







































 
 

 

1 
© 2022 by the authors; licensee Asian Online Journal Publishing Group 
 

Economy 
Vol. 9, No. 1, 1-8, 2022 

ISSN(E) 2313-8181/ ISSN(P) 2518-0118 
DOI: 10.20448/economy.v9i1.4016 

© 2022 by the authors; licensee Asian Online Journal Publishing Group 

 
 

 
 
 
Resource-Use Efficiency and Firm Performance in Nigeria’s Manufacturing Industry 

 
Dahunsi, Olusola Joseph1   

Soetan, Olufunmilayo Rosemary2   

  
( Corresponding Author)  

1,2Obafemi Awolowo University, Ile-Ife, Nigeria. 
1Email: olusoladahunsi@yahoo.com Tel: +2348036922004 
2Email: rsoetan@oauife.edu.ng Tel: +2348037169069 

 
Abstract 

The study investigated the existence of technical efficiency among manufacturing firms and 
examine its effects on firm performance in Nigeria using firm level data between 2001 and 2017. 
Variables such as capital, labour, total overhead inputs, total firm output, competition, capital 
intensity, firm market share, technical efficiency scores and firm profitability were used in this 
study. Using the stochastic frontier analysis (SFA) to generate the technical efficiency scores, the 
study adopted System-GMM to examine the effects of technical efficiency on firm performance 
among quoted manufacturing firms operating in consumer (food beverages and tobacco), 
industrial and health (pharmaceutical) sectors in Nigeria. The findings revealed that 29% of the 
variation between the observed and optimal outputs is attributed to inefficiency among 
manufacturing firms. However, firm competitiveness significantly increases the efficient use of 
resources among manufacturing firms. This study further showed that technical efficiency 
variable has positive effects on manufacturing firm performance in Nigeria. The paper concluded 
that competition increases the efficient utilization of resources which positively improves firm 
performance in Nigeria’s manufacturing industry. Finally, the paper recommended that industrial 
policies should be geared towards promoting healthy competition (and not collusion) among 
manufacturing firms to attain optimal economic efficiency of resources. 

 
Keywords: Efficiency, Competitiveness, Capital intensity, Market share, Firm performance, Manufacturing firms. 

JEL Classification: D22, L25, O14. 
 

Citation | Dahunsi, Olusola Joseph; Soetan, Olufunmilayo 
Rosemary (2022). Resource-Use Efficiency and Firm Performance in 
Nigeria’s Manufacturing Industry. Economy, 9(1): 1-8. 
History:  
Received: 25 April 2022 
Revised: 30 May 2022 
Accepted: 15 June 2022 
Published: 28 June 2022 
Licensed: This work is licensed under a Creative Commons 

Attribution 4.0 License  
Publisher:  Asian Online Journal Publishing Group 
 

Funding: This study received no specific financial support. 
Authors’ Contributions: Both authors contributed equally to the conception 
and design of the study. 
Competing Interests: The authors declare that they have no conflict of 
interest. 
Transparency: The authors confirm that the manuscript is an honest, 
accurate, and transparent account of the study; that no vital features of the 
study have been omitted; and that any discrepancies from the study as planned 
have been explained. 
Ethical: This study followed all ethical practices during writing. 

 

 

Contents 
1. Introduction ......................................................................................................................................................................................... 2 
2. Literature Review ............................................................................................................................................................................... 2 
3. Stochastic Frontier Model ................................................................................................................................................................ 3 
4. Results and Discussions ..................................................................................................................................................................... 5 
5. Conclusion ............................................................................................................................................................................................ 7 
6. Policy Recommendations .................................................................................................................................................................. 7 
References ................................................................................................................................................................................................. 8 
 

 
 

 

 

 

mailto:olusoladahunsi@yahoo.com
mailto:rsoetan@oauife.edu.ng
https://creativecommons.org/licenses/by/4.0/
https://creativecommons.org/licenses/by/4.0/
https://www.doi.org/10.20448/economy.v9i1.4016
https://orcid.org/0000-0002-5007-9402


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Contribution of this paper to the literature 
This study investigated the existence of technical efficiency among manufacturing firms and 
examine its effects on firm performance in Nigeria’s manufacturing industry between 2001 and 
2017. The study revealed that 29% of the variation between the observed and optimal outputs is 
attributed to inefficiency among manufacturing firms. The study also added that intense 
competition among manufacturing firms increases the efficient use of resources which positively 
improves firm performance in Nigeria’s manufacturing industry. 

