WPS8520 Policy Research Working Paper 8520 The Shifting Natural Wealth of Nations The Role of Market Orientation Rabah Arezki Frederick van der Ploeg Frederik Toscani Middle East and North Africa Region Office of the Chief Economist July 2018 Policy Research Working Paper 8520 Abstract This paper explores the effect of market orientation on experiment whereby economies in Latin America and (known) natural resource wealth using a novel dataset of sub-Saharan Africa remained closed, they would have only world-wide major hydrocarbon and mineral discoveries. achieved one quarter of the actual increase in discoveries Consistent with the predictions of a two-region model, they have experienced since the early 1990s. The results call the empirical estimates based on a large panel of countries into question the commonly held view that natural resource show that increased market orientation causes a significant endowments are exogenous. increase in discoveries of natural resources. In a thought This paper is a product of the Office of the Chief Economist, Middle East and North Africa Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/research. The authors may be contacted at rarezki@worlbank.org, rick.vanderploeg@economics.ox.ac.uk, and FToscani@imf.org. The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. Produced by the Research Support Team The Shifting Natural Wealth of Nations: The Role of Market Orientation Rabah Arezki, Frederick van der Ploeg and Frederik Toscani*§ JEL Classification : E00, F3, F4. Keywords: natural resources, discoveries, market orientation, liberalization, institutions, endogenous reserves * Middle East and North Africa, World Bank and Kennedy School of Government, Harvard University (Arezki) and Western Hemisphere Department, International Monetary Fund (Toscani) Department of Economics, University of Oxford (van der Ploeg) Contact e-mail: rarezki@worlbank.org ; rick.vanderploeg@economics.ox.ac.uk; FToscani@imf.org. § We thank Mike Horn and Richard Schoodle for providing us with data on major discoveries and for their guidance during the project. We thank Romain Wacziarg for kindly providing us with updated data on indicators of market orientation. We are grateful to Daron Acemoglu, Olivier Blanchard, Tim Besley, Jim Cust, Julien Daubanes, Torfinn Harding, Jean Imbs, Olivier Jeanne, Gian Maria Milesi-Ferretti, Maury Obstfeld, Bob Pindyck, Michael Ross, Katheline Schubert, and Tony Venables for helpful comments on an earlier draft and helpful discussions. We thank seminar and conference participants for helpful comments. We thank Vanessa Diaz Montelongo for outstanding research assistance. The views expressed in this paper are those of the authors and do not necessarily reflect those of the International Monetary Fund, its Board of Directors or the countries they represent. All remaining errors are ours. 2    I. INTRODUCTION The literature on economic development often assumes that natural resource endowments are exogenous. The consensus in that literature is that resource endowments alongside institutions or legal origin, and geography play a crucial role in determining economic and social outcomes.1 In contrast, the resource economics literature has emphasized that the resource base is endogenous to investment in exploration and extraction.2 That literature has, however, overlooked the role that market orientation and institutions play in driving investments in the resource sector. Our aim is to bridge the gap between these two literatures and explore the effect of market orientation on the discovery of proven (known) natural resource wealth. Countries with weak rule of law, high political or default risk, underdeveloped financial markets, or high transaction cost and deficiencies in governance may attract only limited investment flows even if they offer high rates of return (Shleifer and Wolfenzon, 2002).3 Specifically for the natural resource sector, empirical evidence suggest that a stable political environment, a low risk of expropriation, and a favourable investment climate boost investment (Bohn and Deacon, 2000; Stroebel and van Benthem, 2014).4 5 We present systematic evidence that policies geared toward economic liberalization and increased market orientation lead to major natural resource discoveries that eventually boost extractive activities in those countries. We thus demonstrate that increased market orientation in developing countries is an important determinant of proven natural resource wealth. The experience of the United States during the nineteenth and early twentieth century provides a historical account of the role of market orientation in driving natural wealth. Although the United States at the time of independence was considered to be a country of “abundance of                                                              1 See Acemoglu, Johnson and Robinson (2002), Easterly and Levine (2002), Glaeser and Shleifer (2002), Hall and Jones (1999), and Rodrik, Subramanian and Trebbi (2002). 2 See Pindyck (1978), Arrow and Chang (1982), and Devarajan and Fisher (1982). 3 The literature linking institutions and international capital flows relates to the so-called “Lucas’ paradox” (Lucas, 1990). Policies and institutional factors have been shown to play an important role in explaining the magnitude and nature of capital flows to developing and emerging economies (Alfaro, Kalemli-Ozcan and Volosovych, 2008). 4 Irreversible investments in the resource sector involve sunk costs and are subject to holdup and the political risk of expropriation (Long, 1975). 5 Exploiting variations in market orientation and oil deposits sitting on either side of the border, empirical evidence suggests that institutions substantially affect oil and gas exploration (Cust and Harding, 2017). 3  land but virtually no mining potential” (O’Toole, 1977), by 1913 it was the world’s dominant producer of virtually every major industrial mineral (David and Wright, 1997). Rather than being driven by a comparative advantage in geological endowments, this resource-based development of the United States was driven among other things by an open market orientation and an accommodating legal environment with the government claiming no ultimate title to mineral rents (e.g., Wright and Czelusta, 2004).6 In stock terms, proven reserves of natural resources are today significantly higher in advanced countries than in developing countries (World Bank, 2006). In flow terms, however, we observe a shift in resource discoveries from developed to developing countries over the past decades that coincides with increased market orientation in developing countries. The trend towards more market orientation seems to have led to a shift in the geographic distribution of discoveries. As a consequence, the share of worldwide resource discoveries in Latin America and the Caribbean and Sub-Saharan Africa has doubled over the past decades (see Figure 1). Figure 1: Number of Natural Resource Discoveries by Region and Decade Note: Based on data from MinEX for natural resource discoveries and Mike Horn for hydrocarbon discoveries. High income countries include OECD members as well as Bahrain, Brunei, Cyprus, Equatorial Guinea, Kuwait, Oman, Qatar, Saudi Arabia, Trinidad and Tobago, and the United Arab Emirates.                                                              6 Economic historians argue that natural resources may be under-produced due to a lack of effective property rights (e.g. Anderson and Libecap, 2011) and that private mineral rights became more explicit as mine values increased (Demsetz, 1967; Libecap, 1976). With increased competition for valuable resources, informal rules were insufficient to reduce risk and support long-term investment to develop the mines. Making property rights more formal boosted mining investment.     4  Anecdotal evidence suggests that increased market orientation was followed by increased discoveries across continents and types of natural resources (see Table 1). The increase in discoveries after countries open up to the global economy appears to be quite stark. In Peru, for example, discoveries more than quadrupled, in Chile they tripled, and in Mexico they doubled. In Ghana, discoveries only started to occur after the opening of the economy. Table 1: Number of discoveries before and after opening – Country Examples Country Chile Ghana Peru Indonesia Mexico Date of Opening 1976 1985 1991 1970 1986 Number of Discoveries 10 years before opening 5 0 5 3 12 Number of Discoveries 10 years after opening 15 6 23 15 21 Source: Based on data from MinEX for natural resource discoveries and Mike Horn for hydrocarbon discoveries. Dates of opening are from Wacziarg and Welch (2008). To motivate our empirical analysis, we put forward a simple two-region model of endogenous reserves based on Pindyck (1978) where multinational corporations are faced with an implicit tax which proxies for how closed market orientation is, and seek the lowest cost location. The model explores the interplay between market orientation and other channels such as the increase in the marginal cost of discoveries and (demand driven) natural resource price shocks. In turn, key model predictions of our model are then taken to the data. For our empirical analysis we build a unique and hitherto unexploited dataset of the universe of world-wide major natural resource discoveries since 1950, covering 128 countries, 33 types of natural resources and over 60 years. Our main explanatory variable is a generic measure of market orientation. We provide OLS results as a benchmark but to account for the endogeneity of the market orientation variable, we then use an instrumental variable approach based on Buera et al (2011): a country’s choice to liberalize its economy depends on the policies of neighbouring countries in general, but also on how successful other countries with liberalized and closed economies, respectively, performed. We include country fixed effects as well as year-by-resource fixed effects in our panel estimates to control for time-varying resource- specific factors such as technological progress in extractions of the different types of minerals     5  and hydrocarbons, as well as time-invariant country characteristics such as geographic location to capture that different areas have different natural resource endowments. Consistent with our predictions, our empirical analysis shows that market orientation causes a statistically and economically significant increase in natural resource discoveries. Our point estimates indicate that going from a closed to an open market orientation increases discoveries by 80-140 percent. We verify the mechanism through which this occurs by showing that exploration spending (a key determinant of the probabilistic process of discovering new sources of natural resources) also significantly increases following changes in market orientation (by over 100 percent). In a thought experiment whereby economies in Latin America and sub-Saharan Africa remained closed, they would have only achieved one quarter of the actual increase in discoveries they have experienced since the early 1990s. Our results are robust to a wide array of empirical checks including the use of alternative dependent variables (discoveries per capita or a simple dummy variable), separating out mineral and hydrocarbon resources, including additional controls, the use of an alternative estimator and the use of an alternative measure of market orientation. Our paper is related to the theoretical and empirical literature on exhaustible resource exploitation and exploration. Natural resource exploration and discoveries have been investigated either as a deterministic or a stochastic process (e.g. Pindyck, 1978; Arrow and Chang, 1982; Devarajan and Fisher, 1982). The canonical model is the exploration model developed by Pindyck (1978) where a social planner maximizes the present value of the social net benefits from the consumption of oil and the reserve base can be replenished through exploration and discovery of new fields.7 We apply this model to a two-region world to explore the relationship between exploration investment and discoveries where multinational corporations are faced with explicit taxes and implicit taxes (as proxy for lack of market orientation) on their investment in the South but none in the North and arbitrage between different locations.                                                              