EMPLOYMENT GROWTH, SERVICE SECTOR, AND …

Ife Journal of Economics and Finance (2020), Vol. 9 (1), 145-162

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EMPLOYMENT GROWTH, SERVICE SECTOR, AND MANUFACTURING VALUE-ADDED IN SUB-SAHARAN AFRICA

Ojo, Segun Michael

Department of Economics

Redeemers University, Ede

Email: [email protected]

Ogunleye, Edward Oladipo

Department of Economics

Ekiti State University, Ado Ekiti

Email: [email protected]

Abstract

This study examined the interaction among employment growth, service sector, and manufacturing

value-added in sub-Sahara Africa. The study utilized secondary data spanning 1990 to 2019. The data

was analyzed using a panel vector error correction model (PVECM). The result reveals that long-run

causality runs from manufacturing value-added and service sector to employment growth. The result

shows that the manufacturing sector and service sector generate employment in the economy. It further

reveals that there is no long-run causality running from employment growth and service sector to

manufacturing value-added in SSA. Finally, the analysis reveals that there is long-run causality running

from manufacturing value-added and employment growth to the service sector. The direction of

causality reveals in this study shows that the service sector is crowding out the manufacturing sector in

SSA.

Keywords: service sector, manufacturing value-added, employment growth, deindustrialization,

causality

1. Introduction

The service sector’s expansion over the

manufacturing sector is said to be an

inhibiting factor on the progress of

manufacturing activities globally

(Bosworth and Triplett, 2000). This does

not leave out the sub-Saharan African

countries (SSA). For instance, the average

service value-added share of GDP in sub-

Saharan Africa over the period 1981 to

2018 is estimated at 47.5%, while the

manufacturing-value-added share of GDP

is 13.1%. One of the immediate

consequences of the deindustrialization

menace is the jobless growth

manufacturing sector (ADB, 2017). The

service sector crowding effect on the

manufacturing sector and the jobless

growth problem is not peculiar to the SSA,

rather it is a global problem. But the

attendant consequences bit harder in

developing countries like the SSA than in

the developed countries.

For instance, Sub-Saharan Africa (SSA) is

said to have the lowest standard of living in

the world, and the region is the lowest on

the table of human development index

globally (UNIDO, 2016). The region is

vulnerable to external shocks due to

structural imbalances like high employment

rate, poor infrastructural facilities, weak

institutional frameworks, low per capita

income, the prevalence of abject poverty

and high import dependence. In the face of

those structural imbalances, how can the

mailto:[email protected]

mailto:[email protected]


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SSA countries sustain service-led growth

(Hansda, 2006)? Couple with the fact that

they do not have the prerequisite

industrialization experiences required for

the service-led growth process. The

developed countries around the world

metamorphosed from manufacturing-led

growth systems to service-driven

economies. Although the latecomer

countries may not necessarily go through

the evolutionary process which the

developed countries went through before

and after their takeoff, the developing

countries attempt to leapfrog to the service-

led economic system without laying the

foundation of industrialization and

manufacturing value-added may not yield

the desired results.

Figure 1, presents the unemployment rate in

sub-Saharan Africa, East Asia and Pacific

(EAP) and the world. The graph shows that

the unemployment rate in SSA is higher

than the world average unemployment rate.

The unemployment rate is lower in the East

Asia and Pacific region. The prevalent of

unemployment in SSA is worrisome

because it is one of the root causes of the

observed economic backwardness, moral

and social vices that are rampant in the

African sub-region.

Source: author’s computation

Fig. 1 Unemployment Rate in Sub-Saharan Africa, East Asia And Pacific and the World

In figure 2, the trend in service value-added

share of GDP shows that SSA is lower than

EAP and the world in terms of service

value-added. But in figure 4, the trend in the

SSA manufacturing value-added share of

GDP is downward sloping which implies a

persistent deindustrialization scenario in

the African sub-region. In other words,

figures 3, 4, and 5 contain two lines each.

The curve lines are the graph plots of the

movement in manufacturing value-added

share of GDP in the world, SSA and EAP

respectively. The straight lines are the fitted

regression lines (the regression lines depict

the latent trend in the data) of the data series

over the period. So, the regression line in

figure 3 depicts the movement in the world

aggregate manufacturing value-added share

of GDP, while the regression line in figure

5 depicts the trend in manufacturing value-

added share of GDP in the EAP over the

period. The world aggregate and the EAP

manufacturing value-added share of GDP

are upward sloping from left to the right,

which implies an increase in manufacturing

value-added over the period. But the


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situation in the SSA is the opposite of the

trend in the world and EAP. This shows

among other things that SSA is not keeping

pace with the rest of the world in terms of

productivity and development.


