See discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/328654648 Effect of gold mining on total factor productivity of farmers: Evidence from Ghana Article  in  Acta agriculturae Slovenica · September 2018 DOI: 10.14720/aas.2018.111.2.08 CITATIONS READS 0 103 4 authors: Yaw Osei-Asare Michael Owusu Ansah University of Ghana North Dakota State University 34 PUBLICATIONS   173 CITATIONS    2 PUBLICATIONS   0 CITATIONS    SEE PROFILE SEE PROFILE Akwasi Mensah-Bonsu John K. M. Kuwornu University of Ghana Asian Institute of Technology 21 PUBLICATIONS   95 CITATIONS    132 PUBLICATIONS   530 CITATIONS    SEE PROFILE SEE PROFILE Some of the authors of this publication are also working on these related projects: Climate Change and Sustainability Studies View project Cassava Marketing Channel and Marketing-Risk Shift in Thailand View project All content following this page was uploaded by John K. M. Kuwornu on 01 November 2018. The user has requested enhancement of the downloaded file. doi:10.14720/aas.2018.111.2.08 Original research article / izvirni znanstveni članek Effect of gold mining on total factor productivity of farmers: Evidence from Ghana 1 1 1 2 Yaw B. OSEI-ASARE , Michael O. ANSAH , Akwasi MENSAH-BONSU , John K. M. KUWORNU Received November 23, 2017; accepted May 08, 2018. Delo je prispelo 23. novembra 2017, sprejeto 08. maja 2018. ABSTRACT IZVLEČEK Gold mining comes with several benefits to developing VPLIV ZLATOKOPOV NA CELOKUPNI DEJAVNIK countries, manifested mainly in the form of employment and PRODUKTIVNOSTI KMETOV: PRIMERI IZ GANE revenue, but simultaneously impacts negatively on the immediate environment. It affects the economic structure Zlatokopi prinašajo v dežele v razvoju številne koristi, ki se including agriculture and its productivity. Hence, this study kažejo v obliki zaposlitev in prihodku, a imajo hkrati investigated the effect of gold mining on total factor negativne učinke na neposredno okolje. Vplivajo na productivity of farmers in Ghana using 110 cocoa farmers gospodarstvo, vključno s kmetijstvom in njegovo from Asutifi North and Asutifi South districts of the Brong produktivnostjo. V raziskavi je bil na osnovi ankete med 110 Ahafo Region, categorised into mining and non-mining areas pridelovalci kakava na območjih Asutifi North in Asutifi respectively. About 83 % of the farmers in the mining areas South, regije Brong Ahafo preučevan vpliv zlatokopov na were affected by gold mining through channels such as land skupno produktivnost kmetov v Gani, ki so bili razdeljeni na disputes, relocation of farm/residence, high cost of labour, območja z in brez rudarjenja. Okrog 83 % kmetov na illegal small-scale mining and dust settlement on crops. Also, območjih z rudarjenjem je bilo prizadetih zaradi te aktivnosti about 64 % of cocoa farmers in the mining areas lost their in sicer zaradi prepirov za zemljišča, premestitev farm lands (between 0.4 and 3.64 ha as a result of gold kmetij/bivališč, velikih stroškov dela, ilegalnega mining. The Tornqvist Total Factor Productivity (TFP) indices malopovršinskega rudarjenja in usedanja prahu na posevke. for cocoa farmers in the non-mining areas (mean TFP of Okrog 64 % pridelovalcev kakava je na območjih z 1.404) were also statistically higher than those in the mining rudarjenjem izgubilo svoja kmetijska zemljišča (od 0,4 do areas (mean TFP of 0.371). The study concluded that gold 3,64 ha kot posledica zlatokopov). Indeksi Tornqvistove mining activities adversely affect productivity of farmers in skupne faktorske produktivnosti (TFP) pridelovalcev kakava the catchment areas. The study recommends, among others, so bili na območjih brez rudarjenja statistično značilno večji that a policy of land-for-land should be in place and (poprečje TFP = 1,404) kot na območjih z rudarjenjem effectively implemented to ensure that mining companies in (poprečje TFP = 0,371). V raziskavi je bilo ugotovljeno, da order to enhance and ensure continuity of livelihoods must zlatokopi negativno vplivajo na produktivnost kmetov na fully replace lands lost through mining activities. preučevanem območju. Na osnovi raziskave lahko priporočamo med drugim, da je učinkovita uporaba doktrine Key words: gold mining; total factor productivity; cocoa; menjave zemljišča za zemljišče primerna, da zagotovi, da se Tornqvist; Ghana ob delovanju rudarskih družb spodbuja in zagotavlja kontinuiteta kmetijstva preko popolne nadomestitve zemljišč, izgubljenih zaradi rudarjenja. Ključne besede: zlatokopi; skupna faktorska produktivnost; kakav; Tornqvist, Ghana 1 Department of Agricultural Economics and Agribusiness, College of Basic and Applied Sciences, P.O. Box LG 68, University of Ghana, Legon, Ghana 2 Department of Food, Agriculture and Bioresources; School of Environment, Resource and Development, Asian Institute of Technology, PathumThani 12120, Thailand Acta agriculturae Slovenica, 111 - 2, september 2018 str. 327 - 340 Yaw B. OSEI-ASARE et al. 1 INTRODUCTION Ghana’s agriculture is vastly dominated by smallholder (Amankwah & Anim-Sackey, 2003). A lot of studies