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April 2014, Vol 4 No 1. Pp. 17-34
Outcomes of Worker Effort and Supervision in Tanzanian Labour Market
Godius Kahyarara, PhD.1
This paper examines the impact of work effort and supervision in the Tanzanian labour
market. In particular it focuses on the extent to which observed earnings and
productivity of a worker might be influenced by both the individual effort of a worker
and intensity of supervision. To assess the earnings effect of work effort, the paper
estimates the hourly earnings equation, which includes work effort and monitoring
intensity among the determinants of the hourly earnings. The estimates control for
unobserved firm specific effects and GMM production functions. Key findings of the
paper are that a worker who exerts higher effort at work increases hourly earnings by
about 27 per cent. Estimates of productivity affect via GMM shows that increase in the
monitoring intensity increased the gross output per employee by about 34 per cent. The
estimated coefficient is stable even after a range of factors are controlled for. The paper
concludes that labour market reforms introduced in Tanzania on increased autonomy
and flexibility of firm level work supervision and pay have positive outcomes for both
employers and employees.
Key Words: worker effort. Labour market, Tanzania, Supervision, outcomes
1
Lecturer, Economics Department, University of Dar-es-Salaam, Tanzania
[email protected]
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Introduction
This paper examines the impact of work
effort and work supervision in the labour
market. In particular it focuses on the
extent to which observed earnings and
productivity of a worker might be
influenced by both the individual effort of
a worker and intensity of supervision. The
paper focuses on Tanzania manufacturing.
The labour market reforms introduced in
Tanzania especially from the early 1990s
increased autonomy and flexibility of firm
level work supervision and reduced the
role of state intervention in pay
determination and work regulations. These
changes may have had significant effects
on the role of both work supervision and
work effort in pay determination and
productivity. Incentive pay theories for
example, predict that in setting wages
employers face both adverse-selection and
moral hazard problems: only workers
know the difficulty of their jobs, and they
can shirk so as to obscure this information
from employers. Therefore, in the absence
of state intervention in wage setting in
Tanzanian labour market, firm level work
supervision is one of the potential
strategies for eliciting hidden actions and
hidden information of the workers.
Although the changes introduced in
the Tanzania labour market have potential
effects on the level of earnings received by
workers along with firm level productivity,
little is known about the relationship
between monitoring and worker earnings
differences, or about the impact of worker
effort on individual earnings and firm level
productivity. A better understanding of the
structure and impact of internal
supervision of work and worker effort is
needed to understand their role in
manufacturing development especially
after the reforms. The goal of this paper is
therefore to document several facts
regarding work effort and intensity of
work
supervision
in
Tanzanian
manufacturing.
The overall objective of the paper is thus
to examine the impact of worker effort on
observed productivity and earnings.
The specific objective of the paper is;
 To assess the extent to which
individual effort of the worker
influence the level of productivity of
labour in a particular firm
 To analyse the link between observed
earnings of an individual worker and
the contribution he/she makes via the
effort excreted at work
 How a combination of the level of
earnings and productivity is a function
of the efforts of their employees.
The overall objective of the paper is thus
to examine the impact of worker effort on
observed productivity and earnings.
The specific objective of the paper is;
 To assess the extent to which
individual effort of the worker
influence the level of productivity of
labour in a particular firm
 To analyse the link between observed
earnings of an individual worker and
the contribution he/she makes via the
effort excreted at work
 How a combination of the level of
earnings and productivity is a function
of the efforts of their employees.
The paper acknowledges the fact that
there are measurement problems
associated with estimating the impact
of work effort and supervision
heterogeneity. For example, while
higher effort can lead to higher
earnings, it is also possible that higher
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earnings can be an incentive to elicit
more effort. Likewise, there is a
possibility that the employer can
choose both the level of monitoring
and earnings. If this is so then OLS
estimates will be inconsistent. To
address such problems, the paper
estimates regression models that
control for unobserved firm specific
effects
and
GMM
production
functions. The data used has
information on methods of pay, i.e.
time rate and piece rate. Such
information
provides
potential
instruments that allow the paper to
estimate two stage instrumental
variable
methods.
After
this
introduction, the second section
describes pay policy and work
supervision in Tanzania over the
period 1960-2012. In the third section
theoretical framework underlying the
assessment of earnings impact of pay
method and work supervision is
described. The discussion in this
section reviews agency and efficiency
wage theories that have been central to
various models for analyzing pay
method and work supervision. The
Principal-Agent theory, shirking model
of efficiency wage and other
explanations of pay practices such as
moral hazards, self -selection, and
sociological explanations are briefly
described in relation to work and
supervision. The fourth section
presents the models estimated in this
paper
along
with
possible
measurement problems.
Methods
employed to mitigate estimation
problems are also discussed in this
section. Section five presents the
estimation results whereas the sixth
section summarizes and concludes.
It is worth noting that, the 1990s have seen
a reversal of many policies in Tanzania.
Specific reform measures undertaken in
the Tanzania labour market that may have
direct impact on previous wage policy are:
gradual elimination of fixed wages and
introduction of flexible wage bargaining at
enterprise level (both individually and
collectively bargained wage); and allowing
employers to provide fringe benefits (such
benefits as food allowances, housing
allowance, transport etc) to their
employees when they find it appropriate
(see Tsikata et al, 1999 Mjema et al, 1998,
Mans, Darius 1994).
Following the
reforms in pay and work organization
described above, several firms including
the state owned ones, introduced
performance based pay scheme, and most
of the profitable firms increased wages
substantially (Mans, Darius 1994)1. During
pre-reform period, earnings differentials
were low due to pay compression policy.
The standard rate pay system, and absence
of performance related pay and various
constraints limited employers’ powers and
authority over the employees. After the
reforms, the current average earnings
among the workers and across firms might
differ significantly2. This study therefore
addresses the question, whether and to
what extent variation in earnings can be
1
Performance based pay scheme was introduced in
Tanzania Breweries Limited, Tanzania Cigarette
Company, TPDC, Tanzania Electrical Company, IPP
limited, Somaia group and others.
2 For example, Tanzania Breweries Limited raised the
monthly minimum wage from Tanzania shillings 5,000
(about $ 16) in 1991 to 100,000 in mid 1990s and
186,000 recently (about $ 177) (Kileo, 2003). In addition
to paying higher minimum wage, companies like
Tanzania Breweries provide free medical treatment and
free meals at lunch.
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accounted for by heterogeneous pay
method and supervision intensity during
the post reform era. In the next section we
describe the theoretical framework of our
study.
Literature on Work and Efforts.
Previous studies in this area (see for
example Strobl and Walsh, 2003) have
argued that to correctly specify the impact
of monitoring on earnings, the interaction
between monitoring and work effort needs
be modelled.
The principal-agent
framework (see for example, Ross 1973,
Grossman and Hart 1983; and Sappington
1991 for details), is based on designing
efficient explicit or implicit contracts
between the principal (employer) and the
agent (employee) in a situation where
there are incentives for employees to cheat
about their true work effort, largely arising
from information asymmetries and
monitoring costs. Related to the PrincipalAgent explanation is the theory of implicit
contracts (Azariadis (1975), Baily (1974)
and Gordon (1974)). Based on this theory,
if a firm is risk averse, it would like its
workers to take a wage cut in bad states.
However, if only the firm and not the
workers can observe the state, wages
cannot be made to depend on the state
directly (Malcomsom, 1999).
In the
shirking model of Shapiro and Stiglitz
(1984), workers can choose whether to
work or shirk. Sociological explanations
associated with George Akelof (1982)
have also been a source of theoretical base
for modeling monitoring, pay and
earnings. According to Yellen (1984),
sociological explanations can explain
economic phenomenon related to pay such
as reasons for forbidding piece rates even
when feasible, or why workers might
exceed firms set work standards.
In studies by (Lindberg and
Snower (1987); Ewing and Payne (1999))
it is shown that workers earn more where
monitoring is more difficult. Brown
(1992); Ewing (1996); Parent (1999);
Booth and Frank (1999) reveal that
workers who are employed in jobs that
have pay based on performance earn more.
In other studies (such as Leonard (1987),
Kruse (1992), Green and Mclntosh (1998),
Groshen and Krueger (1990) etc), it is
confirmed that there is a negative
correlation between supervisory intensity
and earnings. Such findings suggest that a
worker will get more pay if he/she is less
supervised. Yet, there is hardly any
estimate of the earnings effect of
monitoring intensity in Tanzanian labour
market on a national level representative
data for the recent period.
Theoretical Framework
In this section the theoretical framework
for estimating the effect of pay method and
work supervision are described. Possible
estimation problems that may be
encountered during estimating the link
between monitoring work supervision and
earnings also form part of our discussion
here.
The
theoretical
frameworks
underlying much of the previous work on
the link between work effort, monitoring,
earnings and productivity are based on
agency
theory
(principal-agent
framework), and efficiency wage theory,
shirking model, adverse selection, moral
hazard and sociological explanations.1 The
1
(see Ross 1973, Grossman and Hart 1983; Sappington
1991;Carlo Shapiro and Stiglitz, 1982; Steven Stoft,
1982; Samuel Bowles, 1981,1983;James Malcomson,
1981; Stiglitz, 1979b; Andrew Weiss, 1980;George
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principal-agent framework (see for
example, Ross 1973, Grossman and Hart
1983; and Sappington 1991 for details), is
based on designing efficient explicit or
implicit contracts between the principal
(employer) and the agent (employee) in a
situation where there are incentives for
employees to cheat about their true work
effort, largely arising from information
asymmetries and monitoring costs.
Employers are therefore forced to prepare
contractual arrangements that are often
self-enforcing, in the sense that they are
designed to elicit the private information
of the employees, providing them an
incentive to provide their true efforts. The
formal description of the Principal-Agent
model is described below followed by
other models from in the literature the area
of pay method and monitoring.
Principal-Agent Model
The models, consider a production
function of an employee (the agent) such
as;
Q = X (e) + ε,
Where P is the realized wage and C is the
cost of effort. The utility function is
strictly concave in net payoff, that is, U '(P
– C) > 0 and U''(P – C) < 0.
The cost function is strictly convex in
effort, that is, C' (e) > 0 and C''(e) > 0,
except at e = 0 where C´(0)= 0. The model
assumes a linear pay contract for the agent,
P = α
component of pay or insurance pay, and β
performance sensitivity. In the literature of
incentive strength. The agent chooses
effort to maximize his or her expected
utility, E [U (P – C)], where E is the
expectation
operator,
yielding
the
following
incentive
compatibility
constraint:
βX'(e) – C'(e) = 0.
[3]
The constraint dictates that effort increases
with the incentive parameter. The principal
is risk-neutral in a competitive labor
market and earns zero expected profit.
Then, E (Q- P) = 0, or
[1]
Where e is the employee’s (agent’s)
investment
of
effort,
which
is
unobservable to the employer (principal),
and X (e) defines expected output, which
is strictly concave in effort, that is, X' (e) >
0 and X''(e) < 0, and where X (0) = 0. The
second term, ε, is a random variable with
zero mean. The variance of variable ε
could be regarded as a measure of the risk
of output. The employee’s (agent’s) utility,
denoted as U, is a general function of his
or her net payoff,
P – C,
[2]
α = (1 – β)X(e)
[4]
The optimisation problem of the model is
to maximize the agent’s expected utility
subject to the incentive compatibility
constraint, equation (1), and the zero
expected profit condition, equation (2).
Before we describe how this framework
will be useful for the analysis of this paper,
we first describe other models that
potentially link the pay method and
monitoring with earnings.
Akerlof 1982; Edward Lazear (1981), Armen Alchian
and Harold Dmsetz (1972), Stephen Ross (1973)).
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discussion on data includes a detailed
account of how various variables used in
our estimation are created. The model
specifications are given below.
Model Specification and Data
The purpose of this section is to outline the
specification of the models to be estimated
in analysing the effect of pay method and
work supervision in our data. Also data
available used in the study and the
variables estimated are discussed. Two
models are specified. First is the earnings
function for assessing the effect of pay
method, work effort and monitoring on
earnings level. Second, the production
functions for assessing the productivity
effects of monitoring and effort are
specified. The data used in this study is
also described in this section. The
Model Specification
The paper first specifies an earnings
equation in which work effort and
monitoring are among the regressors in the
earnings function. The earnings equation
[1] specified below include control
variables for a range of firm and worker
characteristics that may account for
earnings differences.
Earnings Function
LnW  o   1H   2 Ε   3   4 F   5T   j  v
ijt
ijt
ijt
Where i, j and t are subscripts of
individual, firm and time respectively,
LnW is the log of real earnings of worker i
in firm j during time t. Ei is the measure of
work effort exerted by individual i. We
construct the measure of work effort from
self-reported measurement of the level of
effort supplied by the workers. We will
describe the computation of this effort
variable below. M is the measure of work
monitoring intensity. This is calculated as
the percentage of managers and supervisor
employed in a given firm. Hi is a vector of
observable characteristics of worker i at
time t (these characteristics could be
human capital such as years of schooling,
job tenure, work experience, job training
and characteristics such as union
membership etc), F is a vector of other
control
variables
such
as
firm
characteristics of ownership, location etc.
The variable π represents unobservable
characteristics i.e. omitted variables that
may be correlated with explanatory
jt
ijt
ijt
ijt
[1]
variables in our earnings function and ν is
the error term.
Production Function
For estimating the productivity effect of
monitoring and work effort, a real a gross
real output production function is specified
in equation 2.
The gross real output production function
is specified as follows;
LnQjt =α0 + α1LnKjt + α2LnLjt+ α3JTjt+
α4LnOHjt + α5LnRMjt + α6LnINDjt + α7Ejt +
α6Mjt + α8Cjt + µj + єjt
Whereby j and t are firm and time
subscripts,
LnQ
=
log of real gross output
LnK
=
log of physical capital,
LnL =
log of a number of labour
available in a firm,
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JT
=
training,
variable for firm level job
OH
=
other
human
capital
variables of weighted averages of
schooling, age and tenure
E
=
Variable
for
weighted
Average Firm level Effort
M
=
Variable for Firm Level
Monitoring Intensity
C
=
observable
firm
characteristics such as firm location, sector
ownership,
age export and others.
LnRM =
log of raw materials
LnIND =
log of indirect costs.
µ
=
Firm Fixed effects and;
є
=
error term.