 
1. Introduction 

Manufacturing industry has been identified as a crucial driver of industrialization given its significant roles in 
the development of the contemporary developed and emerging economies. This could be attributed to the 
technological processes involved in the transformation of raw materials to finished goods as well as the 
accompanied technical efficiency generated from using factor inputs to achieve maximum outputs. The efficient use 
of both human and capital resources, therefore, has great potential for the production of differentiated goods, job 
creation, development of the industrial sector and firm performance. Firm performance expressed in terms of 
profitability, growth, market value and some other socioeconomic indicators (Selvam, Gayathri, Vasanth, 
Lingaraja, & Marxiaoli, 2016) can be achieved by improving on the efficient use of factor inputs that enhances 
competitiveness of manufacturing firms both locally and internationally. 

Since independence in 1960, Nigeria has aimed at industrialization with the hope of changing the economy 
from an agricultural and import-dependent to a more dynamic and export-oriented economy, especially through 
the export of industrial outputs (import substitution strategies). However, Nigeria has continued to experience a 
concomitant decline in the manufacturing sector output. According to the National Bureau of Statistics (2014), 
manufacturing sector contributed 10% to Nigeria’s outputs compared with the 70% contributed by the primary 
sector (agriculture) before crude oil exploration in the 1970s. With the windfall gain from crude oil, the 
manufacturing sector’s contribution to the national outputs declined drastically to 7.83% in 1982. In a bid to 
increase the performance of the industrial sector, the Nigeria Enterprise Promotion Decree of 1977, the Structural 
Adjustment Programme (SAP) of 1986; and other industrial development policies were directed towards domestic 
production of previously imported consumer products and the promotion of Nigeria’s industrial sector to attain 
sustainable growth. With continued dependence on revenue from crude oil exports, the manufacturing sector 
performance decline in growth and value-added, contributing minimally to economic growth despite the emphasis 
placed on science, technology and innovation (STI) as key drivers of the economic reform of the National Economic 
Empowerment and Development Strategy (NEEDS) between 1999 and 2007 and the economic policy blueprint of 
Vision 20:2020 in Nigeria Chete, Adeoti, Adeyinka, and Ogundele (2014).  

Given the role of industrialization in attaining economic growth and development, efficient use of resources in 
the manufacturing industry has substantial implications for firm performance. Efficient firms weed out less efficient 
ones because efficient firms tend to be competitive and, as a result, resources would be reallocated from inefficient 
firms to the efficient ones (Ahn, 2002; Obembe & Soetan, 2015; Soames, Brunker, & Talgaswatta, 2011). As the 
market becomes more competitive, efficient firms will increase their productivity and market share while inefficient 
firms lose their market share to the efficient ones, and either way firm profitability will be affected. Thus, the 
relationship between the efficient use of resources and firm profitability ultimately influence the overall firm 
performance. However, studies by Baten, Kamil, and Fatama (2009); Essmui, Berma, Shahadan, and Ramlee (2013); 
Helali and Kalai (2015) revealed that inefficiency exists in the use factor inputs among manufacturing firms, 
although, the effects of the inefficiency on firm performance is controversial (Addai-Asante & Sekyi, 2016; Din, 
Ghani, & Mahmood, 2007). Hence, this study estimates the resource utilization efficiency as well as examine the 
effects of the technical efficiency on firm performance in the Nigeria’s manufacturing industry. 

The technical efficiency measures the extent to which factor inputs are optimally utilized to produce outputs 
and this was achieved by separating inefficiency scores from random error (Battese & Coelli, 1995). This was 
estimated through the stochastic frontier analysis (SFA) as proposed by Aigner, Lovell, and Schmidt (1977) and 
Meeusen and van Den Broeck (1977) to estimate resources efficiency in applied economic research. The stochastic 
frontier analysis (SFA), a parametric approach, separates the deviations from the production frontier to white noise 
and technical inefficiency unlike the data envelopment analysis (DEA) which is non-parametric approach that 
allocates the deviation to technical efficiency/inefficiencies (Iinuma, Sharma, & Leung, 1999). Hence, the SFA 
technique reveals a general relationship between output and inputs and also accounts for random shocks (white 
noise) which are lacking in non-parametric DEA (Silva, Tabak, Cajueiro, & Dias, 2016). This study adopts the SFA 
approach because, in reality, the deviations from the production frontier in the manufacturing industry are not 
solely attributable to the inefficiency of manufacturers (Bolarinwa & Adegboye, 2020), certain deviations result from 
risk and uncertainty that surround the supply of materials and fluctuations in the market price of intermediate and 
finished goods of manufacturing firms. 

