The Abundance and Poverty Paradox: Empirical Analysis on Resource Curse in Philippine Mineralized

Abstract

This paper inquires into the channels through which the socio-economic outcomes of Philippine mineralized provinces are affected. Primarily, it interrogates the association that exists between mineral abundance and poverty incidence as compounded by other factors such as immigration, agricultural production, employment, financial transfers and governance, drawing from the assumptions of the Resource Curse Thesis of Richard Auty (1993), claiming that the higher the mineral resource abundance, the lower the economic performance is of a mineral-rich country. In effect, the study will test the applicability of this thesis using microeconomic indicators as opposed to macroeconomic indices. It is the attempt of this paper to discover the relationship between mineral resource extraction and poverty in mineralized provinces using bivariate statistical treatment through multiple regression analysis. The study will conclude which among the factors can influence the socio-economic outcomes of mineralized provinces.

Keywords: resource curse, poverty, mineral resources, mining, mineralized provinces, multiple regression.

Introduction

Various policies governing mineral resource management have been enacted by the Philippine government, including “Republic Act 7942, otherwise known as the Philippine Mining Act of 1995.”The liberal approach to mineral industry hopes to attract more investors, especially foreigners, to develop the untapped mineral resources of the country.

The goal of mineral resource governance in the Philippines is simple - to provide an impetus for economic growth both on a national and local scale. But nearing the twenty-fifth year of the Philippine Mining Act in 2020, the answer still remains elusive as to whether or not, mining has done its role in developing the economic stature of Filipino communities.

According to IBON Foundation (2009), mining had the highest poverty incidence among industry groups at 48.71%. This was the highest poverty incidence since 1988, even surpassing the agriculture sector, which has historically topped poverty incidence across industries. The 2015 poverty statistics (PSA, 2015) show that regions hosting these mining activities are the poorest, next only to the Autonomous Region of Muslim Mindanao (ARMM). Poverty incidence among individuals in Caraga (Region XIII) is the second highest in the country at 39.1%. The Eastern Visayas (Region VIII) posted the third highest poverty incidence at 38.7% followed by Soccsksargen (Region XII) at 37.3%, Bicol (Region V) at 36.0% and Zamboanga Peninsula (Region IX) at 33.9 percent.

This regional poverty map reflects the minimal contribution of mining to the Gross Regional Domestic Product (GRDP) across the leading mineral regions of Mindanao. Zamboanga Peninsula (1.06%), Northern Mindanao (0.66%), Southern Mindanao (3.97%), SOCKSARGEN (0.14%) and CARAGA (6.38%) regions revealed that the mining industry has no compelling influence to regional economic development (Mines and Geosciences Bureau, 2010). These data affirm the report of the Mines and Geosciences Bureau, MGB (2016) that mining has contributed only an average of 0.91% to the Gross Domestic Product (GDP) from 2000-2009, peaking only in 2007 at 1.44%. Mining’s share of total exports has only averaged 3.5% from 2000-2009 (Chavez, 2017) making Philippines one of the least performing economies in the region (ASEAN, 2017).

The dismal economic performance of these mineralized regions are pronounced despite the enormous mineral wealth that the Philippines is endowed with. The total estimated production value for metallic minerals of the country is PhP102.73 Billion with an estimated Gross Value Added (GVA) of PhP 87.2 Billion in mining at current prices in 2016 (MGB, 2016). This was top billed by the country’s gold, nickel and copper production in the same year generated from at least 50 metallic operating mines across the different regions. “Undeniably, there is wealth lying underneath the Philippine soil and that makes the country the fifth most minerally-endowed country in the world with its 32 known metallic and non-metallic resources spread across the archipelago (Ecoteneo, 2012).”

This paradox showing the rich mineral resources on one hand, and low economic performance on the other is what Richard Auty (1993) called the “resource curse.” Countries that are blessed with some of the world’s prime natural resources also suffer in turn from the scourge of inequitable economics, poor governance and resource-based conflicts. The resource curse shows that there is an inversely proportional relationship between natural resource abundance and economic performance of a given country. Where there is enormous natural resource, there is sluggish growth in terms of economic and human development.

Seemingly, the logic purporting that mineral wealth can trigger economic boom appears to be flawed when applied in the Philippines since some mineralized provinces register poor economic performance. The irony, therefore, raises some questions on the cogency of the resource curse theory when applied across provinces in the Philippines. It begs the questions - If some provinces in the Philippines are mineral rich, how come they remain poor? What factors determine the poverty incidence of mineralized provinces in the Philippines? Will the resource curse thesis find conclusive affirmation in Philippine mineralized provinces?

Statement of the Problem

While the resource curse thesis advanced by Auty (1993) is supported by subsequent studies (Sachs and Warner,1995; Sala-i-Martin, 1997; Doppelhofer, Miller, & Sala-i-Martin,2000; Gylfason, 2001; Sala-i-Martin and Subramanian, 2003; Sanglimsuwam, 2010), resource abundance alone cannot be named as solely responsible for poverty in mining affected communities. There appears to be some competing and conflicting claims as to why it exists. Investigating other factors that can possibly affect a mineralized province’s socio-economic outcomes is in order.