Figure 2 Service Value Added as Percentage of GDP

Service value-added is rising all over the

world. But the SSA case is exceptional due

to the deindustrialization menace that

accompanies the service intensity of the

region's economy. In the world and EAP,

service value-added is rising much as

manufacturing value-added share of GDP is

on the rise. Consequently, unemployment is

lower compare to the rate in SSA.

However, SSA is also recording an increase

in service value-added, alongside a

negative trend in the manufacturing value-

added share of GDP. The employment

sector in SSA is shrinking due to the decline

in manufacturing activities.


Figure 3, The World Manufacturing Value-Added Share of GDP

4

6

8

10

12

14

16

1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 2020

Y e a r


4


Figure 4, SSA Manufacturing Value-Added Share of GDP


Figure 5, EAP Manufacturing Value-Added Share of GDP

9

10

11

12

13

14

15

1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 2020

Y e a r

4

8

12

16

20

24

28

32

1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 2020

Y e a r


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Hence, the need to ascertain the direction of

causality among manufacturing value-

added, service sector, and employment

growth in SSA. Manufacturing sector

generates more employment than other

sectors, owing to the multi-stages of

interconnectivities among manufacturing

firms that serve as sources of employment

in an economy. In contrast, the modern

economic system is witnessing jobless

growth in the manufacturing sector because

productivity in the manufacturing sector is

growing faster than the manufacturing

output (ADB, 2017), due to the

computerization and robotization of the

production process which reduced the need

for workers in manufacturing production

(WTO, 2013). Consequently, it has been

argued that a fall in manufacturing output

implies a fall in employment, and a fall in

employment implies higher poverty and

crimes.

2.1 Literature Review The unprecedented expansion in the service

sector that accompanies the globalization

explosion of the early 1990s, has been an

issue of concern to economic managers,

policymakers, scholars, and development

partners around the world. The growth of

the service sector per se is not the bone of

contention, but the decline in employment

and the fall in manufacturing output that

trail the upsurge in services are the basis for

concern. Economic scholars from different

ends have been responding to this

development through various empirical

investigations and analyses of the cause and

effects of this issue. This section of this

study reviews some of the empirical studies

that have been done, investigating how

service sector impacts the manufacturing

sector development in different economies

around the world.

For instance, Haraguchi (2016), carried out

a study on the topic "The importance of

manufacturing in economic development:

has this changed?" this study sought to

determine whether the backwardness in

manufacturing development in developing

countries are as a result of the global

deindustrialization menace that is plaguing

the global manufacturing market. The study

utilized simple regression analysis and

found that the backwardness in

manufacturing development in the

developing countries is a result of the

failure to develop the manufacturing

sectors in the developing countries.

Similarly, Coad and Vezzani (2017),

carried out a study that investigated the

links among manufacturing sector,

productivity growth, exports, and R&D?

Using non-parametric plots and regressions

analytical technique. It observed that many

technologically advanced countries had

recorded acute reduction in the relative

share of manufacturing sectors lately.

Feng and Sivakumar (2016) studied the role

of collaboration in service innovation

across the manufacturing and service

sectors in Germany using the probit

regression technique in a sample of 3,060

firms across 22 different industries. The

result revealed that the effect of service

innovation on innovation performance is

greater for service firms than

manufacturing firms. This study and the

preceding ones attested to the fact that the

developed countries are shying away from

manufacturing in favour of services.

Behuria and Goodfellow (2019) used a

descriptive method to investigate the

plausibility of a services-led developmental

state in Rwanda. The study argued that

service-led growth policy may work for the

Asian countries due to the kind of

integrated transformation they exhibited

before and after they take off. Such a

service-led growth policy cannot work for

Rwanda because the country is not as

prepared as the Asian countries in terms of

technological know-how, prerequisite

manufacturing spectrum, and market

infrastructure.

Das and Saha (2011) used a growth model

and descriptive statistics to explain how and

why the services sector may grow faster

than manufacturing, in the Indian economy.


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The study used a two-sector growth model

(service sector and the manufacturing

sector) to show how diff erences in returns

to scale between the two sectors and the

employment frictions in manufacturing

explain how the growth rate of the services

sector may be higher. This study was able

to identify the key factors responsible for

the rise of the service sector over

manufacturing. A service-oriented

organization needs less labour to operate

and the few labours would be skilled

workers being augmented with

sophisticated machines and equipment.

Consequently, in the service sector; average

labour productivity is higher than other

sectors including the manufacturing sector

because the service sector has a higher

concentration of skilled workers.