7 We use the depletion and exploration investment rates as decision variables. This is a metaphor, since not the depletion rate but the investment in rigs follows a Hotelling rule (Anderson, Kellogg and Salant, 2017).     6  This paper is also related to the literature on the so-called “resource curse”.8 In particular, empirical evidence suggests that the curse in terms of the effect of natural resources on growth is less severe and can even turn into a boon if the quality of institutions is beyond a certain threshold (e.g., Mehlum, Moene and Torvik, 2006; Boschini, Pettersson and Roine, 2007). While this literature has long focused on the direction of causality running from natural resource endowments and institutions to growth and conflicts, our empirical results suggest that causality running from policies and market orientation to natural resource endowments is equally important. Our focus is on “upstream” rather than “downstream”. As such we are concerned with “external” policies and institutions that are geared toward foreign investors who conduct exploration activities. Our results do not contradict the fact that subsequent to a discovery, countries with poor “internal” institutions (e.g. weak state capacity) may experience poor economic performance and/or social outcomes or even civil strife. The remainder of the paper is organized as follows. Section II puts forward some predictions based on a simple two-region model of depletion and discoveries, and discusses our empirical strategy. Section III presents the data used in the empirical analysis. Section IV presents the main results and key robustness checks. Section V concludes. II. ANALYTICAL PREDICTIONS AND EMPIRICAL STRATEGY We specify a simple two-region, three-period model of endogenous reserves and natural resource discoveries with international resource companies (IRCs) arbitraging to decide on the optimal geographical allocation of their exploration and depletion activities. The North has free access for IRCs but the South is less open for IRCs. Lack of openness in the South is interpreted captured by a virtual tax. IRCs decide where to explore reserves depending on local extraction costs which depend on market orientation and availability of subsoil resources. Global IRCs relocate exploration across the globe until costs of resource exploration across the globe are equalized. We can then get the following analytical predictions (see Appendix A):                                                              8 See Frankel (2012), Venables (2016), Ross (2012), and van der Ploeg (2011) for recent surveys.     7  - Discoveries of natural resources in both the North and the South increase with demand for these resources or with the world price of natural resources. - A lower stock of in situ natural reserves corresponding to more cumulative discoveries in the past depresses discoveries today, albeit that in the short run we expect the effect on discoveries to be positive due to probing and increases probability of discovery. - Better geological conditions and a more easily accessible stock of natural resources depress extraction costs, and thus lead to more discoveries and higher depletion rates. - Higher taxes on IRCs or a less outward orientation for IRCs holds back discoveries of natural reserves. Other (not mutually exclusive) forces include the rise in global resource demand emanating from emerging economies (e.g., China and India) that prompts more exploration efforts and discoveries and increases in the marginal cost of exploration. In our empirical analysis, we allow discoveries to depend not only on ease of access for IRCs but also on global resource demand shocks and changes in marginal costs of discoveries due to depletion forces. Taxes and restrictive access in the South lead to a shifting of exploration activities and discoveries from the South to the North due to tax shifting causing a rise in world resource prices As the South liberalizes and gets more open to IRCs, the world price of resources falls and exploration activities and discoveries shift from North to South. However, we do not test this last prediction directly but instead allow for the effect of prices and demand shocks on discoveries. We develop an empirical strategy that allows us to test the above analytical predictions. Our main concern is the estimation of the impact of market orientation on natural resource discoveries. Let , , be the number of discoveries for country at time of resource . Our baseline empirical model estimates a 3-way panel defined by (I) , , , , , , . , where , is an indicator for whether country is open at time 1, , , is a set of controls which depending on the case vary at country-year, country-resource or country- resource-year level, is a country fixed effect, , is a year-resource fixed effect and . , is     8  an error term.9 The country fixed effects control for time-invariant country-specific characteristics such as geographic location and geological conditions. The year-resource fixed effects control for time-varying resource-specific factors such as international prices and technological progress. The additional controls included in , , are a measure of the previous stock of discoveries of resource in country as well as the square of this stock measure. This allows us to capture the country-specific dynamics related to the clustering of discoveries over time due to the probing effect and the depletion of geological reserves after large numbers of discoveries pushing extraction costs.10 While the combination of country and year-by-resource fixed effects is our favoured specification, we also estimate regressions with year and country-resource fixed effects instead as this allows us to explicitly include natural resource prices (which we believe to yield a coefficient of interest in its own right) as a variable in , , . We cluster standard errors at the country-resource level but the significance of our results is unchanged if we cluster at the year-resource level (see Supplementary Appendix). Identification As discussed at length in the literature, resource discoveries can impact policies and institutions. For instance, discoveries may trigger conflicts over natural resources and erode political institutions (Ross 2001, 2012). As a first step to avoiding reverse causality, all explanatory variables are included with a lag.11 Nevertheless, a naïve OLS estimation of (I) faces serious concerns. To try and isolate variation in openness which is exogenous to resource discoveries, and in the absence of a large-scale natural experiment, we require a strong instrument. Since both cross-sectional and time variation are important in our setting, we need the instrument to be time varying. This is not easy, since most previous instruments used for comparable variables in the literature tend to rely exclusively on cross-sectional variation. Our solution, which rests on a number of assumptions to be discussed below, is to construct an instrument for openness based on the idea that neighbours’ market orientation, and in particular                                                              9 The model is essentially a difference-in-difference specification where the key coefficient of interest is . 10 In those specifications where we include the past stock of discoveries (essentially the sum of lagged dependent variables) Nickel bias might be a concern (Nickell, 1981). However, given that it tends to 1/T and we have roughly 50 years of annual data, this bias is likely to be small. 11 The reverse causality generally discussed in the literature would tend to bias our results downwards – resource- rich countries are often shown to do worse on a number of institutional measures.     9  the relative success of neighbours who choose to be open or not, are strong drivers for the choice of own market orientation. Intuitively, the instrument tries to capture the fact that average openness in other countries ( as well as the relative success of open and closed economies (i.e., those with and without market orientation) should not be influenced by future resource discoveries in country and should only influence discoveries in country via their impact on openness in country and not directly. We closely follow the reduced-form specification in Buera et al. (2011) for this exercise.12 Specifically, we use the idea that the liberalization decision of country in period ( , ) depends positively on a distance- weighted measure of other countries’ policies ( , ) and negatively on the distance- weighted average growth rate over the previous 3 years of other countries which remain closed ( , | 0 )13. Hence, we specify (II) , .. , , | 0, where our hypothesis is that 0 and 0. For the instruments to be valid, they need to be strongly correlated with the openness of markets in country but uncorrelated with resource discoveries, conditional on openness of markets in country . The instruments satisfy the inclusion condition as we will show below. One concern with the exclusion restriction might be that discoveries in a neighbour (after that neighbour opens up) make exploration in country more attractive, independent of whether country also opens up (e.g., because of additional geological information). While this effect cannot be ruled out, it is likely to be at most a local effect. Information gained from a successful discovery only applies to a very limited geographical area, usually not more than several square kilometres. Nevertheless, we directly control for a distance-weighted measure of discoveries in other countries in the past year in a robustness exercise and find that our main empirical insights are unchanged.                                                              12 Buera et al. (2011) include the lagged market orientation index in their reduced form specification which we exclude given the endogeneity concern. Their aim is not to construct an instrument, but to motivate a structural estimation. Furthermore, Buera et al. also include the distance-weighted growth rate of open countries, , | 1 ,as a complement to , | 0 . Including it does not change any results. But since the coefficient on , | 1 is never significant in the first stage we exclude it to have the most parsimonious instrument possible. 13 The weights are based on distance data obtained from the CEPII: http://www.cepii.fr/CEPII/fr/bdd_modele/presentation.asp?id=6     10  If both (regional) opening of markets and discoveries are driven by a (third) outside factor, omitted variables might also pose a threat to our identification. We try to address this in several ways. First, directly controlling for a lagged, distance-weighted measure of discoveries in other countries (as just discussed) addresses not only the regional spill-over problem but also the problem of omitted variables related to an unknown deep driver (such as US foreign policy perhaps or the fall of the Soviet Union) to some degree. Secondly, we show that the results are a general phenomenon, which holds for different time periods and regions – no one region or time period (which might be affected by a specific omitted variable) is responsible for the results. More generally, the rich fixed-effects structure we use, in particular the year-by- resource effects, rule out a number of alternative stories such as one in which by coincidence the arrival of a new technology for discoveries of a particular resource happens concurrently with a wave of opening up of markets. The robustness section discusses this in more detail. III. DATA AND STYLIZED FACTS Here we discuss the various datasets that we use. We focus on the novel data on major hydrocarbon and mineral deposits as well as the data on market orientation (see Appendix B for a more comprehensive list of data and sources as well as additional summary tables). Discoveries Discoveries are our main dependent variable in our empirical model (I). The oil and gas discovery dataset is from Horn (2014). Horn reports discoveries of giant oil (including condensate) and gas fields which we refer to jointly as hydrocarbon or simply oil discoveries. A giant discovery is