Central to this study is the question: “What is the association between mineral abundance along with other independent variables (immigration, agricultural production, employment, financial transfers, governance) and poverty incidence of select mineralized provinces in the Philippines?”

This central question is also informed and inspired by the stark contrasts offered by mineralized provinces with relatively low performing economies and relatively high performing economies. For instance, while majority of mining provinces rank among the lowest in terms of poverty incidence - Eastern Samar, 67.1%; Zamboanga del Norte, 56%, Davao Oriental, 55.7% (PSA, 2012) – some others are performing economically well – Benguet, 6%, Rizal 9.4%, Bataan 10.8% (PSA, 2012). The same can be said in terms of their Human Development Index (HDI) rankings.

Apparently, these two competing claims on mining establishes some ambiguous forms of association in the role it plays to people’s quality of life, economic development and poverty incidence. On one hand, environmental protection supporters claim that communities playing host to mining concessions suffer the most from social, environmental and even economic destruction advancing that miners plunder local resources and leave nothing beneficial to the local populations (Lopez, 2017). On the other hand, mining companies assert that communities actually benefit from the exploitation, exploration and development of mineral resources especially that they provide the host communities sufficient resources like housing, schools, health centers, and livelihood opportunities (Domingo, 2016).

Elucidating further explanatory notes to clarify such disparity required this study to answer if there are some forms of linear association between mineral abundance, immigration, agricultural production, labor, financial transfers and governance on one end and poverty incidence on the other.

The study offers both theoretical and practical relevance lending credence to the current debate that puts environment and economy on opposing sides of the fence. This study is primarily relevant in expanding a robust literature on resource curse, both on its economic and political ramifications. This theoretical import will further elaborate the relationship between mineral abundance and poverty from the tools of microeconomic indices. Furthermore, the data in this study will become a useful guide for the country’s economic managers in making public choices especially when deciding whether or not to pursue an extractive industry liberalization policy.

Philippines and its Natural Resource

Undeniably,Philippines, which consists of 7,107 islands, is considered as one of the mega-biodiversity hotspots in the world (DENR, 2002). It is one of the only 17 countries across the globe named both as a mega-biodiversity and a biodiversity hotspot, states that are richest in flora and fauna with more than 52,177 species identified and half of them are endemic to the country.

It is also blessed with rich mineral resources, well-endowed with both base and precious metals (Holden, et.al., 2011). In fact, it is ranked among the world’s top mineral producing countries being third in gold deposits, fourth in copper and fifth in nickel reserves (Arillo, 2014). According to the Mines and Geosciences Bureau (2016), a total of 30 Million hectares have mineral potentials in the country and can be translated to huge economic promises for the Philippines.

The total estimated production value for metallic minerals of the country is PhP 102.73 Billion with an estimated Gross Value Added (GVA) of PhP 87.2 Billion in mining at current prices in 2016 (MGB, 2016). This was top billed by the country’s gold, nickel and copper production in the same year generated from at least 50 metallic operating mines across the different provinces.

However, a footnote must also be made that the Philippines also belongs to the Pacific typhoon ring and belt of fire making natural hazards conventional rather than exceptional. It often suffers the brunt of regular typhoons, storm surges, earthquakes and even volcanic eruptions. Some portions of the country are also classified as natural disaster hotspots (Cathal, et.al, 2007). Coincidentally, these hotspots are also biodiversity areas where most of the minerals are found and the indigenous peoples reside (Holden et.al., 2011:143, citing Ballard and Banks, 2003).

Seeing the full potential of the Philippines for economic development, global financial institutions like the World Bank and the Asian Development Bank called for the liberalization of the country’s mining policy in the early part of 1990 (Holden et.al., 2011:145). This led to the passage of RA 7942, otherwise known as the Mining Act of 1995 which made mining an attractive investment to foreign companies and since then has become the vessel through which these investments found their way to the Philippines.

Philippine Mining Policy

Republic Act 7942, or the Philippine Mining Act of 1995, provides the basic policy framework which governs all mineral resource exploration, exploitation, utilization and development in the country. As a response to the global call to liberalize the mineral industry in the Philippines, President Fidel V. Ramos signed into law, RA 7942 in March of 1995 in the hope of attracting more foreign investors to develop the untapped mineral resources of the country.

On the over-all, the law regulates mining in all phases of its operation from exploration to rehabilitation. The Mining Act of 1995 has two approaches to mineral resource extraction: 1] the Mineral Production Sharing Agreement (MPSA) and 2] the Foreign Technical Assistance Agreement (FTAA) which permits 100 percent foreign ownership of mining operations.

The MPSA is an agreement that the government grants to a contracting party the right to mine within a contract area without necessarily granting title over the land. In this scheme, the government gets a share in the mineral production, being the owner of the minerals. Any individual, corporation, partnership, association or cooperatives with at least 60% of its capital is owned by Filipinos may enter into this kind of agreement. A mineral agreement shall have a term not exceeding twenty-five (25) years from its execution and may be renewed for another twenty-five (25) years. There are two other forms of mineral agreements, namely Co-production Agreement and Joint Venture Agreement, where the former requires the government to provide inputs other than just the minerals and the latter expects the government to have equity share with the contractor (MGB, 2005a).

