Authors
Prof. Ifeoma Mary Okwo, FCNA,Catherine Amoge Eneh,
Abstract
This study empirically investigated the response of firm productivity on human capital expenditure in oil and gas firms in Nigeria. It spanned for the period of ten years (2009-2018). Specifically, the study examined the effect of employee expenditure on education and training, expenditure on salaries and wages, and expenditure on health on the turnover of Oil and Gas firms in Nigeria. Research design adopted was ex-post facto design while analytical tools employed were descriptive statistics and Ordinary Least Squares (OLS) panel regression analysis technique. From the regression result, it was discovered that employee expenditure on education and training (coeff. = 0.070, t* = 0.381, p=0.7056>0.05) had positive and insignificant effect on the turnover of Oil and Gas firms in Nigeria. Expenditure on salaries and wages (coeff. = 
0.383, t* = 1.784, p=0.0836>0.05) and expenditure on health (coeff. = 0.040, t* = 0.146, p=0.8849>0.05) had negative and insignificant effects on the turnover of Oil and Gas firms in Nigeria. A joint estimate as shown by Fisher’s statistics (F=17.264, p=0.0000<0.05) revealed that human capital expenditures have significant effect on turnover of Oil and Gas firms in Nigeria. In conclusion therefore, expenditures in human capital are essential factors for growth in turnover of Oil and Gas firms in Nigeria. On these backgrounds, the study recommended among other things that the oil and gas firms should invest more in training their workers than increasing their salaries and wages for increased turnover and overall productivity.
Key Words: Productivity, Expenditure, Human Capital, Panel Least Squares Regression
Full Text
1.0 INTRODUCTION
It is the goal of every organization to generate high revenue and maximize profit. However, investment on workers is an essential factor towards achieving this target. Firms believe that when they invest on their workers, the resultant effect is improved productivity. In other words, the success of any organization is largely dependent on the commitment of their workers. In an organization, investment in human development ensures that employees (staff) are well trained and reasonably motivated to deliver both physically and mentally on their responsibilities. This commitment encourages increase in firm productivity and sustains competitiveness (James, 2015).
In today’s global economy, particularly in manufacturing firms where knowledge, skills and other human capabilities are very crucial to the existence and survival of companies, workforce has evolved into arguably the biggest competitive differentiator for organizations in virtually all industries.. Also, companies place a premium on its human capital development with the conviction that this would translate to improved efficiency in the business and bring about strategic advantage over competitors. The companies appreciate this fact and usually state in their annual report that “our employees are our greatest asset”. But still, staff move from one industry to another at slightest opportunities with both the genetic and specific investments made on them, thereby leaving the departed company with the option to reinvest in another staff so as to cover such vacuum created and be able to move forward. The question therefore, is, what is the value of this great asset and their contributions to the productivity of manufacturing firms in Nigeria? The broad objective of this study is to ascertain the response of firm productivity to human capital expenditures in oil and gas manufacturing companies in Nigeria for the periods of ten (10) years (2009-2018). The specific objectives were: to investigate the effect of expenditure on education and Training (EETR); salaries and Wages (ESW) and expenditure on Health (EHT) on the Turnover (TVR) of oil and gas firms in Nigeria.
The following hypotheses guided this study
1) Expenditure on education and training does not significantly influence Turnover (TVR) of oil and gas firms in Nigeria.
2) Expenditure on salaries and wages has no significant effect on Turnover (TVR) of oil and gas firms in Nigeria.
3) Expenditure on health has no significant effect on Turnover (TVR) of oil and gas firms in Nigeria.
2.0 REVIEW OF RELATED LITERATURE
2.1 Conceptual Review
Omodero (2019) defines human capital expenditure as spending that are related to recruiting, training and retraining, compensation, salaries and allowances, retaining and pension incurred on the employee of particular firms in the hope of gaining return on this investment in terms of being more productive, more competitive and above all, more profitable in future. They are expenditures burn out from employee training, motivation, condition of health, e.t.c. for increased productivity and expansion. Ogujiuba (2013) stressed that investing in human capital development is critical as it is targeted at ensuring that the nation’s human resource endowment is knowledgeable, skilled, productive and healthy to enable the optimal exploitation and utilization of other resources to engender growth and development. Human capital expenditure consists of salaries and wages; expenditure on education , training, health among others.
Turnover is the net sales generated by a business. Turnover according to Kenton (2019) is an accounting concept that calculates how quickly a business conducts its operations. It can also refer to the proportion of employees that leave a business within a specific period, also sometimes known as ‘churn’ (Hall, 2018). Most often, turnover explains how quickly a company collects cash from accounts receivable or how fast the company sells its inventory. In the investment industry, turnover is defined as the percentage of a portfolio that is sold in a particular month or year. A quick turnover rate generates more commissions for trades placed by a broker.
