Job Absorption Capacity of Nigeria’s Mining and Quarrying Sector

http://jbar.sciedupress.com Journal of Business Administration Research Vol. 10, No. 1; 2021 Job Absorption Capacity of Nigeria’s Mining and Quarrying Sector Adetunji Adeniyi1 1 Tunji Adeniyi and Associates Limited, Lagos, Nigeria Correspondence: Adetunji Adeniyi, Tunji Adeniyi and Associates Limited, Lagos, Nigeria. www.tunjiadeniyi.io. Tel: 234-(0)-805-700-0700. E-mail: tunjiadeniyi@icloud.com Received: April 18, 2021 doi:10.5430/jbar.v10n1p51 Accepted: May 3, 2021 Online Published: May 8, 2021 URL: https://doi.org/10.5430/jbar.v10n1p51 Abstract The mining and quarrying sector account for 10.6 per cent of the GDP and 0.2 per cent of employment in 2014, according to the records of the National Bureau of Statistics. Relative to the gross value added of the mining and quarrying sector, its contribution to aggregate employment is small. Meanwhile, unemployment is one of the most pressing macroeconomic problems in Nigeria today. It is against this background that the job absorption capacity of the sector was investigated to facilitate job creation policies in the sector. Time series secondary data covering 1981 to 2014 on the rebased Gross Domestic Product (GDP) and sectoral Gross Value Added (GVA) at 2010 constant basic prices, employment, wage rate, inflation rate and interest rate were collected from the National Bureau of Statistics and the Central Bank of Nigeria. Sectoral employment elasticities of growth were measured using Vector Error Correction Model (VECM) regression at α0.05. Mining and quarrying sectoral elasticity of employment was -0.05, but was not significant. However, there were significant inter-sectoral and inter-temporal relationships on which job creation policies may be based. Keywords: economic growth, employment elasticity, gross value added, mining and quarrying sector 1. Introduction Nigeria has unacceptably high levels of unemployment at 33.3 per cent at the last count (NBS 2021). Economists have postulated in literature that economic growth generates employment. It is against this background that it was expected that the growth regime of 1981 to 2014 should have helped to reduce unemployment, although, economists have acknowledged the advent of ‘jobless growth” whereby unemployment co-exists with economic growth (Adeniyi, 2021). Source: Adeniyi, 2019; and, 2021 Figure 1. Rising Unemployment Co-existing with Economic Growth Published by Sciedu Press 51 ISSN 1927-9507 E-ISSN 1927-9515 http://jbar.sciedupress.com Journal of Business Administration Research Vol. 10, No. 1; 2021 According to the National Bureau of Statistics (2015) the rate of unemployment was 8.2 per cent by the end of the second quarter of 2015, despite the growth performance of the preceding years. The situation, which has further deteriorated due to subsequent economic decline, was recently accentuated by the outbreak of the COVID-19 pandemic, as unemployment rose to 27 per cent by the second quarter of 2020; and more recently to a record high of 33.3 per cent in the fourth quarter of 2020 (FGN, 2017; FGN, 2020; NBS 2020; and, NBS 2021). Petroleum constitutes the major export product of Nigeria, accounting for about 95 per cent of government’s external revenue (Adeniyi, 2019). The solid mineral subsector is, however, largely undeveloped, and accounts for insignificant contribution to government revenue. The subsector is dominated by unorganised and unregulated artisanal miners, which further makes it difficult to account for their economic contributions. The total sectoral contribution to GDP was 10.6 per cent in 2014, down from 33.1 per cent in 1981, while its contribution to employment has remained stagnant at 0.2 per cent across the period under review (Adeniyi, 2019). Consequently, policy makers should be interested in the job creating capacity of the mining and quarrying sector in order to expand the job creating capacity of the better organised oil and gas subsector and to harvest the employment creating potentials of the solid mineral subsector. The pertinent research question then is, what is the job absorptive capacity of the sector? Therefore, this study sets out to investigate the employment intensity of output growth in the mining and quarrying sector for the purpose of advancing policy recommendations to improve its employment generating capacity. 