On the other hand, the FTAA is an agreement entered into by “the government for the large-scale exploration, development, and utilization of copper, nickel, chromite, lead, zinc and other minerals except from cement raw materials, marble, granite, sand and gravel and construction aggregates (MGB, 2005b). Any Filipino citizen or Filipino-owned or foreign-owned corporation, partnership, association or cooperative may enter into this kind of agreement with a term of twenty-five (25) years renewable for another twenty-five (25) years.”

Among the features of the law that make the policy environment favorable to foreign investors include exemptions in value added tax, deductions and holidays in income tax, tax-free capital equipment importation, exclusive rights to water and timber within mining area and freedom from expropriation (Holden, 2011:146). In fact, the government only partakes of the mineral rents in the form of the 2% excise tax and the 1% royalty fee for tribal communities should the mining tenement be located within the indigenous people’s ancestral domains. Because of these, the volume of request for mining permits increased by 400 percent between 1994 and 1996 (Holden, et.al, 2011:146, citing USGS, 1998).

The liberalization of mining industry began with President Cory Aquino’s efforts to privatize hundreds of government institutions when she issued Executive Order No. 279 to promote investment in mineral resource extraction (Chaloping-March, 2014). This issuance empowered the Secretary of Environment to enter into joint ventures and production sharing agreements in behalf of the government of the Republic of the Philippines, thus, facilitating the entry of mining into the country. This was further expanded during the term of President Fidel Ramos upon the enactment of the enabling law regulating the mining industry through RA 7942, which adopts a neo-liberal framework to attract mining investors. This was all part of Ramos’s desire to hasten the industrialization of the Philippines under his Philippines 2000 development plan. The legislation was an interpretation of the 1987 Constitutional provision stating that,

“The exploration, development, and utilization of natural resources shall be under the full control and supervision of the State. The State may directly undertake such activities, or it may enter into co-production, joint venture, or production-sharing agreements with Filipino citizens, or corporations or associations at least 60 per centum of whose capital is owned by such citizens. Such agreements may be for a period not exceeding twenty-five years, renewable for not more than twenty-five years, and under such terms and conditions as may provided by law.”(Article XII, Section 2, 1987 Philippine Constitution)

Following Ramos’s liberalization policy, President Gloria Macapagal-Arroyo issued Executive Order No. 270 which declares that that the government’s policy was to promote “responsible mineral resource operation, development and utilization in order to enhance economic growth.” This policy invokes the principles of sustainable development, justice, equity, sensitivity to the culture of the Filipino people and respect for Philippine sovereignty. It further recognizes the role of investments in mineral exploration and development as a means to address poverty in the country.

However, this attempt to liberalize the mining industry was not met with equal enthusiasm in the local government units. Empowered by the Local Government Code of the Philippines (RA 7160) with the devolution of certain powers including environmental management and police powers to ensure the general welfare of the people, some local government units enacted anti large scale mining ordinances. This includes the provincial governments of South Cotabato, Albay, Bukidnon, Zamboanga del Norte, Palawan, Romblon, and Occidental Mindoro but many of them faced and some continue to face legal challenges as their constitutionality is being questioned in different courts in the Philippines.

Apart from the local governments standing in the way of mining companies, the civil society organizations including the Catholic Church, indigenous peoples communities, academe and non-government organizations, fomented a force strongly challenging the entry of mining projects in the local communities. In order to allay the growing tension between and among the government, the mining companies and the civil society organizations, President Simeon Benigno Aquino III, issued Executive Order No. 79 in 2012, which established and expanded the no-go-zones or areas that are off limits to mining. In this issuance, a Mining Industry Coordinating Council (MICC) was also created to ensure that mining laws are properly enforced and that stakeholders are coordinated. Furthermore, a moratorium on the issuance of mining permits is also mandated in the order “until a new legislation rationalizing the current revenue sharing schemes and mechanisms” are legislated. This moratorium holds true until the current Duterte administration.

The Politics and Economics of Resource Curse

While there appears to be a solidarity among experts on the veracity of the resource curse thesis, the debate on why the curse exists seems to divide the scholars for being unable to agree on its elucidations (Ross, 1999:297). The articulations can be as many as there are researchers analyzing the hits and misses of states in optimizing the potentials of their wealth in resources. There are some prominent economic explanations supporting the resource curse: 1] Dutch disease theory, 2] the Lucas Paradox, 3] capital flights and 4] failure of trickle down economics.

Corollary to these, political factors affecting resource curse are further articulated in another set of theories: 1] Institutional weakness, 2] theory of policy failures, 3] regime influence and 4]resource-based conflicts.