Thus, this study generated a resource-use efficiency variable and examined its effects on firm performance 
among forty-three (43) quoted manufacturing firms operating in consumer (food beverages and tobacco), industrial 
and health (pharmaceutical) sectors by using stochastic frontier analysis. This was done in line with the Nigerian 
Economic Recovery and Growth Plan (ERGP) framework (2017-2020) that encourages STI to drive economic 
growth through the agro-processing sub-sector comprising firms operating in food, beverages, tobacco, and other 
manufacturing sub-sector. 

 

2. Literature Review 
Koh, Rahman, and Tan (2004) separated productivity growth into technical efficiency, technical progress and 

scale of economies effect and examined their contribution to productivity among 18 manufacturing industries 
between 1974 to 1998 in Singapore. Using the stochastic frontier analysis, it was revealed that the variation 
between the frontier and actual outputs resulted from technical inefficiency but the efficiency level tends to increase 
over time. The study further showed that technical progress contributed 1.5 per cent per annum, technical 



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efficiency change contributed 0.5 per cent per annum and scale of economic efficiency contributed 0.8 per cent per 
annum to the productivity growth in the Singapore manufacturing industry. The study concluded that the 
Singapore manufacturing industry requires technology to improve on technology growth. 

Din et al. (2007) studied the efficiency level of the large scale manufacturing sector in Pakistan. Stochastic 
frontier analysis and data envelopment analyses were employed to investigate efficiency level among 101 industries 
in Pakistan, the result of the stochastic frontier analysis showed that there is a small increase in the efficiency level 
of the large scale manufacturing firms. The result of the data envelopment analysis further supported that the 
efficiency level of the manufacturing sector has greatly improved and exert positive effects on firm performance as 
a result of economic reforms in the Pakistan manufacturing industry. 

Furthermore, Baten et al. (2009) examined the effect of technical inefficiency on manufacturing industry 
performance among 279 industries in Bangladesh. The study employed stochastic frontier analysis and the 
ordinary least square method and showed the existence of technical inefficiency in the Bangladesh manufacturing 
sector, although, the inefficiency declines over the studied period. The study further revealed that the inefficiency 
of capital, labour (manual and non-manual) and raw material cost have significant effects on the manufacturing 
output level. 

Similarly, Essmui et al. (2013) investigated the level of inefficiency and its effects on the performance of 207 
firms in the Libya manufacturing industry. Stochastic frontier model was used and the study showed the existence 
of technical inefficiency in the Libya manufacturing industry as capital, labour and material inputs are not optimally 
utilized. The study concluded that outputs in Libya manufacturing industry can be increased without necessarily 
increase capital, labour and material inputs. 

In a related vein, Helali and Kalai (2015) investigated technical, allocative and economic efficiencies and 
examined their effect on production output among 6 sectors in the Tunisian manufacturing industry between 1961 
and 2010. The study adopted the stochastic and Bayesian model and found that technical inefficiency existed in the 
manufacturing industry and that output efficiency can be improved using the same inputs in production. It was also 
revealed that a lack of innovation and investment in technology in the manufacturing industry resulted in negative 
productivity growth. 

Addai-Asante and Sekyi (2016) examined technical/resources efficiency in production among 39 Ghanaian 
Pharmaceutical firms. Using stochastic frontier analysis and the ordinary least square method, the study revealed 
that technical inefficiency exists in the pharmaceutical industry and resources inefficiency exerted a diverse impact 
on output level. It further showed that capital was more productive in the production of capsule compared to labour 
(skilled and unskilled). Although, skilled and unskilled labour was productive more than capital in the production of 
syrup. The study concluded that age and maintenance of plant, as well as the size of the professional labour, are 
technically efficient. 

 

3. Stochastic Frontier Model 
In stochastic frontier analysis (SFA), several functional forms are often used, however, the two most commonly 

used forms are Cobb-Douglas and transcendental logarithmic (trans-log) production functions. Following the study 
of Battese and Coelli (1995), this study adopted the Cobb-Douglas stochastic frontier production model rather than 
the trans-log production function given the likelihood ratio (LR) test statistics. The Cobb-Douglas production 
function is specified as; 

( ; )i iY f X =           (1) 

Where iY is the optimum output obtainable; iX  is the vector of inputs and   represents unknown parameter. 