defined as a discovery of an oil and/or gas field that contains at least 500 million barrels of ultimately recoverable oil equivalent. Ultimately recoverable reserves refer to the amount that is technically recoverable given existing technology. The data on mineral deposits discoveries is from MinEx. The list of minerals included in the dataset is comprehensive and includes precious metals and rare earths. As in the case of hydrocarbons, we only capture discoveries above a certain threshold, corresponding roughly to a mineral deposit which has the capacity to generate an annual revenue stream of at least     11  USD 50 million after accounting for fluctuations in commodity prices. The hydrocarbons and the mineral datasets themselves were constructed from many underlying sources. Minex constructed the data from company public reports (Annual Reports, press releases, NR 43-101 studies, etcetera), technical and trade journals (such as Economic Geology, Northern Miner and Mining Journal) and Government Files (from the various Geological Surveys). The data was up to date as of August 2013. Minex defines the discovery date of a mineral discovery as the moment when it was realized that the deposit has significant value, usually the date of the first economic drill intersection. Some deposits might have had small-scale operations in place prior to the discovery date – if there is an order-of-magnitude increase in the known size of the deposits, the date of the increase in the size of the discovery is taken as the discovery date. Overall, we believe our data set to have the most comprehensive list of giant or big natural resource deposits for the period since 1950. Nevertheless, there are a number of constraints. First, while only deposits above a certain threshold size are even considered for inclusion in the dataset (see the list in Appendix B), there is likely to still be a certain measurement bias towards larger deposits given better documentation for larger deposits. Secondly, data for certain countries such as Russia and China might be under-measured given less accessible data. Last, our dataset excludes iron ore and bauxite. Since they are more abundant than other metals, there are not always well-timed discoveries to speak of and Minex excluded them from the dataset. Exploitation decisions tend to be based more on proximity to port facilities for iron ore and substantial energy availability for bauxite than other factors. Figure 2 shows a map with all natural resource discoveries included in our dataset. Figure 3 plots the total number of worldwide discoveries (split between minerals and hydrocarbons) by year. Since the early 1980s the average number of discoveries has been fairly stable at the global level. In our empirical work we use discoveries disaggregated by 128 countries and 33 types of resources.     12  Figure 2: Map of Worldwide Natural Resource Discoveries Note: Based on data from MinEX for mineral discoveries and Mike Horn for hydrocarbon discoveries. Figure 3: Number of Natural Resource Discoveries by Year 50 45 40 35 30 25 20 15 10 5 0 1950 1952 1954 1956 1958 1960 1962 1964 1966 1968 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 Total Number of Mineral Discoveries Total Number of Hydrocarbon  Discoveries Note: Based on data from MinEX for mineral discoveries and Mike Horn for hydrocarbon discoveries. Exploration We will also use exploration effort, a key driver of discoveries, as dependent variable in our empirical model (I). To measure exploration effort, we use disaggregated data on exploration expenditures from Rystad for oil and gas and from SNL Metals and Mining for selected minerals including copper, nickel, zinc, diamonds, uranium, and platinum. The SNL Metals     13  and Mining dataset only starts in 1994 and thus limits the sample period for this part of our empirical analysis. Similar to the data on discoveries, data on exploration spending are broken down by country, type of natural resource and year. Measure of market orientation Our prime explanatory variable in our empirical model (I) is market orientation or openness. For this we use data on the timing of economic liberalization for 133 countries during the years 1950 to 2004, originally constructed by Sachs and Warner (1995) (SW thereafter) and revised and extended by Wacziarg and Welch (2008). Following SW, the following criteria are used to classify a country as open: (i) the average tariff rate on imports is below 40%; (ii) non-tariff barriers cover less than 40% of imports; (iii) the country is not a socialist economy (according to the definition of Kornai (1992)); (iv) the state does not hold a monopoly of the major exports; and (v) the black market premium is below 20%. The resulting indicator is a dichotomous variable. If in a given year a country satisfies all of these above criteria, SW define it to be open and set the indicator to 1. Otherwise, SW set the indicator to 0.14 While this indicator was originally designed to capture openness to trade, we follow Rodriguez and Rodrik (2001) and Buera et al. (2011) by viewing the SW indicator as proxy for broader economic openness and market orientation. Trade liberalization is usually just one part of a government’s overall reform plan for integrating an economy with the world system. Other aspects of such a program almost always include price liberalization, budget restructuring, privatization, deregulation, and the installation of a social safety net (Sachs and Warner, 1995). For our purposes, we capture a broad measure of market orientation with policy implications which often reverberate on the “openness” of the resource sector. Indeed, investment in exploration is worthwhile only if there are prospects for further extractive activities. Such a generic measure of market orientation allows us to capture a combination of factors such as favourable business climate including fiscal terms, political risks and access to relevant equipment and financing.15 We thus use the indicator as proxy for country’s degree of market orientation.                                                              14 Unfortunately, the disaggregated data for the components of the SW indicator are not available. 15 We prefer this to a resource-specific measure of market orientation, because generic measures typically capture de facto conditions and/or multilateral commitments (e.g., to the World Trade Organization) which are more     14  It must be noted that a binary variable such as the SW indicator is necessarily restricted in how much it can measure. While we think of the SW indicator as proxy for the above list of factors, it is interesting to ask which of those which might be the most important ones in determining natural resource discoveries. Given that no other meaningful variable is available for most of the time period we are studying, we approach this question in section IV.D by investigating with which alternative measures of openness and institutions the SW index is most closely correlated. A first look at the data: effect of opening markets on discoveries As a first look at the data, we conduct an event-study type of analysis where we calculate the average number of discoveries prior and after liberalization for all such episodes in the updated Wacziarg and Welch dataset.16 Figure 4 shows that the number of discoveries significantly increases once economic liberalization has led to a more market-oriented economy. The average number of discoveries per year and country rises from 0.2 prior to liberalization to 0.36 afterwards.17 The pattern linking liberalization and discoveries seems to hold across geographical regions and time periods. Looking at all episodes of market opening in our dataset (82 cases), we observe that in 54 percent of cases countries discovered more natural resources in the 10 years following a shift to a market-oriented economy than in the ten years before, in 24 percent of cases there was no difference, and in 22 percent of cases they discovered less.                                                              difficult to reverse and thus more credible than say sectoral regulations that govern rather narrower aspects of a bilateral relationship between firms and national authorities. 16 In practice, we regress the number of discoveries on a set of period fixed effects while controlling for event fixed effects. We then retrieve the coefficients of the period fixed effects and plot them. 17 Discoveries seem to start increasing about 1-2 years prior to opening, perhaps due to an anticipation effect.      15  Figure 4: Average number of discoveries before and after liberalization Note: The blue line shows the point estimates of a regression of the number of countries per country and year on a set of period fixed effects (while controlling for event fixed effects). The grey area shows the 90 percent confidence interval. There are a total of 82 events (countries switching from closed to open economies) in the dataset. The event analysis does not include any controls and does not address endogeneity issues or other statistical confounding influences. Nevertheless, it provides us with a window into what the data have to offer and motivates our main hypothesis that economic opening and market orientations increases the number of resource discoveries. IV. MAIN EMPIRICAL RESULTS A. Benchmark Results We now turn to our benchmark results. Table 2 gives the OLS estimates of the impact of openness on natural resource discoveries. In all specifications openness is estimated to have a highly significant positive impact on discoveries. Column 1 only includes year and country- by-resource fixed effects, as well as our basic variable of interest. In columns 2, 3 and 4 we add as additional controls the level of resource prices as well as the stock of past discoveries and its square. In column (5) we use country and year-by-resource fixed effects (and     16  consequently we have to drop the price variable).18 Given that price data is only available since the mid-1960s for many natural resources (and even later for some), we lose a fairly large number of observations in columns (2)–(4).19 This, combined with the demanding nature of the year-by-resource fixed effects, makes column (5) our preferred specification of Table 2. Table 2: The Impact of Liberalization on Resource Discoveries (OLS) (1) (2) (3) (4) (5) Number of  Number of  Number of  Number of  Number of  VARIABLES Discoveries Discoveries Discoveries Discoveries Discoveries SW/Wacziarg Openness, lagged 0.00716*** 0.0171*** 0.0169*** 0.0137*** 0.00875*** (0.00179) (0.00442) (0.00442) (0.00392) (0.00185) (log) Price, lagged 0.0146** 0.0150*** 0.0103** (0.00677) (0.00533) (0.00449) Stock of Discoveries, lagged ‐0.000839 0.0260* 0.0382*** (0.00503) (0.0141) (0.00500) Stock of Discoveries squared, lagged ‐0.000451** ‐0.000284*** (0.000176) (8.26e‐05) Country FE / / / / YES Year FE YES YES YES YES / Natural Resource FE / / / / / Country by Natural Resource FE YES YES YES YES / Year by Natural Resource FE / / / / YES Observations 161,160 57,976 57,976 57,976 161,160 R‐squared 0.272 0.328 0.328 0.339 0.226 Estimation OLS OLS OLS OLS OLS Country by  Country by  Country by  Country by  Country by  Error Clustering Natural  Natural  Natural  Natural  Natural  Resource Resource Resource Resource Resource Note: The table reports results for regressions of the number of natural resource discoveries by country, year and resource on countries’ SW/Wacziarg openness indicator as well as a number of controls. The number of observations in columns (1) and (5) are larger than in the other columns because columns (2) – (4) include resource prices as a control and price data are not available for certain years and resources. Column (5) uses country as well as year-resource fixed effects instead of the year and country-resource fixed effects employed in columns (1) – (4). *** denotes significance at the 1% level, ** at the 5% level, and * at the 10% level. In terms of quantification, this specification also provides what we see as a safe lower bound – first it is a demanding specification and second, as we will see below, we believe the OLS estimates to be biased downwards given that the 2SLS estimates are substantially larger. The                                                              18 For some resources, price data have limited time series information. Appendix B provides detailed information on available price data by natural resource. 