Among the economic explanations to support the resource curse claim is the “Dutch disease” (The Economist, 1977) theory, which says that when there is a sudden surge in one sector such as the natural resource industry, the other sectors like manufacturing, agriculture and others suffer as affected by the changes in the currency exchange rates. Furthermore, Robert Lucas (1990), observed that poorer countries where investments by richer countries are made continue to suffer in poverty because, surprisingly, too little capital flow to poor countries because of imperfections in the international capital market. This is known as the “Lucas paradox.” Akin to this, is the concept of “capital flight,” which implies that financial assets and capital of a nation are able to escape because of varied factors like political unrest, financial instability or because of the imposition of capital controls, who mostly come from developed countries. In effect, when capital flies out of the country, benefits do not trickle down to the people. Leakage happens. This magnifies the failure of trickle-down economics in highly neo-liberal regimes due to the monopolization of rents by the capital control.

Resource curse could also be explained politically according to Michael L. Ross (1999), in “The Political Economy of the Resource Curse,”(1999:308-319). He proposed some possible political explanations based on general theories of policy failures which he classified as the “cognitive theories, social theories and statist theories.” Simply put, resource abundance is not optimally reinforced by sound economic policies, hence, the potentials for development are able to leak. This supports the observations that in mineral-rich countries, the governments do not reinvest the rents from the resource extraction wisely. Atkinson and Hamilton (2003) found that governments which consume the resource revenues suffer from the curse compared to those who use the rents efficiently and sustainably. Brunnschweiler and Bulte (2008) buttressed this by saying that institutional quality and capability play a major role in the economic performance of resource rich countries, although this claim is underexplored. To date, this has not been verified by quantitative analysis. When institutional capacity is low, such that corruption exists, it compounds the effects of the resource curse. It can be argued that resource curse only shows when institutional quality is low. Furthermore, there are some claims that political and economic regimes of a country may have an influence to the existence of resource curse. Merchant-Vega (2011) looks at authoritarianism as closely associated with the curse especially when economies are centrally controlled by those in power. However, it is also observed that democracies with neoliberal regimes can also lead to the curse as exemplified by capital’s natural tendency for overconsumption and greed for profit (Cabarde, 2018). Often, this competition for the limited resources, between capitalists and the locals like the indigenous peoples, lead to resource-based conflicts. Supporters of the conflict theory advances that when conflicts emerge, its natural and logical consequences include the strains the government must hurdle in setting up robust economic and political controls in the affected communities.

A legion of many other theories are put forward to explain why the resource curse exists. However, these are general statements that must be analyzed contextually given the unique circumstances that can closely capture and elaborate why mining communities suffer mostly in poor economic and human development. Garidner (2017) concluded, based on literatures, that there is an association between mining and the level of economic well-being within mining towns (2017:111). However, he admits that there is complexity in factors affecting such condition of development of communities and that no consensus has been reached among scholars and experts that can fully explain the relationship between the poor quality of life and the resource dependence of mining communities.

Resource Curse Conceptual Model

Often observed, mining communities in the Philippines suffer from relatively low economic performance exhibiting some of the highest poverty incidence rates in the country. The resource curse literature posits that there is a negative relationship between mineral abundance and economic development. However, the amount of minerals extracted cannot be the sole indicator that explains why mining provinces are poor. There are several channels through which mining can influence the socio-economic outcomes of mineralized provinces.

This study looked into the association between selected independent variables along with mineral resource extraction rates and the poverty incidence of people living in selected mineralized provinces in the Philippines. These variables (shown in figure 1.0) are generally classified into six (6) different channels which were hypothesized to have potential direct impact on the socio-economic situation of mining provinces.

Figure 1 Conceptual Model

Poverty in mining communities is primarily tied with the resource curse thesis of Richard Auty (1993). This thesis he advanced some twenty-five years ago fundamentally says that countries blessed with some of the world’s prime natural resources also suffer in turn from the scourge of inequitable economics, poor governance and resource-based conflicts. The resource curse shows that there is a negative relationship between natural resource abundance and economic performance of a given country. Where there is enormous natural resource, there is sluggish growth in terms of economic and human development. The resource curse thesis of Auty (1993) has been supported by many other studies as logically plausible. Sachs and Warner (1995) for instance, found out that abundance in natural resources was correlated negatively with economic growth upon their examination of a diverse set of resource-dependent economies covering the period between 1970 and 1989. Regionally, Dr. Karnjana Sanglimsuwan (2010) concluded that “countries with abundance of natural resources seem to have lower economic growth and poor economic performance than countries with fewer natural resources,”after correlating the resource abundance and economic growth rates of twenty-two (22) countries across Asia Pacific from years 1970 to 2008.

While many scholars agree that the curse exists (Auty, 1993; Sachs and Warner,1995; Sala-i-Martin, 1997; Doppelhofer, Miller, & Sala-i-Martin,2000; Gylfason, 2001; Sala-i-Martin and Subramanian, 2003; Sanglimsuwam, 2010), there appears to be competing and conflicting claims as to why it exists. However, experts affirm that resource abundance cannot be solely responsible for the curse as other determinants can possibly affect a community’s socio-economic outcomes.