Therefore, the return to scale is higher in the

service sector. Investment in the service

sector is more lucrative than in the

manufacturing sector. Manufacturing firms

employ different categories of labour to

undertake different tasks at different stages

of manufacturing activities. Hence,

manufacturing generates employment

opportunities for both the skilled and

unskilled workers which enhances the

aggregate income, alleviates poverty, and

stimulates development. The service sector

attracts investors' funds due to its

competitive advantages in return to scale

and higher relative efficiency.

A similar situation is approaching in Japan

following the result reported in a study by

Fukao (2010). The study investigated the

service sector productivity in Japan using

descriptive statistics. The study focused on

three major observations; one, how bad the

productivity performance in Japan’s service

sector has been. Two, why it is important to

accelerate total factor productivity growth

in the service sector. Three, why total factor

productivity has stagnated in Japan’s

service sector. The result revealed that total

factor productivity growth in the

manufacturing sector is much higher than

that in other sectors but the manufacturing

sector’s share is falling speedily.

Hussin and Ching (2013) carried out a

study on the relative contribution of sectors

to economic growth in Malaysia and China.

The study utilized time series data spanning

1978 to 2007 using multiple regression

techniques. Three sectors were included in

the study; agricultural sector,

manufacturing sector, and service sector.

The result of the correlation analysis

revealed that the three sectors (agriculture,

manufacturing, and service sector) share a

positive relationship with GDP per capita in

Malaysia and China over the period.

Besides, the multiple regression analysis

shows that the service sector accounts for

the highest share in Malaysian GDP while

the manufacturing sector accounts for the

larger proportion of China’s economic

growth. Incidentally, a similar study was

conducted for Nigeria in that same year by

Oluwatoyese and Dewi (2013). The study

was titled; effect of agricultural,

manufacturing, and services sectors

performance in Nigeria. It utilized time

series data spanning 1980 to 2011, using

ordinary least square (OLS). The result

shows that the agricultural and services

sector of the non-oil export component

contributed significantly to the economic

growth (GDP) of Nigeria than the

manufacturing sector. The mixed results

about how service and manufacturing

impact the economic growth in the different

countries is a cause for concern, regarding

how to manage the latest structural changes

in the global economy in the modern

economic system.

However, more recently and very

comprehensively Attiah (2019) carried out

a study involving 10 developed countries

and 40 developing countries on the role of

manufacturing and service sectors in

economic growth. The study is a time-

series study that spanned over 65 years

(1950 to 2015). It examines the relative role

of manufacturing and service sectors in

economic development across the 50

countries, using a panel regression

analytical technique. The results support

the popular hypothesis which postulated


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that manufacturing is the engine of growth.

As the manufacturing share of GDP is

positively related to economic growth in

developing countries, such a result is not

found for the service sector. Santacreu and

Zhu (2018) also worked on manufacturing

and service sector roles in the evolution of

innovation and productivity using

descriptive statistics across 24 countries

over the period 2000 to 2014. The findings

include the fact that the contribution of the

service sector to employment and value-

added surpassed that of manufacturing. But

manufacturing exceeded service in terms of

exports, innovation, and productivity

growth. The study further argued that

services and manufacturing are interwoven

such that services are needed in the

manufacturing outfits and manufacturing

products are used in the process of service

delivery. The thin line of demarcation

between them is being accentuated by

innovation and globalization intensity.

Therefore, innovation and sophistication

are the forces that will likely determine the

industrialization of the developing

countries and the likelihood of catching up.

Mbate (2017), investigated the structural

change and industrial policy in Ethiopia,

using the country’s leather sector as case

study. The paper examined the basis for

industrial policy in developing countries

and why it has not been effective in most

African countries. It also considered the

policy measures that can be used to spur

industrial development in Africa. It

examined the implementation of industrial

policies, using the descriptive methods of

analysis in the Ethiopian leather product

sector. The study observed that

industrialization and manufacturing

activities are on the decline in developing

countries. On this basis, it itemized some

policy tools that can be used to spur

manufacturing sector development in

developing countries. The policy tolls

include; institutionalization of industrial

policy in the national development plan,

such that will set achievable goals and put

necessary supervisory and monitoring

measures in place for effective

implementation of programs and projects. It

also recommends human capital

enhancement through the accumulation of

technology and skills upgrading.