Stochastic frontier production function incorporates measurement error and unobservable shocks into the model as; 

i i iY X  = +   for 1,...,i n=                                  (2) 

0i i i iY X V U = + + −                                          (3) 

Where i i iV U = −  

Here, 
iV ∼i.i.d. N(0,

2

v ) and 
iU ∼i.i.d. N(μ, 2

 ) are independently and identically distributed of each other and 

other explanatory variables. Following Battese and Coelli (1995) model which assumes truncated normal 
distribution, this study estimated the technical efficiency of firms in Nigeria’s manufacturing industry through the 
Cobb-Douglas stochastic frontier model. The linear form of Equation 3 can be rewritten as: 

0 ,

1

( )
n

it j j it it it

j

lnY ln X V U 
=

= + + −  for 1,..., , 1,...,i n t T= =   (4) 

0 ,

1

n

it j j it it it

j

lnY lnX V U 
=

= + + −                          (5) 

0 ,

1

exp( )
n

it j j it it it

j

Y lnX V U 
=

= + + −                           (6) 

Equation 6 can be rewritten as: 

0 ,

1

exp( ) exp( ) exp( )
n

it j j it it it

j

Y lnX V U 
=

= +   −     (7) 

Where 
0 ,

1

exp( )
n

j j it

j

lnX 
=

+  are the deterministic component and the composite error term, it , which is 

decomposed into noise effect itV  and efficiency parameter itU . Thus, technical efficiency (TEi) is expressed as the 

ratio of observed output to optimum output. The technical efficiency (TEi) for the i-th firm at time t is defined as: 



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exp( )

exp( )

it it it
it

it it

X V U
TE

X V





+ −
=

+
                                    (8) 

Equation 8 presents the technical efficiency model which measures the extent to which factor inputs are 

efficiently or optimally utilized to produce outputs and this is done by separating efficiency scores from random 

error. 

Equation 8 can be rewritten as: 

exp( )it itTE U= −                                                    (9) 

Equation 9 argues that the expected value of the exponential (-Uit) is the technical efficiency.  
 

Given the truncated-normal assumption in the stochastic frontier analysis, the technical efficiency model is 
specified thus; 

it i it itU Z W= +                                                     (10) 

Equation 10 represents the technical efficiency level with respect to firm competitiveness. 
 

Zit represents the exogenous variable which in this case is competition (COM), Wit represents the error term of 

the efficient model and δ is the estimated parameter. A negative coefficient of δ connotes an increase in technical 
efficiency or a reduction in technical inefficiency. 

 

3.1. Effects of Resource-use Efficiency on Firm Performance 
The system GMM estimator (S-GMM) theorized by Arellano and Bover (1995) which Blundell and Bond 

(1998) developed was adopted by the study as its estimation technique to examine the effect of resource-use 
efficiency on firm performance using secondary data obtained from forty-three (43) quoted manufacturing firms 
operating in consumer (food beverages and tobacco), industrial and health (pharmaceutical) sectors. The S-GMM is 
preferred to difference GMM (D-GMM) because the presence of a lagged dependent variable as an independent 
variable in the D-GMM violates the orthogonality assumption. This is because the lagged dependent variable 

1itPEF −
  depends on

1it −
. However, system GMM augments difference GMM under the assumption that the first 

difference of instrumental variables is not correlated with the level errors. Besides, the system GMM estimator is 
preferred to the OLS and the fixed effects techniques in that the OLS and the fixed effect results generally suffer 
from the problem of endogeneity (Obembe & Soetan, 2015). This study, therefore, adopted System GMM 
framework that accommodates the endogeneity problem inherent in the explanatory variables and it also gives 
consistent parameter estimates for a small period of time, t, and large cross-sectional dimension, N. To examine the 
effects of resource-use efficiency on manufacturing performance, this study employed the S-GMM model specified 
as follows; 

'

1it it it it itPEF PEF EFF X   −= + + +              (11) 

In Equation 11, itPEF  represents performance for firm i over period t; 1itPEF −  entails the lagged value of the 

dependent variable for firm i over period t, itEFF  represents resource-use efficiency, X represents other control 

variables included in the model such as capital intensity (CIN) and firm market share (FMS) for firm i over period t. 
To solve the problem of endogeneity inherent in the explanatory variables, this study examined the effects of 
resource-use efficiency on firm performance using the system generalized method of moment (S-GMM) estimator 
as propounded by Arellano and Bover (1995). 
 

