signiﬁcantly different between these two groups. While purely descriptive in nature, these ﬁndings suggest that general assumptions regarding the importance of differing strategies between state-owned and publicly quoted ﬁrms are not as strong as may be assumed, and therefore need to be evaluated more cautiously. In order to control for the impact of size on efﬁciency, we calculated a two-factor ANOVA with state-owned vs. privately owned as factor 1 and small vs. large with regard to total assets as factor 2. Factor 2 was constructed through a median split of the total assets.

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Table 4 Crosstabs for the Fisher’s Exact Test, based on efﬁciency scores (Table 2). Model 1

Model 2 State owned

Efﬁcient

No Yes Total

Total

No

Yes

9 5 14

8 2 10

17 7 24

The dependent variable was the efﬁciency value. There were no signiﬁcant differences between state-owned and privately owned ﬁrms (p = 0.90), no differences between large and small ﬁrms (p = 0.17), and no interaction between the two factors (p = 0.19). The model has not been signiﬁcant either (p = 0.32, df = 3, F = 1.24).

4. Conclusion and outlook In the analysis, 24 of the largest PR mining ﬁrms were included (21 in the reduced set). In terms of industry leaders, illustrated in Fig. 2, we ﬁnd that of the state-owned or state-controlled ﬁrms (OCP, JPMC, and Ma’aden), only OCP is considered technically efﬁcient in Model 1. In contrast to the publicly quoted ﬁrms, PhosAgro and PotashCorp (PCL) show a technical efﬁciency of 1 throughout all the models. We ﬁnd differences in the average efﬁciency scores and reduction targets (Tables 2 and 3) as well as a higher share of efﬁcient publicly quoted ﬁrms in Table 4 (Model 1, 2: 36%, 36% for publicly quoted and 20%, 10% of state-owned ﬁrms). However, the frequencies of efﬁciency performance would not differ in such a way that the Fisher Exact Test shows statistical signiﬁcance for these data, if we were to assume that the company data was randomly drawn from a population of companies. These ﬁndings suggest that state-owned enterprises, although some might focus on a stronger long-term material-resource efﬁciency in order to secure a sustainable operation for their country as well as bearing potential social responsibilities toward the local population, cannot easily be dismissed as signiﬁcantly less economically efﬁcient than publicly quoted ﬁrms. Moreover, larger ﬁrms are not necessarily more efﬁcient than smaller ﬁrms, as suggested by the results of our twofactor ANOVA with total assets and efﬁciency. A limitation of the research is the considered time horizon since only one year is taken into account with information from the annual company reports. Annual accounts tend to be declarationday oriented, thus providing a lot of space for interpretation. By considering time-series data (which are, at least, not publically available), it would be possible to analyze efﬁciency developments over a number of years, resulting in a more resilient picture of the PR mining sector. Additionally, the sensitivity of efﬁciency scores gained from a DEA might be of further interest. In our case we introduced two additional models (1b, 2b) to examine whether large enterprises, which are operating only partly in PR mining (Monsanto, Agrium, and Yara) may affect the overall results of this study. Furthermore, we performed a linear regression analysis to underpin the selection of inputs and outputs. Past developments in DEA research have introduced further methods of including sensitivity analyses, such as Simar and Wilson (1998, 2000) or Gocht and Balcombe (2006), who proposed the application of a smoothed homogeneous bootstrapping procedure in order to extend the size of the initial dataset. This paper was a ﬁrst step in introducing DEA in PR mining; the addition of an extensive sensitivity analysis was not within the authors’ scope but will deﬁnitely be of interest in future research.

