metadata: authors: 0: author: Arezki,Rabah
1: author: van der Ploeg,Frederick
2: author: Toscani,Frederik
count: Africa,Latin America,World
docna: 0: docna: The shifting natural wealth of nations : the
            role of market orientation
docty: Policy Research Working Paper
subtopic: Global Environment,Coastal and Marine Resources,Energy and Natural Resources,Mining & Extractive Industry (Non-Energy),International Trade and Trade Rules
teratopic: Industry,International Economics and Trade,Energy,Water Resources
abstracts: cdata!: This paper explores the effect of market
            orientation on (known) natural resource wealth using a novel
            dataset of world-wide major hydrocarbon and mineral
            discoveries. Consistent with the predictions of a two-region
            model, the empirical estimates based on a large panel of
            countries show that increased market orientation causes a
            significant increase in discoveries of natural resources. 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. The results
            call into question the commonly held view that natural
            resource endowments are exogenous.
repnme: repnme: The shifting natural wealth of nations :
            the role of market orientation
display_title: The shifting natural wealth of
            nations : the role of market orientation
listing_relative_url: /research/2018/07/30251496/shifting-natural-wealth-nations-role-market-orientation
topicv3: Mining Services m1326410 1701,Natural Resources m1326446 1979,Trade m1326924 698,Mining Sector m1327113 554,Environment, Natural Resources and Blue
            Economy m1327391 566
publishtoextweb_dt: 2018-07-11T00:00:00Z
historic_topic: Industry,International Economics and Trade,Energy,Water Resources
no_of_pages: 49
author_keys: 0: author_key: 1596791
model_extractions: 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 (known) natural resource wealth using a novel dataset of world-wide major hydrocarbon and mineral discoveries. Consistent with the predictions of a two-region model, the empirical estimates based on a large panel of countries show that increased market orientation causes a significant increase in discoveries of natural resources. 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. The results call into question the commonly held view that natural resource endowments are exogenous.

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*<sup>§</sup>

## **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 ; <u>rick.vanderploeg@economics.ox.ac.uk; FToscani@imf.org.</u>

§ 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.<sup>1</sup> In contrast, the resource economics literature has emphasized that the resource base is endogenous to investment in exploration and extraction.<sup>2</sup> 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).<sup>3</sup> 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).<sup>45</sup> 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).
<!-- Start of picture text --> 250<br>200<br>150<br>100<br><!-- End of picture text --> <!-- Start of picture text --> ; in oll<br>High Income Latin America andthe Sub-Saharan Africa<br>Caribbean<br>TM 1950-59 1960-69 TM 1970-79 TM1980-89 TM1990-99 m 2000-09<br><!-- End of picture text -->
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 resourcespecific 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.<sup>7</sup> 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”.<sup>8</sup> 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 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 countryresource-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.<sup>10</sup> 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.<sup>11</sup> 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 – resourcerich countries are often shown to do worse on a number of institutional measures.
j#i) i it OPEN. OPEN, 1-1 E;4-1[Z|OPEN = 0] E[OPEN,,|..] = Q,OPENit-1 + P2Eit—-1[Z|OPEN = 0],

~,>0 Q2 <0.

i i ii i E¢-1[z|OPEN = 1] Ei4-1[zZ|OPEN = 0] E;4-1[Z|OPEN = 1]
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-byresource 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.
<!-- Start of picture text -->
- 3 A ye ‘es arn : < ahi _ are<br>Sai Ge een Ogre ee<br>Discoveries (1950-2013) ba . re<br>ara tah uO Boe al<br>® = fs - 4 ms L<br>© OilMineraland Gas Me<—- . ~ _ " ~ _ &<br>s<br><!-- End of picture text -->
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.<sup>14</sup> 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.<sup>15</sup> 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.<sup>16</sup> 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.<sup>17</sup> 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.
<!-- Start of picture text --> oo I<br>I<br>I<br>I<br>|<br>I<br>a oo 1t<br>21<br>a<br>3<br>ao<br>‘S<br>a<br>oO<br>=<br>a<br>= ty<br>I<br>I<br>= I<br>9-8-7-8 -5 4-3 2-1 0 hut le 38 4 85 UB UB U8 10<br>Years Before or After Opening of the Economy<br><!-- End of picture text -->
16 consequently we have to drop the price variable).<sup>18</sup> 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).<sup>19</sup> 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)**