1.1 Literature Review According to Kapsos, 2005; Ajilore and Yinusa, 2011; and, Adeniyi, 2019, when a country experiences positive GDP growth, the employment elasticity figures can be explained as follows: Employment elasticity greater than 1 implies, positive employment growth; and, negative productivity growth. Employment elasticity between 0 and 1 implies, positive employment; and, positive productivity growth. Higher elasticity within this range implies more employment (lower productivity) intensive growth. Negative employment elasticity implies, negative employment growth; and, positive productivity growth. 1.2 Review of Empirical Literature According to Adeniyi, 2019, in Nigeria, Sodipe and Ogunrinola (2011) estimated the impact of economic growth on employment using time series data. Ordinary Least Square (OLS) regression model was employed to analyze the data. The result revealed that economic growth impacted positively and significantly on employment. However, a negative and significant relationship between employment growth rate and the Gross Domestic Product (GDP) growth rate was observed. Also, Oloni, (2013) investigated the impact of aggregate economic growth in Nigeria had on employment generation using Johansen Vector Error Correction Model. The findings revealed that, although economic growth had positive relationship with employment, the relationship was not significant. He did not disaggregate his analysis by sectors. Although, Ajakaiye et al, 2016, attempted a sectoral analysis using the Shapley disaggregation, their methodology is not as robust as the Vector Error Correction Model employed in this study, which focuses specifically on the mining and quarrying sector of Nigeria. 2. Methodology The study examined the job absorptive capacity of the mining and quarrying sector of the Nigerian economy. The employment intensity of the sectoral gross value added (GVA) growth between 1981 and 2014 was estimated. The secondary data used for the study were collected from the Central Bank of Nigeria (CBN), and the National Bureau of Statistics (NBS). The variables collected, collated, analysed and presented were the figures of mining and quarrying sectoral gross value added, mining and quarrying sectoral employment, minimum wage rates, weighted average prime lending rates and inflation rates from 1981 to 2014. Similar data were collected for the other sectors. Estimation methodology of elasticity of employment, in deference to Ajilore and Yinusa (2011); Mkhize (2015); and, Adeniyi (2019) was used to analyse the data. Specifically, we used the Vector Error Correction Model (VECM). 2.1 Theoretical Framework The national output of an economy, and by extension, any sector of the economy, is produced by combining labour input (demand for labor) with other factors of production in that economy or sector. The demand function for labor can be derived by assuming a constant elasticity of substitution (CES) production function and solving the marginal product of labor (MPL) equation for the labor input variable (Mkhize, 2015, and Adeniyi, 2019) as follows: Published by Sciedu Press 52 ISSN 1927-9507 E-ISSN 1927-9515 http://jbar.sciedupress.com Journal of Business Administration Research Vol. 10, No. 1; 2021 GVAt = A{αK –ρ+ (1-α) E –ρ}–η/-ρ (1) where, GVAt = Gross Value Added (sectoral output) Kt = Capital (input) in year t; Et = Employment/labor (input) in year t. A = Efficiency parameter; A > 0 η = Returns to scale parameter; η > 0 α = Distribution parameter; 0 < α < 1 ρ = Extent of substitution (between K and E) parameter, ρ > -1, and related to elasticity of substitution; σ = 1 / 1+ ρ The derivative of labor (i.e. marginal product of labor (MPL)) from Equation (1) can be written as: dGVA /dE =η(1-α)/Aρ/η.GVA (1+ρ)/η/E ρ+1 (2) The above MPL expression is solved for the Et input variable in order to derive the empirical labor (employment) demand function: η(1-α)/Aρ/η.GVA (1+ρ)/η = Et ρ+1 [η(1-α)/Aρ/η.GVA (1+ρ)/η]1/ρ+1 = Et Et = [η(1-α)/Aρ/η.GVA (1+ρ)/η]1/ρ+1 = [η (1-α) / A ρ/η ]1/ ρ+1 . GVA (1+ρ/η)(1/ρ+1) Et =β0 GVA β1 (3) where, β0 = [η (1-α) / A ρ/η ]1/ ρ+1 β1= (1+ρ/η)(1/ρ+1) β1= 1+ρ/η . σ σ (elasticity of substitution) = 1/ρ+1. However, if we log-transform Equation (3) above, we obtain the following employment function: ln Et = ln β0+ β1 ln GVAt = β0 + β1 ln GVAt + ... βn lnXnt +εt (4) In order to estimate the sectoral employment elasticity of the mining and quarrying sector of the economy and the elasticity of employment with respect to wage rate, inflation and user cost of capital in the economy during the period under review, a double-log linear regression equation was constructed for the parameters as follows: (5) where, t = 1, …, n years. The dependent variable, , represents aggregate employment (formal and informal, public and private) in thousands of persons in the specific economic sectors, in