State owned

Efﬁcient

No Yes Total

Total

No

Yes

9 5 14

9 1 10

18 6 24

An analysis in order to check for confounders (i.e., exogenous variables inﬂuencing the dependent as well as the independent model variables) within linear regression analysis was precluded by the lack of additionally required data (i.e., wages essentially inﬂuence operating costs, which subsequently inﬂuence EBIT, whereby the overall amount for wages depends vastly on the number of employees). This study was limited by the availability of data as well as by the relatively small number of corporations included, which restricted further statistical tests. In general, the results of this analysis rely on the data available; we carefully extracted all information for the year 2012 from the ORBIS ﬁnancial database. In addition, for single cases where data was missing, we obtained information directly from company reports. As previously stated, the efﬁciency scores reﬂect the company ﬁgures reported for the ﬁscal year 2012; delayed reporting or later adaptations might not be included in this analysis and could cause deviations in the interpretation. Country- or region-speciﬁc external inﬂuences, such as legislative regulations, could not be included in this analysis because of missing uniform quantiﬁcation. Still, the DEA itself would allow for the inclusion of ﬁxed exogenous variables that cannot be inﬂuenced by a DMU (Banker and Morey, 1986) but these variables may inﬂuence the overall results of an analysis. From an economic and/or environmental perspective, another ﬁeld of interest emerges when the focus is narrowed from the organizational level (ﬁrm level) to the mine and beneﬁciation level. In addition to economic inputs and outputs, relevant factors in terms of eco-efﬁciency could be included as well; however, as previously emphasized, obtaining this data might be a major challenge. It would also be very interesting to add further dimensions, especially at the mining level, such as social parameters, including data on health and safety, education, and community engagement. The introduction of the so-called undesired outputs (e.g., emissions) requires further adjustments of DEA modeling, such as a directional distance function approach suggested by Färe and Grosskopf (2004). The results of the DEA indicate high levels of inefﬁciency within a large number of major ﬁrms in the PR mining industry. On one hand, our analysis quantiﬁes these inefﬁciencies; on the other hand, it outlines the potential for various forms of innovation in order to address them.

Acknowledgments This paper is a cornerstone in the follow-up of the Global TraPs project. We thank Eva Schernhammer of Harvard University for critically reading the manuscript and for helpful discussions, especially in regard to the statistical analysis; Marko Hell of the University of Split for input on the DEA; as well as the editors Roland W. Scholz and Thomas Hirth and three anonymous reviewers for their scientiﬁc input and particularly for their suggestions regarding extended DEA methods. Additionally, we thank Elaine Ambrose for the thoughtful language editing.

Please cite this article in press as: Geissler, B., et al., Efﬁciency performance of the world’s leading corporations in phosphate rock mining. Resour Conserv Recy (2015), http://dx.doi.org/10.1016/j.resconrec.2015.10.008

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Appendix A. A.1. Expert report The following expert report draws heavily on Michael C. Mew’s view and opinion as one who has spent his professional life analyzing the international phosphate sector as an independent consultant through the Fertecon Research Centre, as well as later on through CRU International. Several of the statements should be seen as propositions that may call for some quantitative validation. Expert report in Geissler and Steiner (2015), Appendix A. Phosphate ore is mined mainly from sedimentary deposits, which are unevenly distributed globally. Some igneous intrusions are also mined for their phosphate content. In both cases, mined ore is usually upgraded to marketable concentrate by a combination of techniques, depending on ore characteristics; these may include sizing, washing, ﬂotation, calcination, magnetic separation, and other less common separation techniques. Most PR-Ore is mined by conventional earthmoving machinery, predominantly in open pit-operations. Over time, there has been a trend toward less underground mining and, for those underground mines that have continued to operate, toward increasing mechanization. Produced PR-M is either sold to the export market, or used in captive downstream plants. Although PR-M constitutes a relatively low-value commodity, its transport is fairly expensive compared to the movement of other derived products (mostly phosphoric acid intermediates or phosphate fertilizer products). This factor, coupled with the desire to ‘add value’ to a basic resource, has prompted a strong trend in past decades toward converting PR-M into downstream chemicals near the site of production. Thus, PR-M trade has seen a decline both in volume and relative to overall production: While in 1970, trade in PR-M accounted for almost 50% of all PR-M use, by 2013, it had fallen to just 23% of the total PR-M production (IFA, 2015). Countries have varying levels of consumption of ﬁnished P products. Some of the largest producers of PR-M (e.g., Morocco, Jordan, and Saudi Arabia) have only small domestic requirements for phosphate products and therefore few alternatives other than accessing the export market (IFA, 2015). Moroccan OCP, for example, sells PR-M, PA, and DAP fertilizer to multiple countries worldwide such as India. In the case of India, OCP is competing with its own DAP fertilizers against DAP fertilizers produced in India using imported OCP PR-M or PA. The development and increase of captive capacity for processing PR-M has potential beneﬁts in terms of both economic and material-resource efﬁciency. For example, production cost can be reduced by obviating the need to dry the PR-M product before its use or transport. Furthermore, downstream plants can be engineered speciﬁcally for a particular type of rock. This is important because PR-M products are not homogeneous; rather, they vary in grade and consist of other elements as well, impacting the downstream production process (CRU International, 2014a). Plant operators therefore tend to be very conservative in changing from one PR source to another since this usually entails a period of plant adjustment and – in the worst case scenario – can even temporarily halt the normally continuous process. This, together with the geographical distribution of large PR-M exporters and importers, has led to fairly stable trade patterns within the PR-M market in the past (Mew, 2000). The existence of a large domestic PR-M market (e.g., in China) or the presence of a large nearby market to which competing PRM suppliers have a much longer shipping distance (e.g., Jordan supplying India) clearly provides an economic advantage to certain producers. The segmentation of the market according to grade and quality also provides some producers with a natural economic advantage. For example, producers of PR-M that is useable in PA