Chen and Ravallion (2010), carried out a

study titled; 'the developing world is poorer

than we thought, but no less successful in

the fight against poverty using descriptive

methods of analysis and found that one-

third of the world's population lived in

poverty in 1981, but by 2001 it had

decreased to 18%. This was due to the

economic growth of some population rich

countries like China and India in recent

decades. Industrialization and

manufacturing value-added helped China

and India during their take-off. In the

developing African countries, the

proportion of the poor in their population

has been on the increase due to economic

backwardness and deindustrialization that

worsen the unemployment situation. The

basic factor that demarcates between the

African countries and the Asians is

industrialization which helped the Asian

countries to break the vicious cycle of

underdevelopment.

The global decline in employment is one of

the emerging issues that is puzzling to

economic managers the world over. The

world's overall productivity is rising but

employment is falling leading to

joblessness, underemployment, and

poverty. This has motivated empirical

studies from different scholars at different

times from various ends. For instance,

Singh and Mitra (2017) investigated the

cyclical asymmetries and short-run relation

between employment and output in Indian

manufacturing, using time series data

spanning 1990 to 2012. The study utilized

panel regression and descriptive statistics to

estimate the data. The result shows that

both output and employment fall sharply

during the recession than their rate of rising

during recovery. Besides, a fall in output

dampened the employment rate than it stirs

it when output is rising. Rodrik (2015)


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worked on premature deindustrialization in

Latin America, using panel regression. The

study observed that the Latin American

countries are losing their industrial

potentialities too early and it is happening

at a relatively low level of income. The

Latin American countries have recorded a

considerable and continuous decline in

employment as a result of persistent and

premature deindustrialization. The study

further argued that globalization and

labour-saving technological progress in

manufacturing are responsible for this

development.

Felipe, Mehta, and Rhee (2014) carried out

a study that was titled; manufacturing

matters, it's the jobs that count. They used

the panel regression technique to analyze

time-series data in 50 countries. The result

revealed that economies that create many

manufacturing jobs grow at a high rate than

those that do not. Two, the result shows that

manufacturing employment shares have

fallen. This calls for urgent intervention by

the policymakers because manufacturing

employment boost growth and welfare than

manufacturing output.

Majid (2000) studied employment, output,

and productivity in Pakistan using

regression analysis conducted for the

period 1980 to 1997. The result shows that

labour productivity in Pakistan has been

influenced by changes in capital intensity.

This implies that manufacturing activities

in the countries are being dominated by

labour-saving devices and equipment.

Capital intensive approach to production

requires a huge startup and running capital

than a labour-intensive method. And the

return to scale under capital intensive is

higher than that of labour intensive. So,

every investor is interested in a huge return

on capital but the government can prioritize

labour through regulatory measures to

employ the unemployed. However, in

Pakistan, labour productivity and capital

intensity have a significant positive

relationship because, under a capital-

intensive method of production, the few

labours employed will be highly

productive. So, the concern is about the

proportion of the total workforce that is

gainfully employed in the economy.

Pierce and Scoott (2016), carried out a

study titled "the surprisingly swift decline

of US manufacturing employment". They

used the generalized ordinary least square

method and found that the US has recorded

fall in the relative share of their

manufacturing sectors lately due to policy

adjustment that favoured import over

domestic production.

Bernard, Smeets, and Warzynski (2016) did

a study for Denmark on the topic

“rethinking deindustrialization” using pool

regression and found that manufacturing

employment and the number of firms have

been shrinking as a share of the total and in

absolute levels. They further observed that

firms are switching from the manufacturing

sector to the service sector resulting in the

shrinking of the manufacturing sector and

the employment level. Scholars across the

globe had seen a decline in the industrial

output of the developed countries as a cause

for concern (Alex and Antonio, 2017).

2.2 Theory

This study is premised on the principles of

Baumol’s “cost disease” theory. Baumol in

1967 carried out a study on the American

economy titled "the macroeconomic of

unbalanced growth: the anatomy of urban

crisis". In the study, he argued that

deindustrialization is due to faster

productivity growth in manufacturing, such

that the manufacturing sector has

decreasing labour requirements, while

labour-intensive services have little scope

for mechanization, scale economies, capital

accumulation, or productivity growth. This

phenomenon is called the Baumol disease,

which simply implies that low productivity

growth and economic stagnation are due to

the low manufacturing output and increased

share of the service sector. Baumol was the

first scholar to perceive the clash between

the manufacturing sector and the service


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sector which poses a threat to the

sustainability of manufacturing

development in the face of continuous

growth in technology.