year t. The exogenous variables are: Wt = minimum wage rate in time t, measured in thousand Naira. rt = is the user cost of capital in time t, represented by the weighted average prime lending rate in the economy. πt = inflation rate in time t. GVAt = mining and quarrying sectoral GVA in constant 2010 basic prices. GVA_MIN&QUA= Gross Value Added in the mining and quarrying sector in year t. TIME (Tt) = yearly time trend variable, where t = 1 is year ended December, 1981 and t = 34 is year ended December, 2014. εt = error term. From the model, the equation to analyse is: EMP_MIN & QUA =f(GVA_ MIN & QUAt , , , (6) Where: Published by Sciedu Press 53 ISSN 1927-9507 E-ISSN 1927-9515 http://jbar.sciedupress.com Journal of Business Administration Research Vol. 10, No. 1; 2021 The above model postulates that employment of persons in the mining and quarrying sector, will vary with gross value added in mining and quarrying, and macroeconomic variables of wage rate, interest rate, and inflation rate, and that employment decisions by economic units in the mining and quarrying sector are a function of previous year’s information. 2.2 Description of the Variables Gross Value Added (GVA) is the value of goods and services produced in a sector. It is the output of the sector less intermediate consumption in that sector. Yearly mining and quarrying GVA series at 2010 constant basic prices were collected from NBS for the period 1981 to 2014. The series, which were in billions of Naira, were produced after the GDP rebasing exercise of 2014 which used 2010 as the base year. (Adeniyi, 2021). Time trend: In a time-series analysis, time is a variable as the other variables and the relationships among them changes or stabilises over time. The lagging approach employed in the analysis took care of the time trend in determining / explaining employment level in the economy (Adeniyi, 2021). Wages: Wage series were not available from the National Bureau of Statistics and other relevant organisations. Furthermore, NBS has not produced the re-based GDP using expenditure approach as of the time of this study. The latter would have been decomposed to obtain the wage component. Although there are various concepts of wages we adopted the minimum wage in the economy for the following reasons which outweigh its limited variability since it does not change annually: It is more relevant to policy making; more determinable with exactitude; better known to everybody; more relevant to the economic strata where employment expansion is most desired, more relevant in determining the minimum financial welfare in the economy, etc. According to ILO (1970) the minimum wage represents the amount of compensation that an employer is required to pay wage earners for the work performed during a giving period, which cannot be reduced by collective agreement or by an individual contract. Minimum wage is, therefore, the lowest compensation that employers may legitimately pay to workers. This implies that it is the price floor below which a worker may not legally sell his labour services (Adeniyi, 2021). Furthermore, recent debates among the three tiers of Government in Nigeria, the Labour Union, the Legislators, Non-Governmental Organisations, and Social Commentators on minimum wage did not only support this choice but seems to have heavy impact on the ethnic - or geo - political organisation, reorganisation and/or viability of the federating units of Nigeria (Eme & Ugwu, 2011; Ajimotokan and Obi, 2016; Buhari, 2016). It is more relevant in employment decision making particularly in the government sector that is very wage elastic, but expected to be employment intensive. For example, according to the Senate of the Federal Republic of Nigeria in its plenary of July 21, 2016, ‘27 states of the federation can no longer pay the salary of their workers.’ Other wage concepts are: average wages in the public sector, average wages in the private sector, average wages in the junior staff category and average salaries and emoluments of senior staff categories both in the public and private sectors (NECA, 2003; and, Adeniyi, 2021). For this study, minimum wage change history was obtained from NBS and from this; the minimum wage series was generated. Interest rate: There are various concepts of the user cost of capital (Ajilore and Yinusa, 2011 Mkhize, 2015). This study used the Weighted Average Prime Lending Rate (WAPLR) of banks operating in the economy during the period, because it is more relevant considering that it affects every economic borrowing decision in the economy. It is subject to regular (weekly) professional determination and reviews at the Assets and Liability Management Committees (ALCOs) of all the banks operating in the economy. Besides, the determination of WAPLR also bears reference to the weighted average cost of generating loanable funds by lenders in the economy. Long-term lending, available only to prime bank customers, is consummated at around the Prime Lending Rate (CBN, 2015; and, Adeniyi, 2021). Unemployment Rate: The data of unemployment rate was collected at the National Bureau of Statistics (NBS). Inflation Rates: Annual Inflation Rates data were also collected from the National Bureau of Statistics. 