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production allows them to access this sector of the market, which tends to provide a higher value per unit of P content compared to lower-grade- or quality PR-M that, might be suitable only for the production of lower-grade fertilizers (CRU International, 2014b). The PR-M export market is thus segmented both by freight costs and by product grade and/or quality. The degree of competition within this decreasing market has been varying over time in the past decades, further impacting PR-M price levels. While historically ﬂat generally, PR-M prices have shown two distinct periods of sharp increase, one in the mid-1970s and another in 2008. In both cases, prices jumped a multiple of 6–8 times before falling back to their baselines in subsequent years. Generally, pricing is governed by the balance between supply and demand in the PR-M market, but higher prices are also maintained by increasing the value of downstream products. During both periods of price spikes, phosphorus fertilizer prices increased substantially, as did freight levels. In between, PR-M prices remained relatively subdued, providing existing producers only a small return and therefore discouraging new entrants into the market (Mew, 2015b). In 1980, US exporters supplied 30% of the PR-M export market. However, by the end of the 1990s PR-M export was no longer considered viable due to deteriorating orebody grades and quality as well as rising environmental costs. This US decline led to a substantial increase of Morocco’s share in the PR-M market, up to 45% from 35% in 2007 when the market slowly tightened (CRU International, 2014b). The subsequent increase in PR-M prices was further driven by price hikes in a number of other market segments, such as energy and transportation costs as well as crop prices. The price peak was followed by a dramatic fall in PR-M demand and trade in 2009, which pulled prices down to lower, albeit still above, pre-2008 price levels (Mew, 2015b). As previously illustrated, local conditions have higly signiﬁcant impacts on operations, decisions, and strategies for local ﬁrms. The following sections provide more-detailed information about major producing countries and their market speciﬁcs; given the complexity of the industry, no claim on completeness can be made. A.2. Brazil All of Vale’s and Fosfato’s (two of the largest Brazilian producers) Brazilian production is sold in the domestic market, where it competes against imports. The geographical location in Brazil, with respect to its coastal ports and major fertilizer consuming areas, is an important factor for economic efﬁciency. Large distances and cross-state tax systems often determine the economics for supplying fertilizers and raw materials (CRU International, 2014a). A.3. China In the late 1970s, the Chinese PR industry started to develop new mines. The created capacities were labor intensive (as a substitute for mechanical capacity), small scale, and preferentially mining higher-grade weathered portions of deposits. They quickly developed a large elemental phosphorus industry based on lower-quality deposits, using low-cost energy in Sichuan and other provinces. The 1980s and 1990s saw a massive increase in the development of new PR deposits, mainly in Yunnan, Guizhou, and Hubei Provinces. At the same time, downstream capacity was built to meet the increasing demand for chemical fertilizers. The latter resulted from a push by the government to increase the level of self-sufﬁciency in food products. The pace of developments allowed PR-M exports for a number of years, but as domestic demand for the PR-M increased, exports vastly decreased (Fertecon Research Centre, 2004). Additionally the government tried to curb exports of phosphate fertilizers by using tariffs, which were mitigated when capacities exceeded domestic demand.