3. Method of Estimation

This analysis seeks to determine the

relationship among manufacturing value-

added, service sector and employment

growth in the sub-Saharan countries to

ascertain how the three variables interplay

in the manufacturing sector of the African

sub-region. Studies have proved that

sophistication has a positive impact on a

country's industrial production, export

structure and growth, but a negative impact

on employment generation (job loss)

resulting in depletion of the manufacturing

output and growth (Hausmann, Hwang, and

Rodrik 2007). However, the way

sophistication and the attendance service

sector expansion act upon manufacturing

activities is of paramount importance in the

modern globalized economy (page, 2011).

Panel vector autoregressive

(PVAR) technique will be used to estimate

the dynamic interaction among the

variables if the Cointegration test indicates

no cointegration in the model. The

empirical model is stated in PVAR form as;

(22)

𝑀𝑉𝐴𝑖𝑡 = 𝛼𝑖1 + 𝛽𝑖𝑗∑𝑀𝑉𝐴𝑖𝑡−𝑗 + 𝛾𝑖𝑗∑𝑆𝐸𝑅𝑖𝑡−𝑗 +

𝑛

𝑗=1

𝑛

𝑗=1

𝜌𝑖𝑗∑𝐸𝑃𝑌𝑖𝑡−𝑗 +

𝑛

𝑗=1

𝜀1𝑖𝑡

𝑆𝐸𝑅𝑖𝑡 = 𝛼𝑖2 + 𝜃𝑖𝑗∑𝑀𝑉𝐴𝑖𝑡−𝑗 + 𝜇𝑖𝑗∑𝑆𝐸𝑅𝑖𝑡−𝑗 +

𝑛

𝑗=1

𝑛

𝑗=1

𝜎𝑖𝑗∑𝐸𝑃𝑌𝑖𝑡−𝑗 +

𝑛

𝑗=1

𝜀2𝑖𝑡

𝐸𝑃𝑌𝑖𝑡 = 𝛼𝑖3 + 𝜋𝑖𝑗∑𝑀𝑉𝐴𝑖𝑡−𝑗 + 𝜏𝑖𝑗∑𝑆𝐸𝑅𝑖𝑡−𝑗 +

𝑛

𝑗=1

𝑛

𝑗=1

𝜋𝑖𝑗∑𝐸𝑃𝑌𝑖𝑡−𝑗 +

𝑛

𝑗=1

𝜀3𝑖𝑡

where, t = 1, 2, 3 4, ………. T and I = 1, 2,

3, 4, ………. N, MVAi,t is manufacturing

value-added, MVAi,t-1 is one year lag of

manufacturing value-added, 𝑆𝐸𝑅𝑖𝑡−𝑗 is

service sector and 𝐸𝑃𝑌𝑖𝑡−𝑗 is employment

growth. 𝜀𝑖 is the error term. This analysis is

meant to model and determine the dynamic

behaviour among manufacturing value-

added, service sector and employment

growth in SSA. If there is cointegration in

the model, a panel vector error correction

model will be used to estimate the model.

The panel vector error correction model

(PVECM) form of the above VAR model

can be stated as;