Data and variables
Data
source
is
the
Tanzanian
manufacturing firm surveys over a long
span of time. The data were originally
collected under the Regional Program of
Enterprise Development (RPED) surveys
by World Bank, later continued by the
University of Oxford and later the
University of Dar-es-Salaam. Work effort
variable is measured using self-reported
measure of the effort level exerted at a
worker level. This is derived from a
psychological question asked during the
data collection. In particular, a worker is
asked “How tired are you at the end of the
day? Each worker is expected to choose
one out of the following four answers: 1)
very tired, (2) tired (3) not really tired (4)
not tired at all. Using these four responses
we construct a zero-one dummy variable
of effort that takes the value of one if a
worker is either very tired or tired and zero
otherwise. Specifically, if a worker feels
very tired or tired then is regarded as
“High Effort worker” and Low Effort
worker if he feels not really tired or not
tired at all. We denote this variable as E in
the model [1] above.
The weighted average of work
effort within a firm is derived from firm
level information about individual level of
self-reported effort. To arrive at the firm
level effort variable, the individual level
effort value is weighted by the proportion
of workers in a given occupational
category in a firm. Earnings variable is the
hourly rate of earnings obtained using
information on the number of hours
worked and the total current earnings
received. The hourly earnings include
salary/wage, plus any allowances received.
Other variables are as defined in the
previous papers.
Empirical Results
This section presents empirical results
based on the earnings and production
functions. The paper first estimate the
earnings effect of work effort and
monitoring from earnings function, and
then present the results of the estimates of
productivity effect of these variables using
the production function. The OLS results
are presented first and then consideration
of other options of estimates mentioned
earlier, of the firm effect within
regressions and GMM (for the productivity
effects) is effected. While the earnings
effects of work effort and monitoring are
presented in section 5.1, we present the
productivity effect of these two variables
in section 5.2.
Estimates of earnings effect of pay
methods and monitoring on earnings
In estimating the results, the key question
here is whether there is any evidence of
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earnings effect of high work effort in our
data. This hypothesis is supported by the
results. As the results in column two of
table 1, controlling for human capital,
there are earnings differences accounted
for by the fact that a worker exerts higher
effort. The estimated coefficient on work
effort is 0.269. It is highly significant at 1
percent. This coefficient has positive sign
suggesting that a worker who exerts higher
effort at work increases hourly earnings by
about 27 per cent. Therefore, the results
support the hypothesis that controlling for
the pay method and human capital those
who work hard are paid more. Hence, the
monitoring level will influence the
earnings effect of work effort. We
therefore move to the third column and see
how monitoring in our data influences
hourly earnings. The major hypothesis
tested using the results in column [3] are
whether high monitoring can reduce the
necessity of paying higher earnings. The
results reported in this third column
strongly accept this hypothesis. There is
evidence of a negative correlation between
monitoring intensity and hourly earnings
in our data. The coefficient on monitoring
is 0.554. It has a negative sign suggesting
that highly monitored workers are less
paid.
To account for the effect of such
influence in our estimation we interact the
work effort with monitoring level. The
results of earnings function that estimate
the earnings effect of the interaction
between work effort and monitoring are
presented in column 4 of table 1. The
results indicate a negative effect of the
interaction between work effort and
monitoring on the individual effect of
monitoring on earnings, although the
coefficient estimate is not statistically
significant. Specifically, we find that after
we control for the joint effect of the
interaction between work effort and
monitoring, the coefficient size of
monitoring does not change, while the
statistical significance is reduced by half
although is still significant. We interpret
this as some evidence that the earnings
effect of monitoring work through work
effort.
However, work effort and the level
of monitoring are likely to be influenced
by worker characteristics and firm
characteristics. Our estimates in columns
[1-4] do not control for other factors that
might be picked up by the pay methods
and monitoring coefficients. For this
reason, the paper stepwise adds the control
variables
of
firm
and
worker
characteristics
in
columns
[5-6]
respectively. The Tanzania Manufacturing
Enterprise Survey from which our data is
drawn contains various firm and individual
characteristics such as location, sector,
occupation, and ownership. The estimates
first add job classification, sector
ownership and location in column [5].
Moving to the right hand side of the table,
it is certainly clear that the earnings effect
of work effort and monitoring operate
through these other factors. For instance it
is confirmed that controlling for
occupation and other firm characteristics
in column [5] significantly changes the
coefficient size of all our variables of
interest. The coefficient estimate of work
effort is more than halved, and becomes
weakly significant. Such findings suggest
that the earnings effect of work effort work
through other characteristics such as
occupation. In column [5] the estimates
control for observable characteristics that
have potential influence on or earnings
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effect of work effort and monitoring.
are still observed.
Evidences of earnings effect of monitoring
Table 1: Firm level hourly earnings effect of work effort and monitoring in Tanzanian
manufacturing
OLS1
-0.034
(1.87)
Education Squared 0.006
(4.74)**
Currently Job training 0.016
(0.28)
Past Job Training
0.123
(3.11)**
Age
0.074
(6.81)**
Age squared
-0.001
(6.19)**
Years of Tenure
0.001
(0.50)
High Work Effort
OLS5
FFE-M
-0.028
-0.019
(1.56)
(1.20)
0.005
0.004
(3.58)**
(3.09)**
0.018
-0.099
(0.33)
(1.80)
0.085
0.012
(2.19)*
(0.34)
0.064
0.056
(6.05)**
(5.69)**
-0.001
-0.001
(5.62)**
(5.04)**
0.001
0.004
(0.39)
(1.52)
0.108
0.091
(1.52)
(1.52)
Monitoring Intensity
-0.917
-0.692
(4.47)**
(2.07)*
Effort*Monitoring
0.330
0.303
(1.46)
(1.47)
Administration
-0.081
-0.200
(0.65)
(1.88)
Cleric
-0.345
-0.384
(2.88)**
(3.81)**
Sales
-0.436
-0.481
(3.18)**
(4.10)**
Supervisor
-0.227
-0.418
(1.90)
(4.06)**
Technical
-0.374
-0.427
(2.96)**
(3.90)**
Production
-0.510
-0.591
(4.20)**
(5.69)**
Apprentices
-0.731
-0.615
(4.34)**
(3.95)**
________________________________________________________________________________________________________________
CONTROL VARIABLES
Location
NO
NO
YES
YES
YES
YES
Ownership
NO
NO
NO
YES
YES
YES
Sector
NO
NO
NO
NO
YES
YES
Firm Fixed Effects NO
NO
NO
NO
NO
YES
Observations
1821
1821
1821
1821
1821
1821
R-squared
0.32
0.34
0.35
0.35
0.37
0.26
Years of Education
OLS2
-0.026
(1.44)
0.005
(3.82)**
0.019
(0.34)
0.124
(3.14)**
0.072
(6.64)**
-0.001
(6.08)**
0.001
(0.23)
0.251
(5.61)**
OLS3
-0.032
(1.74)
0.006
(4.19)**
0.001
(0.02)
0.117
(2.99)**
0.069
(6.47)**
-0.001
(5.97)**
0.000
(0.15)
0.267
(6.01)**
-0.564
(5.73)**
OLS4
-0.031
(1.73)
0.006
(4.14)**
0.001
(0.02)
0.117
(2.99)**
0.069
(6.47)**
-0.001
(5.97)**
0.000
(0.14)
0.267
(6.01)**
-0.530
(2.81)**
-0.044
(0.21)
Absolute values of t-statistics are in parentheses. Significance at the 1 per cent, 5 per cent and
10 per sent level is indicated by ***, ** and * respectively.
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Estimates of the Impact of Work Efforts
and Monitoring on Firm Level
Productivity
This section uses firm level measures of
work monitoring and effort to test if there
is any evidence of productivity effect of
these variables. It does so by estimating
the firm level gross output production
functions in which weighted average work
effort and firm level monitoring intensity
of a firm are used as the determinants of
firm level productivity. The results of the
estimates of productivity outcomes of
monitoring and efforts are presented in
table 2 below. To begin with the estimate
checks if constant returns to scale are
accepted. The test results for constant
returns to scale reported in column [1] of
Table 2 indicates that the constant returns
to scale is rejected. Further findings are
that weighted average work effort has
positive effect on firm level productivity
measured by gross output. The estimated
coefficient on average work effort is 0.21.
It has a positive sign implying that a 1 per
cent increase in average work effort of the
firm will increase gross output by 0. 21
percent. However, the results are weakly
significant (t=1.34). The coefficient on
monitoring has a negative sign but its level
of statistical significant is very low
(t=0.89). Therefore, based on these results,
controlling for production inputs and
human capital, there are weak evidences of
positive effect of work effort on
productivity.
The second column, add exports
and firm age variables. The results show
that when export and firm age are
controlled for, there is slightly decline in
the coefficient estimate and the statistical
significance. But there is still evidence that
average work effort of a firm has a
10 |
positive effect on firm level productivity
measured by gross output. In the third and
fourth columns, more firm characteristics
and
time
invariant
unobserved
characteristics
are
controlled
for
respectively. The results show that, when a
broad range of firm characteristics is
controlled for both the coefficient size and
statistical significance of the firm level
effort variable falls substantially. In
particular, it is found that control for the
firm fixed effects eliminates the statistical
significance the effort variable (as t falls to
0.36). Further results are that the
productivity impact of monitoring
increases substantially. The estimated
coefficient is now positively correlated
with productivity and the statistical
significance nearly doubles although still
insignificant (t=1.39). The results suggest
that the productivity effect of work effort
work and monitoring partly work through
unobserved firm fixed effects. Thus the
OLS productivity effect of work effort
observed in the OLS gross output
production function might be picking up
these unobserved firm fixed effects.
The results reported in tables 2 are
based on OLS hence do not control for
endogeneity problem we discussed above.
To account for this problem, the paper
adds estimate the gross output production
functions using GMM. The results are
reported in table 3. Based on the results,
the estimates based on the gross output
per employee in table shows a significant
positive impact of monitoring on gross
output per employee. The coefficient
estimate is 0.34. It suggests that increase
in the monitoring intensity increases the
gross output per employee by about 34 per
cent. The estimated coefficient is stable
even after a range of factors is controlled
DBA Africa Management Review
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April 2014, Vol 4 No 1. Pp. 17-34
for. The results partly reflect the size
effect. The level of monitoring intensity
decreases with firm size. Therefore an in
gross output per employee is likely to be a
increased efficiency of monitoring.
Table 2: OLS estimate results of the impact of work effort on firm level gross real output
OLS1
OLS2
OLS3
FE (Within)
0.016
0.018
0.015
0.009
(1.28)
(1.36)
(1.12)
(0.07)
Log of labour
0.167
0.170
0.185
0.127
(6.61)**
(6.72)**
(7.47)**
(1.44)
Log of Raw Materials
0.649
0.649
0.646
0.703
(25.98)** (26.28)** (26.44)** (17.46)**
Log of indirect Cost
0.198
0.192
0.185
0.092
(6.81)**
(6.59)**
(6.55)**
(1.76)
Weighted Average Effort
0.210
0.207
0.187
0.084
(2.34)
(2.31)
(2.19)
(1.46)
Weighted Average Monitoring
0.061
0.052
0.044
0.024
(1.89)*
(1.75)*
(1.63)*
(1.39)
Weighted Past Training
0.044
0.067
0.062
-0.056
(0.59)
(0.91)
(0.84)
(0.47)
Weighted Current Training -0.123
-0.127
-0.103
0.581
(1.10)
(1.12)
(0.93)
(2.45)*
Average Years of Education -0.004
-0.005
-0.002
0.025
(0.45)
(0.53)
(0.19)
(1.84)
Average Years of Tenure
0.006
0.006
0.007
-0.012
(1.67)
(1.70)
(1.79)
(1.97)
Average Years of Experience 0.005
0.005
0.005
0.004
(1.77)
(1.78)
(1.67)
(0.79)
Exports
0.049
0.043
0.042
(0.93)
(0.81)
(0.38)
Firm Age
-0.001
-0.001
-0.005
(1.18)
(1.02)
(0.11)
______________________________________________________________________________________
CONTROL VARIABLES
Location
NO
NO
YES
YES
Ownership
NO
NO
YES
YES
Sector
NO
NO
YES
YES
Firm Fixed Effect
NO
NO
NO
YES
Observations
297
297
297
297
R-squared
0.99
0.99
0.99
0.84
CRS1test Σßi=1(p-value)
0.034
0.050
0.040
0.64
Log of Capital
Absolute values of t-statistics are in parentheses. Significance at the 1 per cent, 5 per cent and 10 per
sent level is indicated by ***, ** and * respectively. CRS test is an F-test for constant returns to
scale that the coefficients on inputs sums to unity. The weighted average of schooling, tenure, job
training and age are derived from firm level information about individual highest level of education
completed, the occupational specialization, work tenure whether an individual attended job training
and experience. Each value is weighted by the proportion of workers in a given occupational category
in each firm to obtain a weighted average for each firm. The occupational categories included are
managers, administration, sales, clerical supervisor, technicians, production workers and support staff.
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Table 3: GMM estimate results of the impact of work effort on firm level gross real output
per employee
Log Capital Per Employee
Log of Raw Materials Per Employee
Log of Indirect Cost
Weighted Average Past Training
Weighted Average Current Training
Average Years of Education
Average Years of Tenure
Average Years of Experience
Weighted Average Work Effort
Firm Level Monitoring Intensity
GMM1
0.132
(1.58)
0.699
(7.91)**
0.066
(0.90)
0.021
(0.13)
-0.130
(0.57)
0.002
(0.19)
0.003
(0.55)
0.009
(2.12)*
0.144
(0.67)
-0.344
(1.79)
Exports
Firm Age
Observations
Robust z-statistics in parentheses
(1.56)
172
GMM2
0.158
(1.70)
0.662
(6.37)**
0.072
(0.95)
0.063
(0.41)
-0.148
(0.68)
0.001
(0.07)
0.003
(0.45)
0.009
(1.95)
0.121
(0.52)
-0.391
(1.90)
0.009
(0.07)
-0.005
(1.48)
172
GMM3
0.143
(1.53)
0.678
(8.15)**
0.078
(0.92)
0.075
(0.49)
-0.160
(0.72)
0.004
(0.33)
0.003
(0.64)
0.008
(2.16)*
0.101
(0.43)
-0.359
(1.57)
-0.034
(0.35)
-0.004
(0.79)
172
______________________________________________________________________________________
CONTROL VARIABLES
Location
NO
NO
YES
Ownership
NO
NO
YES
Sector
NO
NO
YES
Firm Fixed Effect
NO
NO
NO
2000
0.000
0.000
0.000
Observations
114
114
114
CRS1test Σßi=1(p-value)
0.27
0.25
0.42
GMM4
0.118
(1.89)
0.669
(9.56)**
0.103
(1.64)
0.115
(0.71)
-0.251
(0.99)
-0.003
(0.18)
0.003
(0.50)
0.007
(2.00)*
-0.056
(0.22)
-0.370
(2.25)*
-0.114
(1.31)
-0.002
129
YES
YES
YES
NO
0.000
114
0.38
Absolute values of t-statistics are in parentheses. Significance at the 1 per cent, 5 per cent and 10 per
sent level is indicated by ***, ** and * respectively. CRS test is an F-test for constant returns to
scale that the coefficients on inputs sums to unity. The weighted average of schooling, tenure, job
training, work effort and age are derived from firm level information about individual highest level of
education completed, the occupational specialization, work tenure whether an individual attended job
training and experience. Each value is weighted by the proportion of workers in a given occupational
category in each firm to obtain a weighted average for each firm. The occupational categories
included are managers, administration, sales, clerical supervisor, technicians, production workers and
support staff.