famers; many commodities including cocoa, maize, have established linkages between mining and cassava and yam produced predominantly on small agriculture with the effects of gold mining either farms. According to Chamberlin (2007), more than beneficial or detrimental to the affected population or 70 % of Ghanaian farms are 3 hectares (ha) or smaller communities. Despite these linkages, the impact of in size and cocoa and maize represent the two most mining on agriculture has not been extensively studied cultivated crops in Ghana by smallholder farmers in Ghana especially at the micro level and available (Millennium Development Authority (MiDA) 2010; results are mixed. According to Mining Facts (2012) (a Asuming-Brempong et al., 2007). Resource for Canadian Mining Information), agriculture is growing in some areas as a result of mining and Cocoa takes a remarkable position in Ghana’s economy declining in others, depending on local circumstances. since it has long played an important role in Ghana’s According to Aragon and Rud (2013) and Van der economic development and remains an important source Ploeg (2011), most modern mines in the developing of rural work and national income. It also remains the world are located in rural areas, where agriculture is country’s most important agricultural export crop noted to be the main source of livelihood and thereby (Asuming-Brempong et al., 2007; International Cocoa having both direct and indirect effects on them. Gold Organization (ICCO) 2010; Boadi-Kusi et al., 2016). mining and agriculture are linked directly through the Ghana is currently the world’s second major producer of dependence on same or similar inputs (land, water cocoa beans, after Cote d’Ivoire with 21 % share of resources and labour). The competition between gold world cocoa production. Cocoa provides the second mining and agriculture for key inputs (such as land and largest source of export earnings after gold, representing labour) and environmental pollution from mining about 19 % of Ghana's total export earnings in 2015 creates potential negative spillovers to farmers (Aragon (Ashitey, 2012; ISSER, 2016). & Rud, 2013). They are also indirectly linked where mining firms have improved infrastructure in a way that Mining has also been an important component of supports agricultural development. A study by Cartier developing country economies. The grandness of the and Bürge (2011) found that mining has the potential to mining sector, particularly gold mining in the economy kick-start local economic development, such as of Ghana has increased considerably since the 1980’s agriculture and service oriented industries and also (Akabzaa, 2009). The Ghana Chamber of Mines report concluded that small-scale agriculture and mining are in 2008 indicated that, mining activities generated not livelihood alternatives, but are instead livelihood around 45 % of total export revenue, 12 % of complements and therefore have the potential to government's fiscal revenue and attracted almost half of contribute to more sustainable rural livelihoods. Foreign Direct Investment (FDI). Gold exports revenue in 2015 represented 41 % of the total exports of Ghana, In Ghana, gold mining coincidentally takes place in followed by cocoa beans, which account for 19 % rural communities/areas where lands earmarked for gold (Observatory of Economic Complexity, 2016). This mining are arable lands that farmers cultivate or have mining expansion has been attributed to the structural reserved for future use. Gold mining therefore reduces reforms in the 1980s that encouraged foreign investment farmers’ access to their farmlands and degrades the in large-scale mines, especially in gold mining (Ghana environment where farm lands are located (Aragon & Chamber of Mines, 2008; Akabzaa, 2009). Ghana has a Rud, 2013) and these factors have the potential to affect long tradition of gold mining with an estimated 2,488 the productivity of farmers. metric tons (80 million ounces) of gold produced between the periods of 1493 to 1997. It is the second There have been concerns over the low productivity and largest gold producer in Africa, after South Africa, the environmental impacts on cocoa production which third-largest African producer of aluminium metal and makes the long-term sustainability of the sector manganese ore and a significant producer of bauxite and uncertain (Gockowski, 2007). Average annual cocoa -1 diamond (Coakley, 1999). Mining, specifically gold yield in Ghana is about 400 kg ha in recent years and mining, has contributed immensely to the economy of this is among the lowest in the world compared to -1 Ghana through employment generation, attracting countries such as Cote d’Ivoire (800 kg ha ) and -1 foreign direct investment, contributing to export Malaysia (1880 kg ha ). The low productivity has been earnings and Gross Domestic Product (GDP). attributed to environmental conditions (climatic and atmospheric) and other factors such as hybrid seed type, Gold mining has effects on economic, social, input variables and cultural