2.3 Unit Root Test Time series data are most useful when they do not contain noise or unit root problems. However, frequently Published by Sciedu Press 54 ISSN 1927-9507 E-ISSN 1927-9515 http://jbar.sciedupress.com Journal of Business Administration Research Vol. 10, No. 1; 2021 associated with time series data is the problem of noise. Consequently, it is necessary to test for and remove unit roots when and if they exist in any series. If they do, the noise must first be removed before proceeding with analysis in other that the results are not spurious, in other words, so that we can rely on the results for interpretation. When there is no unit root or the noise has been removed, the series is said to be stationary. Several tests of stationarity have been developed to examine whether a series is stationary or non-stationary. If the series under analysis is stationary at level, this implies that the series contains no noise. Therefore, the series is said to be I(0). However, if the series being analysed is non-stationary in its level form, but stationary in the first difference form, then, it is said to be integrated of order 1 or I(1). Most time series can be classified as being integrated of order d, I(d). This means that the series must be differenced d times to become stationary. The most common test of the stationarity of a time series is the Augmented Dickey-Fuller (ADF) test proposed by Engle and Granger in 1987 as follows (Adeniyi, 2021): Δ𝒀𝒀𝒕𝒕=𝜷𝜷𝟏𝟏+ 𝜷𝜷𝟐𝟐t+ 𝜹𝜹𝒀𝒀t-1+ (7) where Yt is the relevant time series, t is time trend, εt a white noise error term ; where ΔYt-1 = (Yt-1 – Yt-2 ), ΔYt-2 = (Yt-2 –Yt-3 ) (8) The hypothesis of the ADF test will be specified as follows: Null hypothesis: Ho: β = 0 Alternative hypothesis: H1: β < 0 If the null hypothesis is not rejected, then the series is non-stationary, but if it is rejected, it means the series is stationary or I(0). A time series is stationary when the process by which the data is generated is the same over time. That is, the series’ mean, variance and covariance with lagged values of itself should not change with time (Hansen & King, 1996; Mkhize, 2015; Adeniyi, 2019). According to Mkhize, (2015) ADF test tends to over-reject the null hypothesis when using too few lags and to reduce the degrees of freedom when there are too many lags. Song and Witt (2000) in their study of tourism demand modelling and forecasting, justified the importance of appropriate lag length for time series data. In determining the appropriate lag length for the ADF test in the study, Schwarz Information Criterion was used. 2.4 Cointegration Test According to Stock and Watson (2017) when variables individually non-stationary are co-integrated, two (or more) variables may have common underlying stochastic trends along which they move together on a non-stationary path. For simple instances of few variables and one co-integrating relationship, an error-correction model (ECM) is the appropriate econometric specification. In this model, the equation is differenced and an error-correction term estimating the previous period’s (t-1) deviation from long-run equilibrium is included. The most common tests to investigate the number of common trends among the series in a VAR/VEC were developed and proposed by Johansen (1995). The approach is very similar to testing for unit roots in the polynomial representing an Auto Regression (AR) process. If we have n I (1) variables that are modelled jointly in a dynamic system, there can be up to n – 1 co-integrating relationships linking them. Stock and Watson (2017) thought of each co-integrating relationship as a common trend interconnecting some or all the series in the system. The co-integrating rank of the system is the number of such common trends, or the number of co-integrating relationships (Adeniyi, 2021). To select the co-integrating rank r, a sequence of tests was performed. First, the null hypothesis of r = 0 against r ≥ 1 to investigate if there is at least one co-integrating