2.2 Theoretical Review
This study is anchored on human capital theory propounded by Becker in 1962. Central to Human Capital Theory (HCT) is the fact that any investment in the development of the human person in advancement of his skills translates to the increase in the desired workforce needed to advance the goals and objectives of an organization. In relation to this study, the theory advances that investments in people in terms of education, skill, training, etc., would result in increase in the individuals’ output.
2.3 Empirical Review
Ukenna, Ijeoma, Anionwu and Olisa (2010) examined the effect of investment in human capital development on organizational performance. Analysis of Variance (ANOVA), t-test, multiple regression analysis, simple regression analysis, and Pearson’s correlation coefficient were employed analyze the data. Finding revealed that training and skill are stronger predictors of human capital effectiveness over and above knowledge and education. Perera and Thrikawala (2012) investigated the impact of investment in human capital on financial performances of the companies in Sri Lanka. Findings revealed that there is a significant relationship between investment in human capital and firm financial performances.
Using the ordinary least square analytical technique, Edom, Inah and Adanma (2015) examined the impact of human resource accounting on the profitability of Access Bank of Nigeria Plc, from 2003 to 2012. The finding showed that there is a positive relationship between the indicators of human resource cost and the profit of the organization Also, there was a significant relationship between training cost, development cost and the profit of the bank. However, the number of staff does not have a significant effect on profit of the bank.
By employing panel least squares multiple regression analysis, Ubesie, Eneh and Udeh (2019) examined the effect of human capital expenditures on corporate social responsibility of oil and gas firms in Nigeria for the period of 10 years (2008-2017). The result revealed that human capital expenditures proxy by expenditure on salaries and wages, on education and training and expenditure on health have significant positive effect on Corporate Social Responsibility (CSR) of oil and gas firms in Nigeria.
Okafor, Ofobruku, Obi-Anike and Agbaeze (2019) examined the effects of human capital development on employees’ performance in Nigeria public hospital using linear regression statistical technique to analyze the data collected. The finding revealed that the lacks of articulate human capital development strategy geared towards filling identified skills, knowledge and attitude gap were responsible for the meagre employees’ performance in Nigeria hospitals.
Chukwu, Ugo and Osisioma (2019) used regression analysis to examine the effect of human capital on the market value of banks in Nigeria for the period 2010 to 2014. The result showed that only one variable - the proportion of highly paid employees - had a significant effect on the market value of firms.
3.0 METHODOLOGY
This study adopted ex-post facto research design. data were extracted from annual accounts and financial statements of the four selected oil and gas firms in Nigeria namely MRS oil, TOTAL oil, Conoil, and Oando Oil and Gas firms were studied. This study employed descriptive statistics and panel least squares regression analysis to justify the objectives of this study. Model framework for this study is the panel least square regression model which took its basis from the classical linear regression model. The model is specified thus:
= 
- (Eq. 2)
= Turnover at time t (Dependent variable),
= Expenditure on Salaries and Wages at time t,
= Expenditure on education and training at time t,
= Expenditure on Health at time t,
= Constant/intercept of the regression model,