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Today, China is the largest producer of PR-M globally due to ﬁrms such as Yuntianhua Group, Wengfu, and Kailin, with a market share of approximately 50%. Growth in domestic demand for fertilizers has slowed, in parallel with a decline in population growth. Since energy and transport costs have increased, export markets are now becoming increasingly interesting as an outlet for domestic production. In addition, labor costs are rising, and therefore a trend toward larger production units is driving the transformation from manual labor toward increased mechanization. Despite the substantial growth of the phosphate industry, there are still numerous small mines, many of which are underground operations and many of which are not operating economically (CRU International, 2014b). A.4. Jordan Jordan’s PR-M industry is represented through the state-owned JPMC, which has held the mandate to exploit the country’s large sedimentary PR resources since 1949 (JPMC, 2014). There is very little demand for phosphate products in Jordan; hence, essentially all production is exported. Jordan has been largely an exporter of PR-M historically, but in recent times it has increasingly focused on material-resource efﬁciency by building downstream plants (within joint venture agreements) in Jordan and recently in Indonesia. Currently, approximately one-third of its PR-M output is converted domestically; still, JPMC has maintained a 10–15% share of the global PR-M export market. Most recently, competition for DAP sales to India increased sharply when Saudi Arabia began exporting large volumes in 2011 (CRU International, 2014a). A.5. Morocco OCP SA has been the state-controlled mining company in Morocco since 1920 (OCP, 2015). Despite having formed a number of joint ventures to build downstream capacity in Morocco and overseas, OCP continues to control the sale of its raw materials (PR-M and PA) in these ventures. The formerly tight link with the government has loosened toward more commercial funding and operating activities over the last decade. Nonetheless, OCP remains one of the main employers in Morocco, and its policies reﬂect the reality of regional employment matters, especially for the country’s youth. Morocco has a relatively small requirement for phosphate fertilizers domestically, so practically all its production is exported globally, while it is facing increasing competition from Saudi Arabia, which has as its main export destination India, due to the geographical logistic advantage. Current investments to expand capacity are also aiming to reduce costs while improving the environmental footprint (e.g., PRM slurry pipelines to avoid shipping by train). As a state-controlled company, OCP has a mandate to maximize the value of its PR resource. This entails an element of both economic and materialresource efﬁciency. Therefore, lower-grade PR beds were mined in the past in consideration of future-improved technology (CRU International, 2014a). A.6. Russia Russia’s largest producers (PhosAgro, Acron, and Eurochem) all produce PR-M from igneous deposits. PhosAgro and Acron operate the Khibiny (Kirovsk) mines on the Kola Peninsula, while Eurochem’s main production is at Kovdor to the west of Kirovsk, where it produces phosphate and iron ore as co-products. The Kirovsk deposits are operated by open-pit as well as underground methods. The harsh environment adds signiﬁcantly to the cost of production, which is already relatively high as a result of the multiple ﬂotation steps that are necessary. The PR-M is of relatively high