(23) ∆𝑀𝑉𝐴𝑖𝑡 = 𝛼𝑖1 +∑𝛽𝑖𝑗∆𝑀𝑉𝐴𝑖𝑡−𝑗 +∑𝛾𝑖𝑗∆𝑆𝐸𝑅𝑖𝑡−𝑗 +

𝑛−1

𝑗=1

𝑛−1

𝑗=1

∑𝜌𝑖𝑗∆𝐸𝑃𝑌𝑖𝑡−𝑗 +𝜑1𝑖𝑒𝑐𝑚𝑖𝑡−1 +

𝑛−1

𝑗=1

𝜀1𝑖𝑡

∆𝑆𝐸𝑅𝑖𝑡 = 𝛼𝑖2 +∑𝜃𝑖𝑗∆𝑀𝑉𝐴𝑖𝑡−𝑗 +∑𝜇𝑖𝑗∆𝑆𝐸𝑅𝑖𝑡−𝑗 +

𝑛−1

𝑗=1

𝑛−1

𝑗=1

∑𝜎𝑖𝑗∆𝐸𝑃𝑌𝑖𝑡−𝑗 + 𝜑2𝑖𝑒𝑐𝑚𝑖𝑡−1 +

𝑛−1

𝑗=1

𝜀2𝑖𝑡

∆𝐸𝑃𝑌𝑖𝑡 = 𝛼𝑖3 +∑𝜋𝑖𝑗∆𝑀𝑉𝐴𝑖𝑡−𝑗 +∑𝜏𝑖𝑗∆𝑆𝐸𝑅𝑖𝑡−𝑗 +

𝑛−1

𝑗=1

𝑛−1

𝑗=1

∑𝜋𝑖𝑗∆𝐸𝑃𝑌𝑖𝑡−𝑗 +

𝑛−1

𝑗=1

𝜑3𝑖𝑒𝑐𝑚𝑖𝑡−1 + 𝜀3𝑖𝑡

where,t = 1, 2, 3 4, ……….T and I = 1, 2,

3, 4, ………..N, MVAi,t is manufacturing

value-added, MVAi,t-1 is one year lag of

manufacturing value-added, 𝑆𝐸𝑅𝑖𝑡−𝑗 is

service sector and 𝐸𝑃𝑌𝑖𝑡−𝑗 is employment

growth. 𝜀𝑖, is the error terms. Panel vector

error correction model (PVECM) is the first

difference of panel vector autoregressive

(PVAR) model. Therefore, the optimum lag

for the PVECM is n-1 because when we

take the first difference we shall lose one

lag. 𝑒𝑐𝑚𝑖𝑡−1 is the error correction terms

while 𝜑3𝑖 is the speed of adjustment if there

is deviation from the long-run estimator.

Data and Measurement of Variables

Manufacturing Value-added (MVA) is the

total value of the net output of all resident

manufacturing activity units obtained by

adding up outputs and subtracting

intermediate inputs. This will be captured

by the manufacturing value-added data

published in global input-output tables

(WIOD, 2020). In this study, service value-

added is used as a proxy for the service

sector (Su and Yao, 2016). Employment

growth refers to the rate at which jobs are

being created in a country over a period of

time. This is measured as the ratio of the

labour force to the unemployed in the

economy (Freeman, 2008).

4. Results

4.1 Cross-sectional Dependence

The result of the cross-sectional

dependence test is presented in table 1. The

test statistics is statistically significant at a


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1% level of significance. This implies that

the null hypothesis of cross-sectional

independence is rejected at a 1% level of

significance. Therefore, the null hypothesis

of cross-sectional independence is rejected.

In other words, there is cross-sectional

dependence among the error residuals of

the cross-sectional units in this study.

Table 1: Cross-sectional Dependence

xtcsd, Pesaran abs

Pesaran's test of cross-sectional independence Probability

84.409 0.0000 Source: author’s computation

4.2 Result of Unit Root Test

The cross-sectional dependence test in the

previous section establish that there is error

cross-sectional dependence among the

cross-sectional units in the study.

Therefore, cross-sectional augmented

Dickey-Fuller (CADF) and CIPS unit root

tests (Pesaran, 2007) are employed to carry

out the stationarity test on the variables in

the study. CADF and CIPS are second-

generation unit root test techniques that

account for error cross-sectional

dependence. The results of the unit root

tests on the variables are presented in Table

2. The CADF unit root test shows that

manufacturing value added, service sector,

and employment growth have unit root at

level. All the variables are stationary at first

difference in the presence of error cross-

sectional dependence. It shows that all the

variables are integrated of order one that is

they are all I(1) series. The results of the

CIPS unit root test confirm the results of the

CADF unit root test, as the CIPS test also

indicate that all the variables are I(1). This

justifies the reliability and robustness of the

empirical analysis and satisfies the

condition for adopting the panel vector

error correction model (PVECM) for

estimation.

Table 2 Panel Unit Root Analysis with Cross-Sectional Dependence

Variables

CIPS CADF

Critical

Values

Level First

Differen

ce

Level First Difference

MVA

10% -

2.040

-1.252 -4.860 -0.542 -2.752***

SER

5% -

2.110

-0.537 -2.942 -0.668 -2.402***

EPY

1% -

2.230

-1.047 -3.427 -1.230 -2.519***

Source: author’s computation, ***, **, * denote significant at 1, 5 and 10% respectively

5.3 Lag Length Selection

The lag length selection result is presented

in table 3. The rule of thumb is to select the

criterion that exhibits the lowest lag length.

But in this analysis, all the criteria selected

the same lag length which is lag 1. So, any

of the criteria can be utilized for lag

selection.


11

Table 3 Lag Length Selection

Lag LogL LR FPE AIC SC HQ

0 -1804.925 NA 2.602049 3.794176 3.809474 3.800004

1 -849.2934 1903.241* 0.350945* 1.790752* 1.811149* 1.798523*

2 -849.1992 0.187445 0.351613 1.792653 1.818149 1.802366

3 -848.6938 1.004289 0.351978 1.793691 1.824287 1.805347

4 -847.7590 1.855978 0.352026 1.793828 1.829523 1.807426


* indicates the criterion and the lag order selected

4.3 Cointegration Test Cointegration is about the existence of a

long-run relationship between economic

variables. It is about the time dimension of

the economic data. However, cointegration

analysis should not be limited to the time

dimension of economic variables because

in a panel data set the cross-sectional

dimension could enhance the accuracy and

power of the estimation. Many studies have

not been able reject the null hypothesis of

no cointegration concerning economic

relations where theories advocate for a

long-run relationship due to low statistical

power. Consequently, this study adopts an

error-correction based cointegration test for

panel data (Westerlund panel cointegration

test), because it accounts for the cross-

sectional dimension of the panel model.

