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Volume XII
Issue 5 (51) Fall 2017
ISSN-L 1843 - 6110
ISSN 2393 - 5162
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Editorial Board
Editor in Chief
PhD Professor Laura GAVRILĂ (formerly ŞTEFĂNESCU)
Managing Editor
PhD Associate Professor Mădălina CONSTANTINESCU
Executive Editor
PhD Professor Ion Viorel MATEI
International Relations Responsible
PhD Pompiliu CONSTANTINESCU
Proof – readers
PhD Ana-Maria TRANTESCU – English
Redactors
PhD Cristiana BOGDĂNOIU
PhD Sorin DINCĂ
PhD Loredana VĂCĂRESCU-HOBEANU
European Research Center of Managerial Studies in Business Administration
http://www.cesmaa.eu
Email: jaes_secretary@yahoo.com
Web: http://cesmaa.eu/journals/jaes/index.php
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Editorial Advisory Board
Claudiu ALBULESCU, University of Poitiers, France, West University of Timişoara, Romania
Aleksander ARISTOVNIK, Faculty of Administration, University of Ljubljana, Slovenia
Muhammad AZAM, School of Economics, Finance & Banking, College of Business, Universiti Utara, Malaysia
Cristina BARBU, Spiru Haret University, Romania
Christoph BARMEYER, Universität Passau, Germany
Amelia BĂDICĂ, University of Craiova, Romania
Gheorghe BICĂ, Spiru Haret University, Romania
Ana BOBÎRCĂ, Academy of Economic Science, Romania
Anca Mădălina BOGDAN, Spiru Haret University, Romania
Giacommo di FOGGIA, University of Milano-Bicocca, Italy
Jean-Paul GAERTNER, l'Institut Européen d'Etudes Commerciales Supérieures, France
Shankar GARGH, Editor in Chief of Advanced in Management, India
Emil GHIŢĂ, Spiru Haret University, Romania
Dragoş ILIE, Spiru Haret University, Romania
Cornel IONESCU, Institute of National Economy, Romanian Academy
Elena DOVAL, Spiru Haret University, Romania
Camelia DRAGOMIR, Spiru Haret University, Romania
Arvi KUURA, Pärnu College, University of Tartu, Estonia
Rajmund MIRDALA, Faculty of Economics, Technical University of Košice, Slovakia
Piotr MISZTAL, Technical University of Radom, Economic Department, Poland
Simona MOISE, Spiru Haret University, Romania
Mihail Cristian NEGULESCU, Spiru Haret University, Romania
Marco NOVARESE, University of Piemonte Orientale, Italy
Rajesh PILLANIA, Management Development Institute, India
Russell PITTMAN, International Technical Assistance Economic Analysis Group Antitrust Division, USA
Kreitz RACHEL PRICE, l'Institut Européen d'Etudes Commerciales Supérieures, France
Mohammad TARIQ INTEZAR, College of Business Administration Prince Sattam bin Abdul Aziz University
(PSAU), Saudi Arabia
Andy ŞTEFĂNESCU, University of Craiova, Romania
Laura UNGUREANU, Spiru Haret University, Romania
Hans-Jürgen WEIßBACH, University of Applied Sciences - Frankfurt am Main, Germany
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Journal of Applied Economic Sciences
Journal of Applied Economic Sciences is a young economics and interdisciplinary research journal, aimed
to publish articles and papers that should contribute to the development of both the theory and practice in the field
of Economic Sciences.
The journal seeks to promote the best papers and researches in management, finance, accounting,
marketing, informatics, decision/making theory, mathematical modelling, expert systems, decision system support,
and knowledge representation. This topic may include the fields indicated above but are not limited to these.
Journal of Applied Economic Sciences be appeals for experienced and junior researchers, who are
interested in one or more of the diverse areas covered by the journal. It is currently published quarterly in 2 Issues
in Spring (30th March), Summer (30th June), Fall (30th September) and Winter (30th December).
Journal of Applied Economic Sciences is indexed in SCOPUS www.scopus.com, CEEOL www.ceeol.org,
EBSCO www.ebsco.com, and RePEc www.repec.org databases.
The journal will be available on-line and will be also being distributed to several universities, research
institutes and libraries in Romania and abroad. To subscribe to this journal and receive the on-line/printed version,
please send a request directly to jaes_secretary@yahoo.com.
Journal of Applied Economic Sciences
Journal of Applied Economic Sciences
ISSN-L 1843 - 6110
ISSN 2393 – 5162
Table of Contents
Elena LIKHOSHERST, Lev MAZELIS, Aleksandr GRESKO, Kirill AVRENYUK
Fuzzy Set Model of Project Portfolio Optimization Inclusive for Requirements of Stakeholders 1263
Halil Dincer KAYA
The Impact of the Global Economic Crisis on Rural and Urban Poverty Gap 1274
Júlia ĎURČOVA, Rajmund MIRDALA
Tracing Value Added and Job Creation Across Industries in the Slovak Republic 1285
Aleksandr Mikhaylovich BATKOVSKIY, Victor Antonovich NESTEROV, Elena Georgievna
SEMENOVA, Vladimir Anatolievich SUDAKOV, Alena Vladimirovna FOMINA
Developing Intelligent Decision Support Systems in Multi-Criteria Problems of Administrative-
Territorial Formations Infrastructure Projects Assessment 1301
Radovan BACIK, Beata GAVUROVA, Jaroslava GBUROVA
Social Media, Corporate Website and its Impact on Consumer Purchasing Decisions 1312
Luminiţa PISTOL, Rocsana BUCEA-MANEA –ŢONIŞ, Radu BUCEA-MANEA –ŢONIŞ
Assumptions on Innovation into a Circular Economy 1319
Zhanna MUSATOVA, Boris MUSATOV, Sergey MKHITARYAN, Irina SKOROBOGATYKH
Analysis of Russian Companies’ Practice of Marketing Orientation 1328
Woraphon WATTANATORN, Sarayut NATHAPHAN
The Predictable Market and Mutual Fund’s Superior Performance the Evidence from the Higher
Moment Method 1341
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Journal of Applied Economic Sciences