|VARIABLES|(1)<br>Number of<br>Discoveries|(2)<br>Number of<br>Discoveries|(3)<br>Number of<br>Discoveries|(4)<br>Number of<br>Discoveries|(5)<br>Number of<br>Discoveries|
|---|---|---|---|---|---|
|SW/Wacziarg Openness, lagged|0.00716***<br>(0.00179)|0.0171***<br>(0.00442)|0.0169***<br>(0.00442)|0.0137***<br>(0.00392)|0.00875***<br>(0.00185)|
|(log) Price, lagged||0.0146**<br>(0.00677)|0.0150***<br>(0.00533)|0.0103**<br>(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** <br>(0.000176)|‐0.000284***<br>(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-yearresource 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)**

|VARIABLES|(1)<br>SW/Wacziarg<br>Openness|(2)<br>SW/Wacziarg<br>Openness|(3)<br>SW/Wacziarg<br>Openness|(4)<br>SW/Wacziarg<br>Openness|(5)<br>SW/Wacziarg<br>Openness|
|---|---|---|---|---|---|
|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|
|Controls||(log) Price lagged|(log) Price lagged,<br>Discoveries Stock<br>lagged|<br>(log) Price,<br>Discoveries Stock<br>lagged,<br>Discoveries Stock|<br> <br>Discoveries Stock<br>lagged,<br>Discoveries Stock<br>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|
|Error Clusterin|Year by Natural|Year by Natural|Year by Natural|Year by Natural|Year by Natural|
|g|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)**

|VARIABLES|(1)<br>Number of<br>Discoveries|(2)<br>Number of<br>Discoveries|(3)<br>Number of<br>Discoveries|(4)<br>Number of<br>Discoveries|(5)<br>Number of<br>Discoveries|
|---|---|---|---|---|---|
|SW/Wacziarg Openness, lagged|0.0209***<br>(0.00432)|0.0756***<br>(0.0161)|0.0760***<br>(0.0152)|0.0641***<br>(0.0147)|0.0140***<br>(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** <br>(0.000175)|‐0.000284***<br>(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.<sup>20</sup> 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.<sup>21</sup> 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).<sup>22</sup> 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 activity<sup>23</sup> , 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 where _yitk_ and _sitk_ are the number of discoveries and the logarithm of exploration spending in millions of constant (2010) U.S. dollars<sup>24</sup> , 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 _p_ We estimate equation (III) for _p_  1,2,3  using OLS and then test whether _H_ 0 :  0 _bh_  0. 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 o**|**f Wald Test (H0: E**|**ffect is 0)**|
|---|---|---|---|
|Dependent variable|1 Lag|2 Lags|3 Lags|
|Number of Discoveries|0|0|0|
||**Point Est**|**imate (Sum of coe**|**fficients)**|
|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)**

|VARIABLES|(1)<br>(log) Real<br>Exploration<br>Spending|(2)<br>(log) Real<br>Exploration<br>Spending|(3)<br>(log) Real<br>Exploration<br>Spending|(4)<br>(log) Real<br>Exploration<br>Spending|(5)<br>(log) Real<br>Exploration<br>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***<br>(0.193)|0.968***<br>(0.192)|0.966***<br>(0.192)||
|Stock of Discoveries, lagged|||0.0424***<br>(0.0119)|0.0641***<br>(0.0169)|0.223***<br>(0.00724)|
|Stock of Discoveries squared, lagged||||‐0.000325** <br>(0.000136)|‐0.00237***<br>(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 countrynatural 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).<sup>25</sup> 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

> 25This 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).<sup>26</sup> 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.<sup>27</sup> To predict excess zeros we use the lag of the previous stock of discoveries.<sup>28</sup> 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 �<sup>�.���</sup> ~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 twoway 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 �.

> 27 Let prob( _yitk_ )  _e_   _jtk_  _jtk Nitk_ / _yitk j_ !, where _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).<sup>29</sup>

# **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**

|Variable|Partial<br>Corr.|Semipa<br>Corr.|rtial<br>Partial<br>Corr.^2|Semi<br>Corr.^|partial<br>Significan<br>2<br>Value|ce|
|---|---|---|---|---|---|---|
|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.

Our results provide novel evidence in support of the primacy of outward market orientation by calling into question the view that natural resource endowments are an exogenous feature of an economy. And while we find that market orientation increases discoveries, they do not rule out that the resulting resource boom may lead to aggressive rent seeking and conflicts, potentially worsening welfare for citizens. Our results thus suggest that the relationship between resource endowments and institutions (in the larger sense) is complex. Further research should attempt to better “unbundle institutions” and revisit some of the key empirical findings including in the resource curse literature on the mediating role of institutions for resource endowments.