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Conclusion
This paper set out to assess the impact of
work effort and work supervision on
earnings and productivity in Tanzanian
manufacturing over the period 1990-2012.
The paper makes use of self- reported
measures of effort to test if there are any
gains from working hard. The OLS based
finding is that high effort workers are
compensated for exerting more effort at
work. In particular, the results show that a
worker who exerts higher effort at work
increases hourly earnings by about 27 per
cent. The paper argues that such results
support the hypothesis that controlling for
the pay method and human capital those
who work hard are paid more. In
estimating the earnings effect of
monitoring, the results are evident that
controlling for human capital monitoring
intensity reduces earnings by 0.554.
The argument partly reflect size effect but
most interestingly are consistent with the
incentive theories which suggest that
loosely monitored workers receive more
real earnings than highly monitored ones.
Connected to this finding the paper
confirms earnings effect of monitoring
work through work effort. However, the
results based on firm fixed effects
regressions have revealed that to a large
extent the OLS based effects are
influenced by the unobserved time
invariant firm attributes. We argued that
this reflected that the effects picked up by
OLS are plagued with these effects.
Estimates of productivity affect via GMM
shows a significant positive impact of
monitoring on gross output per employee.
The coefficient estimate suggests that
increase in the monitoring intensity
increased the gross output per employee
by about 34 per cent. The estimated
coefficient is stable even after a range of
factors are controlled for. The level of
monitoring intensity decreases with firm
size. Therefore an increase in gross output
per employee is likely to be a reflection of
efficiency in production from effective
monitoring. The paper concludes that
labour market reforms introduced in
Tanzania on increased autonomy and
flexibility of firm level work supervision
and pay have positive outcomes for both
employers and employees. This is so
because prior to the centrally controlled
pay system significantly reduced the
ability of manufacturing firms to design
establishment level motivation such as
performance based and productivity
enhancing schemes. Hence any increase in
work effort or monitoring cold not affects
pay or incentives to work more.
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The Mediation Effect of Customer Perception on the Relationship Between
Quality Drivers and Customer Satisfaction in Large Maize Flour Mills in
Nairobi, Kenya
Ngungu kabare, PhD.1, Francis N Kibera, PhD.2 and Justus M Munyoki, PhD.3
This study sought to establish the influence of quality drivers on the satisfaction of direct
business customers within large Maize Flour Mills in Nairobi and assess the mediation effect of
customer perception on this relationship. The quality drivers studied were product quality,
service quality, complaints handling, ease of doing business and product price. Customer
perception constructs studied were customer’s desire for features critical to quality, brand
imagery, firm imagery and reference to competitive substitutes. Primary data were collected in
February 2013 by use of questionnaires from 81 direct Business Customer firms randomly
selected from 13 Maize Flour Mills in the study area grinding at least 15 MT of maize per day.
Results showed that the influence of quality drivers on customer satisfaction is both direct and
partially mediated by customer perception, both influences being positive and statistically
significant (p< 0.01). Quality of service significantly influenced customer satisfaction (β= 0.441,
p< 0.01) and most of the other quality drivers and intention to recommend. Brand imagery
significantly influenced satisfaction (β= 0.531, p< 0.01) followed by desire for features critical to
quality (β= 0.259, p< 0.01). These results have implications for marketing theory. The finding
that customer perception partially mediated the process of customer satisfaction agrees with the
consumer attitude theories which postulate that attitude and subjective norms in conjunction
with cognitive and emotional considerations influence intentions which in turn give impetus for
action. The study contributes to the evolution and adaptation of customer satisfaction models by
adding customer perception as mediator variables. Further, the results have implications useful
at national policy level. Kenya’s strategy for revitalizing agriculture and vision 2030 both aspire
to increase the country’s regional and global trade through improved efficiency and
competitiveness at firm level, agro-processing and the marketing system including the wholesale
and retail sectors. The volume of trade within the East African Community is expected to
increase as member states reduce trade barriers. This will open new trade opportunities but
could increase competition. Training local firms on the issues of quality drivers and customer
perception can help to improve their regional and global competitiveness. For managerial
practice, the results demonstrate that frequent feedback on customer perception is necessary
and that improvements in the quality of service go a long way in improving customer perception
concerning other quality drivers and satisfaction. It is concluded that customer satisfaction
enhancement programs and evaluation models need to integrate primary drivers of quality with
key drivers of customer perception. The study was limited in a number of ways. Due to time,
cost and other constraints a cross-sectional research design was used and focused on firms in
Nairobi. Data were collected from respondents once to get their views and perceptions
concerning a limited number of variables and constructs. However, perceptions vary over time
and across markets or regions as influenced by changes in consumer preferences or economic
changes that influence purchase and consumption patterns. Opportunities therefore exist for
longitudinal and wider studies in the same area of research.
Key Words:Quality drivers, customer perception, customer satisfaction (CS), Nairobi, Kenya
1
Lecturer in Marketing, School of Business, Kenyatta University
Professor in Marketing, School of Business, University of Nairobi
3
Senior Lecturer in Marketing, School of Business, University of Nairobi
2
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Introduction
Customer satisfaction refers to the overall
evaluation of how pleasurable one’s
interaction with an organization is
including the buying and use experience,
relative to what is anticipated (Anderson et
al, 1997; Kotler and Keller, 2006; Ronald,
2010). At higher levels satisfaction can
lead to loyalty which is a deeply held
commitment to repurchase or re-patronize
a preferred product consistently over time
even with stiff competition (Oliver, 1999).
Satisfaction is a moving target that is often
shaped by various attributes upon which
customers form perception. The attributes
could be related to the product such as
quality, value-price relationship, benefits
and features, design, reliability and
consistency and product/ service range.
They can be related to service such as
delivery, complaint handling and problem
resolution. Other attributes are related to
the buying process such as convenience,
courtesy,
communication,
staff
competence
and
firm
reputation
(Crawford, 2007; Dutka, 1993).
Kenya’s manufacturing sector contributes
10% of the country’s GDP. Food
processing contributes about two thirds of
the manufacturing GDP and about a fifth
of the country’s export earnings (Osano et
al, 2008). Trade in maize flour plays a key
role in this sector, especially because
maize meal is the staple food in the
country (Wangia et al, 2002). On average
households in Nairobi spend 27% of their
food budget on staples with maize meal
taking the lead (Kamau et al, 2011).
However, other carbohydrate sources such
as wheat, rise, potatoes, sorghum and
cassava are gaining popularity (Kamau et
al, 2011; Muyanga et al, 2005). New
challenges have come by way of
legislation such as the VAT Act 2013 that
has moved cereal milling byproducts and
several other commodities from zero rating
to the standard VAT rating of 16%. This
will increase the cost of these supplies and
is likely to affect demand thereby
increasing competition (Deloitte, 2011). In
view of this, research on the dynamics of
quality drivers of the maize flour would be
useful in policy issues related to the
country’s strategy of promoting agroprocessing and food security. In a study on
Kenyan urban consumption of maize meal,
Mukumbu and Jayne (1994) found that the
key quality drivers on purchase decisions
were price and convenience followed by
taste and nutritive value. The current study
sought to establish the effects of customer
perception on the relationship between
quality drivers and customer satisfaction
within large maize flour mills in Nairobi.
Literature Review
Perception relates to how individuals see
the world around them. It is a process by
which people select, organise, and
interpret stimuli into meaningful and
unified pictures or images of situations
(Schiffman and Kanuk, 2007). Through a
positive image a consumer perceives a
brand or a firm to be stable, dependable
and suitable for satisfying the needs of the
consumer. Such an image can strengthen a
firm’s credibility, lead to more sales and
help to fight competition. Consequently
many companies strive to develop, project
and maintain positive images of their
brands and the firm. Both the brand and
corporate image reinforce one another in
that if the brand image is positive, it
reflects favourably on the corporate image
and vice versa (Haedrich, 1993).
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Satisfaction shifts as customer perception
of quality changes, evolves, and grows to
encompass more expectations (French et
al, 2005). Issues can arise concerning
established brands, product features,
processes or procedures including
complaints. Competitors can offer better
alternatives or changes in other fields such
as technology or culture can shift customer
perception (Armitage and Conner, 2001;
Ferrell and Hartline, 2005). Customers
develop expectations depending on how
they perceive attributes and base decisions
on perceptions rather than on the basis of
objective reality (Schiffman and Kanuk
2007). The relative importance played by
respective quality drivers and other enabler
variables in fostering customer satisfaction
varies over time as marketing conditions
and other aspects of life change. This
dynamism needs to be reflected in
satisfaction
assessment
tools
and
associated frameworks if they are to
remain robust in capturing the voice of the
customer. The scope and nature of drivers
used in satisfaction models therefore needs
to be reviewed from time to time so as to
keep abreast of changes in consumer
behaviour and related fields (Johnson et al,
2001).
likely share their unfortunate experience
with up to ten people (Ronald, 2010).
Loyal customers tend to buy more, are less
price sensitive, speak well of the firm and
are harder for competitors to win
(Schiffman and Kanuk, 2007).
Satisfaction is closely linked to future
purchase behaviour and willingness to
recommend and is thus a strong predictor
of loyalty and customer retention (Ferrell
and Hartline, 2005; Turkyilmaz and
Ozkan, 2007). Satisfaction therefore helps
to reduce customer turnover and lower
transaction costs related to contract
negotiations, order processing, and
bargaining (Fornell, 1992). Satisfied
customers are most likely to share their
experiences with about five or six people
while a dissatisfied customer is more
19 |
Methodology
This study used a descriptive crosssectional design to check for significant
associations between the study variables
and make generalisations concerning the
target population. The population of study
comprised all direct Business Customers in
the sifted maize flour sector within the
administrative boundaries of Nairobi City.
These included distributors, wholesalers,
supermarkets, and other institutions that
bought maize flour directly from the maize
mills. With the help of the Maize Flour
Many firms in Kenya are increasingly
focussing on enhancement of customer
satisfaction due to increasing competition.
An increasing number are registering with
industry and global quality standards such
as ISO 9001:2008 Quality Management
System and, for food related operations,
the FAO/WHO Codex Alimentarius
HACCP Food Safety System. Assessment
of CS is a key feature of these standards
(Hashim, 2007; Kimbrell, 2000). This way
firms hope to compete more effectively
locally and against imports as well as in
the export markets. Anyango and Wanjau
(2011) observed improved company
performance in Nairobi with respect to
perceived quality, competitive advantage,
corporate image and market share
associated with adoption of ISO 9001
certification. Furthermore the certification
impacted positively on financial resource
management (p=0.001) and customer
satisfaction (p=0.03).
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Mills’ marketing and sales managers, a
random sample of 10 direct Business
Customers was selected from the customer
data bases of each of thirteen maize mills
in the study area grinding at least 15 MT
of maize per day. Primary data were
collected by use of a semi- structured
questionnaire.
Respondents
were
purchasing managers because they interact
with the mills and are responsible for the
flour sourcing function.
A total of 81 questionnaires were received
back out of the 130 questionnaires sent
out. Rating was done on a ten point scale
(ranging from 1 to 10) to increase the level
of scale details. Pearse (2011) and Preston
and Colman (2000) report that rating
scales with less than seven points tend to
have inadequate granularity. They
obtained the most reliable scores from
scales with seven to ten response
categories. Likewise Reichheld (2003)
observes that scales with more points offer
wider options especially because cstomers
tend to refrain from top scores.
Data were cleaned, edited and coded
followed by analysis and reporting. The
statistical programme Software Package
for the Social Sciences (SPSS) version
12.0 was used to analyse the data using
both descriptive and inferential statistics.
Normality of distribution was checked
through skewness and kurtosis tests.
Correlations were used to examine
variable relationships. Simple and multiple
linear regressions were used to test for the
study hypotheses. The coefficient of
determination (R2) indicated the amount of
variation explained by the model.
Mediation was tested in accordance with
the four steps regression procedure
described by Baron and Kenny (1986) and
Fairchild and MacKinnon (2009). Figure 1
shows the mediation path diagrams next to
the conceptual model in which X is the
independent variable (quality drivers), M
the
mediating
variable
(customer
perception) and Y the dependent variable
(customer satisfaction).
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April 2014, Vol 4 No 1. Pp. 17-34
Figure 1:
Mediation model path diagram
Conceptual model
H2
H3
X
Y
M
H4
H1
Mediation paths
M
a
b
cl
X
Y
X = Independent variable; Y= Dependent variable;
M= Mediating variable; a, b, and c = beta coefficients
Reference for mediation paths: Fairchild and MacKinnon (2009), A General Model for
Testing Mediation and Moderation Effects. Prevention Science, 10:87-99.
In step one of mediation testing, the
dependent variable Y was regressed on X
dependent variable Y was regressed on the
while controlling the effect of M on Y, by
independent variable X and the
performing a hierarchical regression
standardized regression coefficient (beta
analysis that placed M and X in successive
for path c) examined to determine the size
independent variable boxes in the SPSS
and direction of the relationship and
program. If both coefficients for paths a,
checked for significance. This beta for
and b are significant, then M mediates the
path c was significantly different from
relationship between X and Y and cl is
zero and therefore in step two, the
assessed to check the link strength
mediator M was regressed on the
(Fairchild and MacKinnon, 2009; Bennett,
independent variable X to estimate the
2000; Shaver, 2005; Sharma et al, 1981).
standardized beta regression coefficient for
Findings
path a, which was examined to determine
Correlations
the size and direction of the relationship
Service quality emerged as the feature
and was significantly different from zero.
with the most profound positive influence
In step three, Y was regressed on M to
on other quality drivers and on satisfaction
determine the beta coefficient for path b,
and intention to recommend. Service
which was significant. In step four, the
quality had influence on product quality (r
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April 2014, Vol 4 No 1. Pp. 17-34
= 0.46, p< 0.05) and moderate correlations
good service with superior quality of
with complaints handling and ease of
product and associated processes. Figure 2
doing business as well. It had a positive
highlights the correlations between the
non-significant correlation with product
level of service performance and the other
price. The implication of this is that
attributes.
