Location Factors for Non-Ferrous Exploration Investments

Transcription

1 Locaton Factors for Non-Ferrous Exploraton Investments Irna Khndanova Unversty of Denver Ths paper analyzes the relatve mportance of geologcal potental and nvestment clmate for nonferrous mnerals exploraton nvestments. The analyss s based on log-lnear and truncated models of exploraton fundng wth geologcal potental and nvestment envronment as locaton factors. In order to account for countres szes, we nclude the populaton varable. Models are estmated usng the Metals Economcs Group s exploraton expendtures data, one measure of geologcal potental, and one ndcator of nvestment clmate. Our analyss shows that exploraton does not smply follow geologcal potental. The nvestment envronment plays a sgnfcant role n allocatng exploraton budgets by mnng companes. Ths result confrms that a mneral rch country cannot expect large amounts of exploraton money wthout establshng a favorable nvestment clmate. INTRODUCTION Ths paper analyzes the relatve mportance of geologcal potental and nvestment clmate for nonferrous exploraton nvestments. Many metals, coal, and uranum producng countres resort to prvate companes to explore and mne ther mneral deposts (Jara, Lagos, and Tlton, 2008). Because the mnerals projects requre lump sum nvestments under consderable uncertanty, the companes are reluctant to nvest unless they are guaranteed generous terms (Buckley, 2008). The hgh fxed costs are an mportant aspect of companes barganng power. Once the uncertanty fades away and the mnerals developments begn to operate proftably, the sgnfcant fxed costs turn nto a lablty. The companes cannot smply abandon projects f the host countres mpose harsher terms. Ths progresson s recurrng. In order to brng new nvestments or expand exstng projects, the countres have to mprove nvestment clmate and offer better condtons. However, the new deals become obsolete. Such an nteracton between natural resource nvestors and a host country Raymond Vernon (1971) descrbed as the obsolescng barganng. The obsolescng bargan model explans a cyclcal shft of barganng power from the foregn nvestor to the host country and back. Ths paper attempts to analyze how much barganng power mneral producng countres have. If publc polcy takes a good porton of any rents assocated wth new dscoveres, wll ths or wll ths not cause exploraton to move elsewhere? Fgure 1 shows how exploraton spendng vares among countres. 1 Some of the dfferences among countres are smply due to dfferences n sze what can be assocated wth land areas - mneral potental (Johnson, 1990). But large ntercountry varatons reman even after controllng for land areas, see Fgure 2. These varatons could be nfluenced by varatons n mneral nvestment polcy. The central queston then s how much of the dfferences n country exploraton nvestments are due to country dfferences n geologcal potental and to dfferences n country mneral polces? 38 Journal of Appled Busness and Economcs vol. 12(1) 2011

2 FIGURE 1 THE 2006 EXPLORATION EXPENDITURES Exploraton expendtures, mllons US$ Countres We are not aware of studes, whch conduct econometrc analyss of locaton factors of non-ferrous exploraton spendng. Our study s one of the frst attempts to model countres non-ferrous mnerals exploraton nvestments and to estmate the relatve mportance of geologcal potental and nvestment envronment. The analyss draws upon works by Johnson (1990), Eggert (1992 and 2008), Otto (1992a and 1992b), the Fraser Insttute (2006), and Jara, Lagos, and Tlton (2008). These papers pont to the two domnant groups of locaton factors of exploraton nvestments: geologcal potental and nvestment clmate. Smlarly, Dunnng (1998), Bllngton (1999), Campos and Knoshta (2003), Buckley et al (2007), and UNCTAD (2007) emphasze mportance of avalablty of natural resources and nvestment condtons for resource seekng Foregn Drect Investment. FIGURE 2 THE 2006 EXPLORATION EXPENDITURES DIVIDED BY LAND AREAS Normalzed exploraton expendtures, n US$/sq.km Countres The paper bulds two models of exploraton nvestments: log-lnear and truncated. In the models, the log-transformed exploraton expendtures are the dependent varable; geologcal potental, nvestment clmate, and populaton are explanatory varables. The populaton factor s ncluded to account for economes szes. 2 The countres exploraton nvestments data were kndly provded by the Metals Economcs Group (MEG). MEG reports exploraton expendtures of at least $100,000. It order to take nto account effects of ths truncaton, we consder a one-lmt truncated regresson model for exploraton expendtures. We estmated models usng one ndcator of geologcal potental (land areas) and one Journal of Appled Busness and Economcs vol. 12(1)