Irina Petrovna SAVEL'YEVA, Il'ya Markovich TSALO, Ksenia Valerevna EKIMOVA, Tamara
Petrovna DANKO, Aleksei Ilish BOLVACHEV, Olga Alekseevna GRISHINA,
Vladimir Dmitrievich SEKERIN
Indicating the Impact of Changes in the Global Markets Environment on Russian Regional
Processes 1349
Abiola John ASALEYE, Israel OLURINOLA, Elizabeth Funlayo OLONI, Olufemi OGUNJOBI
Productivity Growth, Wages and Employment Nexus: Evidence from Nigeria 1362
Svetlana V. OREKHOVA
Economic Growth Quality of Metallurgical Industry in Russia 1377
Nadezhda Nickolaevna SEMENOVA, Svetlana Gennadyevna BUSALOVA, Olga Ivanovna
EREMINA, Svetlana Mihialovna MAKEYKINA, Yulia Yr´evna FILICHKINA
Influence of Monetary Policy on Economic Growth in Russia 1389
Suraya MAHMOOD, Musibau Hammed OLUWASEYI, Rana Muhammad Adeel FAROOQ,
Raheem Ibrahim DOLAPO
Stock Market Performance and Macroeconomic Fundamentals in the Great Nation: A Study of
Pool Mean Group 1399
Ján BULECA, Alena ANDREJOVSKA, Grzegorz MICHALSKI
Macroeconomic Factors Impact on the Volume of Household Savings in the Visegrad Four Countries
1409
Oleg Anatolievich TSEPELEV, Stanislav Gennadievich SERIKOV
Peculiarities of Regional Development and Industrial Specialization of the Far East of Russia 1422
Abd. Hamid PADDU
The Influence of Decentralization with Autonomy Power, Decentralization with Authority Power,
Factor Mobility, the Construction Cost Index, and Inflation Rate Toward Labor Absorption Rate
and its Implication Toward Regional Inequity in Indonesia 1433
Aliya NIYAZBAYEVA, Sailau BAIZAKOV, Aigul MAYDIROVA
Competitiveness of the Tourism Cluster of Kazakhstan: Comparative Analysis of Key Indicators 1443
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Journal of Applied Economic Sciences
Aleksandr G. DRUZHININ, Gennady M. FEDEROV, Nikolay V. GONTAR, Vasilisa V. GOROCHNYA,
Stanislav S. LACHININSKII, Andrey S. MIKHAYLOV, Denis A. VOLKHIN
Typology of Coastal Zones in the European Part of Russia: Modern Particularities within the
Trend of Cross-Border Clustering 1451
Anna TYKHONENKO, Veronika SULIKOVA
Catching-Up Process and Gross Domestic Product Synchronisation in the European Union:
Bayesian Shrinkage Estimation and Distance Based Approach 1461
Yuliana Vladimirovna SOLOVIEVA, Maksim Vasilyevich CHERNYAEV,
Anna Vadimovna KORENEVSKAYA
Transfer of Technology in Asian-Pacific Economic Cooperation States. Regional Development
Models 1473
Jozef FECENKO, Zuzana KRATKA, Katarína SAKALOVA
Why We Cannot Fully Understand the Variability of the Insurance Portfolio 1485
Serik B MAKYSH, Zhanat M. BULAKBAY, Dinara Zh. KENESBAYEVA, Baurzhan M. ISKAKOV,
Aida OZHAGYPAROVA, Gulim Sh. MUKHATAY
The Effect of the European Сentral Bank’s Unсоnvеntiоnal Mоnetary Pоliсiеs to the Financial
Stability of the Eurozone 1495
Sophia JOHNSON PREMILA
Status of Financial Literacy Among Small Scale Entrepreneurs: A Case Study 1508
Alexander CHURSIN, Pavel DROGOVOZ, Tatiana SADOVSKAYA, Vladimir SHIBOLDENKOV
The Dynamic Model of Elements’ Interaction within System of Science- Intensive Production
under Unstable Macroeconomic Conditions 1520
Anna MALTSEVA, Natalia BARSUKOVA, Alexandra GRIDCHINA, Tatiana KUZMINA
Analytical Review of the Contemporary State of the Russian Scientific Organizations from the
Development Management Position 1531
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Journal of Applied Economic Sciences
1362
Productivity Growth, Wages and Employment Nexus: Evidence from Nigeria
Abiola John ASALEYE
Economics Department, College of Business and Social Sciences
Landmark University, Kwara State, Nigeria
asaleye.abiola@lmu.edu.ng; asaleyebiola@yahoo.com
Israel OLURINOLA
Economics Department, College of Business and Development Studies,
Covenant University, Lagos, Nigeria
olu.ogunrinola@covenantuniversity.edu.ng; ranti.rinola@gmail.com
Elizabeth Funlayo OLONI,
Economics Department, College of Business and Social Sciences,
Landmark University, Kwara State, Nigeria
oloni.elizabeth@lmu.edu.ng; olonielizabeth@yahoo.com
Olufemi OGUNJOBI
Economics Department, College of Business and Social Sciences
Landmark University, Kwara State, Nigeria
ogunjobi.olufemi@lmu.edu.ng; jolujobi@yahoo.com
Suggested Citation:
Asaleye, A.J., Olurinola, I., Oloni, E.F., Ogunjobi, O. 2017. Productivity growth, wages and employment nexus: Evidence from
Nigeria. Journal of Applied Economic Sciences, Volume XII, Fall 5(51): 1362–1376.
Abstract:
The nexus amongst productivity growth, employment and wages have generated debate in literature. Nigeria has witnessed
increase in economic growth rate in the last decade which some scholars termed as jobless as unemployment has been
growing all along. Therefore, this study joins this debate to investigate the impact of the growth on labour market performance
in Nigeria using auto-regressive distributed lag (ARDL). The main advantage of this approach lies in the fact that it can be
applied irrespective of whether the variables are I (0) or I (1). The ARDL revealed that using the RGDP (productivity growth),
E (employment) and RW (real wages) as dependent variable, there is an existence of long run relationship. Also, it was showed
that the output growth does not translate into employment gains both in the short and long-run while the influence of wages is
not statistically significant. The implication is that the wages do not adjust to reflect the cost of living both in the short and long-
run. The work suggested amongst others that government should aim to integrate employment and wages into the growth
system both in the short and long run through targeting variable such as interest rate.