Business customers are likely to associate
Figure 2: Correlation Coefficients- service quality and other parameters
Correlation with service level
0.5
0.455
0.46
0.427
0.45
0.4
0.35
0.3
0.294
0.269
0.25
0.2
`
0.148
0.15
0.1
0.05
**
**
**
**
*
ns
0
**
p< 0.01, * p< 0.05, ns: not significant, N= 81, Source: Primary Data
The highly statistically significant
correlation between service performance
and intention to recommend (r= 0.46, p<
0.01) implies that the higher the level of
service performance experienced by a
business customer, the higher the
likelihood that the customer will
recommend the brand or firm to a
colleague or friend. The highly statistically
significant correlation coefficient relating
to ease of doing business (r= 0.427, p<
0.01) suggests that a high level of service
performance experienced by a business
customer reassures the client that any
problem arising from the purchase will be
attended to expeditiously.
Correlation coefficients between customer
perception and quality drivers (Figure 3)
were positive and statistically significant
for all quality drivers except for price
whose coefficient fell slightly outside the
threshold of p< 0.05. The correlation
coefficients were also positive and
statistically significant for both overall
satisfaction and intention to recommend.
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April 2014, Vol 4 No 1. Pp. 17-34
Correlation with Customer Perception
Figure 3:
**
0.5
0.45
0.4
0.35
0.3
0.25
0.2
0.15
0.1
0.05
0
Correlations Between Quality Drivers and Customer Perception
0.384
0.374
0.281
0.236
**
*
0.238
*
0.266
0.208
`
**
*
*
ns
p< 0.01, * p< 0.05. Source: Primary Data
The correlation coefficients were highly
statistically significant (p< 0.01) for
service quality and overall satisfaction.
The managerial implication is that any
improvements on attribute performance
(quality drivers) translates to improved
positive customer perception about the
Flour Brand or Mill and that good quality
service has a particularly profound effect
on enhancing customer perception.
The results further indicated that customer
perceptions had a positive and statistically
significant
influence
on
customer
satisfaction (r= 0.374, p< 0.05) and
intention to recommend (r= 0.236, p<
0.05). This implies that sellers stand to
gain more on customer satisfaction by
focussing on both product attributes and
customer perception as opposed to
working on the attributes alone.
Mediation (Intervening) Effects of
Customer Perception
The study had hypothesized that customer
perception mediates the relationship
between quality drivers and customer
satisfaction as shown in Figure 4.
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Figure 4: Mediation Testing Steps
Step 2
Customer
Perception
Step 3
(H3)
MV
(H2)
Quality
Drivers
Step 1
IV
Customer
Satisfaction
(H1)
DV
Step 4 (H4) is done if the
first 3 steps are statistically
significant and predictors
are entered at successive
blocks in SPSS program to
check for change in R2. In
this fourth step DV is
regressed on IV while
controlling for the effect of
MV
IV= independent variable, MV= mediating variable, DV= dependent variable
In step 1 (hypothesis H1) customer
satisfaction was regressed on quality
drivers and the relationship was positive
and statistically significant (β = 0.391, B =
0.406, p< 0.01) and the model accounted
for 15.3% of the variation. This supported
the first condition for testing the effect of
mediation. This hypothesis tested the
direct relationship between quality drivers
and customer satisfaction and was stated
as shown below. Aggregate mean scores of
CS were regressed against those of the
quality drivers. The output is shown in
Table 1 a to c.
H1: There is a statistically significant relationship between quality drivers and customer
satisfaction.
Table 1: Customer Satisfaction regressed on aggregate mean scores of Quality Drivers
a) Model Summary
Model
R
R
Adjusted R2
Std. Error of the Estimate
1
.391(a)
.153
.142
1.08776
a Predictors: (Constant), Quality Drivers
2
b) ANOVA(b)
Mode
Mean
l
Sum of Squares Df Square
F
Sig.
1
Regression
16.827
1
16.827
14.222 .000(a)
Residual
93.475
79 1.183
Total
110.302
80
a Predictors: (Constant), Quality Drivers, b Dependent Variable: C. Satisfaction
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Model
c) Coefficients(a)
Unstandardized
Standardized
Coefficients
Coefficients
T
Sig.
B
Std. Error Beta
1
Constant
4.831 .836
5.778 .000
QualityDrivers .406
.108
.391
3.771 .000
As shown in Table 1 there was a
drivers would raise the level of customer
statistically significant linear relationship
satisfaction by a factor of about 0.4 of a
between quality drivers and customer
standard deviation.
satisfaction (β = 0.391, B = 0.406, p<
Step 2 (hypothesis H2) involved assessing
0.05) and hence the study failed to reject
whether quality drivers predicted customer
hypothesis H1. The influence of quality
perception and whether the relationship
drivers on customer satisfaction was
was
statistically
significant.
The
moderate as the model accounted for
hypothesis was stated as follows:
15.3% variability (R2 = 0.153). The
resulting simple linear regression model
H2:
There is a statistically significant
that can be used to predict the level of
relationship
between
quality
satisfaction for a one standard deviation
drivers and customer Perception.
improvement in the performance level of
quality drivers can be expressed as:
In this second step aggregate mean scores
of customer perception were regressed on
CS = 4.831 + 0.391QD + e
those of quality drivers and the
............................................................... (1)
relationship was positive and statistically
significant (β = 0.418, B = 0.590, p<0.01)
Where CS = level of customer satisfaction
and the model explained 17.4% of the
and QD = level of quality drivers
variation, supporting the second condition
performance. The standardized beta
for mediation testing as presented in Table
coefficient 0.391 implies that, other factors
2 a to c. The study failed to reject
constant, a one standard deviation
hypothesis H2.
improvement in the performance of quality
Table 2:
Customer Perception regressed on Quality Drivers
a) Model Summary
Model
Adjusted
R Std. Error
R
R Square Square
Estimate
1
.418(a)
.174
.164
1.45732
a Predictors: (Constant), Quality Drivers
of
the
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b) ANOVA(b)
Sum
of
Mean
Model
Squares
Df Square
F
Sig.
1
Regression 35.426
1 35.426
16.681
.000(a)
Residual
167.779
79 2.124
Total
203.205
80
a Predictors: (Constant), Quality Drivers, b Dependent Variable: Customer Perception
Model
c) Coefficients(a)
Unstandardized
Standardized
Coefficients
Coefficients
T
Sig.
B
Std. Error Beta
1
Constant
2.146
1.120
1.916
QualityDrivers .590
.144
.418
4.084
a Dependent Variable: Customer Perception. Source: Primary Data
The resultant regression model that
predicts the level of customer perception
for a given level of quality drivers’
performance can be expressed as:
CP = 2.146 + 0.418QD + e,
................................................................(2)
where CP = customer perception and QD
= quality drivers.
The model shows that for one standard
deviation improvement in the performance
of quality drivers, customer perception
would improve by 0.418 of a standard
deviation.
Step 3 (hypothesis H3) involved checking
whether customer perception predicted
customer satisfaction and whether the
.059
.000
relationship was statistically significant.
The hypothesis was stated as follows:
H3:
There is a statistically significant
relationship between customer
perception
and
customer
satisfaction (CS)
In this third step of mediation testing,
aggregate mean scores of customer
satisfaction were regressed against those of
customer perception and the relationship
was positive and statistically significant (β
= 0.349, B = 0.257, p<0.01) and the model
explained 12.2% of the variation,
supporting the third condition for
mediation testing as shown in Table 3 a to
c. The study failed to reject hypothesis H3.
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Table 3: Customer Perception Predicting CS
a) Model Summary
Model R
R
Adjusted R2
Std. Error of the Estimate
1
.349(a) .122
.111
1.10713
a Predictors: (Constant), Customer Perception
2
b) ANOVA(b)
Model
Sum of Squares Df
Mean Square F
Sig.
1
Regression 13.469
1
13.469
10.989 .001(a)
Residual
96.833
79
1.226
Total
110.302
80
a Predictors: (Constant), Customer Perception, b Dependent Variable: Customer
Satisfaction
Mode
l
1
Constant
Customer Perception
c) Coefficients(a)
Unstandardized
Standardized
Coefficients
Coefficients
B
Std. Error Beta
6.233
.533
.257
.078
.349
T
Sig.
11.701
3.315
.000
.001
a Dependent Variable: Customer Satisfaction. Source: Primary Data
The resulting regression model that predicts the level of customer satisfaction (CS) for a
given level of customer perception (CP) is:
CS = 6.233 + 0.349CP + e ............................................................................................(3)
The model indicates that for a unit
standard deviation improvement in the
level of customer perception about a brand
or firm, customer satisfaction level would
improve by a factor of about 0.349 of a
standard deviation.
The success of the first three conditions for
mediation testing lead to the conduct of the
final test in line with hypothesis H4 which
was stated as follows:
H4:
Customer perception has a
mediating effect on the relationship
between quality drivers and
customer satisfaction.
Customer satisfaction was regressed on
quality drivers while controlling for the
effect of customer perception to check for
the significance of the resultant R2 change
and coefficients for quality drivers.
Statistical insignificance would imply full
mediation otherwise it would be partial
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(Baron and Kenny, 1986; Shaver, 2005).
Customer perception was loaded into
block two in SPSS program to control for
its effect. Both the R2 change (R2= 0.073)
and the coefficient (β= 0.296) were
statistically significant (p<0.05) indicating
partial mediation. Results are shown in
Table 4.
Table 4: Satisfaction regressed on Quality Drivers while controlling for Customer
Perception
Model Summary
Std. Error
Model R
R
of Estimate Change Statistics
R2
F
df
Change Change 1
1
.349-a .122 .111 1.10713
.122
10.989 1
2
.441-b .195 .174 1.06720
.073
7.022
1
a Predictors: (Constant), Customer Perception, b Predictors:
Perception, Quality Drivers
2
Adj
R2
Model
a) ANOVA(c)
Sum
of
Mean
Model
Squares
Df
Square
F
1
Regression
13.469
1
13.469
10.989
Residual
96.833
79
1.226
Total
110.302
80
2
Regression
21.467
2
10.734
9.424
Residual
88.835
78
1.139
Total
110.302
80
a Predictors: (Constant), Customer Perception, b Predictors:
Perception, Quality Drivers, c Dependent Variable: Satisfaction
1
2
df Sig. F
2 Change
79 .001
78 .010
(Constant), Customer
Sig.
.001(a)
.000(b)
(Constant), Customer
b) Coefficients(a)
Unstandardized
Standardized
Coefficients
Coefficients T
B
Std. Error Beta
6.233
.533
11.701
Constant
Customer Perception .257
.078
.349
Constant
4.474
.839
Customer Perception .166
.082
.226
Quality Drivers
.308
.116
.296
a Dependent Variable: Satisfaction. Source: Primary Data
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Sig.
.000
3.315
.001
5.332
2.018
2.650
.000
.047
.010
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The study failed to reject H4. The resulting
regression model from the fourth step of
mediation testing, expressed on the beta
coefficients can be expressed as:
CS = 6.233 + 0.296QD + 0.226CP + e
............................................................(4)
Figure 5 shows a diagrammatic summary of the results for mediation testing.
Figure 5: Summary Results of Mediation Effect Testing
Mediating Variable
Customer
Perception
H2, β =0.418**
H3, β =0.349**
R2 =0.174
Step 3
Step 2
IV
Quality
Drivers
Step 1
H1: β = 0.391**, R2 =0.153 direct effect
R2 =0.122
Customer
Satisfaction
DV
(Step 4= H4: β = 0.296* for QD, β =0.226* for CP, R2 change=0.073* mediated effect)
where QD = quality drivers, CP = customer perception
P< 0.01, * p < 0.05, β = beta coefficient, IV = Independent Variable, DV = Dependent
Variable. Source: Primary Data
of customer perception (β= 0.226) in step
The four regression equations relating to
4 implies that, other factors constant,
the tests for mediation effect, expressed in
business clients probably place slightly
beta coefficients are:
more emphasis on quality drivers.
However, sellers need to foster
Step 1:
CS = 4.831 + 0.391QD + e
improvements in both quality drivers and
Step 2:
CP = 2.146 + 0.418QD + e
customer perception as both influence
Step 3:
CS = 6.233 + 0.349CP + e
customer satisfaction.
Step 4:
CS = 6.233 + 0.296QD +
0.226CP + e
Discussion
This study sought to establish the
where CS= customer satisfaction, QD=
influence of quality drivers on the
quality drivers, CP= customer perception
satisfaction of business customers within
large Maize Flour Mills in Nairobi and
The bigger beta coefficient relating to
assess the mediation effect of customer
quality drivers (β= 0.296) compared to that
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**
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perception on this relationship. Quality of
service emerged as a key precondition for
customer satisfaction. This agrees with the
indications of satisfaction index models
and the Service Profit Chain that service
quality is a key motivator in customer
satisfaction (Heskett et al, 1994; Anderson
et al, 1997; Fornell et al, 1996). It also
agrees with the findings of Silvestro and
Cross (2000) who observed a strong
positive correlation between service
quality and customer satisfaction at the
95% level and concluded that a key aim of
management should be to improve
perceptions of service quality for their
customers. Ramaseshan and Vinden
(2009) reported that quality drivers
accounted for up to 54% of satisfaction
with retail stores. Simmerman (1995)
reported that 70% of customer desertions
were due to poor service compare to 20%
combined for price and product quality.
Adams (2006) found that employee
attitude was often a leading cause of
customer defections (68%) followed by
other dissatisfactions (14%) and desertions
(9%).
imagery
influences
cognitive,
physiological, and behavioural responses.
They further observed that imagery has a
positive influence on incidental learning
and given that much of consumer learning
is incidental, then it is likely that imagery
influences likelihood and timing of
purchasing.
The results showed that the influence of
quality drivers on customer satisfaction is
positive and statistically significant (p<
0.05) and is partially mediated by
customer perception. Flour Mills therefore
need to routinely survey on customer
attitudes. Brand image had positive and
statistically significant effect on customer
satisfaction (β= 0.513, p< 0.05). This
agrees with the attitude theories which
postulate that attitude and subjective
norms in conjunction with cognitive and
emotional
considerations
influence
intentions which in turn give impetus for
action (Bagozzi, 1992; Batra et al, 1996).