grade and generally gains a premium price on the export market. Most of the PR-M output is consumed through the internal downstream plants, although they are not located close to the mines and signiﬁcant transport costs are incurred. PhosAgro and Eurochem are both signiﬁcant exporters of phosphate fertilizers as well as selling products in the domestic market (CRU International, 2014a). A.7. United States of America The largest share of US-based phosphate fertilizers is derived from the huge Central Florida phosphate ﬁeld. The open-pit mines there are operated solely by Mosaic, the world’s largest phosphate fertilizer producer. To some extent, it has been the increasing cost of production in the Florida ﬁeld, coupled with the increasingly stringent environmental regime, that has driven Mosaic to look overseas for PR-M mining investments, such as a joint venture in Peru and shares in a new Saudi Arabian project. The existing mines in Florida will continue to be depleted over the next decade, and although Mosaic has projects to construct new mines in the southern portion of the reserve, the gaining of permits to mine has been and will continue to be a challenging process. In addition to Central Florida, current PR mining takes place in Northern Florida and North Carolina as well as in the Western states (e.g., Idaho), almost exclusively in the form of open-pit operations (CRU International, 2014a). Overall volumes and capacities have decreased in the US over the last several decades due to previously mentioned factors such as price competition or environmental standards and regulations. The second main producer of US PR ore is Canadian PotashCorp with headquarters in Saskatchewan, which is operating two openpit sedimentary PR mines in Northern Carolina and North Florida. The integrated downstream operations produce high-quality PA, fertilizer and animal feed, mostly for the US domestic market. References Banker, R.D., Morey, R.C., 1986. Efﬁciency analysis for exogenously ﬁxed inputs and outputs. Oper. Res. 34 (4), 513–521. Coelli, T.J., Prasada Rao, D.S., O’Donnel, C., Battese, G.E., 2005. An Introduction to Efﬁciency and Productivity Analysis, 2nd ed. Springer, New York. Cook, P.J., 2014. Interactive comment on “Recent revisions of phosphate rock reserves and resources: Reassuring or misleading? An in-depth literature review of global estimates of phosphate rock reserves and resources” by Edixhoven J.D., et al. Earth Syst. Dynam. Discuss. 15 (4), C683–C685. Cook, W.D., Kress, M., Seiford, L.M., 1993. On the use of ordinal data in data envelopment analysis. J. Oper. Res. Soc. 44 (2), 133–140, http://dx.doi.org/10. 2307/2584361. Cook, W.D., Liang, L., Zhu, J., 2010. Measuring performance of two-stage network structures by DEA: a review and future perspective. Omega 38 (6), 423–430, http://dx.doi.org/10.1016/j.omega.2009.12.001. Cooper, W.W., Seiford, L., Tone, K., 2006. Introduction to Data Envelopment Analysis and its Uses: With DEA-solver Software and References. Springer, New York. Cooper, W.W., Seiford, L., Tone, K., 2007. Data Envelopment Analysis: A Comprehensive Text with Models, Applications References and DEA-solver Software, 2nd ed. Springer, New York. Cooper, W.W., Seiford, L.M., Zhu, J. (Eds.), 2011. Handbook on Data Envelopment Analysis, vol. 164. Springer US, Boston, MA, Retrieved from http://link.springer. com/10.1007/978-1-4419-6151-8. Cordell, D., Drangert, J.-O., White, S., 2009. The story of phosphorus: global food security and food for thought. Global Environ. Change 19 (2), 292–305, http:// dx.doi.org/10.1016/j.gloenvcha.2008.10.009. CRU International, 2013. Phosphate Rock Costs. CRU International, 2014a. Phosphate Rock Costs. CRU International, 2014b. Phosphate rock market outlook (quarterly), August. CRU International, 2015. Labour and Capacity Statistics. Daraio, C., Simar, L., 2007. Advanced Robust and Nonparametric Methods in Efﬁciency Analysis: Methodology and Applications. Springer, New York. Das, A., 2012. Who extracts minerals more efﬁciently – public or private ﬁrms? A study of Indian mining industry. J. Policy Model. 34 (5), 755–766, http://dx.doi. org/10.1016/j.jpolmod.2012.02.005. De Ridder, M., de Jong, S., Polchar, J., Lingemann, S., 2012. Risks and Opportunities in the Global Phosphate Rock Market: Robust Strategies in Times of Uncertainty. The Hague Centre for Strategic Studies (No. 17 | 12 | 12). Debreu, G., 1951. The coefﬁcient of resource utilization. Econometrica 19 (3), 273–292.

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Efficiency performance of the world's leading corporations in phosphate rock mining

Efficiency performance of the world's leading corporations in phosphate rock mining

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