Keywords: productivity growth; wages; employment; Auto-Regressive Distributed Lag (ARDL)
JEL Classification: E23; J01; J64; J21; C21
Introduction
Over the years there had not been consensus among economists, policy makers and government agencies
regarding the nexus of productivity growth, wages and employment rate, because the relationship between these
variables have not be ascertained widely in the literature with respect to the time frame perspective. Recent
statistics provided by the Central Bank of Nigeria (2015) suggest that economic growth was on the increasing trend
until the first quarter of 2016, when it was decreasing (trading economics 2016). Despite the fact that the growth
rate of the economy was on the increase, the rate of unemployment has been increasing yearly. Thus, the Economic
growth has not been inclusive.
Unemployment rate published by Nigeria National Bureau of Statistics (NBS) is 23.9% in 2011 up from
19.7% in 2009. In 2014, the unemployment rate was 6.4% and later increased to 7.5% in the first quarters of 2015
(it can be noted that the sharp drop in the unemployment rate is due to the redefinition of unemployment by the
Nigeria Bureau Statistics, NBS). Despite the low unemployment rate between the period of 2014 and 2015, rate of
Journal of Applied Economic Sciences
1363
underemployment stands at 17.9% in 2014 and reduced to 16.6% in the first quarter of 2015. Various scholars in
literature have focused on examining the relationship between macroeconomic performance and labour market
performance with less emphasis on employment and wages. This paper investigates the relationship between
productivity and labour market performance in respect to the time frame perspectives.
1. Literature review
This section focused more on evidence from empirical literature. Oloni, Asaleye, Abiodun and Adeyemi (2017)
examine the relationship between inclusive growth and employment in Nigeria using vector autoregressive model.
The findings of the scholars showed that agricultural output have negative effects on employment and poverty.
It was suggested by Oloni et al. (2017) that Nigerian government should aim at promoting pro-poor growth
by investing in the agricultural sector. Tamasauskiene and Stankaityte (2013) evaluated the relationship between
wages and labour productivity in Lithuania. Their results show that regional dissimilarities of labour productivity are
greater than wages. Correlation analysis was carried out by the scholars and they found that the correlation
coefficient between wages and productivity showed that dissimilarities of wages were higher than that of labour
productivity.
Strauss and Wohar (2004) show that there is long-run relationship between real wages and productivity at
the industrial level for a group of manufacturing industries in the United States over the period 1956 – 1996, and
the increases in productivity were associated with a less than unity increase in real wages.
Using Geweke’s linear feedback technique, Meghan (2002) estimated the relationship between wages and
productivity for several industrialised countries to distinguish between conventional and efficiency wage
behaviours’. The results suggested that efficiency wages were being paid in Canada, Italy and the UK. In contrast,
Sweden, the US and France exhibited no efficiency wage setting, with very negligible wages and productivity
feedback measures. The study also found that economic institutions such as worker unions played an important
role on the wage-productivity settings for this group of industrialized countries.
Sobeck (2014) worked on wages and labour productivity across developed economies between the years
1999 to 2013. In his analysis, it was observed that relationship between wages, compensation, labour productivity
and the labour income share often depends on how certain variables are measured. His work shows the trends in
the relationship between these variables for developed economies between 1999 and 2013. The countries are
Poland, Canada, Norway, Sweden, and Spain among others. In his work CPI (consumer price index) and GDP
deflator were used. The scholar observed that, in half of developed economies, the relationship between wages,
compensation and labour productivity depends on the concept of wages or compensation used and/or the type of
deflator. And also in the other half of developed economies, the choice of deflator and concept (wages versus
compensation) are irrelevant. In 5 of the 11 countries, wage and compensation growth with either inflator always
exceeds that of labour productivity growth, the opposite is observed in 6 countries. Since wages represent a
proportion of compensation which varies from country to country, the relationship between wages and labour
productivity may not be the same compared to compensation and labour productivity. The scholar also stresses
that in most cases, trends in wages (deflated by the GDP deflator) and labour productivity serves as a reasonable
proxy for trends in compensation. In other words, trends in wages and labour productivity generally follow trends in
the labour income share.
Ho and Yap (2001) analysed both the long-run and short-run dynamics of wage formation in the Malaysian
manufacturing industry as a whole and also for 13 selected sub-sectors of the industry using the Engle-Granger co
integration test. They found a positive long-run relationship between labour productivity and real wages and a
negative relationship between unemployment and real wages, and no significant relationship of union density on
real wages. Furthermore, the short-run dynamic model revealed a negative relationship between real wages and
labour productivity suggesting that labour productivity gains did not bring about higher wages in the short run. The
main drawback of the methodology applied in this study is that the authors used the Engle-Granger two step
procedure to test the co integration relationship among four variables, namely, real wages, productivity,
unemployment and union density.
Journal of Applied Economic Sciences
1364
Marika and Hector (2009) studied the role of wage-productivity gap in economic activities. It carried out this
study using some developed countries and a few developing countries such as France, Germany, Spain, Japan,
United States of America (USA) and others. The scholars’ find out that the labour share is negatively associated
with employment even when the conventional assumption of a unitary long-run elasticity of wages with respect to
productivity holds.
Sharpe, Arsenault and Harrison (2008) studied the relationship between labour productivity and real wage
growth in Canada and OECD countries and in their work it was observed that the most direct mechanism by which
labour productivity affects living standards is through real wages, that is, wages adjusted to reflect the cost of living.
Between 1980 and 2005, the median real earnings of Canadians workers stagnated, while labour productivity rose
37%.
Malley and Molana (2007) studied the relationship between output, employment and efficiency wages using
the G7 countries to observe this relationship. They constructed a stylized model of the supply side with goods and
labour market imperfections to show that an economy can rationally operate at an inefficient, or ‘low-effort’, state
in which the relationship between output and unemployment is positive. Data was used from the G7 countries over
1960-2001 and their findings reveal that only German data strongly favour a persistent negative relationship
between the level of output and rate of unemployment. The consequence of this is that circumstances exist in which
market imperfections could pose serious obstacles to the smooth working of expansionary and/or stabilization
policies and a positive demand shock might have adverse effects on employment.
Andres Bosca, Domenech and Ferri (2009) worked on Job creation, productivity growth and labour market
reforms in Spain using Dynamic Stochastic General Equilibrium (DSGE). The DSGE model was used with price
rigidities, and a labour market search frictions Mortensen-Pissarides, to assess the effects of the change in the
growth model on unemployment. It was assumed by the scholars that the vigorous demand shock that has been
mostly responsible for recent low growth of the economy and Spain will be successfully substituted by a productivity
shock as the main driver of Spain‘s economic growth in the future. They analyse the impact of several reforms in
the labour market and evaluate their interaction with the new growth model. Their work concludes that changes in
the economic structure do not make labour reforms any less necessary, but rather the opposite if employment will
be increased.