Macinnis and Price (1987) reported that
30 |
It is concluded that the influence of quality
drivers on customer satisfaction is both
direct and partially mediated through
customer perception. This implies that
Flour Mills need to actively pay attention
to the direct quality drivers such as product
quality and price as well as customers
perception variables such as user imagery
of the brand and firm. Improvements in the
quality of service go a long way in
enhancing customer satisfaction. The study
calls for the incorporation of dimensions
of customer perception in customer
satisfaction evaluation index models and
satisfaction surveys.
Conclusion
The objectives of this study were to
evaluate the influence of quality drivers on
the satisfaction of business customers in
the sifted maize flour sub sector in Nairobi
and to assess the mediation effect of
customer perception on this relationship.
Both the direct and the mediated
relationships were positive and statistically
significant (p< 0.05). The findings
therefore supported the two main
hypotheses of the study. In addition,
quality of service had stronger positive
correlations with customer satisfaction and
intention to recommend compared to other
quality drivers. Customer imagery of the
brand and the firm emerged as a key driver
of customer perception.
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Implications
The findings from this study have
implications for the theory of marketing,
policy and managerial practice. The
finding that customer perception partially
mediated the process of customer
satisfaction agrees with the consumer
attitude theories which postulate that
attitude and subjective norms in
conjunction with cognitive and emotional
considerations influence intentions which
in turn give impetus for action. The study
contributes to the evolution and adaptation
of customer satisfaction models by adding
customer perception as a mediator
variable. Most models of customer
satisfaction focus mainly on primary
quality drivers (Johnson et al, 2001).
The results have policy implications that
could
be
harnessed
to
promote
competitiveness. The volume of trade
within the East African Community is
expected to increase as member states
reduce trade barriers (KPMG, 2013). This
will open new trade opportunities but
could increase competition. Kenya’s
strategy for revitalizing agriculture and
vision 2030 both aspire to increase the
country’s regional and global trade
through
improved
efficiency
and
competitiveness at firm level, including
agro-processing and across the marketing
system (Ministry of Agriculture, 2004;
Ministry of Planning, 2007). Training,
research and development are key
ingredients of the strategy and this can
include the need to consider quality drivers
and perception in satisfaction variables.
For managerial practice, the results
demonstrate that business customers
within the Maize Flour subsector are more
willing to do business with Flour Mills
that offer superior service quality, are
efficient in resolving complaints and have
positive brand imagery. Improvements on
the quality of service influence customer
satisfaction both directly and through
positive influences on other drivers of
quality. They further suggest that
satisfaction surveys need to collect
feedback on both the quality drivers and
customer perception including ratings
relative to competitors.
Areas for Further Research
As this was a cross-sectional research that
studied customer satisfaction dynamics in
a sector at a particular point in time, other
research could use longitudinal research
design to track changes over time. Rust et
al., (1999) reported that besides mere
quality limits, perceived variability and/ or
consistency in quality over time is
important to capture as well. Such deeper
insights on dynamics of quality drivers
would help marketers and brand managers
in a competitive market such as the local
maize flour subsector to refine their
market offerings and customer satisfaction
programmes for a better competitive
advantage.
Secondly more variables can be studied
and wider geographical coverage. Extra
variables can include the increasing
availability of alternative carbohydrate
sources in Kenya, the growing use of
fortified flour blends and increasing
dietary consciousness ((Mukumbu and
Jayne, 1994; Muyanga et al., 2005). These
trends are likely to lead to changes in the
consumption of maize flour which would
limit generalization of study findings for
forecasting and estimation. Furthermore
one can disaggregate quality attributes
along the Kano model’s ‘critical to
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quality’ dimensions with a view to
identifying the key performance factors
that often form the common basis for
competition (Anderson and Mittal, 2000;
Oliver 1999).
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April 2014, Vol 4 No 1. Pp. 62-84
Turn of The Month Effect: Evidence From The Nairobi Securities
Exchange
Ogilo Fredrick1
This study sought to investigate if Turn of the Month effect exists at the Nairobi
Securities Exchange. In carrying out the study, the days of the month were divided into
two, the Turn of the Month (TOM) which included the last trading day of the month
and the first three trading days of the following month. The other trading days of the
month were categorized as Rest of the Month (ROM). The 20 share index was used as
the sampling frame and the daily indices were used to compute the daily returns.
Secondary data was obtained from the Nairobi Securities Exchange data base. The
TOM coefficient was not significant to confirm TOM effect. It is therefore concluded
that there is no TOM effect at the Nairobi Securities Exchange. To practice, the study
will give vital information to brokerage firms as they will advise their clients on the best
time of the month to sell or buy securities. The findings of the study will also be of
benefit to policy formulation aimed at improving capital market efficiency.
Keywords: Turn of the Month, Rest of the Month, Twenty Share Index, Market Efficiency
and Nairobi Securities Exchange.
1
Lecturer, School of Business, Mombasa Campus, University of Nairobi, Kenya
[email protected]
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Introduction
It was Fama (1970) who defined an
efficient market as a market which adjusts
rapidly to new information. He categorized
the capital market in to three different
forms of efficiencies namely, the weak
form, the semi-strong form and the strong
form efficiencies. Rubinstein (1975)
extended the definition of market
efficiency. He said that the market is
efficient with regard to an informational
event if the information causes no
portfolio changes. Kennedy (1996) added
that, markets in the developing and less
developed countries are not efficient in
semi-strong or strong form but in the weak
form.
There has been criticism to the efficient
Market Hypothesis that has generated
concern. Grossman and Stiglitz (1980)
argue that perfectly information efficient
markets are impossible, for if markets are
perfectly efficient, the return to gathering
information is nil, in which case there
would be little reason to trade and markets
would eventually collapse. Campbell, Lo
and MacKinlay (1997) share the same
opinion. They are in favour of the notion
of relative efficiency, that is, the efficiency
of one market measured against another.
Lo and MacKinlay (1999) argue that
Efficient Market Hypothesis, by itself, is
not a well-defined and empirically
refutable hypothesis. To make it
operational, one must specify additional
structure, including investors’ preferences,
information structure and business
conditions. But then a test of the Efficient
Markets Hypothesis becomes a test of
several auxiliary hypotheses as well, and a
rejection of such a joint hypothesis tells us
little about which aspect of the joint
hypothesis is inconsistent with the data.
The Bad Model problem advanced by
Fama (1991) suggests that Efficiency per
se is not testable. That it must be tested
jointly with some model of equilibrium.
Financial anomalies have been advanced
in the EMH theory. A financial anomaly
refers to unexplained results that deviate
from those expected under the financial
theory. Financial anomalies that have been
identified under the EMH include, Low
Price Earnings Effect, low priced stocks,
the small firm effect, the neglected firm
effect and Market overreaction. Others are
the Turn of the calendar anomalies which
include The January effect, the day of the
week effect, the turn of the month effect,
the turn of the year effect among others.
The study narrows down to turn of the
month effect and will seek to establish if it
exists at the Nairobi Securities Exchange.
Turn of the Month Effect
A turn-of-the-month effect is documented
by Ariel (1987) where higher mean stock
returns occur during the initial days of a
trading month than during days later in the
month. Ariel (1987) was the first to
identify this anomaly in US stock prices at
the beginning of one month and end of the
other month. He studied this effect by
considering last day of one month and the
first three days of upcoming month.
Changes in stock prices in these days are
found positive.
Zafar, Shah and Urooj (2009) observe that
different studies have given different
conclusions for on TOM effect. Cadsby
(1989) carried a study which confirmed
that the turn-of-the-month effect is
present in both US and Canada. Further
researches were carried out by Cadsby and
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Ratner (1992) and the results suggest that
the anomaly has shown its presence in the
US, Canada, the UK, West Germany,
Switzerland and Australia but not in
Japan, France, Italy and Hong Kong.
Methods
The study employed an analytical research
design. The design was preferred due to
the fact that the study entailed analyzing
returns during the TOM and ROM
windows to establish the window with the
highest return and so confirm if TOM
effect exists or not. For purposes of this
study the population consisted of all active
firms listed at the NSE for equity trading
as at December 2011. The study used the
NSE 20 share index. Secondary data from
the NSE database was used for years
ranging from 2002 to 2011. The regression
model below was used to determine TOM
effect:
Rt = β0+β1d2t+εt
Where:
Rt = Daily return of stock index
β0 = is the coefficient of ROM
β1 = is the difference in the computed
mean returns of TOM
d2t = Dummy variable for the TOM returns
εt = Error term
Results And Discussions
Table 1: Regression coefficients for TOM effect for individual years from 2002 to 2011
(t-values are in parenthesis)
Periods
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2002-2011
β0
β1
-0.035
(-0.606)
0.252
(3.124)
0.77
(1.466)
0.116
(3.007)
0.144
(0.101)
-0.035
(3.124)
-0.327
(-3.002)
-0.026
(-0.376)
0.123
(2.713)
0.220
(40.20)
0.510
(0.359)
R
0.193
(1.469)
0.397
(0.790)
-0.175
(-2.095)
0.136
(0.239)
0.138
(-0.002)
0.068
(0.631)
0.462
(3.178)
0.058
(-0.2020
0.123
(0.013)
0.243
(1.868)
0.150
(0.304)
P-value
0.009
0.143
0.003
0.002
0.4
30
0.0
37
0.8
11
0.9
99
0.529
0.039
0.002
0.000
0.840
0.000
0.989
0.10
0.063
0.000
0.761
0.17
0.000
0.000
Source: Research data
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The model regression results in table 1
above have shown the coefficient TOM β1
to be insignificant except for two years
2004 and 2008. In year 2008, TOM
coefficient is negatively significant with
β1 = -0.175, P-value = 0.037 which is less
than 0.05 and t-value = -2.095. In year
Table 2: Year 2002 Model Summary
Model
2008 TOM is positively significant with
β1 = 0.462 where P-value is less than 0.5,
that is 0.002 < 0.5 with t-value of 3.178.
TOM coefficient for the whole period of
ten years is also insignificant thus failing
to demonstrate TOM effect at the NSE.
Std.
Error of
R
Adjusted the
R
Square R Square Estimate
a
dimension0 1 .093 .009
.005
.81029
a. Predictors: (Constant), Days of the month
Source: Research data
The coefficient of determination (R2)
equals 0.009. This shows that days of the
month explain 0.9 percent of the total
variation of Daily return of stock index
leaving only 99.1 percent unexplained.
Change Statistics
R Square F
Change Change df1
.009
2.159 1
df2
246
Sig.
F
Change
.143
The P- value of 0.143 implies that the
model of Daily return of stock index is not
significant at the 5 percent significance
level.
Table 3: ANOVA
Model
Sum of Squares df
Mean Square
F
Sig.
1
Regression
1.418
1
1.418
2.159
.143a
Residual
161.516
246
.657
Total
162.934
247
a. Predictors: (Constant), Days of the month
b. Dependent Variable: Daily return of stock index
Source: Research data
The P- value of 0.143 implies that there is
of 0.193 during TOM (value of x equal 1).
no linear regression relationship between
The P- value of 0.143 > 0.05 implies that,
days of the month and the Daily return of
Days of the month as an independent
stock index at 5 percent significance level.
variable is not significant at the 5 percent.
That is, the Daily return of stock index in
TOM coefficient (0.228) is higher than
the year 2002 during ROM (zero value of
ROM coefficient (-0.035) but not
x) was -0.035 and the Daily return of stock
significant to confirm TOM effect in year
index was expected to increase at the rate
2002.
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Table 4: Year 2003 Model Summary
Model
Std.
Error of
R
Adjusted the
R
Square R Square Estimate
a
dimension0 1 .050 .003
-.002
1.14430
a. Predictors: (Constant), Days of the month
Source: Research data
In table 4 above the coefficient of
determination (R2) equals 0.003. This
shows that days of the month explain 0.3
percent of the total variation of Daily
return of stock index leaving only 99.7
Change Statistics
R Square F
Change Change df1
.003
.624
1
df2
247
Sig.
F
Change
.430
percent unexplained. The P- value of 0.43
implies that the model of Daily return of
stock index is not significant at the 5
percent significance level.
Table 5: ANOVA
Model
Sum of Squares Df
1
Regression
.817
1
Residual
323.425
247
Total
324.242
248
a. Predictors: (Constant), Days of the month
Mean Square
.817
1.309
F
.624
P-value
.430a
Source: Research data
The P- value of 0.43 in fig. 4.3.2C above
implies that there is no linear regression
relationship between days of the month
and the Daily return of stock index in year
2003 at 5 percent significance level. That
is, the Daily return of stock index in the
year 2003 during ROM (zero value of x)
was 0.252 and the Daily return of stock
index was expected to increase at the rate
Table 6: Year 2004 Model Summary
Model
of 0.145 during TOM (value of x equal 1).
The P- value of 0.43 > 0.05 implies that,
Days of the month as an independent
variable is not significant at 5 percent.
Though the TOM coefficient is more than
ROM coefficient, it is not significant to
confirm existence of TOM effect at NSE
for year 2003.
Std.
Error of
R
Adjusted the
R
Square R Square Estimate
a
dimension0 1 .131 .017
.013
.74965
a. Predictors: (Constant), Days of the month
Source: Research data
Change Statistics
R Square F
Change Change df1
.017
4.391 1
df2
251
Sig.
F
Change
.037
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The coefficient of determination (R2) in
table 11 equals 0.017. This shows that
days of the month explain 1.7 percent of
the total variation of Daily return of stock
index leaving only 98.3 percent
unexplained. The P- value of 0.037 implies
that the model of Daily return of stock
index is not significant at the 5 percent
significance level.
Table 7: ANOVA
Model
Sum of Squares Df
1
Regression
2.468
1
Residual
141.057
251
Total
143.525
252
a. Predictors: (Constant), Days of the month
b. Dependent Variable: Daily return of stock index
The P- value of 0.37 implies that there is
no linear regression relationship between
Mean Square
2.468
.562
F
4.391
Sig.
.037a
days of the month and the Daily return of
stock index at 5 percent significance level.
Table 8: Year 2005 Model summary
Model
Std.
Error of
R
Adjusted the
R
Square R Square Estimate
a
dimension0 1 .015 .000
-.004
.54664
a. Predictors: (Constant), Days of the month
The coefficient of determination (R2)
equals 0.000. This shows that days of the
month do not explain any variation of
Daily return of stock index but determined
Change Statistics
R Square F
Change Change df1
.000
.057
1
df2
246
Sig.