, 
, and 
= Coefficient of ESW, EETR, and EHT respectively in the model,
= stochastic error (white noise) associated with the model
4.0 DATA PRESENTATION AND ANALYSIS
4.1 Data Presentation
Tables 1: The annual time series data from the selected oil and gas firms( Panel Data)
|
Years |
TVR ( |
ESW ( |
EETR ( |
EHT ( |
|
MRS oil: 2009 |
60,900,243 |
1,511,054 |
8,156 |
12,090 |
|
2010 |
68,671,449 |
1,401,562 |
7,764 |
13,112 |
|
2011 |
71,490,715 |
1,225,372 |
5,309 |
13,320 |
|
2012 |
79,727,349 |
581,257 |
62,902 |
14,976 |
|
2013 |
87,786,323 |
360,419 |
63,319 |
15,213 |
|
2014 |
92,325,405 |
618,953 |
82,150 |
15,021 |
|
2015 |
87,099,216 |
371,609 |
84,431 |
15,342 |
|
2016 |
109,635,054 |
441,056 |
90,025 |
15,735 |
|
2017 |
107,088,347 |
517,599 |
93,217 |
15,813 |
|
2018 |
89,552,819 |
463,706 |
983,204 |
205,025 |
|
Total oil: 2009 |
5,543,326 |
3,829,005 |
45,690 |
13,834 |
|
2010 |
7,256,443 |
4,416,650 |
48,665 |
13,567 |
|
2011 |
7,384,465 |
5,547,322 |
50,132 |
15,433 |
|
2012 |
8,198,723 |
5,983,442 |
47,002 |
17,661 |
|
2013 |
9,105,433 |
6,359,707 |
44,897 |
19,807 |
|
2014 |
11,987,933 |
6,533,412 |
47,331 |
22,116 |
|
2015 |
23,106,210 |
6,805,276 |
47,653 |
24,510 |
|
2016 |
21,132,723 |
6,786,096 |
49,429 |
21,663 |
|
2017 |
32,840,384 |
7,483,750 |
37,049 |
21,765 |
|
2018 |
37,205,480 |
7,863,354 |
35,988 |
22,356 |
|
Oando oil: 2009 |
4,207,854 |
54,778 |
441,409 |
1,408 |
|
2010 |
4,352,005 |
108,075 |
488,961 |
1,155 |
|
2011 |
8,122,502 |
227,148 |
762,193 |
1,634 |
|
2012 |
7,358,881 |
494,860 |
645,227 |
1,698 |
|
2013 |
5,883,304 |
265,416 |
984,022 |
1,943 |
|
2014 |
14,217,468 |
69,994 |
866,119 |
2,004 |
|
2015 |
8,452,665 |
43,720 |
640,553 |
2,337 |
|
2016 |
4,858,182 |
631,710 |
734,939 |
2,509 |
|
2017 |
497,422,483 |
376,141 |
828,103 |
2,794 |
|
2018 |
488,518,160 |
399,707 |
907,216 |
2,113 |
|
Conoil Oil: 2009 |
101,853,173 |
1,856,914 |
132,156 |
55,169 |
|
2010 |
102,878,494 |
1,889,847 |
149,023 |
60,435 |
|
2011 |
157,512,072 |
1,802,721 |
257,308 |
52,977 |
|
2012 |
149,993,261 |
1,562,621 |
898,750 |
83,682 |
|
2013 |
121,803,182 |
1,664,674 |
921,044 |
78,541 |
|
2014 |
104,223,841 |
1,167,803 |
821,500 |
47,788 |
|
2015 |
82,919,220 |
1,994,046 |
450,198 |
48,592 |
|
2016 |
85,023,546 |
1,908,477 |
100,617 |
59,023 |
|
2017 |
70,229,461 |
1,435,469 |
112,010 |
44,256 |
|
2018 |
75,838,134 |
968,502 |
896,682 |
80,032 |
Source: Financial Statement and Accounts of the selected oil and gas firm(2009-2018).
Table 2 Descriptive statistics result and interpretation
|
Estimated Parameters |
LOG(TVR) |
LOG(ESW) |
LOG(EETR) |
LOG(EHT) |
|
Mean |
17.41219 |
13.85243 |
11.92327 |
9.558611 |
|
Median |
18.05606 |
14.08593 |
11.57271 |
9.653953 |
|
Maximum |
20.02495 |
15.87772 |
13.79940 |
12.23089 |
|
Minimum |
15.25246 |
10.68556 |
8.577159 |
7.051856 |
|
Std. Dev. |
1.349985 |
1.401538 |
1.504369 |
1.345656 |
|
Skewness |
-0.139307 |
-0.402987 |
-0.292528 |
-0.360577 |
|
Kurtosis |
1.874780 |
2.548488 |
2.146300 |
2.243188 |
|
Jarque-Bera |
2.239577 |
1.422427 |
1.785157 |
1.821381 |
|
Probability |
0.326349 |
0.491048 |
0.409598 |
0.402246 |
|
Sum |
696.4877 |
554.0973 |
476.9307 |
382.3445 |
|
Sum Sq. Dev. |
71.07594 |
76.60800 |
88.26193 |
70.62080 |
|
Observations |
40 |
40 |
40 |
40 |
Source: Author’s computation using Eviews 10 package
The result shows that all the variables (dependent and independent) are clustered on the left hand side of the normal curve (negatively skewed). There is no excess kurtosis (k>3.0) and the Jarque-Bera estimate of normality in distribution series of the variables (with p-values > 0.05) indicate that each of the series follow normal and smooth curve. The standard deviations indicate that the series are clustered around the mean. However, the parametric statistics are considered in further analysis.