relationship was tested. If and when r = 0 is not rejected, then it was concluded that there were no common trends among the series, in which case, a VEC model is not needed. VAR is then simply used in the differences of the series. If r = 0 is rejected at the initial stage, then at least some of the series are co-integrated. Then, the number of co-integrating relationships is determined. The second step is to test the null hypothesis that r ≤ 1 against r ≥ 2. If the hypothesis of no more than one common trend is not rejected, then we estimate a VEC system with one co-integrating relationship. If the hypothesis that r ≤ 1 is rejected, then the hypothesis r ≤ 2 against r ≥ 3 is tested, and so on. r is chosen to be the smallest value at which the null hypothesis that there are no additional co-integrating relationships is not rejected. Published by Sciedu Press 55 ISSN 1927-9507 E-ISSN 1927-9515 http://jbar.sciedupress.com Journal of Business Administration Research Vol. 10, No. 1; 2021 Johansen proposed many relevant tests that can be employed at each stage. The most common is the trace statistic, which was used in this study. The Stata command vecrank prints the trace statistic or, alternatively, the maximum-eigenvalue statistic. 2.5 Vector Error Correction Model Vector error correction model (VECM) is the regression that takes into consideration the correction of the noise/unit root in the model as well as estimating the part of the noise that is being removed at each short run. (Stock and Watson, 2017). The software used for the regression analysis was Stata. Priori expectations The signs expected for the coefficients in the model are as follows: Wt: negative. If and when the percentage change in nominal wages increases, it reduces employers effective demand for labour, given a constant budget constraint and vice-versa. (Dokpe 2001; Soto 2009; Baah-Boateng, 2013; Adeniyi, 2021). rt: positive or negative. If the interest rate increases, the demand by employers for capital decreases and the demand for consumer goods and services also decreases. The reduced demand for capital (that would become relatively more expensive) will reduce labour productivity and the depressed demand for consumer goods and services will decrease the derived demand for labour, vice versa. In these situations, employment would move in opposite directions to long term interest rates. However, in some industries capital may be a substitute for labour. In that wise, an increase in long term interest rates may depress the demand for capital and enhance the demand for labour, the substitute, vice versa. Consequently, long term interest rates would be a positive correlate of employment. (Malunda, 2012; Nangale, 2012; Baah-Boateng, 2013; Mkhize, 2015; and, Adeniyi, 2021). πt: positive or negative. The effect of inflation rate is expected to either be positive or negative. When and if the rate of inflation increases, the marginal revenue products of labour increases. As a consequence, there is an increase in the demand for labour by employers. On the other hand, an increase in inflation rate may reduce consumer demand for goods and services, thereby depressing the derived demand for labour as a factor of production. (Mkhize, 2015). GVAt: positive. The growth of sectoral real GVA will lead to expanded derived demand for labour because employers will view real sector output growth as an indication of future expansion in demand for consumer final goods and services (Soto, 2009; Sodipe and Ogunrinola, 2011; Temitope, 2013; Mkhize, 2015; and, Adeniyi, 2021). In order to make the model very useful for the analysis, equation (6) is log-linearised. The logarithmic functional form ensures that βi can be interpreted as elasticities (Koop, 2005), where β2 is the elasticity of employment with respect to user cost of capital, while holding all other things constant ceteris paribus. In the same manner, also β3 is the elasticity of employment with respect to output. It estimates the proportional change in the number of labour employed for a proportional change in sectoral GVA, holding other factors constant, ceteris paribus. Consequently, a positive elasticity coefficient of 0.25, for example, indicates that a percentage increase in GVA is associated with a quarter of a percentage increase in the number of people employed. The employment elasticity coefficients that will be calculated from the equation above imply that employment is a direct correlate of output (Soto, 2009; Sodipe and Ogunrinola, 2011; Temitope, 2013; and, Adeniyi, 2021). Consequently, the elasticity coefficients estimated for individual economic sectors are suggestive of the correlation between the number of persons employed and gross value added. 