3 measure of nvestment clmate (Index of Economc Freedom of the Hertage Foundaton and the Wall Street Journal). The paper s structured as follows. The second secton descrbes the data. The thrd secton presents models of exploraton nvestments. It derves estmates of the relatve mportance of geologcal potental and nvestment clmate for exploraton spendng. The forth secton summarzes man fndngs and conclusons. DESCRIPTION AND STATISTICS OF DATA SERIES Ths secton descrbes the data on exploraton expendtures, measures of the geologcal potental and nvestment clmate, and populaton. It also presents statstcs for the data seres. Exploraton nvestments data are from the Corporate Exploraton Strateges 2006 Study of the Metals Economcs Group (MEG, 2006). The data nclude the 2006 budgets of mnng companes for exploraton of nonferrous metals and damonds. The compled seres are based on surveys of 1,624 companes, whch budgeted $100,000 or more on the 2006 exploraton. Total exploraton budgets of surveyed companes add to $7.13 bllon - approxmately 95% of worldwde exploraton nvestments (MEG, 2006). The expendtures are reported for the followng exploraton targets: gold; base metals copper, znc, lead, nckel (does not nclude alumnum); damonds; platnum; and other metals or mnerals. The MEG Corporate Exploraton Strateges Study has the 2006 exploraton data for 124 countres and regons. In our regressons analyss, we examne 103 countres. Several countres were not ncluded n the analyss manly because of lack of the nvestment clmate data for them. Total exploraton expendtures of the 103 ncluded countres consttute around 98% of the 2006 exploraton spendng of surveyed companes. Table 1 contans statstcs of exploraton expendtures for the analyzed countres. Fgure 3 shows a hstogram of the 2006 total exploraton spendng. The range of countres total exploraton nvestments s between $100,000 and $1,378 mllon. TABLE 1 STATISTICS OF THE 2006 EXPLORATION EXPENDITURES Number of countres wth exploraton Statstcs, n mllons $ (M $) expendtures n ranges ,378 Mean Max Mn St.Dev M $ M $ M $ , Fgure 3 llustrates that exploraton expendtures dffer sgnfcantly across countres: n 91 out of 103 countres total exploraton expendtures are between $100,000 and $138 mllon, whle n four countres exploraton spendng s between $139 mllon and $276 mllon, n 8 countres above $276 mllon. To reduce non-homogenety of the data, we take logarthms of the orgnal seres. Such logarthmc transformaton has been employed n an analyss of foregn drect nvestments by Bullngton, 1999; Cheng and Kwan, 2000; and We, A hstogram of the log-transformed total exploraton expendtures s also provded n Fgure 3. In our analyss, we examne one measure of geologcal potental - land areas 3. Land measures were used as ndcators of mneral and resource ndcators n Johnson, 1990; Sachs and Warner, 1995; Stjns, 2005; Brdsall et al, The data on countres land areas s from the World Bank s database World Development Indcators 2005 (World Bank, 2005). For a few countres, the land areas data are from the Central Intellgence Agency s (CIA) publcaton The World Factbook 2005 (CIA, 2005). Table 2 reports statstcs for geologcal potental and nvestment envronment ndcators. 40 Journal of Appled Busness and Economcs vol. 12(1) 2011

4 FIGURE 3 HISTOGRAMS OF THE 2006 TOTAL EXPLORATION EXPENDITURES Number of countres Number of countres More Exploraton expendtures, mllo $ Log-transformed exploraton expendtures We use one ndcator of nvestment clmate - the Index of Economc Freedom, publshed by the Hertage Foundaton and the Wall Street Journal. The ndex reflects countres economc condtons. It s calculated as an equally weghted average of scores for 10 ndcators of economc freedom: busness freedom, trade freedom, fscal freedom, government sze, monetary freedom, nvestment freedom, fnancal freedom, property rghts, freedom from corrupton, and labor freedom (Hertage Foundaton and the Wall Street Journal, 2009). The ndex values vary between 0 and 100. The hgher score ndcates economc condtons or polces more favorable to economc freedom. Values of the 2005 ndex of economc freedom reveal that New Zealand had the most favorable economc envronment among analyzed countres. Its score of economc freedom was the hghest (82.3). Angola had the lowest ndex level (24.3). The mean and the medan values of the ndex are almost the same: the mean equals 58.0 and the medan equals Bolva has the ndex value (58.4) closest to the mean. In our analyss, we use the scores of the ndex of economc freedom for the year Measures TABLE 2 STATISTICS FOR MEASURES OF GEOLOGICAL POTENTIAL AND INVESTMENT CLIMATE Land areas, n 2005, n thousand sq. km Index of Economc Freedom, 2005 Statstcs^ Mean Medan Maxmum Mnmum St. dev 1, , ,425 (Colomba) (Vetnam) (Russa) (Cyprus) (Bolva) (Tanzana) ^Below statstcs we provde names of representatve countres best score (New Zealand) 24.3 worst score (Angola) A sgnfcant dfference between the mean and medan values of land areas n Table 2 suggests a hghly skewed dstrbuton: there are only a few countres wth largest levels of land areas, whle the majorty of countres have smaller terrtores. To reduce varatons of the land areas across countres, we use logarthms of the land areas n our models. We nclude n models the populaton factor to control for countres szes. Most of the data on countres populaton s from the World Bank s database World Development Indcators 2005 (World Journal of Appled Busness and Economcs vol. 12(1)