19 In the shorter time period covered by regressions (ii)-(iv) the point estimates are significantly larger.     17  point estimate in column 5 is 0.00875. The average number of discoveries per country-year- resource in our full dataset is 0.01. Simply as a means of gauging the magnitude of the estimated coefficient, this indicates an increase of 87.5% in the average number of discoveries. This already suggests a large impact of openness on discoveries. Table 3: The Impact of Liberalization on Resource Discoveries (First stage of 2SLS)  (1) (2) (3) (4) (5) SW/Wacziarg  SW/Wacziarg  SW/Wacziarg  SW/Wacziarg  SW/Wacziarg  Openness Openness Openness Openness Openness VARIABLES Distance‐Weighted Average Openness, lagged 0.928*** 0.831*** 0.836*** 0.833*** 0.928*** (0.053) (0.012) (0.012) (0.012) (0.069) Distance‐Weighted Growth Closed Economies, lagged ‐0.169*** ‐0.098 ‐0.113 ‐0.116 ‐0.172*** (0.053) (0.086) (0.086) (0.086) (0.052) Country FE / / / / YES Year FE YES YES YES YES / Natural Resource FE / / / / / Country by Natural Resource FE YES YES YES YES / Year by Natural Resource FE / / / / YES (log) Price,  Discoveries Stock  (log) Price lagged,  Discoveries Stock  lagged,  Controls (log) Price lagged Discoveries Stock  lagged,  Discoveries Stock  lagged Discoveries Stock  Squared lagged Observations 157,284 57,976 57,976 57,976 157,284 F‐Stat (2, 1801) Excluded Instruments  9192 2508 2560 2484 9388 Anderson‐Rubin Wald Test (p‐value) 0 0 0 0 0 R‐squared 0.6589 0.7157 0.7163 0.7165 0.6591 Year by Natural  Year by Natural  Year by Natural  Year by Natural  Year by Natural  Error Clustering Resource Resource Resource Resource Resource Note: The table reports the first stage of a 2SLS estimation where distance weighted average openness and distance weighted growth of closed economies are used as excluded instruments for a country’s SW/Wacziarg Openness indicator. The number of observations in columns (1) and (5) are larger than in the other columns because columns (2) – (4) include natural resource prices as a control and price data are not available for certain years and resources. Column (5) uses country as well as year-resource fixed effects instead of the year and country-resource fixed effects employed in columns (1) – (4). The Anderson-Rubin Wald statistics allows for a test of the joint significance of the excluded instruments in a reduced-form estimation, where the null hypothesis is that the coefficients of the excluded instruments are jointly equal to zero. *** denotes significance at the 1% level, ** at the 5% level, and * at the 10% level. We now turn to the instrumental variable results, which are based on the identification assumptions discussed previously. Table 3 shows the first-stage of the 2SLS estimates. The coefficients on distance-weighted average openness and on distance-weighted growth of closed economies have the expected sign. The former is always highly significant and positive while the latter is only significant in columns (1) and (5) which allow for longer timer series. Overall, the high F-statistics (of the excluded instruments) indicate that the instruments are highly correlated with the endogenous variable.     18  Table 4: The Impact of Liberalization on Resource Discoveries (2SLS)  (1) (2) (3) (4) (5) Number of  Number of  Number of  Number of  Number of  VARIABLES Discoveries Discoveries Discoveries Discoveries Discoveries SW/Wacziarg Openness, lagged 0.0209*** 0.0756*** 0.0760*** 0.0641*** 0.0140*** (0.00432) (0.0161) (0.0152) (0.0147) (0.00410) (log) Price, lagged 0.0146** 0.0149*** 0.0103** (0.00678) (0.00532) (0.00448) Stock of Discoveries, lagged ‐0.000541 0.0257* 0.0382*** (0.00500) (0.0141) (0.00499) Stock of Discoveries squared, lagged ‐0.000441** ‐0.000284*** (0.000175) (8.24e‐05) Country FE / / / / YES Year FE YES YES YES YES / Natural Resource FE / / / / / Country by Natural Resource FE YES YES YES YES / Year by Natural Resource FE / / / / YES Observations 157,284 57,976 57,976 57,976 157,284 R‐squared 0.279 0.323 0.323 0.335 0.227 Estimation 2SLS 2SLS 2SLS 2SLS 2SLS Year by  Year by  Year by  Year by  Year by  Error Clustering Natural  Natural  Natural  Natural  Natural  Resource Resource Resource Resource Resource Note: The table reports the estimates of the second stage of a 2SLS regression for the number of natural resource discoveries by country, year and resource on countries’ SW/Wacziarg openness indicator as well as a number of control variables. Distance weighted average openness and distance weighted growth of closed economies are used as excluded instruments for the SW/Wacziarg Openness indicator. The number of observations in columns (1) and (5) are larger than in the other columns, because columns (2) – (4) include natural resource prices as a control and price data are not available for certain years and resources. Column (5) uses country as well as year-resource fixed effects instead of the year and country- resource fixed effects employed in columns (1) – (4). *** denotes significance at the 1% level, ** at the 5% level, and * at the 10% level. Table 4 shows the second stage of the baseline 2SLS estimates. Across all specifications, regression results consistently show that the coefficient on our measure of market orientation is positive and statistically significantly associated with more discoveries. More open countries are thus more likely to discover new natural resources.20 More open institutions allow for easier transfer of capital and technology and thus exploration is more attractive in those regions, as predicted by the model in section II. Comparing the magnitude of the IV and OLS coefficients shows that the IV estimates are larger by a factor 2 to 4. We believe this to indicate that the                                                              20 Adding the stock of past discoveries (which is not instrumented) could potentially raise identification concerns so we always show a column where they are not included.     19  OLS estimates are biased downwards in accordance with reverse causality (resource discoveries leading to rent seeking and potentially conflict). While significance is not affected, the main coefficient of interest is significantly smaller in columns (1) and (5) than in columns (2), (3) and (4) again, presumably due to the different time periods being covered. Taking column (5) of Table 4 as the 2SLS point estimate for the impact of market orientation on discoveries indicates that liberalization increase discoveries by roughly 140% of the average number of discoveries. With 2392 discoveries (from 1950 to 2004) distributed over roughly 30 resources and 60 years, one country opening up is thus estimated to boost annual average discoveries worldwide by more than 1% (ceteris paribus). Considering that in 1980 only 1 out of 26 countries in our sample in sub-Saharan Africa were open and in 2000 this had risen to 15 countries out of 26 (for Latin America and the Caribbean 2 out of 16 countries in 1980 were open and by 2000 this had risen to 15 countries), the aggregate impact is large indeed. To further quantify the importance of market orientation on discoveries, we calculate the predicted number of discoveries per country, year and resource based on the assumption that openness in all countries did not change since 1950 and we compare it to the predicted number of discoveries using data for economic opening such as it occurred. On the basis of our 2SLS results we find that in this counter-factual world with no shift towards market orientation, the increase in discoveries in sub-Saharan Africa and Latin America in the 1990s and 2000s would be only one quarter as large as the increase with economic opening.21 As discussed in section II, a more open market orientation (a lower implicit or virtual ‘tax’ on IRCs), but also higher prices and the stock of previous discoveries impact the number of new discoveries. Empirically, we find that increases in prices are significantly associated with more discoveries. The result is intuitive, since higher prices make additional exploration activity profitable. The coefficient associated with the stock of discoveries is positive and statistically significant. This suggests that in locations where discoveries have occurred in the past, more                                                              21 Recall Figure 1 which illustrated the actual increase in discoveries in sub-Saharan Africa and Latin America and the Caribbean.       20  discoveries are more likely (an informational probing effect).22 The coefficient associated with the square term in the stock of cumulative discoveries is negative suggesting that the effect is non-linear. In other words, bigger stocks of cumulative discoveries eventually turn out to be associated with a lower likelihood of discovery as the easiest available and cheapest deposits have been discovered (“running out of resources” effect). We interpret this as a trade-off between the initially reduced costs of exploring close to a known deposit with the eventually increased cost due to geological depletion. B. Verifying the Mechanism: Exploration Efforts So far, we have focused on the relationship between market orientation and major discoveries. To examine the underlying mechanism, we explore whether exploration efforts rise following shifts in market orientation. Our hypothesis is that more market-oriented and open economies are able to attract more exploration investment and thus have more resource discoveries. Oil and gas exploration, as well as mineral exploration, are capital intensive and thus costly. Nowadays, over a hundred billion dollars are spent on resource exploration annually according to Rystad and SNL Metals and Mining. And while exploration is a very risky activity23, in which “luck is obviously a major factor” (Harbaugh, Davis, and Wendebourg, 1995), exploration efforts ought to be a key determinant of discoveries. To verify that proposition for our data, we first estimate the following equation (III) yitk  B(L)sitk  i t   k  itk , where yitk   and sitk are the number of discoveries and the logarithm of exploration spending in millions of constant (2010) U.S. dollars24, respectively, of resource k in country i at time t and i ,t and  k are country, year and resource fixed effects. is a p-th order lag operator.                                                              22 Cavalcanti et al (2016) used well-level data on oil drilling for Brazil to show that, after a first wild-cat discovery, follow-up exploration activity and additional discoveries increase significantly in following years. 23 An oil exploration well (wildcat well – a well drilled a mile or more from an area of existing oil production) can have a probability as low as 10% of yielding viable oil, while a rank wildcat (a well drilled in an area where there is no existing production) has an even smaller chance of finding oil. Elf was drilling in 1971 for offshore oil in Norway and found nothing. Recently, it found a huge new field just 3 metres away from the original drilling. Drilling outcomes are therefore highly uncertain. 24 Exploration expenditures data are deflated using the US GDP deflator. Using alternative deflators gives similar results.      