By the immigration of workers to local mining communities, mining is expected to have a positive association with local poverty incidence. Poverty rates increase as migration increases because demand for various services like housing, health, food and others expand to accommodate the needs of a growing population. Also, the cost of labor becomes cheap as migrants and locals compete for the limited resources for sustenance (Gardiner, 2016; Oucho, undated) in many mining provinces. Some literature (Smith et.al., 2001) also showed that social problems such as depression, school drop outs, crimes, and violence are aggravated in mining communities contributing further to poverty. In this context, migration is simply defined as “the spatial mobility or geographic mobility of population that involves a change of usual place of residence,” (van de Walle, 1982, cited in Oucho, undated). This was represented by a proxy indicator, that is, population,to show that provinces with relatively higher population must have more migrants. The boom stage of the mining cycle showed increases in population in mining communities as people rush to access labor or find gold in some artisanal mining areas. Lawrie et.al. (2011) observed that towns with more mineral resources in Australia grew more in population than towns without dramatic expansion of mining activities.

One of the principal explanansilluminating the high poverty incidence in mining provinces is the “Dutch disease” theory (The Economist, 1977). The theory says that when there is a sudden surge in one sector such as the natural resource industry, the other sectors like manufacturing, agriculture and others suffer as affected by the changes in the currency exchange rates. Agricultural production, represented by agricultural farm sizes in this study, was also tested whether or not it has any association with the incidence of poverty in mineralized provinces. The assumption is that the bigger farm areas are, the higher its gross production value is. It is also proposed that agricultural production has negative relation with poverty rates as the sector greatly suffers from natural resource boom according to the “Dutch disease” effect, gravely affecting the provinces’ economic performance. Concentration in the mineral resource extraction results to the decline in the trade of prime and basic commodities (Ross,1999:301-307), thereby, making most of them impoverished.

Data on labor was also investigated as mining purports to bring in new opportunities for work both for the local communities and migrant communities. Labor is expressed in terms of the rate of employment in this study. By influencing local economic activity and providing employment opportunities, mining should have negative association with poverty incidence, that is, the higher the employment rate, the lower the poverty rates should be. Loayza et.al. (2015) argues that poverty rates can drop because most mining companies offer jobs that are relatively better paying than other forms of industries. This attracts better paid immigrants which in effect results to fewer poor people in the province.

The fourth possible determinant of poverty is the financial transfers to local governments. These may include revenues from mining taxes and other incomes stemming from mining activities. In a separate study of Loayza et.al. (2014), it was asserted that mining communities must be performing better economically considering the enormity of rents generated from extractive activities. However, the same study cautions that some ambiguities may exist because different local governments have different abilities in optimizing these rents or rent redistribution may be skewed in favor of the better-off or some may have been misappropriated to corruption. This variable was represented in this study by average provincial income and the internal revenue allotment per province with the assumption that they have a negative relationship with poverty incidence per province.

Finally, this study also looked at the function of governance in explaining resource curse among mineralized provinces. Represented by the provincial good governance index and rate of internal revenue allotment dependency, it is assumed that poverty has negative relation with the former and a positive relation with the latter. This means that the higher the good governance index of the province is, the lower the rate of poverty is and the higher the IRA dependency rate is, the higher poverty rate as well. Ross (1999), explained resource curse by linking resource abundance with the neglect of the governments through poor management of economies and natural resources. He advanced some political explanations based on the general theories of policy failures which he classified as the “cognitive theories, social theories and statist theories” (1999: :308-319).

According to his cognitive theories of policy failures, he argued that increase in resource extraction usually leads to the short-sightedness of policy makers (1999:309) miscarrying the need to provide an enabling policy environment to sustain growth from the resource boom. Policymakers often develop “myopic sloth or myopic exuberance” in the light of sudden increase in resource extraction. On the social theories, he claimed that resource curse happens because the surge in resource exports tend to embolden the policy influence of private players in overshadowing policies that may run counter to their interests (1999:311). This is particularly true when non-state actors have prior claims over the spoils of resource extraction. The third political argument supporting resource curse is the tendency of states to weaken owing to the sudden increase in resources. Ross (1999:312-313, citing Mahdavy (1987), Shambayati (1994) and Chaudry (1989) averred that when resource abundance occurs, states behave as “revenue satisficers, and not revenue maximizers,” which means that states make decisions by exploring alternatives until a choice becomes acceptable meeting their set of requirements instead of opting for one which gives them maximum benefits. He also observed that when states’ demand for revenues decrease, so is the strength of their economic policies.

Given the above assumptions, the conceptual model therefore hoped to predict that mining should have a general negative association with poverty incidence in provinces where metallic mineral extraction takes place. It however, interrogated further the associative links between resource abundance, immigration, agricultural production, employment, financial transfers and governance on one hand and poverty incidence among populations in selected mineralized provinces in the Philippines. This investigation began with the resource curse proposition that is supported by longitudinal cross-country studies as of Auty (1993), Sachs and Warner (1995), Sanglimsuwan, (2010), Sala-I Martin (1997) and many others. This study utilized some microeconomic indicators as opposed to the macroeconomic indices used to advance the thesis of inverse relations between resource abundance and economic performance.

The equation testing the hypothesized associations shows that poverty in mineralized provinces is a result of increase in population, increase in mineral abundance, decrease in agricultural production, decrease in employment, decrease in provincial income, decrease in IRA, decrease in GGI and increase in IRA dependency.