Table 3 Panel Unit Root Test: Levin, Lin & Chu t* Approach
|
Variable |
Levin, Lin & Chu t* |
p-value |
Order of integration |
Comment |
|
Log(TVR) |
-3.604 |
0.0002 |
I(0) |
Stationary |
|
Log(ESW) |
-5.837 |
0.0000 |
I(0) |
“ |
|
Log(EETR) |
-2.745 |
0.0030 |
I(0) |
“ |
|
Log(EHT) |
-2.813 |
0.0025 |
I(0) |
“ |
Source: Author’s Extract from E-views 10.0 output (See Appendix A)
The panel unit root test result indicates that the variables are stationary at their level as their respective probability values of Levin, Lin & Chu t* is less than 0.05. This therefore justifies the use of Ordinary Least Square (OLS) panel regression analysis for the hypothesis testing.
Test of Hypothesis :
Decision rule: Reject the null hypothesis if the p-value is less than 0.05 otherwise, do not reject.( Level of significance (
) = 0.05
Hypothesis One
Ho: Expenditure on education and training has no significant effect on turnover of oil and gas firms in Nigeria.
Table 4 Result of effect of log(EETR) on log(TVR)
|
Dependent Variable: LOGTVR |
|
|
||
|
Method: Panel Least Squares |
|
|
||
|
Date: 02/24/20 Time: 18:04 |
|
|
||
|
Sample (adjusted): 2010 2018 |
|
|
||
|
Periods included: 9 |
|
|
||
|
Cross-sections included: 4 |
|
|
||
|
Total panel (balanced) observations: 36 |
|
|||
|
Convergence achieved after 5 iterations |
|
|||
|
|
|
|
|
|
|
|
|
|
|
|
|
Variable |
Coefficient |
Std. Error |
t-Statistic |
Prob. |
|
|
|
|
|
|
|
|
|
|
|
|
|
C |
17.32770 |
2.508074 |
6.908766 |
0.0000 |
|
LOGEETR |
0.069513 |
0.182392 |
0.381117 |
0.7056 |
|
AR(1) |
0.796843 |
0.100233 |
7.949944 |
0.0000 |
|
|
|
|
|
|
|
|
|
|
|
|
|
R-squared |
0.661196 |
Mean dependent var |
17.48176 |
|
|
Adjusted R-squared |
0.640663 |
S.D. dependent var |
1.324175 |
|
|
S.E. of regression |
0.793773 |
Akaike info criterion |
2.455618 |
|
|
Sum squared resid |
20.79251 |
Schwarz criterion |
2.587578 |
|
|
Log likelihood |
-41.20112 |
Hannan-Quinn criter. |
2.501675 |
|
|
F-statistic |
32.20075 |
Durbin-Watson stat |
2.339711 |
|
|
Prob(F-statistic) |
0.000000 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Inverted AR Roots |
.80 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Source: Author’s Eviews 10 result
The coefficient of employee expenditure on education and training is 0.0695, the t-statistic value is 0.381 with associated probability value of 0.7056>0.05. This indicates that employee expenditure on education and training has positive and insignificant effect on turnover of oil and gas firms in Nigeria. From the result, a unit increase in employee expenditure on education and training will lead to about 0.070 unit increases in turnover of oil and gas firms in Nigeria. The explanatory power of the model as measured by coefficient of determination (R-squared) is 66.1% which is above average of 50.0%. The implication is that the model is a good one as about 66.1% of the total variations in turnover of oil and gas firms can be attributed to employee expenditure on education and training. The unexplained 33.9% are attributable to other relevant variables not included in the model. However, the Durbin-Watson statistic value of 2.339711 following the rule of thumb indicates the model is not suffering from autocorrelation problem.
Hypothesis Two
Ho: Expenditure on salaries and wages has no significant effect on turnover of oil and gas firms in Nigeria.