21  We estimate equation (III) for p 1,2,3 using OLS and then test whether H0 :  b  0. p 0 h Table 5 reports the results of this exercise. We always strongly reject the null hypothesis of no impact of exploration spending on discoveries at the 1% significance level. Table 5: The Impact of Exploration Spending on Natural Resource Discoveries P Value of Wald Test (H0: Effect is 0) Dependent variable 1 Lag 2 Lags 3 Lags Number of Discoveries 0 0 0 Point Estimate (Sum of coefficients) Dependent variable 1 Lag 2 Lags 3 Lags Number of Discoveries 0.013 0.013 0.013 Table 6: The Impact of Liberalization on Exploration Spending (2SLS) (1) (2) (3) (4) (5) (log) Real  (log) Real  (log) Real  (log) Real  (log) Real  Exploration  Exploration  Exploration  Exploration  Exploration  VARIABLES Spending Spending Spending Spending Spending SW/Wacziarg Openness, lagged 1.230*** 0.903** 1.074** 1.079*** 1.804*** (0.424) (0.365) (0.407) (0.405) (0.641) (log) Price, lagged 0.930*** 0.968*** 0.966*** (0.193) (0.192) (0.192) Stock of Discoveries, lagged 0.0424*** 0.0641*** 0.223*** (0.0119) (0.0169) (0.00724) Stock of Discoveries squared, lagged ‐0.000325** ‐0.00237*** (0.000136) (9.36e‐05) Country FE / / / / YES Year FE YES YES YES YES / Natural Resource FE / / / / / Country by Natural Resource FE YES YES YES YES / Year by Natural Resource FE / / / / YES Observations 3,706 3,467 3,467 3,467 3,747 R‐squared 0.931 0.941 0.940 0.940 0.755 Estimation 2SLS 2SLS 2SLS 2SLS 2SLS Year by  Year by  Year by  Year by  Year by  Error Clustering Natural  Natural  Natural  Natural  Natural  Resource Resource Resource Resource Resource     22  Note: The table reports the second stage of a 2SLS estimation regressing the logarithm of real exploration spending (expressed in millions of 2010 US Dollars) by country, year and resource on countries’ SW/Wacziarg openness indicator as well as a number of control variables. Data on exploration spending is available starting in 1994. Distance weighted average openness and distance weighted growth of closed economies are used as excluded instruments for the SW/Wacziarg Openness indicator. Column (5) uses country as well as year-natural resource fixed effects instead of the year and country- natural resource fixed effects employed in columns (1) – (4). *** denotes significance at the 1% level, ** at the 5% level, and * at the 10% level. Having thus established that exploration spending increases the likelihood of discoveries, we test whether openness increases exploration spending to complete the causal chain. To do so, we estimate a regression analogous to equation (I). Table 6 gives the 2SLS estimates where we instrument openness as before. Unfortunately, the period of analysis when using exploration spending is relatively short due to exploration data only being available starting in 1994 (to the best of our knowledge it is nevertheless the best available data). Still, we find a strong positive impact of openness on exploration spending. The point estimates in Table 6 suggest an increase in exploration spending of close to 150% after opening markets when using the lowest point estimate (column 2).25 This fits well with the above quantification of the impact of market orientation on the number of discoveries. Opening up the economy thus leads to a large and significant increase in exploration spending that in turn results in additional discoveries of new reserves according to Table 6. The level of prices is also found to be strongly positively associated with exploration spending, with an estimated elasticity of about one. Furthermore, the stock of cumulative discoveries first has a positive ‘information’ effect and eventually a negative ‘depletion’ effect as also found empirically for the effects on discoveries reported in Tables 2 and 4. C. Robustness We examine robustness by exploring a wide array of alternative specifications, additional controls, alternative definitions of the dependent variable, countries and periods excluded from our sample, splitting up the sample between hydrocarbon and mineral discoveries, using an alternative estimator which specifically takes into account the large number of zeros in the discovery data, and collapsing our data to a two-way, country-year panel (see Tables S1-S11 in the Supplementary Appendix). As discussed in section II, in theory it might be true that the                                                              25 This can be calculated as 100 ∗ 1 which with  = 0.903 gives 147%.     23  exclusion restriction for the distance-weighted instrument is not satisfied as openness of neighbours’ economies increases discoveries there and in the home country as the neighbours’ discoveries provide additional geological information about the home country. By controlling directly for neighbours’ distance-weighted discoveries, this concern is addressed (see Table S1). We find that the point estimate on market orientation is virtually unchanged relative to the baseline regression, but the estimate for the impact of neighbours’ discoveries is always positive albeit not always significant. The significance of our core results in Table 4 is unaffected if standard errors are clustered at the country-resource level instead of at the year-resource level (see Table S2). If the human capital index and GDP from the Penn World Tables as are added as controls in our 3-way panel, the results are broadly the same even though the point estimates of the effect of openness on discoveries are somewhat smaller (see Table S3). To allow for a more even comparison between countries (even though the fixed effects included in all regressions already address issues such as different sizes of countries), we also estimated the regressions with discoveries per capita as the dependent variable (see Table S4). The point estimate on openness remains positive and highly significant. With discoveries per capita as the dependent variable the estimates are in fact particularly robust and are both qualitatively and quantitatively unchanged when additional control variables (such as GDP per capita) are added (Table S5). Instead of using a count variable (the number of discoveries), we have also used a simple dummy variable as the dependent variable, again confirming the significance of market orientation for natural resource discoveries (see Table S6). Our core empirical results also hold when excluding any of the individual country groups (see Table S7) or excluding any particular decade (see Table S8). Across all regressions the coefficient on market orientation remains positive and significant. This is important since it suggests that our results are not driven by one region or one specific time period only. Given our rich data set for discoveries, we can estimate equation (I) individually for different natural resources ∈ , , , , , , , . This list covers over 90% of all discoveries for our period of analysis, 1950-2004, corresponding to 2171 out of a total of 2392 discoveries. The estimates for the impact of openness on discoveries     24  range from virtually no impact for uranium to a (statistically significant) increase of close to 3% for nickel and oil and an increase of over 5% for silver, albeit that the latter is not precisely estimated (see Table S9).26 The results indicate that investments (and thus discoveries) of different natural resources are potentially differentially sensitive to a country’s institutional environment. Table S10 employs the zero-augmented Poisson estimator (ZIP) as an alternative estimator. This allow us to directly model the fact that our dependent variable is count data with a very large fraction of zeros. In particular, ZIP fits a logit model to predict the excess zeros and separately models the count data by fitting a Poisson model.27 To predict excess zeros we use the lag of the previous stock of discoveries.28   The coefficient for the effect of openness on discoveries is positive and significant. The interpretation of the point estimate is now different. For example, opening up increases the expected log count of discoveries by 0.486 (from the . coefficient in column 4 of Table S10) so that discoveries increase by a factor of ~1.7 after a country opens up. This is somewhat larger than the quantitative effect obtained from our OLS or 2SLS estimates in Tables 2 and 4. As an additional exercise, we collapsed our three-way panel (country, year, resource) to a two- way panel (country, year) since the obtained regression coefficients are particularly easy to interpret and can be immediately compared to the preliminary event-study analysis conducted in section III. Opening of the economy increases discoveries by 0.47 per year and country (column 1 of Table S11). Recall that a cursory look at the data as shown in Figure 4 suggested an increase of 0.16, severely underestimating the positive impact.                                                              26 These numbers are obtained by taking the estimated coefficients for each resource k and dividing them by the average yearly number of discoveries of resource .   jtk 27 Let prob( yitk )  e  jtk N / yitk j !, where itk yitk denotes the number of resource discoveries in country i at time t and for a specific resource k. (Silva and Tenreyro, 2006). yitk is assumed to follow a Poisson distribution as follows. Specifying  jtk as a linear function of explanatory variables X jtk , gives the expectation of y jtk conditional on X jtk : L jtk  E   y jtk X jtk  e X jtk . B jk , where X jtk is the row vector of explanatory variables. Taking logs gives the model log E yitk X itk  B jk X itk .   28 The drawbacks of ZIP are that we do not employ an IV strategy and for computational reasons we cannot include a fixed-effects structure which is as rich as in the least squares estimations.     25  Last, one could argue that there are important differences in the role market orientation plays in fostering mineral versus hydrocarbon discoveries. In particular, minerals might be seen as more appropriable than hydrocarbons because mining output does not move through pipelines and takes place exclusively onshore. Instead our results suggest that in fact the effect of market orientation is driven as much by hydrocarbon as mineral discoveries (columns 2 and 3 of Table S11).29 D.   A look at what the SW Openness Indicator might be proxying?  As explained in the data section, market orientation is defined as the absence of the following features: (i) average tariff rate on imports above 40%; (ii) non-tariff barriers covering more than 40% of imports; (iii) a socialist economy; (iv) the state holds a monopoly of the major exports; and (v) a black market premium above 20%. In essence, countries defined as market oriented thus allow for relatively free trade and do not exercise state control over the main exports. One might see it as quite intuitive that such a situation should encourage more exploration investment and thus discoveries than the contrary one. One might also wonder, however, what more detailed features of the economy market orientation might be proxying for. To get a sense of the effects of unbundling market orientation, Table 7 shows partial correlations between the SW/Wacziarg indicator and various International Country Risk Guide (ICRG) Political Risk Rating sub-indices which are available since 1984. Table 7: Partial correlations of SW/Wacziarg Openness Indicator with ICRG Indices  Partial Semipartial Partial Semipartial Significance Variable Corr. Corr. Corr.^2 Corr.^2 Value ICRG Investment Profile 0.2379 0.2061 0.0566 0.0425 0 ICRG Corruption 0.0686 0.0579 0.0047 0.0034 0 ICRG Law and Order 0.0554 0.0467 0.0031 0.0022 0 ICRG Government Stability 0.0859 0.0726 0.0074 0.0053 0 ICRG Internal Stability 0.0837 0.0707 0.007 0.005 0 ICRG Bureaucratic Quality 0.0531 0.0448 0.0028 0.002 0 Note: The table reports partial correlation coefficients between the SW/Wacziarg Openness indicator and ICRG political risk sub-indices. The overlap between the data series is 1984-2004.                                                              29 We lose some power when splitting the sample, hence the reduced significance levels.     26  The most correlated ICRG component is the investment profile index – a direct measure of how attractive it is to invest in a certain country. The index measures contract viability/expropriation, profit repatriation and payment delays. The index goes from 0-12 with a higher score indicating less risk. The average value for Latin America and Sub-Saharan Africa increased from 5 to 7.5 between 1984 and the 2000s, in line with the increase in discoveries observed in these regions. In summary, while we are not able to fully disentangle the channels which link market orientation and natural resource discoveries, it seems that the investment climate – proxied by property rights and payment security – is one of the key factors which the SW index proxies for. V. CONCLUSION We have examined the effect of changes in market orientation on proven (known) natural wealth. Consistent with the predictions of a two-region model, we presented empirical estimates based on a large panel of countries that show that increased market orientation causes a significant increase in discoveries. In a thought experiment whereby economies in Latin America and sub-Saharan Africa remained closed, they would have only achieved one quarter of the actual increase in discoveries they have experienced since the early 1990s. These results help explain the worldwide shift in the geographic distribution of natural resource discoveries, with the economic opening in Sub-Saharan Africa and Latin America contributing to a large increase in the share of worldwide discoveries in these two regions. 