practices (Kolavalli & environmental, agricultural and food security of the Vigneri, 2011; Tom-Dery et al., 2012). Gockowski communities in which the mining takes place (2007) showed that cocoa production has focused on 328 Acta agriculturae Slovenica, 111 - 2, september 2018 Effect of gold mining on total factor productivity of farmers: Evidence from Ghana land expansion and intensive use of labour rather than productivity (Akabzaa, 2009). Though, gold mining and on land productivity. Thus output has increased mainly agriculture have all contributed immensely to the due to increase in area cultivated and partly due to economy of Ghana in general, whether local farmers increase in yield. The arable land and labour used for benefit in any way from gold mining activities within this expansion is also competed for by mining the catchment communities is not well established. companies (Aragón & Rud, 2013). Some farmers as a Instead, environmental regulators and opponents of the result have portions of their farmlands and others their mining industry have focused mostly on other aspects whole farmlands taken over by mining companies. such as risk of environmental degradation, health Talule and Naik (2014) indicated that farmers hazards, and social impacts. What is lacking in the experienced dust settlement on plantations after gold policy debate, however, is the crowding out mining was started in the state of Goa, India which mechanisms such as loss of land and agricultural output impeded crop growth. These conditions (competition for through gold mining. Does gold mining in Ghana land and labour, pollution from gold mining) have the reduces farmers’ productivities in gold mining areas? potential to reduce crop productivity. There is therefore This study seeks to determine the factors through which growing concerns with regard to the real benefits of gold mining affects the total factor productivity of gold mining to the ordinary Ghanaian farmer in the gold farmers in mining and non-mining areas of Ghana. mining communities as it affects their welfare and 2 MATERIALS AND METHODS 2.1 Total factor productivity Differences in indices can be viewed as differences in their abilities to provide approximations to the inter- The two most widely adopted methods employed in temporal changes in prices, quantities or productivity. agricultural productivity estimations are the superlative index approach and the quantity-only based index Four economic index numbers are commonly applied in approach (Bjurek, 1996; Førsund, 1997). The advantage estimating economic index: Laspeyres, Paasche, Fisher of using the superlative index method is more apparent Ideal, and Törnqvist. These indices produce different when it comes to the issue of aggregation consistency: methods of approximation (reflected in the formulae of the superlative index method is robust to various levels their aggregator functions) with correspondingly of disaggregation while the quantity-only index is not different properties. The Laspeyres and Paasche indices (Sheng et al., 2014). A number of different types of have traditionally been widely applied, but the economic indices using the superlative index approach Tornqvist and Fisher Ideal are increasingly used. The exist. Each type of index offers an approximate scalar Laspeyres, Paasche and Fisher output quantity indices measure of a multidimensional change over time in can be defined as follows, using the quantity aggregates prices, quantities or productivity. The different indices given in Equation (1) – (3) respectively approximate these inter-temporal changes in different ways according to their theoretical properties. N N QLst pisqit pisqis (1) i0 i0 N N QPst pitqit pitqis (2) i0 i0 QF L Pst  Qst Qst (3) Where qit = [ qit ,…… qNt ] and pit = [ pit ,…… pNt ] period or firm and qit is the quantity of i-th good in t-th are output and output price vectors respectively; t and s ∗period or firm. The input indices, Laspeyres (𝑄𝐿𝑠𝑡 ), denote time or period or firms; i = [1,..... N] are different ∗ ∗ Paasche (𝑄𝑃𝑠𝑡 ), and Fisher (𝑄 𝐹 𝑠𝑡 ) are obtained in a outputs. Thus, pit is the price of i-th good in t-th similar fashion and the ratio of output index to the corresponding input index gives the Total Factor Acta agriculturae Slovenica, 111 - 2, september 2018 329 Yaw B. OSEI-ASARE et al. Productivity (TFP) index. Therefore, in general terms, The Tornqvist (or Translog) index is an alternative ∗ TFP is expressed as 𝑇𝐹𝑃𝑎= 𝑄𝑎⁄𝑄𝑎 where a = [P, L, index, which is the weighted average of growth rates of F] representing Paasche, Laspeyres and Fisher. microeconomic data. For the output quantity index, this is expressed as follows: is it N 2 QT q  st  it  i1 qis  (4) N    lnQTst  is it  ln qit  ln qis  i1  2  (5) T 1 N  N   N  q lnQ itst   Pis qis /Pis qis   Pit qit /Pit qit  ln 2 i1  i1   i1  qis (6) The ratio of 𝑙𝑛 𝑄𝑇 ∗ to its input counterpart (𝑙𝑛 𝑄𝑇 ) expressed in log-change form for calculation. Tornqvist 𝑠𝑡 𝑠𝑡 provides the Tornqvist TFP index. Fisher is geometric is thus a geometric weighted average, while