Table 5: Result of effect of log(ESW) on log(TVR)
|
Dependent Variable: LOGTVR |
|
|
||
|
Method: Panel Least Squares |
|
|
||
|
Date: 02/24/20 Time: 18:05 |
|
|
||
|
Sample (adjusted): 2010 2018 |
|
|
||
|
Periods included: 9 |
|
|
||
|
Cross-sections included: 4 |
|
|
||
|
Total panel (balanced) observations: 36 |
|
|||
|
Convergence achieved after 7 iterations |
|
|||
|
|
|
|
|
|
|
|
|
|
|
|
|
Variable |
Coefficient |
Std. Error |
t-Statistic |
Prob. |
|
|
|
|
|
|
|
|
|
|
|
|
|
C |
23.62313 |
3.070758 |
7.692930 |
0.0000 |
|
LOGESW |
-0.382911 |
0.214614 |
-1.784182 |
0.0836 |
|
AR(1) |
0.808340 |
0.086327 |
9.363722 |
0.0000 |
|
|
|
|
|
|
|
|
|
|
|
|
|
R-squared |
0.689835 |
Mean dependent var |
17.48176 |
|
|
Adjusted R-squared |
0.671037 |
S.D. dependent var |
1.324175 |
|
|
S.E. of regression |
0.759484 |
Akaike info criterion |
2.367301 |
|
|
Sum squared resid |
19.03494 |
Schwarz criterion |
2.499261 |
|
|
Log likelihood |
-39.61142 |
Hannan-Quinn criter. |
2.413359 |
|
|
F-statistic |
36.69748 |
Durbin-Watson stat |
2.018991 |
|
|
Prob(F-statistic) |
0.000000 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Inverted AR Roots |
.81 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Source: Author’s Eviews 10 result
From the panel ordinary least squares regression as shown above, the coefficient of expenditure on salaries and wages (Log(ESW)) is -0.383, with t-statistic value of -1.784 and associated probability value of 0.0836>0.05. This implies that expenditure on salaries and wages have negative and insignificant effect on turnover of oil and gas firms. The null hypothesis is therefore not rejected. This means that expenditure on salaries and wages has no significant effect on turnover of oil and gas firms in Nigeria. Also, the result shows that a unit increase in the workers’ salaries will lead to about 0.383 unit decreases in turnover of the oil and gas firms in Nigeria.
The explanatory power of the model (R-squared) is 69.0% which indicates that the model is a good one, hence, about 69% of the total variations in turnover of oil and gas firms in Nigeria can be explained by expenditure on salaries and wages of the workers. However, the Durbin-Watson statistic value of 2.018991 according to Guajarati and Porter (2009) or the rule of thumb indicates that the model is free from autocorrelation problem.
Hypothesis Three
Ho: Expenditure on health has no significant effect on turnover of oil and gas firms in Nigeria.
Table 6: Result of effect of log(EHT) on log(TVR)
|
Dependent Variable: LOGTVR |
|
|
||
|
Method: Panel Least Squares |
|
|
||
|
Date: 02/24/20 Time: 18:07 |
|
|
||
|
Sample (adjusted): 2010 2018 |
|
|
||
|
Periods included: 9 |
|
|
||
|
Cross-sections included: 4 |
|
|
||
|
Total panel (balanced) observations: 36 |
|
|||
|
Convergence achieved after 6 iterations |
|
|||
|
|
|
|
|
|
|
|
|
|
|
|
|
Variable |
Coefficient |
Std. Error |
t-Statistic |
Prob. |
|
|
|
|
|
|
|
|
|
|
|
|
|
C |
18.68257 |
3.082319 |
6.061206 |
0.0000 |
|
LOGEHT |
-0.040135 |
0.275160 |
-0.145859 |
0.8849 |
|
AR(1) |
0.809485 |
0.104357 |
7.756851 |
0.0000 |
|
|
|
|
|
|
|
|
|
|
|
|
|
R-squared |
0.659973 |
Mean dependent var |
17.48176 |
|
|
Adjusted R-squared |
0.639365 |
S.D. dependent var |
1.324175 |
|
|
S.E. of regression |
0.795205 |
Akaike info criterion |
2.459221 |
|
|
Sum squared resid |
20.86757 |
Schwarz criterion |
2.591181 |
|
|
Log likelihood |
-41.26598 |
Hannan-Quinn criter. |
2.505279 |
|
|
F-statistic |
32.02557 |
Durbin-Watson stat |
2.361051 |
|
|
Prob(F-statistic) |
0.000000 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Inverted AR Roots |
.81 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Source: Author’s Eviews 10 result
The coefficient of employee expenditure on health (Log(EHT)) is -0.040, with t-statistic value of -0.146 and associated probability value of 0.8849>0.05. This shows that employee expenditure on health has a non-significant negative effect on turnover of oil and gas firms in Nigeria. The null hypothesis is therefore not rejected which means that employee expenditure on health has no significant effect on turnover of oil and gas firms in Nigeria. As shown in the result, a unit increase in employee expenditure on health will lead to about 0.040 unit decreases in turnover of the oil and gas firms in Nigeria.
The R-squared estimate is 66.0%. This implies that about 66.0% of the total variations in turnover of oil and gas firms can be explained by health expenditure. However, the model is a good one. However, the Durbin-Watson statistic value of 2.361051 following the rule of thumb indicates that the model is free from autocorrelation problem.