F
Change
.811
by other factors. The P- value of 0.811
implies that the model of Daily return of
stock index is not significant at the 5
percent significance level.
Table 9: ANOVA
Model
1
Regression
Sum of Squares
.017
df
1
Mean Square
.017
Residual
73.507
246
Total
73.524
247
a. Predictors: (Constant), Days of the month
b. Dependent Variable: Daily return of stock index
Source: Research data
The P- value of 0.811 implies that there is
no linear regression relationship between
F
.057
Sig.
.811a
.299
days of the month and the Daily return of
stock index at 5 percent significance level.
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Table 10: Year 2006 Model Summary
Model
Std.
Error of
R
Adjusted the
R
Square R Square Estimate
a
dimension0 1 .000 .000
-.004
20.17648
a. Predictors: (Constant), Days of the month
Source: Research data
The coefficient of determination (R2)
equals 0.000. This shows that days of the
month do not explain any variation of
Daily return of the stock index. The P-
Change Statistics
R Square F
Change Change df1
.000
.000
1
df2
244
Sig.
F
Change
.999
value of 0.999 implies that the model of
Daily return of stock index is insignificant
at the 5 percent significance level.
Table 11: ANOVA
Model
Sum of Squares df
1
Regression
.001
1
Residual
99330.000
244
Total
99330.002
245
a. Predictors: (Constant), Days of the month
b. Dependent Variable: Daily return of stock index
Source: Research data
Table 12: Year 2007 Model Summary
Model
Std.
Error of
R
Adjusted the
R
Square R Square Estimate
a
dimension0 1 .040 .002
-.002
1.01766
a. Predictors: (Constant), Days of the month
Source: Research data
The coefficient of determination (R2)
equals 0.002. This shows that days of the
month only explain 0.2% of the total
variation of Daily return of the stock index
Mean Square
.001
407.090
F
.000
Sig.
.999a
Change Statistics
R Square F
Change Change df1
.002
.398
1
df2
246
Sig.
F
Change
.529
leaving 99.8% unexplained. The P- value
of 0.529 implies that the model of Daily
return of stock index is not significant at
the 5 percent significance level.
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Table 13: ANOVA
Model
Sum of Squares df
1
Regression
.412
1
Residual
254.763
246
Total
255.176
247
a. Predictors: (Constant), Days of the month
b. Dependent Variable: Daily return of stock index
Source: Research data
Mean Square
.412
1.036
F
.398
Sig.
.529a
The P- value of 0.529 implies that there is
days of the month and the Daily return of
no linear regression relationship between
stock index at 5 percent significance level
.
Table 14: Year 2008 Model Summary
Model
Std.
Change Statistics
Error of
R
Adjusted the
R Square F
Sig.
F
R
Square R Square Estimate Change Change df1 df2
Change
a
dimension0 1 .198 .039
.035
1.54444 .039
10.100 1
247 .002
a. Predictors: (Constant), Days of the month
Source: Research data
The coefficient of determination (R2)
equals 0.039. This shows that days of the
month only explain 3.9% of the total
variation of Daily return of the stock index
leaving 96.1% unexplained. The P- value
of 0.002 implies that the model of Daily
return of stock index is not significant at
the 5 percent significance level.
Table 15: ANOVA
Model
Sum of Squares df
1
Regression
24.091
1
Residual
589.165
247
Total
613.256
248
a. Predictors: (Constant), Days of the month
b. Dependent Variable: Daily return of stock index
Source: Research data
The P- value of 0.002 implies that there is
no linear regression relationship between
Mean Square
24.091
2.385
F
10.100
Sig.
.002a
days of the month and the Daily return of
stock index at 5 percent significance level.
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Table 16: Year 2009 Model Summary
Model
Std.
Error of
R
Adjusted the
R
Square R Square Estimate
a
dimension0 1 .013 .000
-.004
.98878
a. Predictors: (Constant), Days of the month
Source: Research data
Change Statistics
R Square F
Change Change df1
.000
.041
1
df2
250
Sig.
F
Change
.840
The coefficient of determination (R2)
value of 0.840 implies that the model of
equals 0.000. This shows that days of the
Daily return of stock index is significant at
month do not explain any variation of the
the 5 percent significance level.
Daily return of the stock index. The PTable 17: ANOVA
ANOVAb
Model
Sum of Squares df
Mean Square
F
Sig.
1
Regression
.040
1
.040
.041
.840a
Residual
244.421
250
.978
Total
244.461
251
a. Predictors: (Constant), Days of the month
b. Dependent Variable: Daily return of stock index
Source: Research data
The P- value of 0.840 implies that there is
days of the month and the Daily return of
a linear regression relationship between
stock index at 5 percent significance level.
Table 18: Year 2010 Model Summary
Model
Std.
Error of
R
Adjusted the
R
Square R Square Estimate
a
dimension0 1 .001 .000
-.004
.64761
a. Predictors: (Constant), Days of the month
Source: Research data
The coefficient of determination (R2)
equals 0.000. This shows that days of the
month do not explain any variation of the
Daily return of the stock index. The P-
Change Statistics
R Square F
Change Change df1
.000
.000
1
df2
251
Sig.
F
Change
.989
value of 0.989 implies that the model of
Daily return of stock index is significant at
the 5 percent significance level.
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Table 19: ANOVA
Model
Sum of Squares Df
1
Regression
.000
1
Residual
105.269
251
Total
105.269
252
a. Predictors: (Constant), Days of the month
b. Dependent Variable: Daily return of stock index
Source: Research data
Mean Square
.000
.419
F
.000
Sig.
.989a
The P- value of 0.989 implies that there is
days of the month and the Daily return of
a linear regression relationship between
stock index at 5 percent significance level.
Table 20: Year 2011 Model Summary
Model
Change Statistics
Std.
Adjusted Error of R
R
R
the
Square F
Sig. F
R
Square Square Estimate Change Change df1 df2 Change
a
dimension0 1 .118 .014
.010
.07776 .014
3.490 1
248 .063
a. Predictors: (Constant), Days of the month
Source: Research data
The coefficient of determination (R2)
by other factors. The P- value of 0.063
equals 0.014. This shows that days of the
implies that the model of Daily return of
month explain only 1.4 percent of total
stock index is not significant at the 5
variation of the Daily return of the stock
percent significance level.
index leaving 98.6 percent to be explained
Table 21: ANOVA
Model
Sum of Squares df
Mean Square
F
Sig.
1
Regression
.021
1
.021
3.490
.063a
Residual
1.499
248
.006
Total
1.521
249
a. Predictors: (Constant), Days of the month
b. Dependent Variable: Daily return of stock index
Source: Research data
The P- value of 0.063 implies that there no
of the month and the Daily return of stock
linear regression relationship between days
index at 5 percent significance level.
Conclusion
The study did not find evidence to confirm
that TOM effect exists at NSE. The study
was conducted by dividing the month in to
two parts, the TOM and the ROM. Daily
returns for both TOM and ROM were
computed from the daily indices of the
NSE 20 share index. In all the years, TOM
coefficients were insignificant as was the
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overall TOM coefficient as shown in table
1. Though TOM mean returns were higher
than ROM returns in most of the years as
shown in table 2, the regression analysis
did not produce significant TOM
coefficient to conclude that TOM effect
exists at the NSE. In all the ANOVA
tables above, p-values are less than 0.05
indicating that the TOM coefficient is
insignificant. Besides the p-value being
less than 0.05, looking at the correlation
analysis in table 1, it can be noted that all
correlations for all the years are less than
0.7. This shows that the model is
inadequate to explain the variations in
securities returns.
The existence of TOM effect at the NSE
could have been an indicator that the
capital market is not efficient. In
conclusion therefore, it can be stated that
no TOM effect was established at the
NSE. This may be an indication that NSE
efficiency is improving. However, other
factors like the size of the index may have
contributed to the results. The returns in
TOM are slightly more than the return in
ROM but not significant. This means that
traders cannot post higher returns by
trading during the TOM days. This is
different from results of previous studies
conducted in the developed world. Kenya
being a developing country, may present a
scenario that may be the case for the
developing nations security markets.
study will be of benefit to the NSE and the
CMA as a basis for policy formulation
aimed at improving capital market
efficiency. TOM as an anomaly to the
EMH is not desirable if the market is
efficient and so the need for market
regulators to develop policies that improve
market efficiency.
Finally, the results of this study are likely
to lend vital information to the brokerage
firms as critical advisors to investors. They
will be able to advise their clients on the
best time of the month to sell or buy
securities. Further, Investors shall find the
results of the study useful as a guide on the
best time to invest or divest their
securities. The results of a TOM effect
study shall appeal to institutional investors
concerned with the timing of purchases
and sales of securities in the stock market.
Implication on Policy and Practice
The academia will find the study as a
spring board for further studies. The
results may be incorporated in future
studies as reference work. Further, various
studies have been done at the NSE but less
on TOM. This study will therefore bridge
this knowledge gap. The results of this
Banz
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Agrawal, A. & Tandon, K. (1994). Anomalies
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Cadsby, C., & Ratner, M. (1992). Turn-of-themonth and pre-holiday effects on stock
returns: Some
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International evidence. Journal of
Banking and Finance, 16, 487-510.
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Dickinson and Marangu (1994). Market
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Dimson E. and Marsh P. (1986). Event study
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Fama E.F. (1970). Reply to efficient capital
market: A review of theory and
empirical work. Journal of Business
Finance, 25, 383-417.
French, K. (1980). Stock returns and the
weekend effect. Journal of Financial
Economics, 8, 55-69.
Gibbons, R. and Hess P. (1981). Day of the
week effects and assets returns.
Journal of Business, 54, 4, 579-596.
Grossman, S.J. and Stiglitz, J.E. (1980). On
the impossibility of informationally
efficient markets. The American
Economic Review, 70, 3.
Hanzel, R. and Ziemba,W.(1996). Investment
results from exploiting TOM effects.
Levin, R. and Zervos, S (1998). Stock
markets, banks and economic growth.
American Economic Review, 88, 53758.
McKinley, T. (1999). Equity Returns at the
Turn of the Month. Financial
Analysts Journal, 64: 49-64.
Pettengill, G., & Jordan, B. (1988). A
comprehensive examination of volume
effects and
seasonality of daily
security returns. Journal of Financial
Research, 11, 57-70.
Stiglitz,
M. (1980) Securities market
efficiency in an Arrow Debreu
economy.
The
American
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Zafar, R, Shah, S. and Urooj, F. (2009).
Calendar Anomalies: Case of Karachi
Stock Exchange.
Research
Journal of International Studies, 9, 8899.
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Manpower Development In The 21st Century: The Role Of Distance
Education
Guantai Mboroki, PhD.1and Lydiah Wambugu, PhD.2
Generally, education is considered one of the biggest instruments for development; a
means for realizing social, cultural, economic and political needs and aspirations.
However, in many African educational institutions there is an enormous challenge in
training a cadre of highly qualified professionals to fuel such development. There are
inadequate educational resources, due to loss of the best talented faculty to the
developed world. In addition, contemporary educational thought holds that one of the
pivotal causes of inadequate school performance is the inability of schools to adequately
staff classrooms with qualified teachers, especially in fields such as mathematics and
science (Gordon and Thomas, 2007, p.43). To address the issue of teacher shortages in
Kenya, some single mode universities converted themselves into dual-mode. Among
them is the University of Nairobi. Apparently, the belief among academics is that
conventional education is real education. This makes distance education to act as
complementary and worse of all supplementary. This paper sets out to compare
whether there is a significant difference in TP performance between B.Ed (Science)
Distance and on-campus students. A sample of 181 students: face-to-face n=131 vs.
distance learning n= 50 students was used. The instrument of data collection was an
observation guide. Though students taking courses by on-campus mode outperformed
their counterparts in the distance mode of learning, this paper will conclude that
distance learning should be treated as the emerging standard of quality in higher
education and can effectively perform a complementary function, which should alleviate
teacher shortage in Kenya. This study has demonstrated that science can be successfully
taught by distance mode even given the current technological investment levels. This is
important for policy because it leverages on what is available and the change that can
bring especially in the area of manpower development, access and equity. The
researchers argue that raising the technological level is not difficult since this can be
done through collaboration with specialized bodies such as the African Virtual
University (AVU) that is spearheading the infusion of ICTs and e-learning into the
educational arena
Key words: Distance education, face-to-face learning, teaching practice
1
Senior Lecturer, School of Continuing and Distance Education, University of Nairobi, Nairobi, Kenya
[email protected]
2
Lecturer, School of Continuing and Distance Education, University of Nairobi, Nairobi, Kenya.
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Introduction
Conventional or on-campus mode of
learning is the widely accepted and known
mode of learning. Tsolakidis (2000)
system of teaching by someone who is
removed in space and time from the
learner through teaching materials that
have been systematically developed using
different types of media to provide two
way communication (Moore and Kearsley,
1996). By its inherent nature, distance
education system has some features of
In Kenya, the Public Universities
Inspection Board (2006) and Amutabi
(2011, p.3) reports that in Kenya ODL is
looked down upon and ‘very few
universities have embraced the model of
open and distance learning and even in
those institutions where this is used, it is
taken as a second or third class form of
instruction’. This could be a pointer that
the academics do not view the quality of
distance
learning
as
similar
to
conventional learning. What is more, the
academic performance of distance learners
is deemed to be of lower quality. The
Public Universities Inspection Board
reported that:
Experiences from other countries such as
Tanzania, Nigeria, South Africa and the
United Kingdom have demonstrated the
potential of Open and Distance Learning
(ODL) in increasing access. Kenya has
not pursued this mode of delivery in a
consistent and aggressive manner. As a
result, Open and Distance Learning
defines conventional education/on-campus
as the universally accepted approach for
knowledge acquisition. On the other hand,
distance
learning
is
a
openness. Open learning describes a
situation where control of learning is
essentially in the hands of the learner. In
literature, the terms Open and Distance
Education (ODE) and Open and Distance
Learning (ODL) are used synonymously
and in some instances; this paper will treat
the two terms as synonyms.
programs are the individual initiatives of
the local universities with limited
government funding (Republic of Kenya,
2006, p. 19).
Whereas the above concerns called for a
national policy framework that would
address the issue, this has not been done.
Yet the number of students who desire
university education keeps swelling every
year. Some of these students are adults
who cannot afford to enroll on a full-time
basis
because
of
work,
family
responsibilities, and other commitments.
Others are Kenya Certificate of Secondary
Education (KCSE) candidates who attain
the
minimum
university
entry
requirements of grade C+ as shown in
Table 1.1 but miss admission into public
universities because admission is tied on
the bed capacity and resources allocated to
each university by the government
(University of Nairobi, 2008, p. 9). The
present public universities in Kenya absorb
a total of 28-30 percent (MoHEST, 2011).