The formula was amended however, after finding that multi-collinearity existed between population and the other variables indicating a variance inflation factor (vif) of 6.167506841 (See Annex, Table 1). In the ensuing tests, population was removed as an independent variable because this can already be linearly predicted from the other variables with some degree of accuracy.

Table 1. Test of Multi-collinearity result

The model to be tested now appears as,

The data were subjected to a bivariate statistical treatment using a correlational design applying the Multiple Regression Analysis approach since the model involved one dependent variable and multiple independent variables. Holding the alpha at 0.05, the p-values of the variables in the model were analyzed to conclude whether there is an association that exists between and among the variables.

Table 2 Data Requirements

The above equation was tested on thirty-one (31) units of analysis - provinces which are considered mineralized. In this study, mineralized refers to a Philippine province with metallic mineral extractions covered by an active Mineral Production Sharing Agreement (MPSA) and/or Financial and Technical Assistance Agreement (FTAA). Refer to Table 3 (Annex) for the list of mineralized provinces.

Reading the Variables

In 2012, Philippines registered a poverty incidence rate of 27.9% (PSA,2012) among Filipinos. Across the mineralized provinces used as units in this analysis, the mean poverty rate is 37.10% among the population, clearly higher than the national figure. Eastern Samar is most impoverished (67.1%) while Benguet is the least poor province among mineralized provinces (see Annex, Figure 2).

In terms of mineral abundance (see Annex, Figure 3), Benguet ranks as the top metallic mineral producing province in the country with a total gross production value of 16,066,360 (in thousand pesos) in 2011 while Abra ranks the least with a gross production value of 11,580. The average gross production value of metallic mines is at 304,519.32 (in thousand pesos). The other top provinces in metallic extraction are Palawan (14,450,086), Surigao del Norte (11,759,361), Cebu (9,269,811), Masbate (9,069,573) and Surigao del Sur (7,579,375).

Incidentally, many of the mineralized provinces are also agriculturally classified with expansive land areas devoted to agricultural farms (see Annex, Figure 4). The mean size of land area devoted to farming is 139,090.29 hectares the largest being Zamboanga del Norte with 295,295 hectares of farm areas and the smallest being Abra with 22,738. Palawan (225,904 has) and Masbate (207,500) who are among the top mineralized provinces are also among the top agricultural provinces.

In terms of labor rates (see Annex, Figure 5), the province of Masbate ranks first with 99.1% employment rate while Rizal ranks last with 90.6% employment rate among the mineralized provinces. Mean rate is 95.03%, higher by around 2% from the national labor and employment rate of the country in 2011 at 93%.

Financial transfers are also essential in analyzing poverty incidence in mineralized provinces. By this, it means the amount, rents or revenues inuring to local governments in terms of average provincial incomes and internal revenue allotments. Mineralized provinces registered 194,276.61 (in thousand pesos) mean provincial income (see Annex, Figure 6). Rizal has the most income at 376,126 while Sultan Kudarat has only 126,806. Among the provinces with least income are Eastern Samar (127,814), Zamboanga del Norte (130,344), Masbate (132,460) and Sarangani (137,077). Share of mineralized provinces from the national revenues in the form of the internal revenue allotment(IRA) varied (see Annex, Figure 7). Negros Occidental has the largest share with 1,775.4 (in million pesos) while Agusan del Norte has the least with only 520.7 (in million pesos). On average, mineral provinces enjoy 889.8 (in million pesos) internal revenue allotment.

Governance on the other hand, is understood as the collective ability of the government, civil society and private sector in improving the lives of all citizens, especially the poor (PSA, 2010). In this study, it includes the good governance index and the IRA dependency rate. The GGI(see Annex, Figure 8) is a measure used to assess the manner in which power is exercised in the management of the country’s economic and social resources for development (2010). Mineralized provinces recorded a mean GGI of 125.5. Among them, Benguet is considered as the best performing mineralized province in terms of good governance with a GGI of 230.66 and the worst performing is Masbate with a GGI of 90.56. IRA dependency(see Annex, Figure 9) also measures governance as it indicates the ability of local governments to diversify their sources of income other than the IRA. With a mean of 88.4%, it goes to show that many of the mineralized provinces still rely heavily on the IRA as the primary source of income. Sultan Kudarat appears to be the most dependent at 98% dependency ratio and Bataan as the least dependent at 64% dependency ratio.

Finding Associations and Significant Relationships

Prior to running a multiple regression tests, associative links were examined using the Pearson’s Product Moment Correlation Coefficient or Pearson’s r analysis. (All scatterplots are attached as Annex)

Full Model.The full regression results showed that 75% of the data fits into the regression line. There seems to be credence in this claim with a standard error of only 7.1, the mean distance of data points from the line of best fit. Over-all, the model has a p-value of 0.000001 which means that the proposed model is significant. Conversely, when each variable is analyzed independently, the relationships between the independent variables and the dependent variable changed.