Table 7: Multiple Panel Regression Result
|
Dependent Variable: LOGTVR |
|
|
||
|
Method: Panel Least Squares |
|
|
||
|
Date: 02/24/20 Time: 18:02 |
|
|
||
|
Sample (adjusted): 2010 2018 |
|
|
||
|
Periods included: 9 |
|
|
||
|
Cross-sections included: 4 |
|
|
||
|
Total panel (balanced) observations: 36 |
|
|||
|
Convergence achieved after 9 iterations |
|
|||
|
|
|
|
|
|
|
|
|
|
|
|
|
Variable |
Coefficient |
Std. Error |
t-Statistic |
Prob. |
|
|
|
|
|
|
|
|
|
|
|
|
|
C |
24.12180 |
4.734624 |
5.094764 |
0.0000 |
|
LOGESW |
-0.382366 |
0.231137 |
-1.654281 |
0.1082 |
|
LOGEETR |
0.008000 |
0.216061 |
0.037028 |
0.9707 |
|
LOGEHT |
-0.056349 |
0.325758 |
-0.172978 |
0.8638 |
|
AR(1) |
0.815656 |
0.095064 |
8.580072 |
0.0000 |
|
|
|
|
|
|
|
|
|
|
|
|
|
R-squared |
0.690175 |
Mean dependent var |
17.48176 |
|
|
Adjusted R-squared |
0.650198 |
S.D. dependent var |
1.324175 |
|
|
S.E. of regression |
0.783171 |
Akaike info criterion |
2.477314 |
|
|
Sum squared resid |
19.01406 |
Schwarz criterion |
2.697248 |
|
|
Log likelihood |
-39.59166 |
Hannan-Quinn criter. |
2.554077 |
|
|
F-statistic |
17.26414 |
Durbin-Watson stat |
2.046831 |
|
|
Prob(F-statistic) |
0.000000 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Inverted AR Roots |
.82 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Source: Author’s Eviews 10 result
The multiple regression result authenticates that employee expenditure on salaries and wages (Log(ESW)) and expenditure on workers’ health (Log(EHT)) have negative and insignificant effect on turnover of oil and gas firms in Nigeria, while employee expenditure on education and training (Log(EETR)) has positive and insignificant effect on turnover of oil and gas firms in Nigeria.
The joint significant estimate is F = 17.264 with probability value of 0.0000<0.05. This indicates that human capital expenditure proxied by expenditure on salary and wages, expenditure on education and training, and expenditure on health have joint significant effect on the productivity (turnover) of oil and gas firms in Nigeria. The model is a good one as it explains about 69.0% of the total variations in turnover of the firms. With the Durbin-Watson statistic value of 2.046831 indicates that the model is free from first order autocorrelation problem.
4.4 Discussion of Findings
With a coefficient value of 0.070, t-statistic value of 0.381 and associated probability value of 0.7056, this paper established that employee expenditure on education and training has a non-significant positive effect on turnover of oil and gas firms in Nigeria. The meaning is that advancement in education of the employees will help to promote turnover of oil and gas firms in Nigeria. This finding aligns with the findings of Ubesie et al (2019), Ukenna et al (2010), among others.
The study also uncovered that employee expenditure on salaries and wages (with coeff. = 
0.383, t* = 1.784, p=0.0836>0.05) have insignificant negative effect on turnover of oil and gas firms in Nigeria. This finding is in partial support of the work of Yusuf (2011) in Nigerian banks, among others. On the contrary, the outcome of this study disobeys the work of Perera and Thrikawala (2012) that staff salary has positive effect on organizational profitability in Nigeria.
Moreover, this study established that employee expenditure on health with a coefficient value of 0.040, t-statistic value of 0.146 and associated probability value of 0.8849>0.05 is not favourable to turnover of oil and gas firms in Nigeria. The implication of this finding is that when a firm spends much on his workers, the level of turnover (in terms of revenue) of the firm will confidently drop. The insignificant effect of employee expenditure on health on the turnover of oil and gas firms in Nigeria does not support the work of Perera and Thrikawala (2012), Olowolaju and Oluwasesin (2016), among others.
The collective significant effect of human capital expenditures on turnover of oil and gas firms in Nigeria agrees with the work of Perera and Thrikawala (2012) in Sri Lanka. The finding equally supports the work of Ubesie, et al (2019), amongst others. On the contrary, the outcome nods in disagreement to the work of Chukwu et al (2019).
5.0 CONCLUSION AND RECOMMENDATIONS
5.2 Conclusion
Having explored the response of firm productivity to human capital expenditures in oil and gas firms in Nigeria using panel least squares regression analysis techniques for the period of 2009-2018, the study established that firm productivity respond significantly to human capital expenditure in oil and gas firms in Nigeria. The implication is that expenditure in human capital contributes substantially to turnover of oil and gas firms in Nigeria.
5.3 Recommendations
Based on the findings of this study, the following recommendations were made:
1) The oil and gas firms should invest more on education and training of their employees for wider knowledge and exposure on the strategies for increased productivity of the firm.