Laspeyres average and hence may also be a good approximation of and Paasche are arithmetic and harmonic averages, TFP. However, Tornqvist uses share weights often respectively. T Output T index TFP Index  stst InputT (7) indexst lnTFPT Indexst  lnOutput T indexst  ln Input T indexst (8) 1 N 1 K lnTFPTst  is it ln qit  ln qis  δ js  jt ln x jt  ln x js   (9) 2 i1 2 j1 N is number of outputs and K is the number of inputs, q any unknown production function (Diewert, 1976) and is output quantity, x is input quantity,  denotes output these indices can be interpreted as a production function revenue share and δ denotes input cost share. This shift (Technical change) if we assume technical approach (equation 9) of estimation is also known as the efficiency, allocative efficiency and constant return to Hicks-Moorsteen Approach and defines productivity scale. This second order flexibility makes the Fisher and index simply as the ratio of output and input index Tornqvist indices ‘superlative’ indices (Mishra & numbers (Diewert, 1992). Pujari, 2008). Diewert (1976) demonstrated that the Tornqvist index is an exact index for (i.e. is consistent According to Diewert (1976), there are two methods with) a “translog” structure of production whiles fisher used to assess the suitability of an index formula and is exact for quadratic. But the Laspeyres and Paasche they are; economic theory or functional approach (exact employs simplistic linear production function. The and superlative index number) and axiomatic or Test merits of the translog production function include the approach (index numbers that satisfy a number of fact that it places fewer restrictions on input (and desirable properties). The Tornqvist and Fisher indices output) relationships than other functions (Dean et al., provide more accurate approximations to changes than 1996). the Laspeyres or Paasche index because intermediate substitution possibilities are incorporated. According to The Tornqvist index satisfy almost all the basic and the index number theory, Tornqvist and Fisher Ideal commonly used axioms (positivity, proportionality, indices are a group of index numbers whose underlying continuity, units invariance, time-reversal, mean value, formula, as shown in equation 3 and equation 9, factor). However, the axiom of circularity (transitivity) provides a second order differential approximation to and factor reversal test are not satisfied by the Tornqvist 330 Acta agriculturae Slovenica, 111 - 2, september 2018 Effect of gold mining on total factor productivity of farmers: Evidence from Ghana index but the factor reversal test it is not considered between various time periods or among various cross- very serious and important (Diewert, 1992; Mishra & sections (Diewert, 1992; Mishra and Pujari, 2008). EKS Pujari, 2008). The non-transitive indices are method constructs geometric mean of all indirect transformed into transitive ones by applying the Elteto- comparisons via the N firms in the sample. EKS Koves-Szulc (EKS) transformation. The transitive adjustment is a minimum mean squared deviation from property is very important for a proper comparison original index. It is expressed as N 1 ITransitive Nst Isr  Irt  r1 (10) M M lnTFPTransitive 1 1  st   it i ln qit  ln qi  is i ln qis  ln qi  2 i1 2 i1  1 K 1 K  xaxaaaaaaa    jt  j ln x jt  ln x j   js  j ln x js  ln x j  2 j1 2 j1  (11) where M K is  p q  p q  js  pis is is is jsx js  p jsx js i1 j1 qi denotes output, xj denotes inputs and the pi and districts in India from 1956 to 1987 and the study covered five major and fourteen minor crops. They p j are the output price and input cost respectively. The concluded that the main sources of productivity growth bars refer to sample means. The transitive Tornqvist can have been public research and extension and private be calculated directly using equation 11. In productivity research. Sidhu and Byerlee (1992) analysed technical studies, the Fisher index has been used less frequently change and wheat productivity in Punjab, in the post- than the Törnqvist. However, the Tornqvist index Green Revolution period and found that the use of method has been preferred by many researchers in the inputs such as fertilizers and herbicides increased from area of productivity measurement and analysis because the 1970s to the 1980s but the use of labour-saving of the desirable properties outlined above (Dean et al, technologies such as tractors increased rapidly which 1996; Ali & Iqbal, 2004). Tornqvist Total Factor was also synonymous with the TFP changes. Kumar and Productivity approach therefore was used to estimate Rosegrant (1994) assessed TFP growth in 15 states of the TFP index of various respondents for this study. India and examined the sources of productivity growth. They used the Divisia Tornqvist index for computing The Tornqvist TFP has been used by several researches the total output, total input and TFP indices for rice, after its development but mostly at macro levels with using farm-level data from 1971 to 1988. They found few at the micro