3.2. Sources of Data 
Secondary data were sourced from annual reports of quoted manufacturing firms on the Nigerian Stock 

Exchange (NSE). The population of the quoted manufacturing firms is fifty-six (56) consisting of consumer (food, 
beverages and tobacco - 26); healthcare (10); industrial (20) firms as categorised by securities and exchange 
commission (SEC). Total sampling technique was employed by the study, however, only forty-three (43) firms 
consisting of 19 firm operating in consumer (food, beverages and tobacco); 18 firms in industrial and 6 firms in 
healthcare (pharmaceuticals) were used due to unavailability of data for most of the quoted manufacturing firms for 
the period under study. These firms were chosen because they have greater access to foreign and domestic funds 
and they also invest more in research and development compared with non-quoted manufacturing firms. Secondary 
data were specifically obtained from the published annual reports and financial statements of the manufacturing 
firms. 

Firms’ total value of output (TVO) was measured by total annual sales of sampled manufacturing firms (Battese 
& Coelli, 1995). The labour input (LAB) was measured by the labour-to-revenue ratio. It is calculated as total 
labour cost divided by revenue. The labour-revenue ratio measures the efficiency of the labour force in generating 
revenue and it is estimated to be positively related to output. The capital input (CIP) was measured by the asset 
turnover ratio. The total asset turnover is calculated as revenue divided by average total assets which should be 
positively related to total output. The total overhead input (TOH) was measured by the operating expenses ratio. It 
is calculated as total overhead expenses divided by revenue. The operating expenses ratio showed how efficiently an 
organization is being managed and it is expected to be positively related to output (Edwards, Allen, & Shaik, 2006). 
YEAR is the year of the observation involved. 

Technical efficiency scores (EFF) was derived by generating inefficiency from maximum output obtainable 
using stochastic frontier analysis (Battese & Coelli, 1995). Firm performance (PEF) was measured as the ratio of 
profit (profit before tax) to total asset. This shows the ability of a firm to generate profits by using all its asset (Ti 
& Chi, 2016). Capital intensity was measured by the ratio of tangible assets (fixed assets) to the total number of 
employees (Halpern & Muraközy, 2015). Firm market share which is the share of the market that each firm can 



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capture was measured by the ratio of each firm’s revenue to the industry revenue (Edwards et al., 2006). 
Competition (COM) was measured by the Price–Cost Margin (PCM). Intense competition in a market stimulates 
efficiency among incumbent firms (forces inefficient firms out), thereby increasing the average PCM among firms 
(Cranfield, 2002).  

Re .

Re

venue Inventories LabourCost CostOf Materials
PCM

venue Inventories

+  − −
=

+ 
. 

 

4. Results and Discussions 
This study examined the effects of resource-use efficiency on firm performance in Nigeria’s manufacturing 

industry. In a bid to achieve this, the study generated a resource-use efficiency variable for all sampled firms 
through the stochastic frontier analysis (SFA) technique. Following Battese and Coelli (1995) in estimating 
technical efficiency, the Cobb-Douglas and trans-log production functions were estimated and the appropriate 
production frontier between the two production functions was selected using the likelihood ratio test. The 
existence of inefficiency effects was also tested and analyzed using maximum likelihood statistics. This is because, 
manufacturing firms will not be able to achieve optimum technical efficiency until we can identify the sources of 
inefficiency (Tingum & Ofeh, 2017). Finally, the technical efficiency scores (EFF) of sampled manufacturing firms 
in Nigeria were generated using 43 Nigeria’s manufacturing firms operating in consumer (food, beverages and 
tobacco), industrial; and healthcare (pharmaceutical) sectors between 2001 and 2017. The stochastic frontier 
production function consisted of labour input, capital input, overhead expenditure and total revenue. 

 

4.1. Descriptive Statistics of Inputs and Output Data 
In estimating the stochastic frontier analysis, labour, capital, overhead expenditure and total revenue from the 

selected manufacturing firms were sourced for the period between 2001 and 2017 in Nigeria. The dependent 
variable (total value of output) of the model is total revenue while labour input, capital input and overhead 
expenditure are the independent variables. 