Table 4. Regression Statistics

Table 5. ANOVA

Running the regression equation of Poverty in Mineralized as Provinces = b0+ b1MineralAbundance- b2AgriFarmArea– b3Employment–b4ProvincialIncome–b5IRA–b6GGI+ b7IRADependency, it yielded the result

POVERTY = 4.78785 + 0.00000 * MIN_GPV - 0.00002 * FARM_AREA + 0.54343 * EMP - 0.00019 * PROV_INC + 0.00163 * IRA - 0.02461 * GGI + 0.23791 * IRA_DEP

Mineral Abundance.Data showed that there appears to be a positive linear association between mineral abundance and poverty incidence with a coefficient value of 0.00000. This association though is not significant as shown by the p-value result of 0.77200. In short, the model failed to reject the assumption that there is no positive linear association between the variables.

This result shows that mineral extraction has no poverty reduction function nor poverty escalation mechanism which somehow denies the general claim of resource curse. Nothing in the data indicates that poverty grows when mineral resources are abundant. Perhaps this is because mining is a secluded sector (Bautista, 2016) as evidenced by its minimal contribution to the Gross Domestic Product and almost negligible in the Gross National Product. Bautista (2016) also argued that around 80% to 90% of the unprocessed ores are exported abroad resulting to financial leakage, therefore, not creating any footprints of national industrialization. The leakage extends to the mining fiscal regime considering that mining companies enjoy a number of tax holidays and at most only 2% excise tax inures to the Philippine government.

Ross (1999) may have been correct in expanding resource curse as also politically predisposed. The ability of the local government to manage its resources no matter how meager may have some influences on the socio-economic outcomes of the province as exemplified by Benguet which raked almost perfectly well in many of the indicators – 1stin mineral abundance, 1stin HDI, 1stin GGI, 2ndin provincial income and 4thleast IRA dependent local government making it last in the ranking of poverty incidence even if it has the 2ndsmallest farm area and 3rdlowest IRA among the mineralized provinces of the Philippines.

Agricultural Production. As regards the relationship between agricultural production and poverty incidence, the result revealed a moderately positive linear association with an r-value of 0.42715. However, the regression equation indicated a negative association with a coefficient value of -0.000016. But this association is not significant with a p-value of 0.60710. Therefore, the model failed to reject the hypothesis that there is no negative linear association between agricultural production and poverty incidence.

Similar to mineral abundance, agricultural production has no affect to the poverty situation of mineralized provinces. This could probably be explained by the insufficiency of farm areas as representation of agricultural production especially that most mining provinces are also agricultural communities. The cases of Palawan and Masbate supports this claim where economic drivers become indistinguishable since both are top mineral and agricultural provinces. Relying simply on farm areas prove to be inadequate in measuring the effect of mining to the agricultural sector as postulated by the “Dutch disease.” But should the negative association revealed by the regression equation be given credence, one can postulate that perhaps, agriculture may have suffered when provinces become more mineral dependent giving weight to the Dutch disease.

Employment. Meanwhile, employment appears to have a somewhat strong positive linear association with poverty incidence having an r-value of 0.60891 and supported by a coefficient value of 0.543431. This is insignificant however, with a p-value of 0.52221 failing to reject the prediction that there is no negative linear association between employment and poverty. In short, employment in mining communities does nothing to poverty incidence.

The absence of any significant association between employment rates and poverty incidence denies Loayza et.al.’s (2015) claim that poverty rates can drop because mining offers better paying jobs. Instead this affirms many observations that mining is only labor intensive in the initial stage of the mining life but labor absorption declines in the later stages of the operations. MGB (2010) reported that on average, mining industry’s share to total employment was scantily recorded at 0.38%. This was also affirmed by Bauer (2012) of the Revenue Watch Institute reporting that of the 11,400 jobs in Oyu Tolgoi in Mongolia, only 3,500 eventually became permanent.

Provincial Income. Asking if there is a negative linear relationship between average provincial income and poverty, the result of the tests indicate that there is strong negative linear association at -0.89026 p-value. Wth an r-value of 0.00045, the association is deemed significant thereby failing to accept the null hypothesis. In short, there is strong negative linear relationship between provincial income and poverty. The regression result showed that the coefficient value of provincial income is -0.00019, which means that holding all other variables constant, every one thousand pesos increase in provincial income, poverty incidence reduces by .00019.

Of all the variables, it is provincial income that exhibited a significant association with poverty incidence. This shows that rents generated from mineral extraction can help alleviate the poverty situation in mineralized provinces although the data does not clearly identify if these incomes are mining derivatives. However, with a mean of 88% IRA dependency, mineral provinces should diversify its sources of income if it is serious in addressing its poverty issues. Given Loayza et.al.’s (2014) warning on the ambiguities in associating poverty and income, it is possible to infer that mineral provinces have a remarkable ability in optimizing the potential benefit from their revenues. Confusingly, the governance indices in this study did not show any association with poverty. If taken conclusively, this refutes Ross’ (1999) claim that most governments behave as revenue satsficers rather than revenue maximizers if provincial income may have been utilized for poverty alleviation.

IRA.The relationship that exists between IRA and poverty is a weak negative linear association evidenced by its r-value of -0.18807. Its coefficient value on the other hand is 0.001629, showing that there is a positive association with poverty, that is, the bigger the IRA allotted to the province, the poorer it becomes. However, this is also not significant as shown by the p-value of 0.77851. Given this data, the model also failed to reject the prediction that there is no negative association between IRA and poverty.