level (Mishra & Pujari, 2008). Kumar TFP and growth in crop inputs to have contributed and Mruthyunjaya (1992) analysed the TFP growth of roughly 3.5 per cent per year to rice production growth wheat in India. They used the Divisia-Tornqvist index and have enabled India to increase rice production per to compare the total output, total input, TFP and input capita in the presence of high population growth rates price indices for wheat grown in the major states of and limited land resources within the period. India, based on micro-level data. Coelli (1996) investigated productivity growth in agriculture in 2.2 Study area Western Australia using Tornqvist indices using three The Brong Ahafo Region, as shown in Figure 1 is the output groups (crops, sheep products and other) and five second largest region in Ghana with a land area of input groups (livestock, materials and services, labour, 2 39,557 km and 27 administrative capital and land) from 1953/4 to 1987/8. The total factor districts/municipalities. It covers 16.6 % of the productivity was observed to grow at an average annual country’s total land area. The region has an estimated rate of 2.7 %. Rosegrant and Evenson (1992) assessed population of 2,310,983 (2010 census) and located the sources of TFP growth in the crops sector in India, within longitude 00 15’ E-30 W and Latitude 80 45’ N- and compared the same with Pakistan and Bangladesh. 70 30’ S in the west central part of Ghana which is in They used the Tornqvist index to analyse TFP for 271 the transition zone of Ghana. The transition zone Acta agriculturae Slovenica, 111 - 2, september 2018 331 Yaw B. OSEI-ASARE et al. 0 stretches across the centre of the country from East to 1,300 mm and 27 C respectively. The productive soil West, where soils are deep, friable, and well drained, and bimodal rainfall season permit all year round cocoa and there is less dense forest cover. It has a bi-modal production. rainfall with average annual rainfall and temperature of Figure 1: Map of Brong Ahafo Region of Ghana Source: Geography Department, University of Ghana 2 With an arable land area of 23,734 km (60 % of land cassava, maize, cocoyam, rice, potato, pepper, plantain, 2 area) and land under cultivation being 9,746 km (41 % garden eggs, okra, watermelon, ground nut, cowpea, and of arable land area), opportunities exist to expand other tree crops such as cocoa, cashew and mango. cultivated land and improve productivity (MOFA, 2006; Some non-traditional farming activities practiced by the MOFA, 2013). Agriculture plays a very important role farmers include grass cutter rearing and bee-keeping. in the region’s economy as it engages 61 % of the Gold mining is also one of the economic activities in the population. The various farming systems/methods region with Newmont Gold Ghana Limited (NGGL) practiced by the farmers in the region include; shifting being the largest gold mining company situated in the cultivation, continuous cropping, mixed cropping, mono Asutifi North and South Districts (Ghana Statistical cropping, inter cropping, land rotation and bush fallows. Service 2010; Ghanadistrict.com 2014). Some of the major crops cultivated include yam, 332 Acta agriculturae Slovenica, 111 - 2, september 2018 Effect of gold mining on total factor productivity of farmers: Evidence from Ghana Figure 2: The map of Africa showing Ghana Source: https://www.pinterest.com/pin/157414949459332821/ 2.3 Data collection mining and non-mining areas. The farmers in mining areas were farmers who have their farms around the A pretested structured questionnaire was used to collect operational area of NGGL. The farmers in the non- data on output, input and relevant socioeconomic mining communities were farmers with their farms variables of cocoa farmers from January to February located at least 10 kilometres from the operational area 2015 and covered both the major and minor seasons of of NGGL such that they do not experience any direct 2013/2014 cropping year. A multi-stage sampling impact or effect of mining operations, such as hauling technique was employed in this household survey. through or around their farms and dust from mining Purposively, Asutifi North and South districts were operations settling on their crops. Finally, a simple chosen because its land area falls within the forest agro- random sampling of cocoa farmers from each ecological zones of the Brong Ahafo region where community was employed, resulting in 110 cocoa cocoa production is concentrated and all the farmers. Table 1 shows the distribution of respondents communities located close to the operational areas of by communities (69 from the mining areas and 41 from Newmont Gold Ghana Limited (NGGL) involved in the non-mining areas). cocoa production. Respondents were farmers in both the Table 1: Communities and number of cocoa farmers sampled District Community/Town Mining Non-mining Total Asutifi North Kenyasi 40 0 40 Ntotoroso 29 0 29 Obengkrom 0 19 19 Sub-Total 69 19 88 Asutifi South Amanfrom 0 5 5 Achirensua 0 11 11 Nkasiem 0 6 6 Sub-Total 0 