Deepankar and Duncan (2011) studied the dynamics of output and employment in US economy. Real output
is conventionally measured by the scholars as value added corrected for price inflation. The scholars noted that
there are some industries in which no independent measure of value added is possible and existing statistics
depend on imputing value added to equal income. Indexes of output that exclude these imputations are closely
correlated with employment over the whole period, and remain more closely correlated during the current business
cycle. The work by the scholars’ offer insights into deeper structural changes that have taken place in the US
economy over the past few decades, it shows economically significant reduction in the coefficient relating
employment growth and output growth over the business cycles since 1985. Some of this change is due to sectoral
shifts toward services, but an important part of it shows a reduction in the coefficient or the goods and material
value-adding sectors.
Gros (2010) examines the relationship between wages and productivity growth. The scholar findings show
long run positive relationship between wages and productivity. Mishel and Shierholz (2011) describe that there is
a widening gap between growth rates of productivity and wages. Mishel and Shierholz show that labour
compensation growth was particularly low in the private sector, while the growth of average wages was particularly
weak for college educated public sector workers.
Harrison (2009) reports a similar divergence between the growth of real earnings and productivity in the US
and Canada. From the empirical review of developing countries, the following conclusions are also drawn, in
developing countries empirical studies by scholars show that growth in real wage suppresses employment creation
(Nir Klein 2012, Fafchamps et al. 2008, Gilaninia, Monsef and Mosaddegh 2014, Barletta, Castillo, Pereira, Robert
and Suarez 2014).
In conclusion, evidence from both developed and developing economies have shown that the relationship
among productivity growth, wages and employment differs across regions and the effects are attributed to different
Journal of Applied Economic Sciences
1365
time perspectives. This study aimed at investigating the relationship among these variables in Nigeria using Auto
Regressive Distributed Lag (ARDL). The increases in unemployment rate in Nigeria have motivated the study to
examine the nexus among wages, productivity and employment. Unlike, other studies, the inclusion of wages
distinguished the study from previous studies in Nigeria. Real wages have been identified in literature as the
channel in which living standards can be affected through the productivity growth (Bruce 2002, Sharpe, Arsenult
and Harrison 2008). Though some studies in Nigeria have used ARDL to examine economic growth, employment
and trade openness among others. For example: Lawal, Nwanji, Asaleye and Ahmed (2016) that examined the
nexus of economic growth, financial development and trade openness. Nigerian government have introduced
different programmes and policies to improve labour market performance and welfare. Despite all these attempts,
unemployment and low income still remain macroeconomic issues for policy makers. So the question is, given the
dynamic nature of these programmes and policies, what is the impact of productivity growth on labour market
performance? This is main thrust of this study.
2. Theoretical framework and research method
2.1. Theoretical framework
The theoretical framework of this study is built on the Phillips curve. Friedman (1968) stated that if employees
bargain over real wages, there could not exist, a long-run trade-off between inflation and unemployment.
Algebraically, it starts from the following equation:
11 1
e
tt t t t
w P w P prod a b u
−−
−=−+Δ+−
(1)
where:
t
w
is the wage rate at time t;
e
t
P
is the equilibrium price at time t; prod is the output;
t
u
is the unemployment
rate;
1t
w−
and
1t
P−
are pervious wage rate and prices respectively;
a
is the inflation rate.
From equation (1), the accelerationist Phillips curve can be written as:
11tt t
PPabu
−
Δ=Δ+−
(2)
Inflation rate is the function of unemployment and steady if and only if the unemployment equals to ‘Non-
accelerating increasing rate of unemployment’ (u*). This can be defined as:
*
1
a
u
b
=
(3)
Nigel and Stefan (2011) established equilibrium in relation to real wages, unemployment, inflation and
productivity as follows:
12 3 4
()ep bbubprodbe−=+ + +Δ
(4)
where:
()ep−
is the real wage;
u
is unemployment.
The classical theory of the firm justifies the relationship between productivity and real wages. Insider-
outsider models of wage bargaining would consider unemployment as non-significant (b2=0) except for the case
that it was included in the objective function of the labour unions. (b2<0), the relationship between inflation and real
wages depends on the nature of the wage contracts. Increases in real wages could lead to unemployment growth
if firms financed the cost of these increases exclusively. The wages’ growth would increase the participation of the
population in the labour force thus leading to unemployment growth even with a stable number of jobs. Productivity
through ‘specialization’ affects unemployment through two different mechanisms: an increase in productivity leads
to a decrease in the demand for labour for a fixed output level. An increase in unemployment would lead to a
Journal of Applied Economic Sciences
1366
decrease in the aggregate demand; also, an increase in productivity leads to a decrease in the cost of production
and lower product prices.
In the equation below, increase in aggregate demand with lower prices could increase employment as
stated.
12 3
()ubbep bprod=+ −+
(5)
Okun’s law specified a positive relationship between employment and output; standard output model also
specified a positive relationship between output and the factor inputs (labour and capital); finally, marginal
productivity of labour equals to the wage rate.
Equation (5) can be rewritten as follows;
12 3
Ebbwbprod=+ +
(6)
where:
E
is employment and
w
is wages, based on okun’s law, standard output model and marginal productivity
of labour: the three variables of interest (productivity, real wages and employment) can be used as
dependent variables.