3. Results and Discussions Table 1 below presents the result of the VECM estimation of equation 6. Column two of the table contains the estimated regression coefficients with respect to the variables in the first column. These coefficients also represent the elasticity of employment in the mining and quarrying sector with respect to the respective variables. Thus, the elasticity of employment with respect to mining and quarrying GVA is -0.05, but it is not significant at 95% level of confidence. Although, we may not be able to rely on the result for policy, because the coefficient is not significant, the interpretation of the result is that a one per cent change in mining and quarrying GVA will lead to 0.05 per cent change in mining and quarrying employment in the opposite direction. This further implies that output or GVA increases in the sector during the period was achieved by productivity increases rather than by the employment of more persons. In the same manner, the estimated elasticities of employment in the sector with respect to wage rate, inflation rate and interest rate, respectively, are: -0.01, -0.00, and 0.03, and the coefficients are, also, not significant at 95% confidence level. Similarly, were the coefficients to be significant, it would mean that a one per cent change in wage Published by Sciedu Press 56 ISSN 1927-9507 E-ISSN 1927-9515 http://jbar.sciedupress.com Journal of Business Administration Research Vol. 10, No. 1; 2021 rate and inflation rate, respectively, will lead to a 0.01 per cent, and 0.00 per cent change in mining and quarrying employment in the opposite direction, while a one per cent change in interest rate in the economy will lead to a 0.03 per cent change in mining and quarrying employment in the same direction. Although, the estimates of elasticity are not significant, the results are consistent with priory expectations above, particularly, with regard to the direction of signs obtained variously by Dokpe 2001; Soto 2009; Sodipe and Ogunrinola, 2011; Malunda, 2012; Nangale, 2012; Baah-Boateng, 2013; Temitope, 2013; Mkhize, 2015; and, Adeniyi, 2021. Table 1. VECM estimation of employment intensity of mining and quarrying sector in Nigeria EMP_MIN & QUA =f(GVA_ MIN & QUAt , , , Vector error-correction model Sample: 1983 - 2014 Number of obs = AIC Log likelihood = 120,9671 Det(Sigma_ml) = -4,185445 HQIC = 3,58e-10 Equation = -3,365573 SBIC Parms 32 = -1,712016 RMSE R-sq chi2 P>chi2 D_lnemp_minin 10 ,018232 0,7434 63,74114 0,0000 D_lngva_minin 10 ,072302 0,4861 20,81174 0,0224 D_lninflation 10 ,574501 0,6018 33,2551 0,0002 D_lnwap_rate 10 ,171806 0,6028 33,38448 0,0002 ,466728 0,3267 10,67559 0,3833 D_lnminim_wage 10 Coef. Std. Err. z P>|z| [95% Conf. Interval] -0,01 0,994 -0,07 0,07 0,057 -0,00 0,16 D_lnemp_minin _ce1 L1. -0,00 0,04 _ce2 L1. -0,08 0,04 -1,90 0,01 0,01 0,92 0,360 -0,01 0,02 0,14 0,20 0,71 0,477 -0,25 0,53 _ce3 L1. lnemp_minin LD. lngva_minin LD. -0,05 0,04 -1,21 0,225 -0,13 0,03 -0,00 0,01 -0,57 0,566 -0,01 0,01 0,03 0,02 1,34 0,180 -0,01 0,06 LD. -0,01 0,01 -1,38 0,169 -0,03 0,00 _cons | 0,03 0,01 2,76 0,006 0,01 0,05 inflation LD. lnwap_rate LD. lnminim_wage Source: Author’s Analysis of Data collected from the National Bureau of Statistics. Since mining and quarry, particularly the yet undeveloped solid mineral subsector, is expected to contribute Published by Sciedu Press 57 ISSN 1927-9507 E-ISSN 1927-9515 http://jbar.sciedupress.com Journal of Business Administration Research Vol. 10, No. 1; 2021 significantly to job creation, we should be able to design policies to enhance the job absorptive capacity of the sector. Furthermore, the economy consists of other sectors with which mining and quarry sector co-exists and establishes various dynamic linkages, which if estimated, may help explain and stimulate its job absorptive capacity (Adeniyi, 2019). In order to incorporate this inter-sectoral linkages and relationships, a system of six plausible scenarios were developed from a system of six simultaneous equations of aggregate employment from the series as follows: Scenario 1: lntot_empl = f (lnemp_agric, lnemp_non-agric, lngva_agric, lngva_nonagric.) Scenario 2: lntot_empl = f (lnemp_agric lnemp_minin lnemp_manufac lnemp_const lnemp_admin lngva_agric lngva_minin lngva_manufac lngva_const lngva_admin) Scenario 3: lntot_empl = f (lnemp_agric lnemp_mini lnemp_manufac lnemp_const lnemp_admin lninflation lnwap_rate lnminWage) Scenario 4: lntot_empl = f (lngva_agric lngva_minin lngva_manufac lngva_const lngva_admin lninflation lnwap_rate lnminimWage) Scenario 5: lntot_empl = f (lngdp lninflation lnwap_rate, lnminim_wage) Scenario 6: lnemp_agric = f(lnemp_minin lnemp_manufac lnemp_const lnemp_admin lngva_agric lngva_minin lngva_manufac lngva_const lngva_admin) (9) The above equations (9) were then estimated using VECM, and the results present in Table 2 below: Table 2. Employment in Mining and Quarrying Sector Scenario1 Scenario2 Scenario3 Scenario4 Scenario5 Scenario6 Coef.