5 Bank, 2005). For a few countres, the populaton data are from the Central Intellgence Agency s (CIA) publcaton The World Factbook 2005 (CIA, 2005). CROSS-COUNTRY MODELS OF EXPLORATION INVESTMENTS In ths part we analyze the relatve mportance of geologcal potental and nvestment clmate for exploraton spendng. We buld two models: log-lnear and truncated. In the models, the log-transformed exploraton expendtures are the dependent varable; geologcal potental, nvestment clmate, and populaton are explanatory varables. We use logarthms of exploraton expendtures to reduce ther sgnfcant varatons across countres (to reduce heteroskedastcty of models errors terms) and to model non-lnear assocatons of varables. Such logarthmc transformaton has been employed n an analyss of foregn drect nvestments by Bullngton, 1999; Cheng and Kwan, 2000; We, 2000; Buckley et al, In the models we analyze total exploraton expendtures, whch nclude exploraton expendtures by major, ntermedate, and junor mnng companes. Log-Lnear Model of Exploraton Investments In ths secton we estmate the log-lnear model of exploraton nvestments: lexploraton c b1 geology b2nvestment b3lpopulaton, (1) where lexploraton s the log-transformed total exploraton expendtures, lexploraton = ln(exploraton ), exploraton s the total exploraton expendtures (ncludes exploraton expendtures of major, ntermedate, and junor mnng companes); geology s the geologcal potental ndcator; nvestment s the nvestment clmate ndcator, lpopulaton s the log-transformed populaton, lpopulaton ln(populaton ), N[0, 2 ], denotes a country, geologcal potental (log-transformed land areas lland) and one nvestment clmate ndcator (the ndex of economc freedom - econfreedom): Regresson 1: lexploraton c b1 lland b2econfreedom b3 lpopulaton Regresson 1 results are reported n Table 3. The adjusted R 2 value of 0.48 n Regresson 1 s relatvely hgh for cross-country models. In Regresson 1, coeffcents of geologcal potental (land areas) and nvestment clmate (ndex of economc freedom) have expected postve sgns and are sgnfcant. The geologcal potental coeffcent mples that, f geologcal potental mproves by 1%, then exploraton nvestments grow by1.01%. The nvestment clmate coeffcent shows that, f the ndex of economc freedom goes up by 10 unts, then exploraton nvestments rse by 0.31%. For example, f Russa s score of the 2005 ndex of economc freedom were at the Bulgara s ndex level of 62.3, Russa could have seen an exploraton spendng ncrease of about 0.31%. Truncated Model of Exploraton Investments MEG reports exploraton expendtures of at least $100,000. In order to take nto account ths truncaton 4, we estmate a one-lmt truncated regresson model: lexploraton * b' (2) X lexploraton = lexploraton * f lexploraton * > -2.3 (exploraton * > $100,000), 42 Journal of Appled Busness and Economcs vol. 12(1) 2011