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Economic Reform and the Process of Global Integration, Brookings Papers on Economic Activity, 26 (1, 25th A), 1-118. Silva, J.M.C. Santos and Silvana Tenreyro (2006). The Log of Gravity, Review of Economics and Statistics, 88, 4, 641–58. Venables, Anthony J. (2016). Using Natural Resources for Development: Why Has It Proven So Difficult?, Journal of Economic Perspectives, 30(1), 161-84. Wacziarg, Romain and Karen Horn Welch (2008). Trade Liberalization and Growth: New Evidence, World Bank Economic Review, 22(2), 187-231. World Bank (2006). Where is the Wealth of Nations. The International Bank for Reconstruction and Development/The World Bank, Washington, DC. Wright, Gavin and Jesse Czelusta (2014). The Myth of the Resource Curse, Challenge, 47(2), 6-38.     30  APPENDIX A: ANALYTICAL PREDICTIONS IRCs in the North decide on exploration investment in periods 0 and 1, I 0 and I1. This gives initial reserves, S1 (I0 ), and discovery of new reserves, D(I1 )  Aln(I1 ).  At the start of period 2, reserves in the North are S2  S1 (I0 )  D(I1 )  R1, where R1 denotes current depletion. Future depletion cannot exceed remaining reserves. The resource is sold on the world market at prices p1 and p2 . This market is competitive, so IRCs take prices as given. The cost of extraction falls with remaining proven reserves: G(St )   / St with   0. IRCs can freely borrow at the given world interest rate r. Variables, cost and exploration functions for the South are denoted by an asterisk. North and South may differ in extraction costs and initial reserves. Furthermore, the South has access restrictions captured by explicit or implicit taxes T0  0 * and T1  0 on exploration investment. Global IRCs thus maximize their net worth of their * activities in the North and the South: 2 Max   (1  r )  p I t , I t* , Rt , Rt* t 1 t t  G ( S t )  Rt  (1  r ) t 1 I t 1   (A1)   (1  r )  2   pt  G ( S t )  t t 1  Rt  (1  r ) (1  Tt 1 ) I t 1 , * * * t 1 subject to R2  S2  S1(I0 )  D(I1)  R1 and R2  S2  S1 (I0 )  D (I1 )  R1 . This gives the * * * * * * * Hotelling rules governing extraction speeds for the North and South: p2  G ( S 2 )  G '( S 2 ) R2  (1  r )  p1  G ( S1 )  , (A2) p2  G * ( S 2 * )  G * '( S 2 * * ) R2  (1  r )   p1  G ( S1 )  * * . Future rents minus the marginal increase in future extraction cost from extracting an extra unit today must equal current rents plus interest. Maximizing net worth also requires that the marginal rent from discovery investment equals the cost including taxes in each region:  p1 G S1(I0 )   p1  G  S1 (I0 )  D '(I1 )  1  T1 .  D'(I1) 1 and  (A3)  * * *  * * *     31  This gives discoveries D  A ln  ( p1   / S1 ) A and D *  A* ln  ( p1   * / S1* ) A* / (1  T1* )  . Discoveries thus rise with the global resource price. The North has more discoveries if geological conditions are better ( A  A* ), it has a higher or more easily accessible stock of reserves that depresses extraction costs (    or S1  S1 ), and there are less taxes and easier * * access for IRCs in the North ( T1  0 ). Global IRCs relocate exploration across the globe so * that total marginal cost of extracting and discovering a unit of resource is equalized across the I1  * * * I1  globe as can be seen from the global arbitrage condition p1    *  (1  T1 ) * . S1 A S1 A Maximizing net worth also gives the efficiency conditions that yield initial reserves and exploration in much the same way as discoveries follow from (A3):  p1  G  S1 ( I 0 )   G '  S1 ( I 0 )  R1    S1 '( I 0 )  1  r and (A3)  p1  G*  S1* ( I 0 )   G '  S1* ( I 0 )  R1*   S1 '( I 0 )  (1  r )(1  T0 ). * * * * *  The main difference is that initial exploration investment benefits from making future extraction cheaper by ensuring higher proved reserves. We thus get the following comparative statics results for discoveries, exploration investment and reserves:           (A4) D  D ( p1 , R1 , A,  ) and D*  D* ( p1 , R1* , T0* , T1* , A* ,  * ),           (A5) I 0  I 0 ( p1 , R1 ), S1  S1 ( p1 , R1 ), I 0 *  I0 * ( p1 , R1* , T0* ), S1*  S1* ( p1 , R1* , T0* ). where the pluses and minuses indicate the signs of the partial derivatives. Finally, to explain world resource prices one needs to introduce global resource demand. Let world demand for oil in period t be iso-elastic and given by t pt , t  1,2, where   0 is the  price elasticity of demand and t  0 an exogenous shift to oil demand in period t. Market equilibrium on the world oil markets requires (A6) R1  R1*  1 p1 and R2  R2*  2 p2 . Using initial exploration and discoveries as given in (4), the depletion equations become R1  R2  S1 ( p1 , R1 )  D ( p1 , R1 , A,  ) and (A7) R  R  S1*  S1* ( p1 , R1* , T0* )  D * ( p1 , R1* , T0* , T1* , A* ,  * ). * 1 * 2     32  The extraction rates follow from the Hotelling rules (A2) or 1 p1  G  S1 ( p1 , R1 )    p2  G(R2 )  G '(R2 )R2  and 1 r (A8) 1 p1  G* (S1* )    p2  G* ( R2 * )  G* '( R2 * * ) R2  . 1 r  * * Equations (A6)-(A8) can be solved for extraction rate and prices R1, R1 , R2 , R2 , p1, p2 and   thus also for initial and future oil discoveries S1, S1 , D, D * *  in terms of the ease of access for  * * IOCs in the South T0 ,T1 , the extraction cost parameters and geological conditions in the North and the South  ,   and  A, A  , and the global oil demand shocks  ,   . One * * 1 2 could also extend the analysis to allow for discoveries to depend on how much has been explored initially and thereby on geological conditions. One can capture this by making A a * function of I 0 and A* a function of I 0 , but our main conclusions regarding the shifting frontier of natural resources will not be materially affected.     33  APPENDIX B: DATA Table B.I: Data Definition and Sources Variable Source Number of natural resource discoveries per year, country  Horn (2014), MinEx (2014) and natural resource Sachs and Warner (1995), Wacziarg and  Sachs and Warner Openness Indicator Welch (2008)  Rystad (2014) and SNL Metals and  Exploration spending Minerals (2014) Commodities prices [We use the longest available series, taken either from  IMF, Primary Commodity Price System;  UNCTAD, Datastream, Bloomberg or the IMF, depending on  Thomson Reuters Datastream,  the natural resource. UNCTAD is used for Manganese,  Bloomberg, L.P.; and UNCTADstat. Tungsten and Phosphate.] Population Summers and Heston Real GDP and GDP growth Summers and Heston Human Capital Index Summers and Heston Geographic distance between countries CEPII International Country Risk Guide  Political Risk Rating (2015) Polity 2 Score Marshall and Jaggers (2009) Natural Resource Discovery Data Definition of minimum size of included discoveries by resource: Oil and Gas (> 500 million barrels of ultimately recoverable oil equivalent). Gold (>1 Moz Au-equivalent). Silver (>50 Moz Ag). PGE (>1 Moz Au-equivalent). Copper (>1 Mt Cu-equivalent). Nickel (>100 kt Ni). Zinc (> 2.5 Mt Zn+Pb). Lead (> 2.5 Mt Zn+Pb). Cobalt (>1 Mt Cu-equivalent). Molybdenum (>1 Mt Cu-equivalent). Tungsten (>1 Mt Cu-equivalent). Uranium Oxide (>25 kt U3O8).   Overall, discoveries of Gold, Oil, Natural Gas, Copper, Nickel, Uranium, Zinc and Silver account for over 90 percent of all discoveries (followed by Diamonds and Molybdenum which account for 1-2 percent each).     34  Table B. II. Summary statistics Mean Std. Dev. 10% 50% 90% Observations 1950‐1959 Number of Discoveries (by country, year, natural resource) 0.006 0.095 0 0 0             43,860 SW/Wacziarg Opennes 0.292 0.454 0 0 1             20,026 Real GDP Growth  0.049 0.059 ‐0.015 0.046 0.137             15,912 Growth in Natural Resource Price (by year and resource) ‐0.017 0.14 ‐0.21 ‐0.013 0.186 1,548                 1960‐1969 Number of Discoveries (by country, year, natural resource) 0.011 0.137 0 0 0             43,860 SW/Wacziarg Opennes 0.3 0.458 0 0 1             28,424 Real GDP Growth  0.534 0.061 ‐0.007 0.05 0.145             26,146 Growth in Natural Resource Price (by year and resource) 0.029 0.158 ‐0.137 0 0.406             11,997 1970‐1979 Number of Discoveries (by country, year, natural resource) 0.116 0.147 0 0 0             43,860 SW/Wacziarg Opennes 0.313 0.463 0 0 1             30,226 Real GDP Growth  0.051 0.075 ‐0.025 0.048 0.127             28,390 Growth in Natural Resource Price (by year and resource) 0.143 0.321 ‐0.179 0.087 0.687             14,190 1980‐1989 Number of Discoveries (by country, year, natural resource) 0.01 0.161 0 0 0             43,860 SW/Wacziarg Opennes 0.36 0.48 0 0 1             30,940 Real GDP Growth  0.281 0.072 ‐0.045 0.029 0.104             28,560 Growth in Natural Resource Price (by year and resource) ‐0.029 0.277 ‐0.284 ‐0.032 0.238 1,612                 1990‐1999 Number of Discoveries (by country, year, natural resource) 0.017 0.135 0 0 0             43,860 SW/Wacziarg Opennes 0.671 0.469 0 1 1             34,204 Real GDP Growth  0.292 0.089 ‐0.065 0.369 0.101             32,572 Growth in Natural Resource Price (by year and resource) ‐0.173 0.215 ‐0.253 ‐0.276 0.244             21,543 2000‐2009 Number of Discoveries (by country, year, natural resource) 0.01 0.132 0 0 0             43,860 SW/Wacziarg Opennes 0.808 0.394 0 1 1             17,340 Real GDP Growth  0.05 0.083 ‐0.022 0.039 0.12             32,980 Growth in Natural Resource Price (by year and resource) 0.102 0.343 ‐0.299 0.076 0.479             27,219 Natural resource prices The following natural resources are included in the dataset (numbers in bracket indicate first year for which we have price data if that year is later than 1960): Antimony (2005), Boron (2004), Chromium (1990), Copper, Diamonds (2005), Fluorite (no price data), Natural Gas (1985), Gold, Graphite (no price data), Lead, Lithium (1997), Magnesium (2003), Manganese, Mineral sands (no price data), Molybdenum (2012), Nickel, Niobium (2013), Oil, Palladium (1992), PGE (no price data), Phosphate, Platinum (1992), Potash (no price data), Rare earths (no price data), Silver (1968), Soda ash (2007), Tantalum (2009), Tellurium (2013), Tin, Tungsten, Uranium (1980), Vanadium (1987), Zinc, Zircon (1997).     