The result of the cointegration test is

presented in table 4, the result shows that

there is cointegration in the model. The p-

value is '0.0178' which is less than 5%. This

implies rejection of the null hypothesis of

no cointegration at a 5% level of

significance. In other words, there is a long-

run relationship between the variables in

the model.

Table 4: Westernlund Panel Cointegration Test

Statistics p-value

Variance ratio -2.1007 0.0178 Source: author’s computation

4.4 Result of Panel VECM

The panel VECM result when employment

growth is the endogenous variable is

presented in table 5. The long-run

coefficient of manufacturing value-added is

estimated at '-0.198472' while the long-run

coefficient of service sector is -11.55493.

The error correction term (ECM) is

estimated at '-0.018912'. The speed of

adjustment is negative and significant at a

5% level of significance. It implies that

disequilibrium from the previous year's

shock converges back to the long-run

equilibrium in the current year at the speed

of 1.9%. The significance of the ECM term

represents a joint significance of the long-

run coefficients because the coefficient of

the ECM term embodies the long-run

coefficients. Therefore, the joint long-run

causality can be deduced from the

significance of the ECM term. In other

words, the significance of the ECM term

implies that long-run causality runs from

manufacturing value-added and service

sector to employment growth.

The result of the short-run dynamics is also

presented in table 5. The coefficient of the

lag of the dependent variable is estimated at

'-0.327451' and it is significant at a 1% level

of significance. The coefficient of


sector is estimated at '0.004621' and '-

0.110103' respectively. Manufacturing

value-added is positive but not significant

while service sector is negative and not


12

significant. However, the joint short-run

causality test of the Wald Coefficient Test

fails to reject the null hypothesis of no

causality. The probability of the test

statistics is greater than the 10% level of

significance. This shows that there is no

short-run causality running from


sector to employment growth in SSA.

Table 5 Result of Panel Vector Error Correction Model when Employment Growth is

the Dependent Variable

LONG-RUN

VARIABLES COEFFICIENTS PROBABILITY

EPY(-1) 1.000000 -

LNMVA(-1)

-0.198472

(0.11442)

-

LNSER(-1)

-11.55493

(1.68798)

-

CointEq

-0.018912

(0.007825)

0.0157

SHORT-RUN

EPY(-1)

-0.327451

(0.03331)

0.0000

LNMVA(-1)

0.004621

(0.02752)

0.8667

D(LNSER(-1))

-0.110103

(0.19512)

0.5726

Wald Test

Test Statistic Value Df Probability

Chi-square 0.344570 2 0.8417


The result of the model where

manufacturing value-added is the

dependent variable is presented in table 6.

The coefficients of the long-run

relationship are presented in the upper part

of the table, the long-run coefficient of

employment growth is negative ‘-

5.038489’ while the long-run coefficient of

service sector is also negative ‘-58.21937’.

In this analysis, the probability of the

cointegration term (error correction term) is

approximate ‘0.76%’ which is greater than

the 10% level of significance. This implies

that there is no long-run causality running

from employment growth and service

sector to manufacturing value-added in

SSA. In other words, employment growth

and the service sector are not long-run

drivers of manufacturing value-added in

SSA.

The result of the short-run dynamics when

manufacturing value-added is the

dependent variable is presented in the lower

segment of table 6. The coefficient of the

lag of the dependent variable is negative but

not significant, it is estimated at ‘-

0.011798’. The coefficient of the short-run

dynamics between manufacturing value-

added and employment growth is positive

but not significant, it is estimated at

‘0.020943’. The coefficient of the short-run

dynamics between manufacturing value-

added and service sector is positive and

significant, it is estimated at ‘0.025802’.

This study employs the Wald Coefficient

Test to ascertain the likelihood of short-run

causality running from the exogeneous


13

variables to the endogenous variable. The

test result reported in table 6 indicates a less

than 1% probability value which implies

that the null hypothesis is rejected at a 1%

level of significance. In other words, short-

run causality runs from the employment

growth and service sector to manufacturing

value-added in SSA.