6 where exploraton * s the latent exploraton expendtures varable, lexploraton * s log-transformed latent exploraton expendtures, lexploraton * exploraton *), lexploraton exploraton ), exploraton s a truncated (observed) exploraton expendtures varable, X s a vector of explanatory varables, X geology, nvestment, lpopulaton ), geology s an geologcal potental ndcator, nvestment s an nvestment clmate ndcator, denotes country, N[0, 2 ], We estmate model (2) for one measure of geologcal potental and one measure of nvestment clmate: geology = lland, nvestment = econfreedom. Regresson 2: lexploraton * econfreedom lpopulaton. c b1lland b2 Maxmum lkelhood estmaton results for Regresson 2 are gven n Table 3. Magntudes of coeffcents of explanatory varables and the sgnfcance statstcs n regressons 1 and 2 are close. It appears that truncaton at $100,000 does not sgnfcantly change mportance of geologcal potental and nvestment envronment for exploraton nvestments, comparng to model (1). TABLE 3 ESTIMATION RESULTS FOR THE LOG-LINEAR MODEL (1) AND THE TRUNCATED MODEL (2) Model 1* Model 2** Varables Constant (-7.750) (-7.187) lland (7.966) (7.941) econfreedom (2.210) (2.214) lpopulaton (-1.390) (-1.160) Adjusted R Log-lkelhood * t-statstcs of the model (1) coeffcents estmates are gven n parentheses. The t-statstcs were derved usng the Whte heteroskedaststy consstent standard errors. ** z- statstcs of the model (2) coeffcents estmates are gven n parentheses. The z- statstcs are derved usng the Huber/Whte standard errors CONCLUSIONS Ths work studes varatons n countres non-ferrous mnerals exploraton nvestments. It also analyzes the relatve mportance of geologcal potental and nvestment clmate for attractng exploraton fundng. The analyss s performed usng cross-country models of exploraton nvestments. We construct two models of exploraton expendtures: log-lnear and truncated. In the models we consder two locaton factors: geologcal potental and nvestment clmate. An estmaton of models s based on the Metals Economcs Group s exploraton expendtures data, one measure of geologcal potental, and one ndcator of nvestment clmate. The models quantfy the relatve mportance of geologcal potental and nvestment clmate and show that both factors nfluence exploraton fundng. Exploraton does not smply follow geologcal potental. In order to attract exploraton nvestments, countres rch wth natural resources need to work on formng compettve nvestment envronments. Journal of Appled Busness and Economcs vol. 12(1)

7 The performed study s based on the exploraton expendtures n one year In a follow-up paper we wll look at changes of the countres exploraton nvestments over tme and factors leadng to these changes. ENDNOTES 1. The horzontal axs of Fgure 1 shows countres by ther numbers n our sample. 2. We estmated a model wth an nteracton term between geologcal potental and nvestment clmate to test whether sgnfcance of geologcal potental for exploraton nvestments depends on nvestment envronment. We found that the nteracton term was an nsgnfcant factor. 3. We do not consder mneral reserves estmates as measures of the geologcal potental because of lack of the data for some analyzed countres. Another measure of geologcal potental s the Fraser Insttute ndex of mneral potental (Fraser Insttute, 2006). In 2006, the ndex covered only 36 countres. For ths reason, we do not use the Fraser Insttute ndex of mneral potental. 4. Truncaton results n lower varance than the varance on the orgnal varable. Truncaton from below, as n our sample, produces the hgher mean than the mean of the orgnal varable. REFERENCES Bllngton, N. (1999). The Locaton of Foregn Drect Investment: An Emprcal Analyss. Appled Economcs, 31, Brdsall, N., Pnkney, T. & R. Sabot, R. (2001). Natural Resources, Human Captal, and Growth. Resource Abundance and Economc Growth, R. Auty ed., Oxford Unversty Press. Buckley, P.J. (2008). Do We Need a Specal Theory of Foregn Drect Investment for Extractve Industres? Journal of Chnese Economc and Foregn Trade Studes, 1, (2), Zheng, P. (2007). The Determnants of Chnese Outward Foregn Drect Investment. Journal of Internatonal Busness Studes, 38, Campos, N. F. & Knoshta, Y. (2003). Why Does FDI Go Where t Goes? New Evdence from the Transton Economes. IMF Workng Paper, WP/03/28. Central Intellgence Agency. (2005). The World Factbook Washngton, DC, Cheng, L. K. & Kwan, Y.K. (2000). What Are the Determnants of the Locaton of Foregn Drect Investment? The Chnese Experence. Journal of Internatonal Economcs, 51, Dunnng, J. H. (1998). Locaton and the multnatonal enterprse: a neglected factor. Journal of Internatonal Busness Studes, 29, (1), Eggert, R.G. (1992). Exploraton. In: Peck, M.J., Landsberg H.H., Tlton, J.E. (Eds), Compettveness n Metals the Impact of Publc Polcy. London: Mnng Journal Books, Eggert, R. G. (2008). Trends n Mneral Economcs: Edtoral Retrospectve, Resources Polcy, 33, 1-3. Fraser Insttute. (2006). Survey of Mnng Companes: 2005/2006, Vancouver, Canada. 44 Journal of Appled Busness and Economcs vol. 12(1) 2011

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