2.2. Empirical model formulation
The empirical models of the study are derived from the theoretical framework. Model specification begins with a set
of structural equations made up of three models of system equations as follows: using employment as dependent
variable (Model 1); using wages as dependent variable (Model 2) and using productivity as dependent variable
(Model 3). The Auto Regressive Distributed Lag (ARDL) Model using bounds test approach with unrestricted error
correction model (UECM) was employed to examine the short and long run relationship between labour market
performance (using wages and employment as metrics) and productivity growth in Nigeria. Other variables to be
considered in the models included exchange rate, consumer price index and interest rate. The ARDL modelling
approach, the unrestricted error correction model for model 1 to 3 is stated in the equations below:
Model 1 Using Employment (E) as dependent Variable
1011 12 13 14 15 16 1
0
q
ttitititititijtj
i
E E RGDP RW XD IR CPI RGDP
αδ δ δ δ δ δ β
−−−−−− −
=
Δ=+ + + + + + + Δ
∑
1111
00 00
qq qq
ltl jtj ktk n tn
ii ii
RW M XD IR CPI
γψρε
−−− −
== ==
+Δ+Δ+Δ+Δ+
∑∑ ∑∑
(7)
Model 2 Using Wages (RW) as dependent Variable
1021 22 23 24 25 26 2
0
q
ttitititititijtj
i
RW RW RGDP E XD IR CPI RGDP
βδ δ δ δ δ δ β
−−−−−− −
=
Δ=+ + + + + + + Δ
∑
22 2 2
00 0 0
qq q q
ltl j tj k tk n tn
ii i i
E M XD IR CPI v
γψρ
−−− −
== = =
+Δ+Δ+Δ+Δ+
∑∑ ∑ ∑
(8)
Model 3 Using Productivity (RGDP) as dependent Variable
1031 32 33 34 35 36 3
0
q
ttitititititijtj
i
RGDP RGDP E RW XD IR CPI E
ωδ δ δ δ δ δ β
−− − − − − −
=
Δ=+ ++ + ++ +Δ
∑
3333
00 00
qq qq
ltl jtj ktk n tn
ii ii
RW M XD IR CPI
γψρµ
−−− −
== ==
+Δ+Δ+Δ+Δ+
∑∑ ∑∑
(9)
Journal of Applied Economic Sciences
1367
In equations (7) to (9), the summation terms represented the Error Correction Model (ECM) dynamics and
δI are the coefficients of the long run multipliers (Poon 2010). Where α0, β0 and ω0 are constant for Model 1, Model
2 and Model 3 respectively: ε, v and µ are the white noise.
The symbol Δ represents the first difference operator and q represents the lag length. F statistics will be
used to test joint significance of the variable which will be compared with the critical value bounds. The variables
are; E represents the level of employment; RGDP represents productivity; RW represents real wages; XD
represents real effective exchange rate index; CPI represents consumer price index; IR represents the interest rate.
2.3. Method of research
This section presents the method of research which explains the technique of estimation. This includes the
following, unit root test and ARDL (auto-regressive distributed lag).
Unit Root Test
It is necessary to check the Stationarity of the time series of the variables used, without the test of the unit root (if
variables are non-stationary), it will give spurious result. The paper employed both the Augmented Dickey Fuller
(ADF) and Phillips-perron unit root tests. The equation for the test is as follows:
12 1
1
n
ttiti
i
YtY Y
ββ δ α ε
−−
=
Δ= + + + Δ+
∑
(10)
where:
t
Y
is the variable that is been examined;
ε
is the white noise error term.
The test involving whether
δ
is equal to zero or not. The number of lags to be used was determined using
Akaike Information Criterion (AIC) to avoid serial correlation in the error terms.
Auto-regressive Distributed Lag (ARDL)
The main advantage of this approach lies in the fact that it can be applied irrespective of whether the
variables are I (0) or I (1). This approach also allows for the model to take a sufficient number lags to capture the
data generating process in a general-to-specific modelling framework. Another advantage of the ARDL is that it is
not affected by the pre-testing problem implicit in the standard co-integration techniques (i.e. the Johansen
maximum likelihood or the Phillips-Hansen semi-parametric fully-modified OLS procedures).
Data Sources and Measurement
The data used are obtained from Central Bank of Nigeria statistical bulletin (2015) and Nigeria National Bureau of
Statistics. All variables except the employment rate are obtained from Central Bank of Nigeria statistical bulletin
while the employment rate is obtained from National Bureau of statistics. Quarterly data are available for CPI,
exchange rate, interest rate and GDP. Wages are also transformed into quarterly date using Quadratic match sum,
this approach have also been used in literature by the study of Lowe and Grosvenor (2016) that estimated quarterly
indicators of economic activity for the states of Eastern Caribbean Currency Union. GDP and RW are in log form.
Data for real wages are not available. Wages is then computed using recurrent expenditure minus transfers, social
and community cost.
3. Result and discussion
3.1. Unit Root Test
The Augmented Dickey-Fuller test and the Philips-perron test were conducted for each of the variables in the model
in order to test for the stationarity and non-stationarity of the data used.
Journal of Applied Economic Sciences
1368
Table 1. Phillips-Perron and Augmented Dickey Fuller Unit Root Result for the variables
Variables
ADF Test Statistics at
Levels
ADF Test Statistics
at First Differencing
P-P Test Statistics
at Levels
P-P Test Statistics
at First Differencing
Order of
Integration
CPI
-0.641061
-11.65872
-0.555386
-11.72094
I (1)
RGDP
1.168109
-11.34536
1.901508
-11.34534
I (1)
RW
-0.861372
-8.060668
-0.824874
-8.148445
I (1)
XD
-0.097165
-10.46387
-0.145576
-10.40387
I (1)
IR
-3.761159
-11.57059
-3.560311
-12.41976
I (0)
E
0.450155
-12.20939
0.637025
-12.57831
I (1)
Source: Author’s computation (2016)
Table 1 above presents the Phillips-Perron and Augmented Dickey Fuller unit result of the variables used.
All variables are integrated of order one except variable IR which is stationary at 5% significant level both for
Phillips-Perron and Augmented Dickey Fuller Test.
Figure 1. Graph of variables (after first differencing)
Source: Author Computation using Eviews 9.5
The graphical illustration of the variables used after first difference is presented in Figure 1, all variables
are integrated of order 1 except interest rate (IR). Though, in the figure above, all the series were integrated of
same order.
3.2. Bound Testing for existence of a long-run relationship in Model 1
Table 2. ARDL (9, 9, 1, 1, 0, 12) for Model 1
Significance
Levels
Critical Value Bounds
F-Statistic Value
K
Hypothesis Testing
IO Bound
II Bound
10%
2.08
3.00
5.227570
5
Cointegration exist
5%
2.39
3.38
5.227570
5
Cointegration exist
2.5%
2.70
3.74
5.227570
5
Cointegration exist
1%
3.06
4.15
5.227570
5
Cointegration exist
Source: Author’s Computation using Eviews 9.5
Table 2 presents the ARDL bound test, shows the presence of long run relationship between the variables,
long run relationship exists when the value of f-statistics is greater than the upper bound. From the table the f-stat
is 5.227570, this is greater than the upper bound value which is 3, this means that there is long run relationship
between the variables using E as the dependent variable at 10%, 5%, 2.5% and 1% significance level.
