THE DETERMINANTS OF ARTISANAL AND SMALL-SCALE MINING (ASM) PRODUCTION: EVIDENCE FOR GOLD, Essays (high school) of Earth science

Download THE DETERMINANTS OF ARTISANAL AND SMALL-SCALE MINING (ASM) PRODUCTION: EVIDENCE FOR GOLD and more Essays (high school) Earth science in PDF only on Docsity! R E MSesS ISSN : 2489-2068 Vol 5 - Numéro 2 (2020) ———— Revue des Etudes Multidisciplinaires en Sciences Economiques et Sociales artisanal and small-scale mining THE DETERMINANTS OF ARTISANAL AND SMALL-SCALE MINING (ASM) PRODUCTION: EVIDENCE FOR GOLD IN BATOURI, EAST CAMEROON LES DETERMINANTS DE LA PRODUCTION MINIERE ARTISANALE : CAS DE L’OR A BATOURI, EST-CAMEROUN Adele M. NGO BILONG AMOA Researcher-Senior Lecturer Faculty of Mines and Petroleum Industries Maroua University adelebilong] @yahoo. fr +237 699961635 Ousmanou HAMAWA Mining and Petroleum Economist Ingineer Faculty of Mines and Petroleum Industries Maroua University hamawaousmanou@yahoo fr +237 696283022 Andre TSIMI ELOUNDOU Mining and Petroleum Economist Ingineer Faculty of Mines and Petroleum Industries Maroua University aguitizi@yahoo. fr +237 65685491 Abstract This document aims to present the factors influencing the production of Gold in Batowi. This study was carried out as part of research to demystify the contours of the Cameroonian mining sector. To achieve this, we used the student and fisher tests to better assess the significance of each element in the model. Based on the results obtained, it appears at first glance that production depends more on hours of work than on other variable. Then, men exercise more than women and finally awareness is required to improve the level of artisans to better manage their resources from these activities. It should also be noted that the model has a correlation coefficient of 86.75%, a sign of good correlation. Donor agencies should view the artisanal and small-scale mining sector as a potential vehicle for poverty alleviation. It should be seen as a key part of rural development programs. Keywords: Mining, GoLd, artisanal, Batouri REMSES http://revues.imist.ma/?journal=REMSES&page=index 44 R E MSesS ISSN : 2489-2068 Vol 5 - Numéro 2 (2020) ———— Revue des Etudes Multidisciplinaires en Sciences Economiques et Sociales Résumé : Ce document vise a présenter les facteurs influengant la production d'or a Batouri. Cette étude a été réalisée dans le cadre de recherches visant a démystifier les contours du secteur minier camerounais. Pour y parvenir, nous avons utilisé les tests Student et Fisher afin de mieux évaluer la signification de chaque élément du modeéle. Sur la base des résultats obtenus, il apparait a premiére vue que la production dépend davantage des heures de travail que d'autres variables. Ensuite, les hommes font plus d'exercice que les femmes et enfin une prise de conscience est nécessaire pour améliorer le niveau des artisans afin de mieux gérer leurs tessources issues de ces activités. Il faut également noter que le modéle a un coefficient de corrélation de 86,75%, signe d'une bonne corrélation. Les bailleurs de fonds devraient considérer le secteur minier artisanal et a petite échelle comme un vecteur potentiel de réduction de la pauvreté. I] doit tre considéré comme un élément clé des programmes de développement rural Mots clés : Minier, Or, artisanal, Batouri Introduction Artisanal and small scale mining (ASM) has been widespread throughout the world for over 2000 years (Hilson, 2002a), and today features heavily in the rural economy of many developing countries (Hentschel et al., 2003; Hilson, 2002a). , artisanal and small-scale mining (ASM) is largely a poverty-driven activity which plays an important economic role. It is estimated that in the order of 13 million people in about 30 countries are directly engaged in small-scale mining, a significant proportion of whom are women and children. A further 80 to 100 million people across the developing world could depend on small-scale mining for some aspects of their livelihoods. Inded, Africa has about 30% of the planet’s mineral reserves, most of which are unexploited. The potential for further discovery and exploitation is immense (Hilson, 2002b). Africa already produces several important minerals, including 40% of the world’s gold, 60% of its cobalt and 90% of platinum group metals (Janneh and Ping, 2011; Kogre and Afilaka, 1988; Taylor et al., 2009 All mining experts agree on the subject. Although it is not comparable to the Democratic Republic of the Congo or even South Africa, Cameroon is a real geological scandal, given the large mineral resources available to the country. The inventory of mining potential, drawn up REMSES http://revues.imist.ma/?journal=REMSES&page=index 45 R E MSesS ISSN : 2489-2068 Vol 5 - Numéro 2 (2020) ———— Revue des Etudes Multidisciplinaires en Sciences Economiques et Sociales To collect data, we use survey research design. We chose that way because it’s preferable to conduct researches employing number of people and questioning about their attitudes and opinion towards a specific issue, an event or a phenomenon (Marczyk & Dematteo 2005). The tesearch questions and objectives have been addressed by cross-sectional survey data since the study has been done at a point of time and place. - Data processing. Informations receives have been summarize, the data coded manually and entered into STATA software version 15 and SPSS version 21. IL.2 Empirical strategy II.2.1 Data analysis The methods of data analysis used are descriptive statistics and economettic tools. Descriptive statistics such as mean, standard deviation are use to describe socio-economic and mining characteristics and factors affecting gold production. OLS regression model is use to examine determinants