The descriptive statistics results in Table 1 reveal that the mean and median are in between their maximum 
and minimum values for all the inputs and output variables which show a high consistency level. The skewness 
statistics reveals that all the variables are negatively skewed. The kurtosis of all the variables exceeds 3 implying 
that the series is peaked (leptokurtic) compared to the normal distribution. 
 

Table 1. Descriptive statistics. 

Descriptive Statistics LAB CIP TOH TVO 

Mean -2.40 -0.45 -1.65 15.7 
Median -2.34 0.57 -1.54 15.9 
Standard Dev. 0.80 0.91 0.81 2.07 
Minimum -6.89 -5.56 -5.22 8.39 
Maximum 0.16 2.59 0.64 20.5 
Skewness -1.15 -1.78 -0.98 -0.37 

Kurtosis 7.43 10.8 5.40 3.05 
Observations 610 647 577 658 
Note: LAB, CIP, TOH and TVO represent the natural logarithm of Labour input, Capital input, Total 
overhead expenditure and Total value of output (Sales). 

 

4.2 Stochastic Frontier Analysis Results 
The maximum likelihood estimates of the time-invariant inefficiency model for the Cobb-Douglas production 

function (assuming a truncated-normal distribution) was obtained and reported in Table 2. The Cobb-Douglas and 
the transcendental logarithmic (trans-log) production models were estimated, however, the Cobb-Douglas model 
was presented rather than the trans-log production function following the likelihood ratio (LR) estimates. The 
probability value of the likelihood ratio (14.42) is not significant at a 5% significant level, thus the rejection of the 
null hypothesis that the Cobb-Douglas production function is nested in full in the trans-log production function. 
This study accepts the alternative hypothesis that the Cobb-Douglas production function is not nested in full in the 
trans-log production function, hence, the Cobb-Douglas production function was presented. 

The results showed that the coefficients of labour (LAB) and overhead (TOH) inputs from the Cobb-Douglas 
model are negative and statistically significant at a 5% level except for capital input (CIP) which is positive at a 5% 
significant level. The negative coefficient of labour (LAB) input could be as a result of incessant activities of labour 
unions (such as strike actions), low wage rate and unfavourable working conditions of workers. These will, in turn, 
affect labour efficiency and reduce firm performance (Tingum & Ofeh, 2017). The MLE further explains the 
concepts of elasticity and returns to scale. The output elasticity is explained by the estimated coefficients of input 
variables (labour, capital and overhead) from the MLE. 

The results revealed that the elasticity coefficients of labour input (-0.6084) and total overhead input (-0.5361) 
are negative which implies that labour input (LAB) and total overhead expenditure (TOH) had significant negative 
effects on the outputs level in Nigeria’s manufacturing industry. By implication, a percentage increase in all the 
input variables will result in less than a proportionate increase in firms’ outputs and revenue level. Conversely, the 
elasticity coefficient of capital input (0.2114) is significantly positive, thus, capital input (CIP) has a significant 
positive effect on outputs level among manufacturing firms in Nigeria. Succinctly, the summation of all elasticity 
coefficients (-0.9331) shows that Nigeria’s manufacturing firms operate on a decreasing return to scale. 

 

4.2.1. The Inefficiency Effects 
Table 2 also reveal the existence of inefficiency in the Cobb-Douglas model. According to Ahmadzai (2017), the 

coefficient of gamma implies that the variance between the observed outputs and optimum level of outputs is 
attributable to technical inefficiency. The coefficient ( =0.29) from the model shows that about 29% of the 

difference between the observed output and maximum outputs comes from inefficiency on the part of the 



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manufacturer/production team. By implication, about 29% of the difference between the observed output and the 
frontier outputs results from inefficiencies among manufacturing firms. This shows reductions in the frontier 
outputs using capital input, labour input and total overhead expenditure. However, 71% of the variation is 
attributable to random effects such as risk and uncertainty that surround the supply of raw materials as well as 
fluctuations in the market price of intermediate and finished goods of manufacturing firms. Thus, the null 
hypothesis which states that the variation in production is not attributable to technical inefficiency is rejected. This 
finding is consistent with the study of Helali and Kalai (2015) which showed that the variation in production output 
is attributable to technical inefficiency and random effects.  

The knowledge of technical efficiency will not be useful if we do not understand the source of the efficiency. 