This result simply reveals that IRA alone cannot determine the socio-economic outcome of mineralized provinces. With the significant relationship established by provincial incomes and insignificant relationship with IRA dependency, it is clear that other incomes are necessary in improving the economic conditions of people in mineral-rich communities. But assurance has to be made that these incomes are not misappropriated to malversations or any form corruption as warned by Loayza et.al. (2014)

GGI. On the contrary, there appeared to be a moderately strong negative linear relationship between GGI and poverty having an r-value of -0.66882. This is further supported by the coefficient value of -0.024613. Yet, this is not significant with a p-value of 0.72637. This means to say that the hypothesis, “there is no negative linear association between GGI and poverty,” is not rejected.

IRA dependency. IRA dependency shows similar result with GGI about its association with poverty. While there appears to be a strong positive linear association between the two at 0.70013 r-value, and the coefficient value at 0.237911, the same cannot be said to be significant with a p-value of 0.34879. Therefore, the assumption that there is no positive linear association between IRA dependency and poverty is not dismissed.

Both GGI and IRA dependency indicated that no association exists between them and poverty incidence. This can probably be explained by the absence of more compelling indicators to establish a significant relationship with poverty like corruption index or the role of local economic and environmental policies in poverty alleviation. This insignificant association neither affirms nor denies the theory of policy failures advanced by Michael Ross (1999). In his assumptions, links with private players who can sway policy directions in mineral resource governance or government’s ability to absorb fiscal shocks due to sudden fluctuations in the international market find no relevant explanatory value in this result.

Table 6. Summary of Findings

Conclusion and Recommendation

Resource curse has never been established in this study. There is no persuasive proof pointing to the inverse relations between mineral abundance and economic performance among the thirty-one provinces classified as mineralized. Using microeconomic indicators may have caused the inability to detect the curse, contrary to the use of macroeconomic indices that established the thesis of Richard Auty.

While it is clear that mining cannot be accused of the provinces’ ominous economic condition, the result showing that it has no significant association to poverty is also indicative of its non-linear relation to the locale’s economic development. Mining in short is neither a poverty reduction nor a poverty intensification stimulus to mineralized provinces. It cannot be claimed therefore that mining aggravates poverty nor does it induce development.

Yet, the data shows that mining communities remain to be among those with very high poverty incidence rates. It is unclear however if agricultural production can be affected by mineral extraction to determine if it has any associative link with poverty incidence. The data cannot lead to the conclusion proposed by the Dutch disease theory where agriculture can potentially suffer when communities become reliant towards mineral extraction.

Surprisingly, higher employment rates do not always lead to lower poverty rates. This runs contrary to the claim that with mining, more opportunities for work are made available to the locals and even to immigrants, especially at the boom stage of the mining cycle which can spur economic growth.

Governance did not also exhibit some significant associations with poverty disputing Ross’ policy failures. Perhaps, a more appropriate indicator may have clearly established the link between governance and poverty especially that nothing specific to natural resource governance indicators were used to prove linearity in their relationships.

Apparently, provincial income matters in the poverty situation of mineralized provinces. This cannot be used to infer though that mining rents have contributed positively to the economic outcomes of the mineral-rich provinces since the data is limited and unable to show the amount of income derived from mineral extraction to be able to conclude that revenues from the minerals can be a determinant to poverty.

On the basis of the foregoing findings, the following recommendations are proffered:

1. Given the significance of provincial income in determining the socio-economic outcome of mineralized provinces and their relatively high IRA dependence, local governments must be encouraged to diversify its sources of income along with setting up some mechanisms to ensure that revenues are not wasted to corruption. More specifically, RA 7942 must be amended to secure local government’s equitable share to the resources which rightfully belongs to the state, other than the 2% excise tax and 1% royalty fee to indigenous peoples. Tax holidays must be reconfigured to make the sharing in resource rents more equitable.

2. Considering the result that mineral abundance is not a determinant of poverty incidence, the economic managers must rethink the economic development plan of the country by investing more on industries that empirically contribute to the reduction of poverty rates across mineralized provinces in the Philippines

3. Explore other variables to test the validity of the resource curse claim. Actual provincial data on 1] gross production value of agriculture, 2] gross production value of both metallic and non-metallic minerals, 3] corruption index, 4] natural resource governance index, 5] gross added value of mining to provincial gross domestic product and 6] international market price of metallic and non-metallic minerals, among others. It may also help if these variables will be tested against the quality of life in mineral provinces using the provincial human development index as dependent variable.

4. Expand this analysis by using non-mineralized provinces as a control group to assess if the mining industry makes a difference in the economic outcomes of the Philippine provinces.

5. Further inquiry into the unique case of Benguet must be undertaken. Having been able to escape the curse necessitates an in-depth case study analysis to unearth the factors how it is able to optimize the mineral resource rents to its advantage, making the province among the relatively high performing mineralized economies in the country.

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(Photo Credit: Google Images)

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