-120
-80
-40
0
40
80
1985 1990 1995 2000 2005 2010
D(RW) D(CPI) D(XD)
D(IR) D(E) D(RGDP )
Journal of Applied Economic Sciences
1369
Figure 2. Model Selection Criteria for Model 1
Source: Author’s computation using Eviews 9.5
Figure 1 presents the 20 model results of the ARDL, from the result, ARDL (9, 9, 2, 1, 2, and 12) has the
highest Hannan-Quinn (HQ) Criterion value and ARDL (9, 9, 1, 1, 0, and 12) has the lowest Hannan- Quinn Criterion
value. The lower the HQ value of the model, the more appropriate the model. The most appropriate model for this
analysis is ARDL (9, 9, 1, 1, 0, and 12).
Table 3. Breusch-Godfrey Serial Correlation LM for Model 1
F-Statistic
0.697410
Prob. F(2,84)
0.5007
Obs* R-Squared
2.025388
Prob. Chi-Square (2)
0.3632
Source: Author’s computation using Eviews 9.5
Table 3 presents the Breuch-Godfrey serial correlation LM, from the result the prob. Chi-Square is 0.3632
which is greater than 0.05, therefore the null hypothesis that there are no serial correlations between the variables
cannot be rejected. Hence, there is no serial correlation in model 1
Table 4. Heteroskedasticity Test: ARCH for Model 1
F-Statistic
0.001021
Prob. F(1,121)
0.9746
Obs* R-Squared
0.001038
Prob. Chi-Square (1)
0.9743
Source: Author’s computation using Eviews 9.5
Table 4 above presents the Heteroskedasticity, from the result the prob. Chi-Square is 0.9743 which is
greater than 0.05, therefore the null hypothesis that there is no Heteroskedasticity between the variables will be
cannot be reject.
Figure 3. Stability Test for Model 1
Source: Author’s computation using Eviews 9.5
The graph above shows the stability test for Model 1, using CUSUM test, when the line of the variables is
in-between the upper and the lower boundaries this means that is stability at 5% level of significance; therefore,
the graph above satisfies the above stated condition.
3.148
3.152
3.156
3.160
3.164
3.168
3.172
3.176
3.180
3.184
ARDL(9, 9, 1, 1, 0, 12)
ARDL(9, 9, 1, 1, 2, 12)
ARDL(5, 1, 1, 0, 0, 9)
ARDL(9, 9, 0, 1, 0, 12)
ARDL(10, 9, 1, 1, 0, 12)
ARDL(9, 9, 1, 1, 4, 12)
ARDL(9, 9, 2, 1, 0, 12)
ARDL(9, 9, 1, 2, 0, 12)
ARDL(9, 9, 1, 1, 3, 12)
ARDL(9, 9, 1, 1, 1, 12)
ARDL(9, 10, 1, 1, 0, 12)
ARDL(9, 1, 1, 1, 0, 12)
ARDL(9, 9, 3, 1, 0, 12)
ARDL(5, 1, 1, 1, 0, 9)
ARDL(5, 1, 0, 0, 0, 9)
ARDL(9, 1, 1, 0, 0, 12)
ARDL(9, 9, 0, 1, 2, 12)
ARDL(6, 1, 1, 0, 0, 9)
ARDL(10, 9, 0, 1, 0, 12)
ARDL(9, 9, 2, 1, 2, 12)
Hannan-Quinn Criteria (top 20 models)
-30
-20
-10
0
10
20
30
94 96 98 00 02 04 06 08 10 12 14
CUSUM 5% Significanc e
Journal of Applied Economic Sciences
1370
3.3. Bound Testing for existence of a long-run relationship in Model 2
Table 5. F-Statistics for Testing Existence of Long-run in Model 2
Significance
Critical Value Bounds
F-Statistic Value
K
Hypothesis Testing
IO Bound
II Bound
10%
2.08
3
3.296199
5
Cointegration exist
5%
2.39
3.38
3.296199
5
Inconclusive
2.5%
2.7
3.74
3.296199
5
Inconclusive
1%
3.06
4.15
3.296199
5
Inconclusive
Source: Author’s computation using Eviews 9.5
Table 5 shows the ARDL result using RW as dependent variable, with 5 lags for RW and (0, 0, 0, 0, 0) lag
for CPI, RGDP, XD, IR, E respectively. The appropriate Lag length strength was selected by using Hannan-Quinn
Criterion. From the table, it can be deduced that Cointegration exists at 10% level of significance. The value of the
f-statistic is 3.296199 which is greater than the upper bound value which is 3, this shows that there is long run
relationship between the variables using RW as the dependent variable. At 5%, 2.5% and 1%, the result of the
inference is inconclusive since the computed F- statistics value is between the lower and upper bound.
Figure. 4 Model Selections for Model 2
Source: Author’s computation
Figure 4 presents the 20 model results of the ARDL, from the result, ARDL (5, 0, 0, 0, 0, and 0) has the
highest Hannan-Quinn Criterion value and ARDL (6, 0, 0, 1, 1, and 0) has the lowest Hannan-Quinn Criterion value.
The most appropriate model for this analysis is ARDL (5, 0, 0, 0, 0, and 0).
Table 6. Breusch-Godfrey Serial Correlation LM for Model 2
F-Statistic
0.725040
Prob. F(2,118)
0.4864
Obs* R-Squared
1.590293
Prob. Chi-Square (2)
0.4515
Source: Author’s computation using Eviews 9.5
Table 6 above presents the Breuch-Godfrey serial correlation LM, from the result the prob. Chi-Square is
0.4515 which is greater than 0.05, therefore the null hypothesis that there is no serial correlations between the
variables cannot be rejected.
Table 7. Heteroskedasticity Test: ARCH for Model 2
F-Statistic
0.533026
Prob. F(20,90)
0.9448
Obs* R-Squared
11.75553
Prob. Chi-Square (20)
0.9242
Source: Author’s Computation
Table 7 above presents the Heteroskedasticity, from the result the prob. Chi-Square is 0.9242 which is
greater than 0.05, therefore the null hypothesis that there is no Heteroskedasticity between the variables cannot
be rejected.