Table 2 shows that the coefficient (δ) of competition (-1.1840) in the inefficiency effects section of the production 
function is negative and significant at a 5% level. The negative coefficient of competition showed a reduction in 
technical inefficiency or an increase in technical efficiency. This indicates that in manufacturing industry, firm 
competitiveness motivates the resource-use efficiency of manufacturing firms. This is in line with Esquivias and 
Harianto (2020) who found that competition (in terms of export and import activities) has positive impact on firms’ 
technical efficiency level. This implies that a higher level of efficiency will be attained by competitive firms (firms 
that are export-oriented as well as firms that have access to imported raw materials) compared with less 
competitive firms with no export and/or import activities which face the risk of being driven out of the market by 
competitive firms. Hence, this study revealed that the source of technical efficiency among Nigerian manufacturing 
firms is dependent on the level of industrial competitiveness. 
 

Table 2. Cobb-Douglas maximum likelihood estimates. 

 Method Variable Coefficient Std. Error t-value Prob. 

Production Frontier 
  
  
  
  

Constant 331 284 1.17 0.24 
LAB -0.61 0.12 -5.22 0.00*** 
CIP 0.21 0.11 1.96 0.05** 

TOH -0.54 0.11 -5.10 0.00*** 
YEAR -41.7 37.4 -1.11 0.27 

Inefficiency Effects 
  
  
  
  
  
  

2

u  0.49 0.30 1.63 0.10 
2

v  1.69 0.73 23.2 0.00*** 

 ` 0.29 0.36 0.82 0.41 
Constant 0.87 0.42 2.07 0.04** 

COM ( ) -1.18 0.21 -5.54 0.00*** 
Log-Likelihood -1088    

Wald Chi2 112    
Prob. 0.00    

Likelihood-ratio 
LR Chi2 

Prob. 
14.4 
0.08    

Note: LAB, CIP, TOH and YEAR represent the natural logarithm of Labour input, Capital input, Total overhead expenditure 
and Year of observation involved. 
***, ** significance at 1% and 5% respectively. 

 

4.2.2. Estimation of Technical Inefficiency Scores 
Table 3 reveals the existence of technical inefficiency with a sample mean of 19.15% in the selected sample of 

quoted manufacturing firms. This reveals that there are technical inefficiencies in Nigeria’s manufacturing industry. 
By implication, manufacturing firms operating in consumer (food, beverages and tobacco); industrial; and 
healthcare (pharmaceutical) sectors obtained 80.85% outputs from utilizing available capital, labour and total 
overhead inputs. Put differently, the sampled manufacturing firms only produced 80.85% outputs which were below 
the maximum obtainable output while the rest of their output (19.15%) is attributed to inefficiency. The analysis of 
technical inefficiency scores of sampled firms is also presented in Table 3. 
 

Table 3. Technical inefficiency statistics. 

 
 

 

4.3. Effects of Resource-use Efficiency on Firm Performance 
Under normal market conditions, resource-use/technical efficiency results in higher firm performance in terms 

of high profitability. However, when markets are characterized with imperfections and asymmetric information, 
resource-use efficiency together with other performance-related factors may negatively affect firm performance. 
The study had established that inefficiency existed in the resource utilization among Nigeria’s manufacturing firms 
for the period between 2001 and 2017 (see Table 2). Given the existence of inefficiency in the use of factor inputs 
by manufacturing firms in Table 3, the resource-use efficiency variable (EFF) was derived by generating firms’ 
inefficiency scores from the maximum output obtainable to examine the effect of resource-use efficiency on firm 
performance in Nigeria’s manufacturing industry. 

Table 4 shows the effects of resource-use efficiency (EFF) on firm performance in Nigeria’s manufacturing 
industry using the generalized method of moments approach. The results indicated that the effect of resource-use 
efficiency (0.087%) is positive on firm performance, though not statistically significant at 5% level. By implication, 
the efficient utilization of factor inputs in the manufacturing industry has positive effects on profitability and 
subsequently on firm performance. 

Besides, the results showed that firm performance in the previous period (PEF(-1)) has a significant positive 
effect (0.1155) on the current firm performance at a 5% significant level. This means that a unit increase in the last 

Mean Median Maximum Minimum Std. Dev. 

19.15812 18.9 22.5 17.3 1.07 



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period performance leads to an 11.55% increase in the current period performance. Similarly, firm market share 
(FMS) exhibits a positive effect (1.3738) on firm performance at a 5% level of significance, meaning that a unit 
increase in the firm market share results in 137% increases in firm performance. 