22 22 Total 69 41 110 Source: field survey, 2015 Acta agriculturae Slovenica, 111 - 2, september 2018 333 Yaw B. OSEI-ASARE et al. TFPIP 1.0 software developed by Coelli (1997) was 2.4 Productivity differences among farmers in employed to estimate the transitive TFP indices. The mining and non-mining areas variables used in the estimation include the output and The assumption underlying the differences in cocoa output prices as well as input and input cost of cocoa productivity is that productivity should be the same for produced in 2013/14 production year. Cocoa output farmers in the mining and non-mining areas in the (2013/14 production year) was measured in kilograms absence of gold mining since they are in the same agro- and output price is measured in Ghana Cedis per ecological (transitional) zone, experience similar kilogram. Labour is captured based on the total man- environmental and climatic conditions and encounter days employed by the i-th farm during the production the same input market and cocoa output market year. One man-day for labour is calculated as one adult arrangements and challenges. To determine whether male working for one day (8 hours); one female there is productivity difference, the study adopts and working for one day (8 hours) equals 0.75 man days. performs a number of z-tests (of equality of means) to Seedling is the quantity of seedling used by the i-th analyse whether farmers in the non-mining communities farmer for the production year, measured in number for are more productive than those in mining communities. cocoa seedlings and price per seedling is measured in The mean values of the Tornqvist TFP, inputs and Ghana Cedis. Total quantity of weedicide, fungicide and output indices are estimated and their mean differences insecticide used by the i-th farmer measured in litres. are statistically examined. In the determination of the The price per litre is measured in Ghana Cedis. differences in the values of the means in the two areas, the z-test used for the analysis is given as: y2  y1 Zcal  s2 21 s 2 n1 n2 (12) where y and y are the mean TFP index of the H1: ?̅?1 < ?̅?2 the mean TFP index of farmers in 1 2 mining area is significantly less than the mean TFP of mining and non-mining areas respectively, s1 and s2 are farmers in non-mining area the standard deviations of the two samples, n1 and n2 are the sizes of the two samples. This hypothesis is repeated for the output and input indices. The decision rule is that if zcal is greater than (in 2.5 Statement of hypothesis absolute terms) the zcrit, then we reject the null H0: ?̅?1 = ?̅?2 there is no significant difference hypothesis (H0) in favour of the alternate hypothesis between the mean TFP index of farmers in mining and (H1). non-mining areas 3 RESULTS AND DISCUSSION 1 3.1 Socio-demographic characterisation of years) ages and this implies that quality of labour is respondents good which may positively affect their productivity. Diverse age groups cultivate cocoa therefore Males represent the majority (68 %) of the respondents improvement in cocoa productivity will positively affect which affirms the dominance of males in cocoa livelihoods. The majority (41 %) of the respondents production, mostly because of the laborious and cost have completed middle school or junior high intensive nature of cocoa farming which discourages educational level. However, 28 % of the farmers had no most females from investing into cocoa production. formal education at all. In general, about 72 % of the Also, in Ghana, land is mostly owned and controlled by farmers had access to some level of formal education. the male head of the household which also gives them The educational level of farmers is known to affect an advantage. From Table 2, the age of cocoa farmers farming activities. The majority (67 %) of the ranges between 20-85 years with a mean age of 50 respondents have a household size between 5 and 9 and years. The majority of cocoa farmers (51) fall between one household (1 % of the respondents) has a household 41 to 50 years, representing 46 % of the respondents. size of 15 people. The mean household size is seven (7). One can infer from these results that most cocoa farmers A greater percentage (40 %) of the farmers in the study in the study area are in their economically active (15-60 1Ghana Statistical Service (GSS) (2012) definition for economically active age 334 Acta agriculturae Slovenica, 111 - 2, september 2018 Effect of gold mining on total factor productivity of farmers: Evidence from Ghana area had farm sizes less than 2.02 ha. This suggests that farmers cultivated between 6.47 – 8.09 ha (8 %) and the majority of the farmers are peasant and small-scale above 8.09 ha (3 %). farmers. However, as shown in Table 2, very few cocoa Table 2: Socio-demographic characteristics of the respondents Cocoa Farmers Socioeconomic Item Non- variables Mining Percent mining Percent Total Percent area area Female 23 20.91 12 10.91 35 31.82 Sex Male 46 41.82 29 26.36 75 68.18 20-30 5 4.55 1 0.91 6 5.50 31-40 10 9.09 12 10.91 