Table 6 Result of Panel Vector Error Correction Model when MVA is the Dependent

Variable

LONG-RUN


LNMVA(-1) 1.000000 -

EPY(-1)

-5.038489

(1.60770)

-

LNSER(-1)

-58.21937

(8.65362)

-

CointEq

0.000658

(0.00195)

0.7359

SHORT-RUN

D(LNMVA(-1))

-0.006795

(0.03455)

0.8441

D(EPY(-1))

0.244050

(0.04182)

0.0000

D(LNSER(-1))

0.025802

(0.00781)

0.2041

Wald Test


Chi-square 43.70021 2 0.0000


The model where service sector is the

endogenous variable is analyzed and the

result is presented in table 7. The long-run

coefficient of manufacturing value-added is

estimated at ‘-0.017176’, while the

coefficient of employment growth is

estimated at ‘0.086543’. Manufacturing

value-added is negatively signed while

employment growth is positively signed.

The error correction term (ECM) is

estimated at ‘-0.100854’ and it is

significant at a 1% level of significance. On

the one hand, ECM represents the speed of

adjustment to equilibrium from the

previous time deviation from the

equilibrium part. This implies that

disequilibrium from the previous year’s

shock converges back to the long-run

equilibrium in the current year at the speed

of 10%. On the other hand, the significance

of the ECM term represents a joint

significance of the long-run coefficients

because the coefficient of the ECM term

combines the long-run coefficients.

Therefore, the joint long-run causality can

be deduced from the significance of the

ECM term. In other words, the significance

of the ECM term implies that long-run

causality runs from manufacturing value-

added and employment growth to service

sector

The coefficients of the short-run dynamics

are also presented in table 7. The coefficient

of one lag of the dependent variable is

estimated at ‘-0.043497’ it is negative and

not significant. The coefficient of short-run

dynamics of manufacturing value-added

concerning service sector is estimated at

‘0.004418’ and it is significant. The

coefficient of the short-run dynamics of

employment growth concerning service

sector is estimated at ‘0.012451’, it is not

significant. The short-run causality in the


14

model is determined using the Wald

Coefficient Test. The probability of the test

statistics of the Wald Coefficient Test is

less than 10% but greater than the 5% level

of significance. This implies that the test

statistics are significant at a 10% level of

significance. In other words, there is short-

run causality running from manufacturing

value-added and employment growth to

service sector in SSA.

Table 7 Result of Panel Vector Error Correction Model when Service sector is the

Dependent Variable

LONG-RUN


LNSER(-1) 1.000000 -

LNMVA(-1)

-0.017176

(0.01026)

-

EPY(-1)

0.086543

(0.02813)

-

CointEq

-0.100854

(0.01633)

0.0000

SHORT-RUN

LNSER(-1)

-0.043497

(0.03524)

0.2171

LNMVA(-1)

0.004418

(0.00497) 0.0386

EPY(-1)

0.012451

(0.00602)

0.3741

Wald Test


Chi-square 5.096201 2 0.0782


5. Conclusion

The causal relationship among

manufacturing productivity, the

employment sector, and the service sector

has been highly debated in the literature

without consensus (Bernard, Smeets and

Warzynski, 2016). Hence, this study

ascertained the direction of causality

among these variables in the context of the

SSA economy. The result reveals that long-

run causality runs from manufacturing

value-added and service sector to

employment growth. This result justifies

the fact that the manufacturing sector and

service sector generate employment in the

economy. In other words, the

manufacturing sector and the service sector

contribute to employment generation in

SSA. The short-run analysis reveals that

there is no short-run causality running from


sector to employment growth in SSA. In

other words, the employment impacts of the

manufacturing sector and the service sector

is not immediate, rather it takes a relatively

long time for the employment impact of the

two sectors to materialize.

The study also reveals that there is no long-

run causality running from employment

growth and service sector to manufacturing

value-added in SSA. In other words,

employment growth and the service sector

are not long-run drivers of manufacturing

value-added in SSA. The manufacturing

sector induces employment because

demand creates supply. The service sector

does not induce the manufacturing sector

because the service sector is crowding out

the manufacturing sector (Faggio and

Overman, 2014).


15

Although in the short-run, both the

employment sector and service sector

enhance the manufacturing value-added in

SSA, they do not have a long-run impact on

it.

Finally, the analysis reveals that there is

long-run causality running from

manufacturing value-added and

employment growth to the service sector.

This implies that the manufacturing sector

and employment sector stimulate the

service sector. Besides, there is short-run

causality running from manufacturing

value-added and employment growth to

service sector in SSA. In other words, both

in the short-run and long run the

manufacturing sector and employment stir

the service sector growth.

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