Conversely, the results show that capital intensity (CIN) has significant negative effects (-0.0258) on 
manufacturing firm performance but insignificant at a 5% level. The adverse effect of capital intensity on firm 
performance is not unconnected with the existence of technical inefficiency among manufacturing firms as reported 
in Table 3. 

The generalized method of moments diagnostic test was also reported in Table 4 and it revealed the validity of 
instruments employed as well as the absence of auto-correlation in the model. Hansen statistics explains the 
validity of instruments. The overall validity of instruments holds since the null hypothesis (p-value > 0.05) that 
supports the choice of the instruments at a 5% level of significance cannot be rejected. Similarly, auto-correlation of 
the error term was carried out to test the null hypothesis that the error term is auto-correlated. The probability 
value of the AR(2) in Table 4 which is 0.601 shows that the null hypothesis of no serial correlation cannot be 
rejected, rather, the results imply that the original error term is not correlated at 5% significant level, moment 
conditions are correctly specified and the model does not suffer from second-order auto-correlation. 

 
Table 4. System GMM. 

Variables Coefficient Std. Error t-Statistic Prob. 

PEF (-1) 0.11 0.04 2.71 0.01*** 
EFF 0.00 0.02 0.04 0.97 
FMS 1.37 0.44 3.08 0.00*** 
CIN -0.03 0.15 -1.69 0.09** 
C 0.23 1.77 0.13 0.89 
No. of Obs.    688 
F statistic    4.89 
Group/Instruments    43/21 
AR (2)    0.60 
Hansen Statistic    0.99 

Notes: ***, ** represent significance at 1% and 5% respectively.  
 

 
In addition, the study showed in Table 5 that younger manufacturing firms between 0 – 20 years are the most 

inefficient (21%) compared with much older firms between 51 – 60 years (18.70%), suggesting that younger firms 
are likely to perform poorly compared to older firms in Nigeria’s manufacturing industry. As reported by 
Krusinskas, Norvaisiene, Lakstutiene, and Vaitkevicius (2015) this can be attributed to the fact that small and 
medium-tech firms lagged behind the high-tech firm in innovation.  According to Soames et al. (2011) who opined 
that small and medium firms are not likely to survive if they are inefficient unlike large firms which can profitably 
remain in business even at sub-optimal production levels. This might be attributed to the fact that younger firms 
between 0 - 20 years, at the beginning of business operations, will have large sunk cost and are competing with 
large existing firms who have been in operation for 51 – 60 years.  

However, the high inefficiency scores of firms that are 61 years and above could be attributed to the technology 
employed in their production. Many of these technologies are considered archaic and outdated compared to the 
types of technologies employed by younger firms. By implication, firms that are 61 years and above usually operate 
below production frontier level compared with younger firms.  

 
Table 5. Mean technical inefficiency by firm age. 

Variable Years Inefficiency 

Firm Age 0-20 years 21.00% 
21-30 years 20.60% 
31-40 years 20.00% 
41-50 years 19.50% 
51-60 years 18.70% 

61 and above 19.00% 

 

5. Conclusion  
The study identified the existence of inefficiency in the use of resources among manufacturing firms in Nigeria. 

However, it was observed that competition significantly increases the efficient utilization of resources among 
manufacturing firms. This study further showed that resource-use efficiency has positive effects on manufacturing 
firm performance. The study concluded that intense competition increases efficient utilization of resources and 
resource-use efficiency subsequently improves firm performance in Nigeria manufacturing industry. 

 

6. Policy Recommendations 
The study revealed the existence of technical inefficiency (reduction in the optimum/frontier output) arising 

from capital input, labour input and total overhead expenditure among manufacturing firms. Therefore, policies on 
the efficient utilization of resources (capital input, labour input and total overhead expenditure) that will bridge the 
gap between actual output and potential output should be developed to achieve optimal production efficiency and 
prevent voluntary winding up or outright relocation of Nigerian firms to neighbouring countries. The study also 
showed that firm competitiveness enhances resource-use efficiency, as a result, the study recommends industrial 
policy that will promote healthy competition (and not collusion) among manufacturing firms to attain optimal 
economic efficiency of resources that will enable the economy to compete effectively with the developed and newly 
industrialized countries of the world. 
 



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