35 31.80 Age (years) 41-50 22 20.00 8 7.27 51 46.40 Above 50 32 29.09 20 18.18 18 16.40 (Minimum = 25 Mean = 49.8 Maximum = 85) No Schooling 18 16.36 13 11.82 31 28.18 Primary 7 6.36 7 6.36 14 12.73 JHS/MSLC 28 25.45 17 15.45 45 40.91 Education SHS/O/A Level 14 12.73 3 2.73 17 15.45 Technical/Vocational 1 0.91 1 0.91 2 1.82 Tertiary 1 0.91 0 0.00 1 0.91 1-4 7 6.36 7 6.36 14 12.73 5-9 48 43.64 26 23.64 74 67.27 Household size 10-14 14 12.73 7 6.36 21 19.10 Above 14 0 0.00 1 0.91 1 0.90 (Minimum = 1 Mean = 7 Maximum = 15) <5 26 23.64 18 16.36 44 40.00 6-10 23 20.91 11 10.00 34 30.91 4.45 – 6.07 13 11.82 7 6.36 20 18.18 Land size (ha) 6.47 – 8.09 4 3.64 5 4.55 9 8.18 Above 8.09 3 0 0.00 3 2.73 (Minimum = 0.5 Mean = 7.2 Maximum = 55) Source: field survey 2015 3.2 Perceived effects of gold mining on crop cost of farmland, high cost of labour, and relocation of production farm/residence, illegal small-scale gold mining, land disputes, and settlement of dust on their crops. As The majority (83 %) of cocoa farmers in mining areas shown in Figure 3, Relocation of farm/residence (32 %) (69 farmers) indicated that gold mining has affected and high cost of farmlands (26 %) were the major their crop production. According to them, the channels channels through which gold mining has affected cocoa through which they have been affected included high Acta agriculturae Slovenica, 111 - 2, september 2018 335 Yaw B. OSEI-ASARE et al. farmers. This confirms a study by Taphee et al. (2015) inadequate compensation schemes from mining on the economic efficiency of cocoa production which companies. Another 4 % and 9 % of cocoa farmers in concluded that high cost of production per hectare was a the mining areas mentioned illegal small-scale mining major problem to cocoa farmers in Ondo State, Nigeria. and settlement of dust on their crops respectively as The same reasons were given by Schueler et al. (2011) impacting negatively on cocoa productivity. Dust on the study of the impacts of surface gold mining on settlement on cocoa leaves inhibit the growth as well as land use systems in Western Ghana where farmers injuring the plants and thereby reducing the described their livelihood situation after relocation as productivity. worse, due to the loss of their traditional farmlands and 35% 32% 30% 26% 25% 20% 15% 15% 13% 9% 10% 4% 5% 0% land disputes relocation of high cost of high cost of illegal small dust settle on farms/residence farmlands labour scale mining crops Factors Figure 3: Factors affecting cocoa farming and productivity Source: field survey 2015 Figure 4 shows the average reduction in cocoa farm About 52 % indicated their farmlands reduced by 0.4 to sizes because of the commencement of gold mining 1.21 ha. The study by Mumuni et al. (2012) found operations in the mining areas. About 36 % of cocoa similar results where an estimated 9,575 individual crop farmers in the mining areas indicated that their farm farmers in the Asutifi North and South districts lost sizes have not reduced as a result of the gold mining. 7,500 hectares of farmlands, an average of 0.8 ha per However, the rest of the farmers (64 %) in the mining farmer which were annexed by Newmont Gold Ghana areas mentioned various reductions in farm acreages. Limited for gold exploration. 336 Acta agriculturae Slovenica, 111 - 2, september 2018 Percentage Effect of gold mining on total factor productivity of farmers: Evidence from Ghana 60% 52% 50% 40% 36% 30% 20% 9% 10% 1% 1% 0% 0 1--3 4--6 7--9 above 9 Acre Figure 4: Cocoa farm size lost/reduced through gold mining Source: field survey 2015 3.3 Total factor productivity (TFP) in mining and finds that, farmers have higher averages of indices in non-mining areas non-mining areas as compared to the mining areas. Table 3 presents the summary statistics of the estimated input, output and TFP indices. In general, the study Table 3: Summary statistics of Tornqvist total factor productivity indices Area Observation Total Mining Non-Mining Mean TFP Index 0.371 1.404 0.756 Standard Deviation 0.463 1.512 1.106 Mean Output Index 0.430 1.193 0.714 Standard Deviation 0.641 1.610 1.160 Mean Input Index 1.755 1.770 1.760 Standard Deviation 3.068 2.900 2.993 Source: field survey 2015 Table 4 shows the summary of the compared means. there is not any difference between the inputs index The mean difference between the input indices for between farmers in mining and non-mining areas, the farmers in the two categories was not statistically difference in the output and TFP could likely be different and thus the null hypothesis is not rejected. attributed to the fact that gold mining has significantly The reasons may likely be that both farmer groups have contributed to lower cocoa productivities of farms in the access to same input types and prices from same mining areas mainly through dust settlement on cocoa markets and also utilise similar input amounts. For the trees that impede the growth of cocoa trees and thereby output and TFP index, the mean differences were reducing the productivity of cocoa farms in the mining