of gold’s small size mining production factors in Batouri. The survey comprised 80 mining smallholders. It aims is to upgrade knowledge on gold mining sector in Cameroun. These data are also aimed at providing indicators that capture the living standards of the local population in order to be able to follow up efforts made towards the implementation of the poverty reduction strategy paper (PRSP) and the realization of the Millennium Development Goals objectives. II.2.2 Econometric model To identify the factors affecting gold production in Batouri, Ordinary Least Square (OLS) tegression model was employed. The reason in the continuous nature of dependent variables and the quantity of gold production. Furthermore, according to GUJARATI (2006), with the assumption of classic linear model, OLS estimators are unbiased and have the least variance: they are BLUE ( Best Linear Unbiased Estimators). Also, many others researchers such as Babatunde & Qaim (2009) and Olujendo (2008) had used OLS to address similar issues. Y = By + B:X;+U; where Y is the dependent variable ( quantity of gold produced) , X; , a vector of explanatory variables, B;, a vector of coefficients of the explanatory variables (parameters) and U; the disturbance that assumes to satisfy all OLS assumption ( Gujarati, 2006). The economic model’s specification of variables is: Y; = Bot Bigender+B,age + Bz educ +f, familysz +B,workf+P,senior + B7card +Pghours +U;. Y, is the continuous dependent variable indicating mining production per week of each REMSES http://revues.imist.ma/?journal=REMSES&page=index 48 R E MSesS ISSN : 2489-2068 Vol 5 - Numéro 2 (2020) a Revue des Etudes Multidisciplinaires en Sciences Economiques et Sociales smallholder. Age stands for the age of the miner, educ stands for his level of education, familisz stands for respondent’s family, workf stands for the quantity of work force or the number of workers, senior indicates the number of years of seniority in mining activity, card indicates if yes or no the respondent has a smallholder’s card, and hours represents the number of working hours per week. II.2.3 Description of variables used in model The variables used are descript as follow: Gender: the gender takes two values: 1 if the smallholder is a man and 0 if the smallholder is a woman. Males smallholders are stronger and more capable than females. But females have more opportunities of exchanging mining incomes than males ( Malek & Usami, 2010; Abay & Assefa, 2004); Age: the age is a continuous variable measuring the age of the smallholder; Education: the education is a continuous variable that measure the number of years spend in schooling. In fact, the educated smallholder is supposed to acquire, analyse and evaluate informations on different mining inputs and market opportunities that potentially can increase mining incomes than illiterate smallholders (Uwaboe &al, 2012). Positive coefficient was expected from the regression result; Family size: it’s a continuous variable measured in number. Large and productive family size could increase mining production trough proper labour division. Small and efficient family size could increase mining production by devoting all their time for mining activities as well by employing mining inputs (Amaza & al,2006). It expected effect in mining production was not determined in priori. Seniority: it’s a continuous variable that indicate the mining experience and proper time allocation for mining activities until a certain age limit and thereafter their mining income would decrease (Edabiyi & Okunlola, 2013; Shumet, 2011; Anyarwu, 2009 and Abay & Assefa, 2004). Hence, negative coefficient was expected from the final regression result. Tablel provides the descriptive statistics. Table1: Descriptive statistics Variable | Mean Std. Dev. | Min Max Age 30.025 30.025 15 60 Edu 0.8375 0.7866569 | 0 3 Familysz_| 2.0875 2.200079 | 0 7 Workf 2.175 2.627869 | 0 10 Senior 8.2375 7.503997 | 1 32 Hj 8.65 2.643837 [5 17 REMSES http://revues.imist.ma/?journal=REMSES&page=index 49 R E MS@eS ISSN : 2489-2068 Vol 5 - Numéro 2 (2020) a Revue des Etudes Multidisciplinaires en Sciences Economiques et Sociales Hours S19 15.86302 | 30 102 Gender 0.7625 0.428236 | 0 1 Card 0.3625 0.4837551 | 0 1 Source: Authors’ computation II. EMPIRICAL RESULTS AND DISCUSSION Table 2: Correlation and covariance matrix Hours Gender Edu Card __Familysize_workf Senior Age correlations Hours 1.000 0.091 0.017 0.123 0.068 -0.529 -0.039 -0.094 Gender 0.091 1.000 0.020 0.003 -0.214 -0.274 -0.109 0.098 Edu 0.017 0.020 1.000 0.057 0.304 -0.059 -0.064 -0.104 Card 0.123 0.003 0.057 1.000 0.002 -0.141 -0.364 -0.155 familys 0.068 -0.214 0.304 0.002 1.000 -0.047 -0.057 -0.639 workf -0.259 -0.274 -0.059 -0.141 -0.047 1.000 -0.174 -0.122 Senior -0.039 0.109 0.064 -0.364 -0.057 -0.174 1.000 -0.347 Age -0.094 0.098 -0.104 -0.155 -0.639 -0.122 -0.347 1.000 covariances Hours 0.001 0.002 0.000 0.003 0.01 -0.003 -8.64e-005 0.000 Gender 0.002 0.673 0.008 0.003 -0.054 -0.047 0.007 0.005 Edu 0.000 0.008 0.211 0.026 0.043 -0.059 0.002 -0.003 Card 0.003 0.003 0.026 0.997 0.001 -0.006 -0.029 -0.010 familys 0.001 -0.054 0.043 0.001 0.093 -0.030 -0.001 -0.012 workf -0.003 -0.047 -0.006 -0.030 -0.003 0.044 -0.003 -0.002 Senior -8.64e-005 0.007 0.002 -0.029 -0.001 -0.003 0.007 -0.002 Age 0.000 0.005 -0.003_-0.010 -0.012 -0.002 -0.002 0.004 Source: Authors’ computation We observe that the variables taken for this study are very weakly correlated. However, it appears that the hours and workf variables are fairly moderately and negatively correlated (-0.529) as well as the familisz and age variables (-0.639). Graph: Correlation of variables REMSES http://revues.imist.ma/?journal=REMSES&page=index 50