the impact of electricity on economic growth from
single-country studies has shown divergent findings.
This study will focus on the effect of electricity access
on industrial output at a micro level (agro processing industries), while holding other factors constant
by investigating the effect of electricity consumption, capital, labour, education, and other firm-specific
characteristics like size, location, managerial experience, and government intervention.
2.1 The relevant policies for the problem in question
The Government of Uganda has over the past eight
years embarked on a Power sub-Sector Reform Programme, which has resulted in the implementation
of significant structural changes within the sector
to promote industrial growth. The Uganda National
Development Plan (NDP 2010/11-15) highlights the
need to invest in priority areas, among them facilitating availability and access to critical production inputs
especially in agriculture and industry. The National
Agriculture Policy (NAP) approved in 2013 also is in
place to achieve food and nutrition security through
coordinated sustainable agricultural productivity and
value addition. The Energy Policy for Uganda (2002),
which exists to meet the energy needs and rural electrification policy, was also formulated to increase
electricity access with a target to increase access to
electricity from 7% to 26%.
2.2 Theoretical model
The theory of production shows the relation between
input changes and output changes. It also shows the
maximum amount of output that can be obtained by
an industry from a fixed quantity of resources. The
production function is expressed as:
Industrial output Q = f (K, L, E)
(1)
where Q is industrial output, K represents capital, L
represents labour, and E stands for electricity.
Under standard assumptions underlying an explicitly specified concave production function, inputs have
a positive but a decreasing impact on output. Thus,
electricity is expected to have independent positive
effects on output. Though the effects of individual
inputs on output may be examined, output is not (often)
produced by a single input; therefore, a combination
of inputs is necessary for optimal production (Romer
2012).
While microeconomic theories, such as human
capital and signalling (Mincer 1974; Weiss 1995),
suggests several avenues through which education
can affect productivity, little consensus exists among
economists on how education is related to productivity.
However, empirical research has been limited because
few data sets contain information on both workers’
output and their education.
This study thus estimates a capital-augmenting
model where electricity enters the model multiplicatively
with capital to reflect the role of electricity-operated
capital in the production process of agro-processing
industries. Uganda should thus increase electricity
supply to stimulate industrial output and enhance
economic growth.
The industrial output is produced by the output
growth and value added. That is:
Y i = f (Y
t
i , Y
n
i )
(2)
where, Y i = industrial output for each industry i; I ,
Y
n
i = total value added, n is the number of industries,
and t represents the period.
The industrial output is produced by capital and
labour. However, the capital equipment in the industrial sector runs on electricity: this study then assumes
electricity enters the model in a capital-augmenting
factor.
Y i = (K
γ E
β L
α e
n
j=1
mizi + e i
(3)
where K
γ
= capital employed in the industrial sector, proxied by gross fixed capital and assets owned;
E
β
= electricity supply (consumption); L
α
= labour
employed in the industrial sector, proxied by labour
force participation; e
n
j=1
mizi = other specific factors (location, nature of establishment and government
intervention); and e i = error term.
The differential of the natural logarithm of Equation
(3) yields the elasticity Equation (4):
InY i = γInK i + βInE i + αInL i +
n
j=1
mizi + e i (4)
where γ, β, α are the coefficients; e
n
j=1
mizi is a
set of other firm-specific characteristics (location,
rural/urban; government intervention; and managerial
experience, education). Differentiating Equation 3 to
get elasticity yields:
dY i
dY i
Y i
= γ
dK i
K i
+ β
dE i
E i
+ α
dL i
L i
(5)
Implying that γ = 1% change in K leads to γ %
increase inY, β = 1% change in E leads to β % increase
in Y, and α = 1% change in L leads to α % increase in
Y:
InY i = γInK i + βInE i + αInL i + β i
(6)
Equation (6) contains endogenous explanatory variables, which are electricity consumption, capital,
labour, and education, with γ, β, andα as the coefficients.
3 METHODS
3.1 Methodology and the data
The study used time series data from the World
Bank enterprise surveys (2016) and Uganda Bureau
of statistics (2018). The study used a population of
87
single-country studies has shown divergent findings.