35  Countries included in the dataset The following countries are included in our sample: Afghanistan, Albania, Algeria, Angola, Argentina, Armenia, Australia, Austria, Azerbaijan, Bahrain, Bangladesh, Bolivia, Botswana, Brazil, Brunei Darussalam, Bulgaria, Burkina Faso, Burundi, Cyprus, Cambodia, Cameroon, Canada, Central African Republic, Chile, China, Colombia, Republic of Congo, Democratic Republic of the Congo, Costa Rica, Czech Republic, Denmark, Dominican Republic, Ecuador, Egypt, El Salvador, Equatorial Guinea, Eritrea, Ethiopia, Fiji, Finland, France, Gabon, Germany, Ghana, Greece, Guatemala, Guinea, Guyana, Honduras, Hungary, Israel, India, Indonesia, Iran, Iraq, Ireland, Italy, Côte d'Ivoire, Japan, Jordan, Kazakhstan, Korea (South), Kuwait, Kyrgyz Republic, Lao P.D.R., Lesotho, Liberia, Libya, FYR Macedonia, Madagascar, Malaysia, Mali, Mauritania, Mexico, Mongolia, Morocco, Mozambique, Myanmar, Namibia, Netherlands, New Zealand, Nicaragua, Niger, Nigeria, Norway, Oman, Pakistan, Panama, Papua New Guinea, Peru, Philippines, Poland, Portugal, Qatar, Romania, Russia, Saudi Arabia, Senegal, Serbia, Sierra Leone, Slovak Republic, Solomon Islands, South Africa, Spain, Sri Lanka, Sudan, Suriname, Sweden, Syria, Tajikistan, Tanzania, Thailand, Togo, Tonga, Trinidad and Tobago, Tunisia, Turkey, Turkmenistan, United Arab Emirates, Ukraine, United Kingdom, United States, Uruguay, Uzbekistan, Venezuela, Vietnam, Yemen, Zambia, Zimbabwe. Date of opening and closing according to SW index The following countries opened their economies between 1950 and 2004 (numbers in bracket indicate year of opening): Canada (1952), Costa Rica (1952), Morocco (1956), Bolivia (1956), Germany (1959), Italy (1959), France (1959), Spain (1959), Denmark (1959), Greece (1959), Netherlands (1959), Finland (1960), Sweden (1960), Austria (1960), Australia (1964), Japan (1964), Jordan (1965), Ireland (1966), South Korea (1968), Indonesia (1970), Chile (1976), Sri Lanka (1977), Botswana (1979), Morocco (1984), Bolivia (1985), Israel (1985), Ghana (1985), Guinea (1986), Costa Rica (1986), Mexico (1986), New Zealand (1986), Colombia (1986), Philippines (1988), Guatemala (1988), Mali (1988), Venezuela (1989), Tunisia (1989), El Salvador (1989), Turkey (1989), Poland (1990), Uruguay (1990), Hungary (1990), Brazil (1991), South Africa (1991), Sri Lanka (1991), Nicaragua (1991), Honduras (1991), Ecuador (1991), Bulgaria (1991), Argentina (1991), Peru (1991), Trinidad and Tobago (1992), Dominican Republic (1992), Romania (1992), Albania (1992), Cameroon (1993), Zambia (1993), Ivory Coast (1994), Niger (1994), Kyrgyzstan (1994), Macedonia (1994), Armenia (1995), Mauritania (1995), Tanzania (1995), Egypt (1995), Mozambique (1995), Azerbaijan (1995), Venezuela (1996), Bangladesh (1996), Tajikistan     36  (1996), Madagascar (1996), Ethiopia (1997), India (1997), Burkina Faso (1998), Papua New Guinea (1999), Congo (DRC) (2001), Sierra Leone (2001), Kazakhstan (2001), Pakistan (2001), Nigeria (2002), Congo (Brazzaville) (2002), Ethiopia (2004) The following countries closed their economies between 1950 and 2004 (numbers in bracket indicate year of closing): Turkey (1954), Sri Lanka (1957), Venezuela (1960), Nicaragua (1961), Guatemala (1962), El Salvador (1962), Costa Rica (1962), Honduras (1962), Morocco (1965), Syria (1966), Peru (1968), Bolivia (1980), Ecuador (1983), Sri Lanka (1984), Venezuela (1994), Colombia (2000), Ethiopia (2001), Venezuela (2003)     37  SUPPLEMENTARY APPENDIX Table S.1 adds neighbours’ distance-weighted discoveries as an additional control and finds that the point estimate on market orientation is virtually unchanged relative to the baseline regression whilst the estimate for the impact of neighbours’ discoveries is always positive but not always significant. Table S1: The Impact of Liberalization on Resource Discoveries (2SLS) – Controlling for neighbours’ discoveries (1) (2) (3) (4) (5) Number of  Number of  Number of  Number of  Number of  VARIABLES Discoveries Discoveries Discoveries Discoveries Discoveries SW/Wacziarg Openness, lagged 0.0191*** 0.0708*** 0.0713*** 0.0603*** 0.0122*** (0.00438) (0.0165) (0.0156) (0.0153) (0.00416) Distance‐Weighted Discoveries Neighbors, lagged 0.00648** 0.0108 0.0108 0.00853 0.00651** (0.00262) (0.00661) (0.00660) (0.00635) (0.00273) (log) Price, lagged 0.0146*** 0.0149*** 0.0103*** (0.00461) (0.00366) (0.00314) Stock of Discoveries, lagged ‐0.000568 0.0257*** 0.0382*** (0.00429) (0.00832) (0.00282) Stock of Discoveries squared, lagged ‐0.000442*** ‐0.000284*** (0.000135) (4.44e‐05) Country FE / / / / YES Year FE YES YES YES YES / Natural Resource FE / / / / / Country by Natural Resource FE YES YES YES YES / Year by Natural Resource FE / / / / YES Observations 157,284 57,976 57,976 57,976 157,284 R‐squared 0.280 0.323 0.323 0.336 0.228 Estimation 2SLS 2SLS 2SLS 2SLS 2SLS Year by  Year by  Year by  Year by  Year by  Error Clustering Natural  Natural  Natural  Natural  Natural  Resource Resource Resource Resource Resource Note: The table reports the second stage of a 2SLS estimation regressing the number of resource discoveries by country, year and resource on countries’ SW/Wacziarg openness indicator as well as a number of controls. Distance-weighted average openness and distance-weighted growth of closed economies are used as excluded instruments for the SW/Wacziarg Openness indicator. The number of observations in columns (1) and (5) are larger than in the other columns because columns (2) – (4) include resource prices as a control and price data are not available for certain years and resources. Column (5) uses country as well as year-resource fixed effects instead of the year and country-resource fixed effects employed in columns (1) – (4). *** denotes significance at the 1% level, ** at the 5% level, and * at the 10% level.     38  Table S2 clusters standard errors at the country-resource level instead of at the year-resource level. It turns out that the significance of our core results in table 3 is unaffected. Table S2: The Impact of Liberalization on Resource Discoveries (2SLS) – Clustering at the country-by-natural resource level (1) (2) (3) (4) (5) Number of  Number of  Number of  Number of  Number of  VARIABLES Discoveries Discoveries Discoveries Discoveries Discoveries SW/Wacziarg Openness, lagged 0.0209** 0.0756*** 0.0760*** 0.0641*** 0.0140** (0.00880) (0.0240) (0.0240) (0.0204) (0.00700) (log) Price, lagged 0.0146** 0.0149*** 0.0103** (0.00678) (0.00532) (0.00448) Stock of Discoveries, lagged ‐0.000540 0.0257* 0.0382*** (0.00500) (0.0141) (0.00500) Stock of Discoveries squared, lagged ‐0.000441** ‐0.000284*** (0.000175) (8.24e‐05) Country FE / / / / YES Year FE YES YES YES YES / Natural Resource FE / / / / / Country by Natural Resource FE YES YES YES YES / Year by Natural Resource FE / / / / YES Observations 157,284 57,976 57,976 57,976 157,284 R‐squared 0.279 0.323 0.323 0.335 0.227 Estimation 2SLS 2SLS 2SLS 2SLS 2SLS Country by  Country by  Country by  Country by  Country by  Error Clustering Natural  Natural  Natural  Natural  Natural  Resource Resource Resource Resource Resource Note: The table reports the second stage of a 2SLS estimation regressing the number of resource discoveries by country, year and resource on countries’ SW/Wacziarg openness indicator as well as a number of controls. Distance-weighted average openness and distance weighted-growth of closed economies are used as excluded instruments for the SW/Wacziarg Openness indicator. The number of observations in columns (1) and (5) are larger than in the other columns because columns (2) – (4) include resource prices as a control and price data are not available for certain years and resources. Column (5) uses country as well as year-resource fixed effects instead of the year and country-resource fixed effects employed in columns (1) – (4). *** denotes significance at the 1% level, ** at the 5% level, and * at the 10% level.     39  Table S3 adds the human capital index and GDP from the Penn World Tables as controls. The results are broadly the same, but the point estimates of the effect of openness on discoveries are somewhat smaller. Table S3: The Impact of Liberalization on Resource Discoveries (2SLS) – Additional Controls (1) (2) (3) (4) Number of  Number of  Number of  Number of  VARIABLES Discoveries Discoveries Discoveries Discoveries SW/Wacziarg Openness, lagged 0.00807** 0.00818** 0.00852** 0.00639* (0.00364) (0.00370) (0.00368) (0.00368) Stock of Discoveries, lagged 0.0357*** 0.0360*** 0.0360*** 0.0361*** (0.00296) (0.00299) (0.00299) (0.00300) Stock of Discoveries squared, lagged ‐0.000260*** ‐0.000257*** ‐0.000258*** ‐0.000257*** (4.51e‐05) (4.66e‐05) (4.66e‐05) (4.67e‐05) Human capital index, lagged 0.00104 0.00155 0.00160 0.00648** (0.00294) (0.00297) (0.00297) (0.00319) Revised Combined Polity Score , lagged 0.000198** 0.000159* 7.57e‐05 (8.21e‐05) (8.20e‐05) (8.13e‐05) Population (in millions), lagged ‐4.64e‐05*** 7.43e‐06 (1.78e‐05) (1.72e‐05) Expenditure‐side real GDP  (in mil. 2011US$), lagged ‐1.09e‐08*** (1.96e‐09) Country FE YES YES YES YES Year FE / / / / Natural Resource FE / / / / Country by Natural Resource FE / / / / Year by Natural Resource FE YES YES YES YES Observations 146,234 139,468 139,468 139,468 R‐squared 0.214 0.218 0.218 0.219 Estimation 2SLS 2SLS 2SLS 2SLS Year by  Year by  Year by  Year by  Error Clustering Natural  Natural  Natural  Natural  Resource Resource Resource Resource Note: The table reports the second stage of a 2SLS estimation regressing the number of resource discoveries by country, year and resource on countries’ SW/Wacziarg openness indicator as well as a number of controls. Distance-weighted average openness and distance-weighted growth of closed economies are used as excluded instruments for the SW/Wacziarg Openness indicator. *** denotes significance at the 1% level, ** at the 5% level, and * at the 10% level.     40  Table S4 tests the core results of table 3 by using discoveries per capita as the dependent variable. The point estimate on openness is again positive and highly significant. Table S4: The Impact of Liberalization on Resource Discoveries (2SLS) – Discoveries per Capita as Dependent Variable I (1) (2) (3) (4) (5) Number of  Number of  Number of  Number of  Number of  Discoveries  Discoveries  Discoveries  Discoveries  Discoveries  VARIABLES per Capita per Capita per Capita per Capita per Capita SW/Wacziarg Openness, lagged 0.00107*** 0.00407*** 0.00421*** 0.00414*** 0.000860*** (0.000313) (0.00108) (0.00105) (0.00106) (0.000320) (log) Price, lagged 0.00106*** 0.00113*** 0.00110*** (0.000392) (0.000380) (0.000378) Stock of Discoveries, lagged ‐0.000140 2.73e‐05 0.00126*** (0.000156) (0.000404) (0.000111) Stock of Discoveries squared, lagged ‐2.83e‐06 ‐1.23e‐05*** (6.11e‐06) (1.57e‐06) Country FE / / / / YES Year FE YES YES YES YES / Natural Resource FE / / / / / Country by Natural Resource FE YES YES YES YES / Year by Natural Resource FE / / / / YES Observations 150,484 56,369 56,369 56,369 150,484 R‐squared 0.093 0.117 0.117 0.117 0.039 Estimation 2SLS 2SLS 2SLS 2SLS 2SLS Year by  Year by  Year by  Year by  Year by  Error Clustering Natural  Natural  Natural  Natural  Natural  Resource Resource Resource Resource Resource Note: The table reports the second stage of a 2SLS estimation regressing the number of resource discoveries per capita by country, year and resource on countries’ SW/Wacziarg openness indicator as well as a number of controls. Distance-weighted average openness and distance-weighted growth of closed economies are used as excluded instruments for the SW/Wacziarg Openness indicator. The number of observations in columns (1) and (5) are larger than in the other columns because columns (2) – (4) include resource prices as a control and price data are not available for certain years and resources. Column (5) uses country as well as year-resource fixed effects instead of the year and country-resource fixed effects employed in columns (1) – (4). *** denotes significance at the 1% level, ** at the 5% level, and * at the 10% level.     41  Table S5 also uses discoveries per capita as dependent variable and adds more control variables, mirroring table S4. Again, the point estimate on openness is positive and highly significant. Table S5: The Impact of Liberalization on Resource Discoveries (2SLS) – Discoveries per Capita as Dependent Variable II (1) (2) (3) (4) Number of  Number of  Number of  Number of  Discoveries  Discoveries  Discoveries  Discoveries  VARIABLES per Capita per Capita per Capita per Capita SW/Wacziarg Openness, lagged 0.000862*** 0.000951*** 0.000960*** 0.000926*** (0.000319) (0.000324) (0.000325) (0.000322) Stock of Discoveries, lagged 0.00128*** 0.00127*** 0.00127*** 0.00127*** (0.000113) (0.000114) (0.000114) (0.000114) Stock of Discoveries squared, lagged ‐1.25e‐05*** ‐1.21e‐05*** ‐1.21e‐05*** ‐1.21e‐05*** (1.58e‐06) (1.60e‐06) (1.60e‐06) (1.61e‐06) Human capital index, lagged ‐0.00148** ‐0.00144** ‐0.00144** ‐0.00137** (0.000662) (0.000675) (0.000676) (0.000694) Revised Combined Polity Score , lagged 1.69e‐05 1.59e‐05 1.47e‐05 (1.09e‐05) (1.11e‐05) (1.13e‐05) Population (in millions), lagged ‐1.27e‐06* ‐4.79e‐07 (6.50e‐07) (3.92e‐07) Expenditure‐side real GDP  (in mil. 2011US$), lagged ‐1.59e‐10** (6.50e‐11) Country FE YES YES YES YES Year FE / / / / Natural Resource FE / / / / Country by Natural Resource FE / / / / Year by Natural Resource FE YES YES YES YES Observations 146,234 139,468 139,468 139,468 R‐squared 0.039 0.040 0.040 0.040 Estimation 2SLS 2SLS 2SLS 2SLS Year by  Year by  Year by  Year by  Error Clustering Natural  Natural  Natural  Natural  Resource Resource Resource Resource Note: The table reports the second stage of a 2SLS estimation regressing the number of resource discoveries per capita by country, year and resource on countries’ SW/Wacziarg openness indicator as well as a number of controls. Distance-weighted average openness and distance-weighted growth of closed economies are used as excluded instruments for the SW/Wacziarg Openness indicator. *** denotes significance at the 1% level, ** at the 5% level, and * at the 10% level.     