(z) Coef.(z) Coef.(z) Coef.(z) Coef.(z) Coef.(z) Ce1 0.037(0.52) -1.911(-0.7) 0.081(0.21) -0.138(-1.02) Ce2 0.038(0.48) -1.395(-0.67) 1.096(3.99)*** 0.03(1.31) Ce3 -0.114(-1.57) 0.413(1.81)* -1.624(-2.62)*** 0.173(0.61) Ce4 0.056(0.36) Ce5 -0.065(-1.00) Employment Agriculture(-1) 0.433(0.83) 1.969(1.31) -0.013(-0.03) Employment Agriculture(-2) 0.824(1.15) 0.812(1.15) -0.563(0.1) Employment Mining(-1) 0.096(0.16) -0.603(-0.88) 0.159(0.27) Employment Mining(-2) 1.391(2)** -0.267(-0.41) 0.305(0.64) Employment (-1) Manufacturing 0.428(1.53) -1.07(-1.59) 0.475(1.49) Employment (-2) Manufacturing 0.268(0.89) -0.206(-0.50) -0.321(-0.8) Employment Construction(-1) 0.198(0.25) -1.606(-0.94) 0.62(0.93) Employment Construction(-2) -0.750(-1.07) -0.01(-0.01) -0.392(-0.75) Employment Admin(-1) 0.155(0.23) 0.708(0.96) -0.579(-1.02) Employment Admin(-2) 1.272(1.91)* 0.151(0.2) 0.743(1.37) Employment Trade Employment Non-agric(-1) Employment Non-agric(-2) GVA Agriculture(-1) 0.022(0.41) -0.891(-2.73)*** 0.039(0.43) GVA Agriculture(-2) -0.104(-1.69)* -0.285(-1.12) 0.05(0.71) GVA Mining(-1) 0.009(0.09)* -0.069(-0.18) -0.154(-2.2)** GVA Mining(-2) -0.043(-0.73) 0.206(0.77) -0.064(-0.88) Published by Sciedu Press 58 ISSN 1927-9507 E-ISSN 1927-9515 http://jbar.sciedupress.com Journal of Business Administration Research Vol. 10, No. 1; 2021 GVA Manufacturing (-1) -0.062(-1.04) 1.241(2.67)*** 0.064(1.79) GVA Manufacturing (-2) -0.076(-1.26) 0.566(2.87)*** 0.029(0.96) GVA Construction(-1) 0.068(1.49) -0.166(-0.8) 0.042(0.55) GVA Construction (-2) -0.102(-1.2) -0.447(-1.99) ** 0.079(1.8) GVA Admin (-1) -0.292(-1.04) 0.909(1.63) 0.152(0.54) GVA Admin (-2) -0.234(-1.27) 0.571(0.87) -0.025(-0.1) GVA Trade GVA Non-agric (-1) GVA Non-agric (-2) GDP (-1) GDP (-2) Inflation Rate(-1) -0.004(-0.51) 0.0002(0.01) -0.007(-1.22) Inflation Rate(-2) -0.002(-0.46) 0.007(0.36) -0.008(-1.36) WAPLR(Weighted Average Prime Lending Rate)(-1) -0.018(-0.65) -0.468(-2.82)*** WAPLR(Weighted Average Prime Lending Rate)(-2) -0.002(-0.11) -0.166(-1.65) Minimum wage (-1) -0.009(-1.79)* 0.07(1.49) Minimum wage (-2) -0.006(-0.99) -0.021(-0.60) -0.016(-1.21) -0.01(-0.21) Constant 0.012(0.53) -0.011(-0.98) Source: Author’s Analysis of Data collected from the National Bureau of Statistics. The result indicates that current employment level in the Mining sector is significantly influenced negatively by the immediate past Gross Value Added in agricultural sector. This is attributable to the inter-temporal and off-seasonal shifts of labour between artisanal mining and quarrying, and agriculture. More specifically, the growth elasticity of employment in the Mining sector with respect to Gross Value Added in agriculture is -0.891 and lagged by one year. This means that a one percent change in Gross Value Added in Agriculture in the immediate past year is accompanied by a 0.891 per cent change in the employment level in the current year in the Mining sector in the opposite direction. Also, current employment in the Mining sector of the Nigerian economy in the period under review is significantly influenced negatively by two-year lagged Gross Value Added in the Construction sector. This could be ascribed to the inter-temporal and off-seasonal shifts of labour between artisanal mining and quarrying, and construction. The employment intensity of growth in the Mining sector with respect to Gross Value Added in the Construction sector is -0.447 and lagged by two years. In other words, a one per cent change in prior two years’ Gross Value Added in the Construction sector is accompanied by a 0.447 per cent change, in the opposite direction, in employment in the current year in the mining and quarrying sector. Furthermore, current employment in the mining and quarrying sector of the Nigerian economy is significantly influenced positively by the gross value added (GVA) of the previous year in the manufacturing sector. This is probably because the output of mining and quarrying are used as raw materials in manufacturing industry, while manufactured industrial goods are used in the mining and quarrying sector. Specifically, the employment intensity of growth in