-2.285
-2.280
-2.275
-2.270
-2.265
-2.260
-2.255
-2.250
-2.245
ARDL(5, 0, 0, 0, 0, 0)
ARDL(6, 0, 0, 0, 0, 0)
ARDL(5, 0, 0, 0, 1, 0)
ARDL(5, 0, 0, 1, 0, 0)
ARDL(5, 1, 0, 0, 0, 0)
ARDL(6, 0, 0, 0, 1, 0)
ARDL(5, 0, 0, 0, 0, 1)
ARDL(6, 1, 0, 0, 0, 0)
ARDL(5, 0, 1, 0, 0, 0)
ARDL(6, 0, 0, 1, 0, 0)
ARDL(5, 0, 0, 1, 1, 0)
ARDL(5, 1, 0, 0, 1, 0)
ARDL(6, 0, 0, 0, 0, 1)
ARDL(7, 0, 0, 0, 0, 0)
ARDL(6, 1, 0, 0, 1, 0)
ARDL(6, 0, 1, 0, 0, 0)
ARDL(5, 0, 0, 1, 0, 1)
ARDL(5, 1, 0, 1, 0, 0)
ARDL(6, 0, 0, 0, 2, 0)
ARDL(6, 0, 0, 1, 1, 0)
Hannan-Quinn Criteria (top 20 models)
Journal of Applied Economic Sciences
1371
Figure 5. Stability Test for Model 2
Source: Author’s computation using Eviews 9.5
From the graph above, it can be shown that using CUSUM test, the line is in-between the upper and the
lower boundaries this means that there is stability at 5% level of significance; therefore, the graph above satisfies
the above stated condition, therefore it is significant at 5% level of significance.
3.4. Bound Testing for existence of a long-run relationship in Model 3
Table 8. ARDL Result (ARDL 9, 9, 0, 1, 1, 9) for Model 3
Significance
Critical Value Bounds
F-Statistic Value
K
Hypothesis Testing
IO Bound
II Bound
10%
2.08
3
3.078088
5
Cointegration exist
5%
2.39
3.38
3.078088
5
Inconclusive
2.5%
2.7
3.74
3.078088
5
Inconclusive
1%
3.06
4.15
3.078088
5
Inconclusive
Source: Author’s computation using Eviews 9.5
Table 8 shows the ARDL result using RGDP as dependent variable, with 9 lags for RGDP and 9, 0, 1, 1, 9
lags for CPI, RW, XD, IR, and E respectively. The appropriate Lag length strength was selected by using Hannan-
Quinn Criterion. The results of Model 2 and Model 3 are similar. Cointegration exists at 10% level of significance.
The value of the f-statistics is 3.078088, greater than the upper bound value which is 3, this show that there is long
run relationship between the variables using RGDP as the dependent variable at 10% level of significance. At 5%,
2.5% and 1%, the result of the inference is inconclusive since the computed F- statistics value is between the lower
and upper bound.
Figure 6. Model Selection Summary Result for Model 3
-.84
-.83
-.82
-.81
-.80
-.79
-.78
ARDL(9, 9, 0, 1, 1, 9)
ARDL(9, 9, 1, 1, 1, 9)
ARDL(9, 9, 0, 2, 1, 9)
ARDL(9, 9, 0, 1, 1, 10)
ARDL(9, 10, 0, 1, 1, 9)
ARDL(10, 9, 0, 1, 1, 9)
ARDL(9, 9, 0, 1, 2, 9)
ARDL(9, 9, 0, 1, 0, 9)
ARDL(9, 9, 1, 2, 1, 9)
ARDL(9, 9, 2, 1, 1, 9)
ARDL(9, 9, 1, 1, 1, 10)
ARDL(9, 10, 1, 1, 1, 9)
ARDL(9, 9, 1, 1, 2, 9)
ARDL(10, 9, 1, 1, 1, 9)
ARDL(9, 9, 1, 0, 1, 9)
ARDL(9, 9, 1, 1, 0, 9)
ARDL(9, 10, 0, 2, 1, 9)
ARDL(9, 11, 0, 1, 1, 9)
ARDL(10, 9, 0, 2, 1, 9)
ARDL(9, 9, 0, 2, 1, 10)
Hannan-Quinn Criteria (top 20 models)
Source: Author’s Computation using Eviews 9.5
-40
-30
-20
-10
0
10
20
30
40
86 88 90 92 94 96 98 00 02 04 06 08 10 12 14
CUSUM 5% Significance
Journal of Applied Economic Sciences
1372
Figure 5 above presents the 20 model results of the ARDL, from the result, ARDL (9, 9, 0, 2, 1, and 10) has
the highest Hannan-Quinn Criterion value and ARDL (9, 9, 0, 1, 1, and 9) has the lowest Hannan-Quinn Criterion
value. The most appropriate model for this analysis is ARDL (9, 9, 0, 1, 1, and 9).
Table 9. Breusch-Godfrey Serial Correlation LM for Model 3
F-Statistic
0.046611
Prob. F(2,90)
0.9545
Obs* R-Squared
0.131411
Prob. Chi-Square (2)
0.9364
Source: Author’s Computation using Eviews 9.5
Table 9 above presents the Breuch-Godfrey serial correlation LM, from the result the prob. Chi-Square is
0.9364 which is greater than 0.05, therefore, there is no serial correlations between the variables.
Table 10. Heteroskedasticity Test: ARCH for Model 3
F-Statistic
0.163765
Prob. F(1,124)
0.6864
Obs* R-Squared
0.166187
Prob. Chi-Square (1)
0.6835
Source: Author’s Computation using Eviews 9.5
Table 10 above presents the Heteroskedasticity, from the result the prob. Chi-Square is 0.6835 which is
greater than 0.05, therefore, there is no ARCH effect among the variables.
Figure 7. Stability test for Model 3
Source: Author’s computation using Eviews 9.5
From the graph above, the CUSUM line is in-between the upper and the lower boundaries this means that
the model is stable at 5% level of significance.
3.5. Estimated Long-run and Short-run using the ARDL for Model 1, 2 and 3
The bound test results presented above show the existence of long-run relationship in the model examined, since
the cointegrating vector is identified. The ARDL model of the cointegrating vector is reparameterized into Error
Correction Model (ECM). With the specification of ECM by this study, both the long-run and short-run information
are incorporated. The result is presented below (in Table 11). The reparameterized result shows the short-run
dynamics and the long-run relationship of the variables for Model 1, 2 and 3.