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Table 1.1: Number of KCSE candidates admitted in Private and Public universities
Year
C+ and Above Private universities
Public universities
Left out
2006/2007
62,926
17,706
10,000
35,220
2007/2008
82,134
20,432
16,000
45,702
2008/2009
72,500
22,123
20,000
30,377
2009/2010
81,048
35,179
24,221
21,648
2010/2011
97,134
37,848
29,237
30,049
Source: The Kenya National Examination Council and Joint Admission Board (2011)
Those who are shut out may join private
universities. Private universities absorb
less than 25 percent of students who attain
mean grade of C+ and above at KCSE
level (MoHEST, 2011). Others may join
the public universities through the ‘parallel
programs’ commonly referred to as
Module 2 and Module 3 programs. In
Module 2 students learn in the evenings, or
get integrated with the fulltime students
while Module 3 is the distance learning
mode.
While ODE is credited for enhancing
participation and access in higher
education, there is skepticism on the
quality of learning. This is on the
assumption that it undermines traditional
education, limit student interaction with
peers and lecturers and eradicate the
platform for which a deliberate academic
discourse takes place (Mathews, 1999).
Other scholars assert that distance
education has shown a capacity to help
students
acquire
knowledge
and
communication skills but fails in
developing the skills of analysis, synthesis,
application, judgment and value that are
the ‘characteristics of a truly higher
education’ (Smith and Kelly, 1987, p. 48).
Fox (1998) argues that what is in dispute is
not whether distance education is ideal, but
whether it is good enough to merit a
university degree, and whether it is better
than receiving no education at all. Fox
alludes to an argument that students learn
far too little when the teachers personal
presence is not available because the
student has more to learn from the teacher
than the texts. Hannay and Newvine
(2006, p. 5) add that distance learning
examinations are ‘cheap’ since they test
lower cognitive levels of knowledge and
comprehension. No wonder, distance
education has been described as learning at
the back door (Ding, 1988); second hand
education or the Cinderella of the
educational spectrum (Keegan, 1990) and
an ignored and neglected step child of
education (Peters, 1993).
Despite such concerns, the researchers are
of the view that ODL is as good as oncampus learning as long as the students are
stimulated to order their thoughts and
actively get involved in the academic
requirements. This is because there are
successful single and dual mode
universities in the world that have
continually channelled out quality
graduates into the labour market. For
instance in 2011, the Open University of
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the United Kingdom was rated among the
United Kingdom's top three higher
education institutions. It came third out of
157 universities in the National Student
Survey - placed equally with Oxford. The
rating was based on student satisfaction,
number of graduates and research output
(http//www.open.ac.uk/about/main). Open
University of Tanzania and University of
South Africa are among the top ranked
universities in Tanzania and South Africa
respectively with various programs on
offer. (http//www.open.ac.uk/about/main)
Assessment of Academic Performance
of B.Ed (Science) Course
Assessment may be defined as the process
of determining the extent to which
learning has taken place. Chris (1999)
defines assessment as the systematic
collection of information about student
learning using the time, knowledge and
resources available, in order to inform
decisions about how to improve learning.
In this study, assessment is defined as the
methods used by lecturers to determine the
extent
to
which
students
have
comprehended the course content.
University of Nairobi uses two methods to
assess the Bachelor of Education (Science)
students: written examinations at the end
of each semester and teaching practice at
the end of the third year. Examinations
consist of 30% course work comprising of
written assignments (term papers),
semester tests, practicals, projects; and
70% written examination at the end of
each semester. Each course unit is
examined by a two hour paper and the pass
mark for each paper is 40%. The B.Ed
(Science) students in the two modes of
study sit for different but equivalent
examinations which go through the same
university mechanism of content validity
through internal and external moderation.
Teaching is a complex activity; student
teachers need to develop their capacity in
making intelligent decisions to handle
ambiguous and challenging situations in
schools and beyond. Lam and Fung (2001,
p. 1) notes that teacher education ‘…is
largely a matter of developing a teacher’s
capacities for situational understandings as
a basis for wise judgment and intelligent
decisions in complex, ambiguous and
dynamic educational situations’. This
means that educational and teaching
theories learned in teacher education
programs
may
not
be
applied
straightforwardly to complex and dynamic
educational situations. It is only during
teaching practice when student teachers
are given an opportunity to test theories
learnt in the lecture rooms in real life
experiences. No wonder teaching practice
is compulsory to all students studying for a
degree in Education.
Teaching practice is equivalent to two
units. Each candidate on teaching practice
is assessed a minimum of three times in
each of the teaching subjects and each
assessment is marked out of 100 marks.
Teaching Practice is viewed by many
educational authorities as the measure for
the success of an Education degree (Stones
1992)… ‘because the student teacher is
provided with the opportunity of
practically synthesizing and applying in a
real situation the theoretical learning that
has been provided throughout the teacher
preparation program’ (Molomo as cited in
Digolo, 2002, p. 96). Teaching practice
students are assigned students to teach and
what they teach is real learning content
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that might decide the future of that learner.
At the University of Nairobi, students go
for their teaching practice just before the
beginning of their final year. By this time
they have covered most of their academic
work and they have covered both general
and subject methods of teaching.
Therefore, it is not enough for a person to
show examination grades, but should
demonstrate
that
learning
through
communicative acts (Keegan, 1990, pp.
14-15). Mager (1973, p.2) aptly concludes
that ‘you cannot find out if someone can
ride a unicycle unless you or someone else
watches him ride one. You cannot find out
if the objective is achieved unless you use
items that ask the student to perform what
the objective is about’ (Mager, 1973, pp.
2-3). This then implies that you cannot
find out if a student-teacher can translate
theory into practice unless you watch him
or her in a real classroom environment.
Literature Review
There are numerous research studies that
have been conducted comparing the two
modes of learning. These studies have
presented conflicting conclusions creating
‘a doubt, a barrier, and an indeterminate
situation crying out to be made
determinate’ (Kerlinger and Lee, 2000, p.
67). This will be demonstrated below:
Comparative Studies in Kenya
Mutonga (2011) conducted a comparative
study in Kenya of academic performance
of students in the Registered Community
Health Nurse Upgrading Program under
face-to-face and Distance learning mode of
instructional delivery. The design of this
study was descriptive survey. The target
population was 1,363 students; 943
distance study and 420 face-to-face
students who sat for the Nursing Council
of Kenya (NCK) licensing examination
between 2008 and 2010. The study used
secondary data obtained from NCK’s
electronic database. The study found out
that there was a statistically significant
difference (p=0.000; p< 0.05) between the
performance of distance education and
face-to-face
students.
Face-to-face
students performed significantly higher
than distance study students. The study
also found that there was a relationship
between student’s performance and their
entry qualification. The higher the O-level
grade attained, the higher the licensing
examination mean score.
Mboroki (2007) carried out descriptive
Survey study that compared performance
in teaching practice between 43 Bachelor
of Education (Arts) on- campus students
and 50 distance study students of the
University of Nairobi. The findings of this
study showed that there is parity in
performance in teaching practice between
on-campus and distance study students of
the University of Nairobi in the way the
students are assessed in lesson preparation,
presentation, development and mastery of
content, classroom management and
personal deportment.
Comparative Studies outside Kenya
Unterberg (2003) carried out a study at the
Harvard Medical School Center for
Palliative Care in Boston. The purpose of
the study was to compare learning
outcomes of undergraduate students in a
Physical Therapy Education degree course,
given different learning environments. The
two learning environments were inclassroom environment, where students
met in a classroom with the instructor
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present, and the computer-mediated
distance learning environment, where
students worked independently, receiving
information and communicating with the
instructor via e-mail. Unterberg’s study
found out that 43.0 percent of the distance
group and 49.0 percent of the in-class
group scored above-average, 32.0 percent
and 34.0 percent scored average while
25.0 percent and 17 percent scored below
average respectively.
Urtel (2008) carried out a study at the
Mid-Western
Urban
University,
Indianapolis to compare academic
performance between face-to-face and
distance education course format. The
sample was 116 face-to-face and 269
distance study learners. Data were
collected by use of document analysis.
Examination grades and gender were used
as comparative variables. Results analyzed
at .05 level of significance indicated that
there was a statistically significant
difference between the distance education
and face-to-face groups regarding overall
academic performance as measured by
grade earned (p=0.011). The students
enrolled in the face-to-face mode earned,
overall, an average GPA 3.16/4.00 against
2.28/4.00 of the distance study students.
When analyzing gender interactions, there
was no statistically significant difference
in performance of females versus males
(p=0.2214). Urtel concluded that contrary
to some studies, students in distance
education course do not automatically
perform equally as well, or even better,
than in a face-to-face course and that older
students’ in distance education do not
automatically outperform their younger
counterparts in face-to-face class.
Lizzio, Wilson and Simons (2002)
conducted a study to determine the
influence of Tertiary Entrance (TE) score
in the students’ years 11 and 12 of their
secondary
education
on
academic
outcomes at the University of Griffith,
Australia. A sample of 64 students was
drawn from the Faculty of Business
Studies. Findings showed that TE score
was positively but weakly (ρ = .39)
associated with a high Grade Point
Average (GPA) score measured on a scale
from 1 (low) to 7 (high). The weak
relationship could be a pointer that past
performance is not a determinant of the
present performance probably due to
maturity which is associated with
cognitive development. In contrast,
Adedeji (2001) found a strong positive
correlation (ρ = .85) between students
admission scores and their undergraduate
performance at the Faculty of Technology,
University of Ibadan. A study by Aderson,
Benjamin and Fuss (1994) also found out
that student’s who received better scores
(between 777-999) in high school tended
to have a Grade Point Average score of
above 3.0/4.0 at the university.
Pascallera and Collins (2003) conducted a
randomized
instructional
experiment
between a group of 46 students in a Fire
Fighting Tactics and Strategy course
learning on campus and learning at a
distance at a Community College in Iowa.
In one format students received face to
face instruction in a traditional classroom
on-campus while the rest received the
same course instruction by two-way
Interactive Television on the Iowa
Communication Network (ICN). The
findings of the study were that: The
specific medium of instruction has little
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impact on how students learn. Students can
master course facts and concepts as well
when they receive instruction at a distance
as they can when they receive the same
instruction on-campus in a traditional face
to face environment; and the parity in
outcomes holds irrespective of students
pre-course level of content knowledge,
their prior exposure to post-secondary
education or their related professional
training and experience. Pascarella and
Collins’ study used a different form of
distance education; teleconferencing (elearning) which is more interactive
(Keegan, 1986) than the print medium
used at the University of Nairobi.
Carmel and Gold (2007) examined the
relationship between modality of course
delivery and the level of student Grade
Points Average (GPA), satisfaction and
retention achieved by students attending
either traditional on-site or distance study
courses at the University of Phoenix. A
document analysis incorporated data from
110 courses and 164 students of which 95
students were attending on-site classes
while 69 were in distance study. The
average GPA for distance mode was 3.74
while on-site was 3.77.
Statistical
significance of the means was tested at α =
0.05. Carmel and Gold concluded that
there is no statistically significant
difference between the means of the
groups (p=0.293).
Cano and Garton (1994) conducted a study
that investigated in-service performance of
82 practicing teachers who had majored in
Agricultural Education at The Ohio State
University. These researchers were
interested in the relationship between
instructional format and student’s success
in a college level course. The researchers
based their study on Richard Clark’s
theory which states that any medium of
instruction is capable of delivering
instruction. Cano and Garton (1994). Two
instructional formats were compared; faceto-face learning and distance learning with
minimal face to face interaction. Results
indicated a low positive relationship (ρ =
.30) between instructional format and
academic achievement in the course. The
researchers concluded that instructional
medium does not significantly influence
academic performance.
Neuhauser (2002) conducted a research
that focused on the relationship between
learning environment and academic
outcomes. The researcher analyzed
documents of 54 students enrolled in two
high school psychology classes. 27
students were in distance mode and the
other 27 were in the face-to-face mode of
learning. Results showed that the mean
performance of the face-to- face group was
not significantly higher (M =76.47) as
compared to distance mode mean
performance of 74.21 with p>.05. Thus the
researcher concluded that equivalent
learning activities could be equally
effective for distance and traditional
classroom learners as long as the students
spend ‘quality time’ studying.
Okoh (2010) examined the influence of
age, financial status and gender on
academic performance among a sample of
175 undergraduates; 57 males and 118
females enrolled in a Counseling
Psychology course offered through faceto-face and distance learning. One of the
hypotheses the researcher tested was
‘There is no significant difference in the
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academic performance of undergraduate
students based on gender’. A p> 0.05 was
obtained and the researcher concluded that
there is no significant difference between
male and female respondents in their
academic performance. A probable
explanation to this observation is that since
both male and female undergraduates are
exposed to same social and academic
environment, they are assessed using the
same criteria, then their reaction to grading
(academic performance) is similar.
Magagula and Ngwenya (2004) carried out
a comparative analysis of the academic
performance of off-campus (distance) and
on-campus learners at the University of
Swaziland in 2004. The purpose of the
study was to; a) examine the background
characteristics of 70 off-campus and 70
on-campus enrolled in Bachelor of Arts
and b) the extent to which the academic
performance of off-campus and on-campus
were similar and/or different. Academic
performance was operationalized as the
overall average mark or grade obtained by
a learner in each of the following subjects
in year one of the final examination at the
University of Swaziland; Academic
Communication Skills (ACS), History,
Theology, African Languages, Geography,
and English. The researchers compared the
academic performance based on Rumbles
(1997) assertion that if the entrance
requirements (entry qualification) and
content of both off-campus and on-campus
programs are the same, lecturers are the
same and both off-campus and on-campus
learners write the same final examination
then it should be possible to compare the
academic performance of both modes.
Research Methods
The design of this paper was a survey
where a sample of 50 ODE and 131 oncampus students was drawn from a
population of 58 ODE and 195 on-campus
students respectively. These were the third
year B.Ed (Science) students who were out
for TP in 2012. Simple random sampling
was used. To achieve a simple random
number, each student in the sampling
frame was assigned a number from 1 to
195 for on-campus students and 1-57 for
distance study students. Computer
Random Number Generator was then used
to generate a list of random numbers that
were used to the sample.
An observation guide was used to measure
teaching practice performance. The
university uses a standardized measuring
instrument referred as Teaching Practice
Observation Sheet for awarding marks.