2) They should review their salary structure (downward) to match with the firms’ turnover since increased salary and wages are not favourable to the firm’s productivity.
3) The oil and gas firms should curtail and monitor expenditure on health of their workers as it decreases turnover of the firms.
REFERENCES
Chukwu, G. J., Ugo, C. C. & Osisioma, B. C. (2019). Market valuation of human capital in Nigerian Banks. International Journal of Academic Research in Accounting, Finance and Management Sciences, 9(1), 21–29.
Edom, G. O., Inah, E. U. & Adanma, E. S. (2015). The impact of human resource accounting on the profitability of a firm: Empirical from Acess Bank of Nigeria PLC. European Journal of Accounting, Auditing and Finance Research, 3(7), 76-94.
Hall, J. (2018). How to calculate turnover for your business. Retrieved online from: https://www.simplybusiness.co.uk/knowledge/articles/2018/05/what-is-business-turnover/ on January 24, 2020.
James, U. M. (2015). Local content policy, human capital development and sustainable business performance in the Nigerian Oil and Gas Industry. Journal of Management and Sustainability, 5(1), 75-83.
Kenton, W. (2019). Turnover definition. https://www.investopedia.com/terms/t/turnover.asp Retrieved online on February 22, 2020.
Kum, F., Cowden, R. & Karodia, A. M. (2014). The impact of training and development on employee performance: A case study of ESCON consulting. Singaporean Journal of Business Economics, and Management Studies, 3(3), 72-105.
Makgata, R. M. & Ngwakwe, C. C. (2017). Human capital expenditures and company sales turnover. Corporate Ownership and Control, 14(3), 320-328.
Ogujiuba. K. (2013). The impact of human capital formation on economic growth in Nigeria. Journal of Economics, 4(2), 121-132.
Okafor, C. N., Ofobruku, S. A., Obi-Anike, H. O. & Agbaeze, E. K. (2019). An investigation of the effect of human capital development on employees’ performance in Nigeria public hospitals: A study of the federal medical hospital Keffi. Academy of Strategic Management Journal, 18(4), 1-13.
Omodero, C. O. (2019). Government general spending and human development: A case study of Nigeria. Academic Journal of Interdisciplinary Studies, 8(1), 51-59.
Perera, A. & Thrikawala, S. (2012). Impact of human capital investment on firm financial performances: An empirical study of companies in Sri Lanka. IPEDR, 54(3).
Seidel, M. (2019). What are the factors that affect productivity? Retrieved from https://bizfluent.com/info-10000291-factors-affect-productivity.html on February 20, 2020..
Singh, R. & Molhanty, M. (2012). Impact of training Practice on employee’s productivity: A comparative study. Inter-science Management Review, 2(2), 87- 92.
Ubesie, M. C., Eneh, C. A. & Udeh, N. (2019). Effect of human capital expenditures on corporate social responsibility of oil and gas firms in Nigeria. American Journal of Theoretical and Applied Business, 5(4), 102-112.
Ukenna, S., Ijeoma, N., Anionwu, C. & Olisa, M. C. (2010). Effect of investment in human capital development on organizational performance: Empirical examination of the perception of small business owners in Nigeria. European Journal of Economics, Finance and Administrative Sciences, 94-107.
Voegtlin, C. & Greenwood, M. (2016). Corporate social responsibility and human resource management: A systematic review and conceptual analysis. Human Resource Management Review, 26, 181–97.
Imoughele, L. E. & Ismaila, M. (2013). Determinants of public health care expenditure in Nigeria: An error correction mechanism approach. International Journal of Business and Social Science, 4(13), 222-233.