42  Table S6 tests the robustness of our core results using a simple dummy variable for discoveries. The effect of market orientation on natural resource discoveries remains positive and highly significant. Table S6: The Impact of Liberalization on Resource Discoveries (2SLS) - Discovery Dummy as Dependent Variable (1) (2) (3) (4) (5) Discovery  Discovery  Discovery  Discovery  Discovery  VARIABLES Dummy Dummy Dummy Dummy Dummy SW/Wacziarg Openness, lagged 0.0118*** 0.0446*** 0.0446*** 0.0441*** 0.00781*** (0.00270) (0.0101) (0.00987) (0.00986) (0.00262) (log) Price, lagged 0.00788*** 0.00788*** 0.00771*** (0.00230) (0.00195) (0.00193) Stock of Discoveries, lagged 4.84e‐06 0.000991 0.0225*** (0.00135) (0.00258) (0.00113) Stock of Discoveries squared, lagged ‐1.66e‐05 ‐0.000205*** (3.66e‐05) (2.06e‐05) Country FE / / / / YES Year FE YES YES YES YES / Natural Resource FE / / / / / Country by Natural Resource FE YES YES YES YES / Year by Natural Resource FE / / / / YES Observations 157,284 57,976 57,976 57,976 157,284 R‐squared 0.245 0.280 0.280 0.281 0.199 Estimation 2SLS 2SLS 2SLS 2SLS 2SLS Year by  Year by  Year by  Year by  Year by  Error Clustering Natural  Natural  Natural  Natural  Natural  Resource Resource Resource Resource Resource Note: The table reports the second stage of a 2SLS estimation regressing a dummy variable which takes the value one if there was at least one resource discovery in a given country, year and resource type, on countries’ SW/Wacziarg openness indicator as well as a number of controls. Distance-weighted average openness and distance-weighted growth of closed economies are used as excluded instruments for the SW/Wacziarg Openness indicator. The number of observations in columns (1) and (5) are larger than in the other columns because columns (2) – (4) include resource prices as a control and price data are not available for certain years and resources. Column (5) uses country as well as year-resource fixed effects instead of the year and country-resource fixed effects employed in columns (1) – (4). *** denotes significance at the 1% level, ** at the 5% level, and * at the 10% level.     43  Table S7 shows how remarkably stable our results are to excluding individual country groups as whatever group is removed from the sample the coefficient on openness remains positive and significant. Table S7: The Impact of Liberalization on Resource Discoveries (2SLS) – Using subsamples of countries (1) (2) (3) (4) (5) (6) (7) (8) Number of  Number of  Number of  Number of  Number of  Number of  Number of  Number of  VARIABLES Discoveries Discoveries Discoveries Discoveries Discoveries Discoveries Discoveries Discoveries SW/Wacziarg Openness, lagged 0.0140*** 0.00816** 0.0196** 0.0142*** 0.0406*** 0.0137*** 0.0145*** 0.0107*** (0.00410) (0.00341) (0.00786) (0.00422) (0.0115) (0.00391) (0.00405) (0.00404) Stock of Discoveries, lagged 0.0388*** 0.0354*** 0.0395*** 0.0382*** 0.0359*** 0.0405*** 0.0383*** 0.0398*** (0.00289) (0.00303) (0.00262) (0.00282) (0.00312) (0.00300) (0.00283) (0.00314) Stock of Discoveries squared, lagged ‐0.000294*** ‐0.000172*** ‐0.000399*** ‐0.000284*** ‐0.000249*** ‐0.000312*** ‐0.000286*** ‐0.000305*** (4.43e‐05) (5.56e‐05) (4.97e‐05) (4.44e‐05) (4.79e‐05) (4.61e‐05) (4.46e‐05) (4.77e‐05) Country FE YES YES YES YES YES YES YES YES Year FE / / / / / / / / Natural Resource FE / / / / / / / / Country by Natural Resource FE / / / / / / / / Year by Natural Resource FE YES YES YES YES YES YES YES YES Observations 147,628 143,990 115,090 154,292 128,452 142,154 150,722 118,660 R‐squared 0.229 0.228 0.208 0.228 0.224 0.236 0.230 0.243 Estimation 2SLS 2SLS 2SLS 2SLS 2SLS 2SLS 2SLS 2SLS Year by  Year by  Year by  Year by  Year by  Year by  Year by  Year by  Error Clustering Natural  Natural  Natural  Natural  Natural  Natural  Natural  Natural  Resource Resource Resource Resource Resource Resource Resource Resource High‐income  High‐income  Latin America  Middle East  East Asia and  Europe and  Sub‐Saharan  Excluded Region OECD  non‐OECD  and the  and North  South Asia Pacific Central Asia Africa Countries Countries Caribbean Africa Note: The table reports the second stage of a 2SLS estimation regressing the number of resource discoveries by country, year and resource on countries’ SW/Wacziarg openness indicator as well as a number of controls. Distance-weighted average openness and distance-weighted growth of closed economies are used as excluded instruments for the SW/Wacziarg Openness indicator. Each column excludes one specific geographic area which is specified in the “Excluded Region” row. *** denotes significance at the 1% level, ** at the 5% level, and * at the 10% level.     44  Table S8 shows that the results are robust to excluding individual time periods. Again, across all regressions the coefficient on openness remains positive and significant. Table S8: The Impact of Liberalization on Resource Discoveries (2SLS) – Using subsamples of time periods (1) (2) (3) (4) (5) (6) Number of  Number of  Number of  Number of  Number of  Number of  VARIABLES Discoveries Discoveries Discoveries Discoveries Discoveries Discoveries SW/Wacziarg Openness, lagged 0.0235*** 0.0129*** 0.0159*** 0.0163*** 0.00633* 0.0119*** (0.00672) (0.00449) (0.00456) (0.00443) (0.00357) (0.00429) Stock of Discoveries, lagged 0.0379*** 0.0349*** 0.0352*** 0.0369*** 0.0418*** 0.0435*** (0.00283) (0.00290) (0.00319) (0.00240) (0.00362) (0.00330) Stock of Discoveries squared, lagged ‐0.000277*** ‐0.000232*** ‐0.000250*** ‐0.000304*** ‐0.000286*** ‐0.000349*** (4.46e‐05) (4.47e‐05) (4.74e‐05) (4.54e‐05) (5.85e‐05) (5.29e‐05) Country FE YES YES YES YES YES YES Year FE / / / / / / Natural Resource FE / / / / / / Country by Natural Resource FE / / / / / / Year by Natural Resource FE YES YES YES YES YES YES Observations 143,242 129,710 127,160 126,378 123,454 136,476 R‐squared 0.235 0.235 0.225 0.210 0.233 0.232 Estimation 2SLS 2SLS 2SLS 2SLS 2SLS 2SLS Year by  Year by  Year by  Year by  Year by  Year by  Error Clustering Natural  Natural  Natural  Natural  Natural  Natural  Resource Resource Resource Resource Resource Resource Excluded Decade 1950s 1960s 1970s 1980s 1990s 2000s Note: The table reports the second stage of a 2SLS estimation regressing the number of resource discoveries by country, year and resource on countries’ SW/Wacziarg openness indicator as well as a number of controls. Distance-weighted average openness and distance-weighted growth of closed economies are used as excluded instruments for the SW/Wacziarg Openness indicator. Each column excludes one decade which is specified in the “Excluded Decade” row. *** denotes significance at the 1% level, ** at the 5% level, and * at the 10% level.     45  Table S9 gives the results of estimating equation (I) individually for different natural resources ∈ , , , , , , , . Table S9: The Impact of Liberalization on Resource Discoveries (2SLS) – Results by natural resource (1) (2) (3) (4) (5) (6) (7) (8) Number of  Number of  Number of  Number of  Number of  Number of  Number of  Number of  VARIABLES Discoveries Discoveries Discoveries Discoveries Discoveries Discoveries Discoveries Discoveries SW/Wacziarg Openness, lagged 0.0960 0.106** 0.146*** 0.0868 0.0781** 0.00327 0.0237 0.0311 (0.0783) (0.0403) (0.0355) (0.0545) (0.0365) (0.0139) (0.0210) (0.0194) Stock of Discoveries, lagged 0.0702*** 0.00278 0.0408*** 0.0196* ‐0.00771 ‐0.0731*** ‐0.0281** ‐0.0189 (0.0143) (0.00719) (0.0111) (0.0110) (0.0130) (0.0219) (0.0129) (0.0199) ldiscoveries_stock_squar ‐0.000822*** ‐0.000608*** ‐0.000696*** ‐0.000541** 0.000346 0.00435** ‐0.000178 0.00982*** (0.000203) (0.000184) (0.000155) (0.000257) (0.000470) (0.00189) (0.000732) (0.00263) Country FE YES YES YES YES YES YES YES YES Year FE YES YES YES YES YES YES YES YES Natural Resource FE / / / / / / / / Country by Natural Resource FE / / / / / / / / Year by Natural Resource FE / / / / / / / / Observations 4,626 4,626 4,626 4,626 4,626 4,626 4,626 4,626 R‐squared 0.415 0.286 0.328 0.274 0.188 0.160 0.158 0.276 Estimation 2SLS 2SLS 2SLS 2SLS 2SLS 2SLS 2SLS 2SLS Error Clustering Year Year Year Year Year Year Year Year Natural Resource Gold Oil Gas Copper Nickel Uranium Zinc Silver Note: The table reports the second stage of a 2SLS estimation regressing the number of resource discoveries by country and year on countries’ SW/Wacziarg openness indicator as well as a number of controls. Distance-weighted average openness and distance-weighted growth of closed economies are used as excluded instruments for the SW/Wacziarg Openness indicator. Each column excludes shows results for one main type of natural resource. *** denotes significance at the 1% level, ** at the 5% level, and * at the 10% level.     46  Table S10 present estimates analogous to those in table 3 with the zero-augmented Poisson estimator (ZIP) to allow for count data with a very large fraction of zeros. To predict excess zeros we use the lag of the previous stock of discoveries. The coefficient for the effect of openness on discoveries is again positive and significant in all specifications. Note that the coefficient is not directly comparable to the OLS/2SLS ones given the Poisson regression. Table S10: The Impact of Liberalization on Resource Discoveries (Zero-Inflated Poisson) (1) (2) (3) (4) Number of  Number of  Number of  Number of  VARIABLES Discoveries Discoveries Discoveries Discoveries SW/Wacziarg Openness, lagged 0.486*** 0.418*** 0.602*** 0.525*** (0.127) (0.155) (0.175) (0.165) (log) Price, lagged 0.375** 0.301** 0.267* (0.153) (0.151) (0.144) Stock of Discoveries, lagged 0.0166*** 0.0589*** (0.00425) (0.0124) Stock of Discoveries squared, lagged ‐0.000550*** (0.000154) Country FE YES YES YES YES Year FE YES YES YES YES Natural Resource FE YES YES YES YES Country by Natural Resource FE / / / / Year by Natural Resource FE / / / / Observations 161,160 58,078 58,078 58,078 Estimation ZIP ZIP ZIP ZIP Year by  Year by  Year by  Year by  Error Clustering Natural  Natural  Natural  Natural  Resource Resource Resource Resource Note: The table reports zero-inflated Poisson regressions of resource discoveries by country, year and resource on countries’ SW/Wacziarg openness indicator as well as a number of controls. The number of observations in column (1) is larger than in the other columns because columns (2) – (4) include resource prices as a control and price data are not available for certain years and resources. *** denotes significance at the 1% level, ** at the 5% level, and * at the 10% level.     47  Table S11 collapses the three-way panel (country, year, resource) estimates of table 3 to a two- way panel (country, year). Opening of the economy increases discoveries by 0.47 per year and country (column 1). Splitting the analysis between hydrocarbon and mineral deposits shows that the effect of openness on discoveries remains positive and statistically significant (columns 2 and 3). Table S11: The Impact of Liberalization on Resource Discoveries (2SLS) – Country- Year level regressions (1) (2) (3) Number of  Number of  Number of  VARIABLES Discoveries Discoveries Discoveries SW/Wacziarg Openness, lagged 0.471** 0.575** 0.588* (0.228) (0.268) (0.328) Stock of Discoveries, lagged 0.0421*** (0.0108) Stock of Discoveries squared, lagged ‐0.000205*** (4.74e‐05) Stock of Hydrocarbon Discoveries, lagged 0.0456* (0.0237) Stock of Hydrocarbon Discoveries squared, lagged ‐0.000528*** (0.000141) Stock of Mineral Discoveries, lagged 0.0370*** (0.0122) Stock of Mineral Discoveries squared, lagged ‐0.000204*** (5.14e‐05) Country FE YES YES YES Year FE YES YES YES Observations 4,626 4,626 4,626 R‐squared 0.592 0.575 0.563 Estimation 2SLS 2SLS 2SLS Error clustering Country Country Country Note: The table reports the second stage of a 2SLS estimation regressing the number of resource discoveries by country and on countries’ SW/Wacziarg openness indicator as well as a number of controls. Distance-weighted average openness and distance-weighted growth of closed economies are used as excluded instruments for the SW/Wacziarg Openness indicator. *** denotes significance at the 1% level, ** at the 5% level, and * at the 10% level.