the Mining sector with respect to gross value added (GVA) in the manufacturing sector of the economy is 1.241, positive and lagged by one year. This means, a one per cent change in the level of manufacturing gross value added (GVA) of the immediate past year is accompanied by a 1.241 per cent change in employment in the mining and quarrying sector in the same direction. In addition, employment in the Mining sector of the economy is significantly affected by the Weighted Average Prime Lending Rate (WAPLR) of the immediate past year. Mining and quarrying is a capital intensive sector. Consequently, the higher the cost of capital the lower the investment in the sector and the lower the sector’s ability to create jobs. In specific terms, the intensity or coefficient is -0.468. This implies that a one per cent change in the Published by Sciedu Press 59 ISSN 1927-9507 E-ISSN 1927-9515 http://jbar.sciedupress.com Journal of Business Administration Research Vol. 10, No. 1; 2021 previous year’s WAPLR is associated with a 0.468 per cent change, in the opposite direction, in employment level in the Mining sector of the Nigerian economy. See table 2 above. 4. Conclusions Every sector of the economy of Nigeria must contribute commensurably in proving employment to stem the current unemployment malaise. This must be a cardinal policy issue in the current efforts to diversify the economy. Consequently, government must come up with sector-specific policies for this purpose. Analysed as a stand-alone sector, the estimates of employment intensity with respect to GVA, interest rates, wage rates, and inflation rates for the mining and quarrying sector were not significant. This means they could not be relied on for pin-point policy. However, when the employment function was redefined to incorporate the other sectors in the Nigerian economy, as exists in real life, the estimates are significant and explain some of the real-life issues that have characterised the mining and quarrying sector in Nigeria. In other to take advantage of the job absorptive capacity of the mining and quarrying sector, policy makers should create and implement policies aimed at exploiting the inter-temporal and the inter-sectoral linkages with the other sectors of the economy, through well-developed and well-resourced value chains. Government should create appropriate mining and quarrying investment climate through general macroeconomic stability, and specific policies targeted at the provision of both soft and hard infrastructures for mining and quarrying, processing and marketing infrastructure, and fiscal and monetary incentives. 5. Recommendations. 1) In order to encourage job creation in the mining and quarrying sector, Nigeria will need to enact policies that look beyond mere growth in sectoral output, since employment in the sector is negatively integrated with the sector’s gross value added, because the sector is more capital or technology intensive than it is labour intensive. 2) Furthermore, policy makers would need to design and implement policies that encourage low wage rate in the sector. This will enable employers to be able to employ more people within the limit of their often limited resources. 3) Also, policy makers should facilitate stable general price level and low inflation rate in the in the economy in order to encourage planning and investment that will, in turn, create more jobs. 4) Employment in the Mining and quarrying sector of the economy is significantly negatively correlated with the immediate past year’s interest rate in the redefined model. The higher the cost of capital, the lower the investment in the sector and the lower the sector’s ability to create jobs. Consequently, policy makers should design and implement policies that facilitate low and stable interest rate. This will encourage new and expansionary investments in the sector. Furthermore, policy makers should create and implement policies aimed at taking advantage of the inter-temporal and the inter-sectoral linkages of the mining and quarrying sector with the other sectors of the economy, particularly, References Adeniyi, A. (2021). Designing a framework for employment-growth targeting in Nigeria. Journal of Asia Development. https://doi.org/10.5296/jad.v7i1.18401 Adeniyi, A. (2019). Economic growth and employment nexus in Nigeria’s agricultural and non-agricultural sectors (1981-2014) (PhD Thesis in the Department of Agricultural Economics (in Press)). Ajilore, T., & Yinusa, O. (2011). An Analysis of Employment Intensity of Sectoral Output Growth in Botswana. 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