APPENDICES
APPENDIX A: UNIT ROOT TEST
|
Panel unit root test: Summary |
|
|||
|
Series: LOGTVR |
|
|
||
|
Date: 02/24/20 Time: 17:58 |
|
|||
|
Sample: 2009 2018 |
|
|
||
|
Exogenous variables: Individual effects, individual linear trends |
||||
|
User-specified lags: 1 |
|
|
||
|
Newey-West automatic bandwidth selection and Bartlett kernel |
||||
|
Balanced observations for each test |
|
|||
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Cross- |
|
|
Method |
Statistic |
Prob.** |
sections |
Obs |
|
Null: Unit root (assumes common unit root process) |
||||
|
Levin, Lin & Chu t* |
-3.60442 |
0.0002 |
4 |
32 |
|
Breitung t-stat |
0.52566 |
0.7004 |
4 |
28 |
|
|
|
|
|
|
|
Null: Unit root (assumes individual unit root process) |
||||
|
Im, Pesaran and Shin W-stat |
-0.33755 |
0.3679 |
4 |
32 |
|
ADF - Fisher Chi-square |
11.3807 |
0.1810 |
4 |
32 |
|
PP - Fisher Chi-square |
4.31823 |
0.8273 |
4 |
36 |
|
|
|
|
|
|
|
|
|
|
|
|
|
** Probabilities for Fisher tests are computed using an asymptotic Chi |
||||
|
-square distribution. All other tests assume asymptotic normality. |
||||
|
Panel unit root test: Summary |
|
|||
|
Series: LOGESW |
|
|
||
|
Date: 02/24/20 Time: 17:59 |
|
|||
|
Sample: 2009 2018 |
|
|
||
|
Exogenous variables: Individual effects, individual linear trends |
||||
|
User-specified lags: 1 |
|
|
||
|
Newey-West automatic bandwidth selection and Bartlett kernel |
||||
|
Balanced observations for each test |
|
|||
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Cross- |
|
|
Method |
Statistic |
Prob.** |
sections |
Obs |
|
Null: Unit root (assumes common unit root process) |
||||
|
Levin, Lin & Chu t* |
-5.83711 |
0.0000 |
4 |
32 |
|
Breitung t-stat |
-0.21669 |
0.4142 |
4 |
28 |
|
|
|
|
|
|
|
Null: Unit root (assumes individual unit root process) |
||||
|
Im, Pesaran and Shin W-stat |
-0.38084 |
0.3517 |
4 |
32 |
|
ADF - Fisher Chi-square |
12.3049 |
0.1381 |
4 |
32 |
|
PP - Fisher Chi-square |
5.62330 |
0.6893 |
4 |
36 |
|
|
|
|
|
|
|
|
|
|
|
|
|
** Probabilities for Fisher tests are computed using an asymptotic Chi |
||||
|
-square distribution. All other tests assume asymptotic normality. |
||||
|
Panel unit root test: Summary |
|
|||
|
Series: LOGEETR |
|
|
||
|
Date: 02/24/20 Time: 18:00 |
|
|||
|
Sample: 2009 2018 |
|
|
||
|
Exogenous variables: Individual effects, individual linear trends |
||||
|
User-specified lags: 1 |
|
|
||
|
Newey-West automatic bandwidth selection and Bartlett kernel |
||||
|
Balanced observations for each test |
|
|||
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Cross- |
|
|
Method |
Statistic |
Prob.** |
sections |
Obs |
|
Null: Unit root (assumes common unit root process) |
||||
|
Levin, Lin & Chu t* |
-2.74491 |
0.0030 |
4 |
32 |
|
Breitung t-stat |
1.00772 |
0.8432 |
4 |
28 |
|
|
|
|
|
|
|
Null: Unit root (assumes individual unit root process) |
||||
|
Im, Pesaran and Shin W-stat |
-0.09439 |
0.4624 |
4 |
32 |
|
ADF - Fisher Chi-square |
8.65308 |
0.3724 |
4 |
32 |
|
PP - Fisher Chi-square |
4.29550 |
0.8295 |
4 |
36 |
|
|
|
|
|
|
|
|
|
|
|
|
|
** Probabilities for Fisher tests are computed using an asymptotic Chi |
||||
|
-square distribution. All other tests assume asymptotic normality. |
||||
|
Panel unit root test: Summary |
|
|||
|
Series: LOGEHT |
|
|
||
|
Date: 02/24/20 Time: 18:01 |
|
|||
|
Sample: 2009 2018 |
|
|
||
|
Exogenous variables: Individual effects |
||||
|
User-specified lags: 1 |
|
|
||
|
Newey-West automatic bandwidth selection and Bartlett kernel |
||||
|
Balanced observations for each test |
|
|||
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Cross- |
|
|
Method |
Statistic |
Prob.** |
sections |
Obs |
|
Null: Unit root (assumes common unit root process) |
||||
|
Levin, Lin & Chu t* |
-2.81265 |
0.0025 |
4 |
32 |
|
|
|
|
|
|
|
Null: Unit root (assumes individual unit root process) |
||||
|
Im, Pesaran and Shin W-stat |
-0.00607 |
0.4976 |
4 |
32 |
|
ADF - Fisher Chi-square |
9.58795 |
0.2951 |
4 |
32 |
|
PP - Fisher Chi-square |
6.30419 |
0.6132 |
4 |
36 |
|
|
|
|
|
|
|
|
|
|
|
|
|
** Probabilities for Fisher tests are computed using an asymptotic Chi |
||||
|
-square distribution. All other tests assume asymptotic normality. |
||||
Download PDF