statistically significant at 1 % significance level. The areas. To a lesser effect, the lower use of inputs could Output and Tornqvist TFP indices were higher in non- also contribute to lower productivities. mining areas than in mining areas (see Table 3). Since Table 4: Mean comparison (t-test) of output, input and TFP indices for mining and non-mining areas Cocoa TFP Index T-statistics Significance Input Index -0.0256 0.9796 Output Index -2.8990 0.0056 TFP Index -4.2564 0.0001 Source: field survey 2015 Acta agriculturae Slovenica, 111 - 2, september 2018 337 Percentage Yaw B. OSEI-ASARE et al. 4 CONCLUSIONS The study estimated the TFP difference among cocoa Land Valuation Board (LVB) must review crop farmers in gold mining and non-mining areas using compensation rates to reflect economic realities and micro-level data from the Asutifi North and South must also factor in the sustainability of cocoa trees Districts of the Brong Ahafo Region. Based on the (projected income flows of the economic life of cocoa findings from the study, it is concluded that gold mining trees) when rural livelihoods are at stake. When farms in the study area has a negative effect on productivity of are to be relocated, mining activities need not interfere farmers located in mining areas. The adverse impacts with crop production activities. The findings of the are mainly dust settlement on cocoa trees from mining study also suggest that mining companies should activities, which impedes cocoa growth and thereby adequately compensate for crops. The farmers in Asutifi reducing the productivity. To lesser extent, the use of were compensated based on the Mining and Minerals relatively less productive inputs contributes to lower Law, 1986 (PNDCL 153). The existing policies and TFP for this group of farmers. Cocoa farmers also laws relating to mining should incorporate the education perceived land disputes, relocation of farms/residence, of farmers and mining companies on the effect of high cost of farmlands, high cost of labour and illegal mining activities on crop productivity. small-scale mining as factors contributing to low productivities. Thus, key lessons from the study are that: mining activities impact negatively on cocoa productivity and The uniqueness of this study is rooted in the application rural livelihoods in spite of its contribution to of Total Factor Productivity (TFP) and not the effect of government revenue. Farmers in gold mining catchment one single input (i.e., partial factor productivity) on communities perceive mining activities as inimical to cocoa productivity. Often, qualitative approaches are their food security situation and livelihoods through the adopted to highlight the effect of mining on crop loss of croplands and inadequate crop compensation. production and productivity. Using a quantitative approach, this study has identified and attributed low There is a high level of confidence in the study’s cocoa productivity in mining areas to mining activities. empirical findings: use of primary data collected from statistically representative cocoa farmers and the use of The findings of the study are important to inform policy basic and robust quantitative approach to determine on how to eliminate or reduce the existing negative results. In other words, the approaches adopted in the effect of gold mining on cocoa productivity of rural study have provided enough data and information to farmers. A policy of land-for-land should be in place make informed decisions on the phenomenon under and effectively implemented to ensure that lands lost study. through mining activities (whether currently in use or lying fallow) must fully be replaced by mining The study employed the use of primary cross-sectional companies to enhance and ensure continuity of data and therefore recommends that, subsequent livelihoods. In the absence of this, areas devoted to research should consider the use of a time series or cocoa production will dwindle, labour may shift from panel data for the analyses and also to determine TFP cocoa production and productivity may fall (reducing growth rates. Moreover, future studies could quantify, in government revenue, household income and livelihood). dollar terms, the losses in cocoa productivity and Secondly, if farmers loose crop lands (tree crop and livelihoods resulting from mining activities in food crop lands) adequate crop compensations that catchment communities and compare with cocoa reflect current economic realities must be paid by revenues generated from such areas. mining companies to farmers. Government through the 5 REFERENCES Akabzaa T. (2009). Mining in Ghana: implications for and diamond mining industry of Ghana. Resources national economic development and poverty Policy, 29(3), 131-138. reduction (pp. 25-65). London, Pluto Press. doi:10.1016/j.resourpol.2004.07.002 Ali S., & Iqbal M. (2004). Total Factor Productivity Aragon F. M., & Rud J. P. (2013). Modern industries, Growth in Pakistan's Agriculture: 1960-1996. 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