This study will focus on the effect of electricity access
on industrial output at a micro level (agro processing industries), while holding other factors constant
by investigating the effect of electricity consumption, capital, labour, education, and other firm-specific
characteristics like size, location, managerial experience, and government intervention.
2.1 The relevant policies for the problem in question
The Government of Uganda has over the past eight
years embarked on a Power sub-Sector Reform Programme, which has resulted in the implementation
of significant structural changes within the sector
to promote industrial growth. The Uganda National
Development Plan (NDP 2010/11-15) highlights the
need to invest in priority areas, among them facilitating availability and access to critical production inputs
especially in agriculture and industry. The National
Agriculture Policy (NAP) approved in 2013 also is in
place to achieve food and nutrition security through
coordinated sustainable agricultural productivity and
value addition. The Energy Policy for Uganda (2002),
which exists to meet the energy needs and rural electrification policy, was also formulated to increase
electricity access with a target to increase access to
electricity from 7% to 26%.
2.2 Theoretical model
The theory of production shows the relation between
input changes and output changes. It also shows the
maximum amount of output that can be obtained by
an industry from a fixed quantity of resources. The
production function is expressed as:
Industrial output Q = f (K, L, E)
(1)
where Q is industrial output, K represents capital, L
represents labour, and E stands for electricity.
Under standard assumptions underlying an explicitly specified concave production function, inputs have
a positive but a decreasing impact on output. Thus,
electricity is expected to have independent positive
effects on output. Though the effects of individual
inputs on output may be examined, output is not (often)
produced by a single input; therefore, a combination
of inputs is necessary for optimal production (Romer
2012).
While microeconomic theories, such as human
capital and signalling (Mincer 1974; Weiss 1995),
suggests several avenues through which education
can affect productivity, little consensus exists among
economists on how education is related to productivity.
However, empirical research has been limited because
few data sets contain information on both workers’
output and their education.
This study thus estimates a capital-augmenting
model where electricity enters the model multiplicatively
with capital to reflect the role of electricity-operated
capital in the production process of agro-processing
industries. Uganda should thus increase electricity
supply to stimulate industrial output and enhance
economic growth.
The industrial output is produced by the output
growth and value added. That is:
Y i = f (Y
t
i , Y
n
i )
(2)
where, Y i = industrial output for each industry i; I ,
Y
n
i = total value added, n is the number of industries,
and t represents the period.
The industrial output is produced by capital and
labour. However, the capital equipment in the industrial sector runs on electricity: this study then assumes
electricity enters the model in a capital-augmenting
factor.
Y i = (K
γ E
β L
α e
n
j=1
mizi + e i
(3)
where K
γ
= capital employed in the industrial sector, proxied by gross fixed capital and assets owned;
E
β
= electricity supply (consumption); L
α
= labour
employed in the industrial sector, proxied by labour
force participation; e
n
j=1
mizi = other specific factors (location, nature of establishment and government
intervention); and e i = error term.
The differential of the natural logarithm of Equation
(3) yields the elasticity Equation (4):
InY i = γInK i + βInE i + αInL i +
n
j=1
mizi + e i (4)
where γ, β, α are the coefficients; e
n
j=1
mizi is a
set of other firm-specific characteristics (location,
rural/urban; government intervention; and managerial
experience, education). Differentiating Equation 3 to
get elasticity yields:
dY i
dY i
Y i
= γ
dK i
K i
+ β
dE i
E i
+ α
dL i
L i
(5)
Implying that γ = 1% change in K leads to γ %
increase inY, β = 1% change in E leads to β % increase
in Y, and α = 1% change in L leads to α % increase in
Y:
InY i = γInK i + βInE i + αInL i + β i
(6)
Equation (6) contains endogenous explanatory variables, which are electricity consumption, capital,
labour, and education, with γ, β, andα as the coefficients.
3 METHODS
3.1 Methodology and the data
The study used time series data from the World
Bank enterprise surveys (2016) and Uganda Bureau
of statistics (2018). The study used a population of
87