Table 11. Estimated Long-run and Short-run Parameters
Regressors
Dependent Variable (Coefficients and Probability Value)
Model 1 - E
Model 2 - RW
Model 3 - RGDP
LR
SR
LR
SR
LR
SR
E
0.0000401
(0.6995)
-0.001134
(0.7609)
-0.079891***
(0.0000)
-0.080471***
(0.0000)
RGDP
-4.078490***
(0.0000)
-4.057743***
(0.0000)
-0.002139
(0.9153)
-0.005306
(0.8927)
IR
0.080355***
(0.0413)
0.081519
(0.0208)
0.002950**
(0.0811)
-0.000500
(0.8621)
0.014292***
(0.0073)
0.013689***
(0.0046)
-30
-20
-10
0
10
20
30
92 94 96 98 00 02 04 06 08 10 12 14
CUSUM 5% Signific ance
Journal of Applied Economic Sciences
1373
Regressors
Dependent Variable (Coefficients and Probability Value)
Model 1 - E
Model 2 - RW
Model 3 - RGDP
LR
SR
LR
SR
LR
SR
XD
0.019646
(0.2535)
0.021525
(0.1827)
1.74E-05
(0.9600)
0.001060
(0.3596)
0.005063***
(0.0285)
0.004346***
(0.0499)
RW
0.640503
(0.1562)
0.260235
(0.8009)
-0.41572
(0.4486)
0.092628
(0.4946)
CPI
0.060784***
(0.0000)
0.060309***
(0.0000)
0.000245
(0.4654)
-0.000220
(0.7449)
0.009196***
(0.00000)
0.009265***
(0.0000)
ECM
-0.030798***
(0.0000)
-0.032492***
(0.0000)
0.251623***
(0.0000)
LR represent Long-run: SR represent Short-run
* indicates significance at 10%; ** indicates significance at 5%; *** indicates significance at 1%
Source: Author’s computation using Eviews 9.5
*Note: Probability value are presented in angle brackets
From Table 11 above, in Model 1, the ECM with the value of -0.030798 and a probability value of less than
5%. The coefficient is negative and significant. The ECM shows the speed of adjustment, this implies the existence
of convergence in long-term equilibrium. Also, both in the short and long run, there are negative relationships
between employment and output. This result contradicts the standard growth theory, Okun’s law and the theoretical
framework of this study. The implication of the result is that the output gains have not improved employment
performance in Nigeria. The growth otherwise can be referred to as jobless growth. This result is in line with the
study of Oloni et al. (2017) that examined the relationship between Inclusive growth and employment in Nigeria.
Wages in short and long-run is not significant; contradicts the work of Andrew et al. (2008) that examined the
relationship between wages and productivity in Canada and OECD countries.
In Model 2, The ECM value is -0.032492 and a probability value less than 5%. From the result, the coefficient
is negative and significant. From this, it can be depicted that there is an adjustment from short run to the long run
equilibrium among the variables (RGDP, CPI, RW, XD, IR and E) using RW as the dependent variable. This result
was not in line with some of the studies in literature, for example: Gros (2010) shows there is no long-run relationship
between wages and productivity; Mishael and Shierbolz (2011) show that there is divergence between wages and
productivity in US and Canada. RGDP and E are not significant in Model 2.
In Model 3, ECM value is 0.251623 and a probability value of less than 5%. From the result, the coefficient
is positive and significant; this positive coefficient indicates divergence in the long-run using RGDP as the
dependent variable. There is a negative relationship between RGDP and E (employment) both in the short and
Long-run and no significant relationship between wages both in the short and long run. This result contradicts the
result of Ho and Yap (2001) that stressed a positive relationship between output and wages in the long-run and
negative relationship in the short-run. Though, using wages as dependent variable, it was observed that there is
existence of long-run relationship which was in line with the study of Strauss and Wohar (2004) that examined the
long-run relationship between real wages and productivity at industrial level for a group of manufacturing companies
in United State. In Model 3, the economic implication of the result is that minimal or no impact has been observed
in promoting the wages that adjust to reflect the cost of living, since the channel in which productivity affect living
standard is through real wages.
Conclusion
This study examines the relationship between productivity growth (RGDP) and labour market performance in
Nigeria. The metrics used for labour market performance are wages (RW) and employment (E). Empirical studies
have shown that impact of productivity on wages and employment varies both in developed and developing
economies. This study uses autoregressive distributed lag (ARDL) as analytical tool. The augmented dickey-fuller
and Philips-Perron technique were used in testing the unit root properties of the series. The unit root tests show
that all the series used are non-stationary at 5% level of significance except the interest rate. However, the non-
Journal of Applied Economic Sciences
1374
stationary attained stationary after the first difference. The study specified the Error Correction Model (ECM) to
capture both the short-run and long-run dynamics; the associated ECM model takes a sufficient number of lags to
capture the data generating process to the specified framework using Hannan-Quinn Criterion. This is necessary
to prevent Gaussian error in the ARDL model.
The results from the auto-regressive distributed lag (ARDL) revealed that using the RGDP, E and RW as
dependent variable, there is an existence of long run relationship between the variables. Convergence in the long-
run equilibrium using E and RW as dependent variables was noted while divergence was noted using RGDP as
dependent variable. The sign of relationship between output and employment is negative and vice-versa both in
the short and long-run. From the ARDL results the influence of the value of wages is not statistically significant both
in the short run and long run. It has been observed in literature that the most direct mechanism by which productivity
affects living standard is through real wages. Series of tests was also done to ensure the stability of the data and
models respectively. The economic implication of the result is that minimal or no impact has been observed in
promoting the wages in adjusting to reflect the cost of living and also, the output growth does not translate into
employment gains both in the short and long run.
Based on the findings, it can be concluded that Nigerian government should focus on long term goals
especially in trying to promote employment opportunities and increasing level of income. The following suggestions
are given: the government should focus on long run policies for employment and wages and also ensure
consistency between the policies in order to avoid the complication that occurs as a result of inconsistency in policy
making. So there might be need for the government to develop an institutional framework that will ensure this;
Government should create appropriate enabling environment to promote a sustained effective aggregate demand
in order to maintain the required level of domestic production through targeting variable such as interest rate.
Government should aim to integrate employment and wages into the growth system both in the short and long run
through their policies; the Government should also be able to maintain competitive and favourable real exchange
and interest. Finally, Government should deliberately promote labour- intensive method of production in order to
generate more employment particularly in the real sector.
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