Various behaviors (or elements) are
assessed. These behaviors are Preparation
(20 marks), Presentation (15 marks),
Lesson
development
(35
marks),
Classroom management (10 marks),
Summary/conclusion (10 marks) and
Personal factors (10 marks).
Results and Discussion
The total student sample size for this study
was 181 B.Ed (Science) students. 131
representing 72.4% were on-campus
students while 50 representing 27.6% were
distance study students. Majority of the
on-campus
students
were
males
representing 71.0% while majority of the
ODE students were females representing
56.0% as shown in Table 1.2. This high
percentage of the males could be a pointer
that there is a gender gap in science
performance at KCSE. As per this finding,
males had performed higher in Sciences at
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KCSE than females. The ODE mode of
learning attracted more females than males
a finding that concurs with Aragon et al
(2002) study who found that females
enrolled in distance education classes at a
higher rate than males because of the
flexibility of the mode of learning.
Table 1.2: Gender Distribution of the B.Ed (Science) students
Distance Study students
On-campus students
Gender
Frequency
Percentage
Frequency
Percentage
Male
Female
TOTAL
22
28
50
44.0
56.0
100.0
93
38
131
71.0
29.0
100.0
Teaching Practice (TP) is a compulsory
exercise for every student enrolled in a
B.Ed (Science) course. A common adapted
T.P Lesson Observation Guide was used to
assess the students as they taught in their
TP schools. Just like in examinations, the
TP marks are converted into grade letter
where Grade A ranges from 70-100%,
Grade B from 60-69%, Grade C from 5059%, Grade D from 40-49% while E (fail)
is from 0-39%.
The researchers sought to find out teaching
practice mean performance between ODE
and on-campus students. First, the
researcher determined the teaching
practice
mean
score
performance
translated into letter grades. As shown in
Table 1.3, majority of the students in both
modes scored above 60.0% while none
scored below 49.0%. For instance, 27
(54.0%) ODE and 92 (70.2%) on-campus
students scored Grade A in TP.
Table 1.3: Letter Grade Performance of the B.Ed (Science) students in teaching Practice
Grade performance Distance study students
A (70-100%)
B (60-69%)
C (50-59%)
D (40-49%)
TOTAL
Frequency
27
21
2
0
50
On-campus students
Percentage
54.0
42.0
4.0
0.0
100.0
As per the university evaluation, this
performance may be termed as good. This
may be attributed to the preparation and
Frequency
92
38
1
0
131
Percentage
70.2
29.0
0.8
0.0
100.0
support the students received from the
university and also from the regular
teachers in their TP schools. Using the
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scores assigned to the student, the overall
TP mean score performance for the oncampus students was higher than for the
ODE students. As shown in Table 1.4 the
mean score performance for the oncampus students was 71.73 against a mean
of 69.88 for the ODE students with a
standard deviation of 4.59 and 4.77 and SE
of .40 and .67 respectively. Based on the
normal curve, approximately 68% of the
on-campus students scored between 67.14
and 76.32 while ODE students scored
between 65.11 and 74.65.
Table 1.4: Mean score performance in TP
TP Performance
Overall
in TP
Distance study students
Mean
N
Standard
deviation
performance 69.88
50
4.77
The relationship between the mode of
learning and the overall TP performance
shows a weak positive linear relationship,
Pearson correlation coefficient of r = .176
as presented in Table 1.5. The Table also
shows that this relationship is significant p
< .05. This finding implies that there is a
On-campus students
Mean
N
71.73
131
Standard
deviation
4.59
significant relationship between the mode
of learning and TP performance. Oncampus students perform significantly
higher in TP than the ODE students
despite the latter having prior Training in
Education.
Table 1.5: Correlation between TP performance and mode of learning
Overall
Teaching
practice Performance
1
.
181
Mode
Learning
.176*
.018
181
of
Overall
Teaching Pearson
practice Performance
correlation
Sig. (2-tailed)
N
Mode of Learning
Pearson
.176*
1
correlation
.018
.
Sig. (2-tailed)
181
181
N
*Correlation is significant at the 0.05 level (2 tailed)
Finally, the researchers sought to determine whether the difference in mean performance
between ODE and on-campus is significant. Independent t-test was used and the output is
presented in Table1.6. As shown in the table, there is a significant difference between the
mean performance of ODE students and their counterparts in the on-campus mode of
learning. The students in the on-campus mode of learning performed significantly higher
(M=71.33, SE =0.401) than students in the ODE mode of learning (M=69.88, SE=0.65). This
difference was found to be significant t (179) = -2.392, p<.05.
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The behaviors assessed during teaching practice were categorized into six attributes. These
are lesson preparation, lesson presentation, lesson development, classroom management,
summary/conclusion and personal factors. There is no significant difference in the
performance of students in both modes of learning in Preparation of the lesson (p=.618),
Presentation of the lesson (p=.508), Classroom management (p=.141) and summary and
conclusions (p=606). However, on-campus performed significantly higher in lesson
development (p=.000) while ODE performed significantly higher in personal factors
(p=.036.
Table 1.6: Output for the Mean performance in each attribute
Teaching practice Mode
attributes
learning
Overall TP
performance
mean
Preparation
Presentation
Lesson Development
Classroom
management
Summary
conclusion
Personal factors
and
of N
Mean Std.
Std.
deviation Error
Mean
69.88 4.77
.675
Distance
learning
On-campus
learning
50
131 71.73
4.60
.401
Distance
learning
On-campus
learning
Distance
learning
On-campus
learning
50
11.54
1.88
.265
131 11.37
2.17
.189
50
10.74
1.12
.159
131 10.60
1.37
.120
Distance
learning
On-campus
learning
50
26.16
3.06
.433
131 28.34
2.35
.205
Distance
learning
On-campus
learning
Distance
learning
On-campus
learning
Distance
learning
On-campus
learning
50
6.72
.97
.137
131 6.96
.99
.086
50
.78
.111
131 6.69
1.20
.105
50
8.10
.93
.132
131 7.82
.75
.066
6.60
t
df
-2.392 179
Sig.
(2tailed)
.018
.499
179
.618
.664
179
.508
-5.128 179
.000
-1.480 179
.141
-.519
179
.606
2.115
179
.036
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The researchers expected the ODE
students to perform exceptionally higher
than the on-campus owing to the fact that
majority of the ODE students (92.0%) as
shown in Table 4.8 have prior training in
Education. On the contrary, the on-campus
students performed significantly higher
than the ODE. This might seem as a
challenge to the finding by Mboroki
(2007) that there is parity in teaching
practice performance between on-campus
and distance study students. His findings
were on a bachelor of Education (Arts)
program and may differ from a science
program. An arguable point is that the
University of Nairobi may be assuming
that the ODE students, majority of whom
are practicing teachers do not need to go
through micro teaching while at the
university. This therefore means that the
student goes to the field without being
exposed to a mock secondary school
classroom. This finding is in accord with
Kumar (1997) assertion that conventional
education leads to the development of oral
presentation skills and interpersonal skills
as a result of high teacher/learner and
learner/learner interaction.
They may
therefore not have the skills to teach in a
secondary school. As Odumbe and Kamau
(1986) explain, ODE students lack
confidence in themselves because the
society favors conventional modes of
teaching.
Conclusions
It is the assertion of the researchers that
lack of interactivity which is presumed to
be a major contributor to the difference in
performance between the two programs
will be overcome when the University of
Nairobi fully integrates the e-learning
component that is being provided by the
partnership with the African Virtual
University.
Implication on Policy and Practice
It is not clear why Kenya has so far not
developed a policy framework that would
mainstream open distance education into
the formal delivery of education. Kenya
has a long history of distance education
experience spanning from 1967 when the
Correspondence
Course
Unit
was
established at the University of Nairobi’s
Kikuyu Campus and shortly followed by
the very successful In-service Teacher
Training Programmme of the Ministry of
Education in 1969 at the same venue.
Indeed so successful have been the
Kenyan experiences that countries like
Uganda, Tanzania, Zimbabwe, Botswana,
Swaziland, Namibia, Ghana, Nigeria and
even India have borrowed the Kenyan
model and introduced open universities
and colleges.
The University of Nairobi has been
running a Bachelor of Education (Arts)
programme since 1986 and the science
equivalent since 2003. The results of this
study have demonstrated that a science
programme can be taught successfully at a
distance. 99.2% of the distance study
students scored over 60% in an
examination where a 40% mark would
have been adequate for a pass. The fact
that the research compared both oncampus and distance education students
using the same examination dispenses with
the notion espoused by Hannay& Newvine
(2006)
that
distance
education
examinations are ‘cheap’ since they test
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lower cognitive levels of knowledge and
comprehension. It confirms the assertion
by Fox(1998) that learning by distance
education mode is good enough to merit a
university degree.
The study further confirms that distance
education brings more female learners to
the learning arena at the tertiary level. This
is an encouraging finding because it
demonstrates a direction that can foster
gender equity through education.
What the findings of this study mean for
policy is that distance education can be
used to break the bottleneck of qualified
manpower in Kenya. Research has shown
that the Gross Enrollment Ratio for Kenya
has been 3% against Africa’s 6%(Kenet
2009). Some educational economists have
observed that at least 12-15% of a nation’s
workforce must have tertiary education if
it is going to compete in the new global
economy. They also add that ‘Seeking to
meet
this
demand
requires
a
conceptualization of massification that is
not currently under consideration’ (Taferra
and Altbach, 2003:74). In recent years the
Government has come out strongly in
rhetoric in support of distance education as
the option that will bring about the
massification that will break the manpower
bottleneck. There are plans to establish an
open university by the end of this
year(2014). Among the policy documents
that have called for the establishment of
the Open University include the Sessional
Paper No. 1 of 2005 on Education,
Training and Research (2005), Report of
the Public Universities Inspection Board
(2007), National Strategy for University
Education (2008) and the Road Map for
Open University (Rumble Report 2008).
Others include the Task Force on the
Realignment of the Education Sector to the
2010 Constitution of Kenya (2012), the
Task Force on Alignment of Higher
Education, Science and Technology sector
to the 2010 Constitution of Kenya (2012),
the Sessional Paper No. 14 of 2012, on
reforming education and training sector in
Kenya and the Universities Act, 2012,
This study goes some way in reaffirming
the capacity of distance education to
produce graduates who are as competent
as those produced by the on-campus
system.
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Foreign Direct Investment And Economic Growth: An Empirical Analysis
Of Kenyan Data
Daniel O Abala, PhD.1
The paper investigates the main drivers of real Gross Domestic Product growth in
Kenya as well as those that drive the foreign direct investment (FDI) in Kenya. It is
widely acknowledged that FDI has potential benefits that accrue to host countries. The
view suggests that FDI is important for economic growth as it provides much needed
capital, increases competition in host countries and helps local firms to become more
productive by adopting more efficient technology. Kenya’s record in attracting FDI
from the 1980s has been poor though it was a favoured destination in the 1970s.The
study findings show that FDIs in Kenya are mainly market-seeking and these require
growing GDPs, political stability and good infrastructure, market size as well as
reduction in corruption levels. The prevalence of crime and insecurity would be
impediments to FDI inflow. The policy implications of this study are that Kenya’s FDI’s
tend to be mainly market seeking and for this reason policy makers in Kenya should
focus on improving political stability, emphasize the development of good infrastructure
and growing the country’s GDP. This should be coupled with a serious attempt at
reducing corruption levels as well as a serious assault on the prevalence of crime and
insecurity which are major impediments to this type of FDI inflows.
Keywords: Foreign Direct Investment, Economic Growth, Determinants, Kenya
1
Senior lecturer, School of Economics, University of Nairobi, Nairobi, Kenya
[email protected]
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Introduction
Foreign direct investment (FDI) in Kenya
is defined as investment in foreign assets,
such as foreign currency, credits, rights,
benefits or property, undertaken by a
foreign national (a non-Kenyan citizen) for
the purposes of production of goods and
services which are to be sold either
domestically or exported overseas
(Investment Promotion Centre Act,
Chapter 518). FDI generally refers to an
investment made to acquire a lasting
management interest (normally 10% of
voting stock) in a business enterprise in a
country other than that of the investor
defined according to residency (World
Bank, 1996). Ownership of less than 10%
is regarded as portfolio investment.
Foreign direct investment has grown
enormously in the last three decades. For
example prior to the recent economic
crisis, global FDI has risen to US $ 1,833
billion in 2007 well above the US $ 1,748
billion in 2000(UNCTAD, 2008). The
production of goods and services by
multinational corporations and their
foreign affiliates have continued to rise as
evidenced by increase in FDI from US $
15 trillion in 2007 US $ 18 trillion in 2010
(UNCTAD 2010). The increase in FDI has
been singled out as the most important
factor for poverty reduction (Rose and
Mwega, 2006). Most developing countries
such as Kenya are interested in FDI a
source of capital for industrialisation. This
is because FDI involves a long term
commitment to the host country and
contributes significantly to the gross fixed
capital formation.
FDI has been identified to contribute
significantly to the economic growth of
countries. Governments of many host
countries (recipients of FDI) are using
financial incentives such as tax allowances
and grants in aid among other policies to
attract FDI into their economies due to the
perceived benefits associated with FDI
inflows. It has been suggested in numerous
papers that foreign firms are able to
positively affect the levels of productivity
and growth rates in the industries they
enter and to also promote skill upgrading,
increase employment and increased
innovation (Blomström, 1986; Blomström
and Persson, 1983; Görg and Strobl, 2001;
UNCTAD, 2005). However, it has also
been argued that FDI may lower or replace
domestic savings and investment, transfer
low level or inappropriate technologies for
the host country’s factor proportions target
primarily the host country’s domestic
market and even inhibit the expansion of
indigenous firms thereby limiting growth.
By focusing solely on local cheap labour
and raw materials, foreign firms may not
be helpful in developing the host country’s
dynamic
comparative
advantages
(UNCTAD, 2005). Nevertheless, the
negative consequences of FDI can be
managed with proper business and labour
regulation (Rose and Mwega, 2006;
Kinuthia 2010).
There are at least three major types of
FDIs. The market-seeking FDI usually
serves local and regional market and
involves the replication of production
facilities in the host countries. A variant of
this type of FDI is also known as Tariffjumping or export-substitution FDI and it
is driven mainly by market size and market
growth of the host economy. Due to
market and income considerations FDIs in
small and poor countries are unlikely to be
of the market seeking type (see Lim 2001;
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Campos and Kinoshita, 2003 and
UNCTAD, 1998).
The resource or asset-seeking FDI is
another type of FDI and involves the
relocation of parts of the production chain
to the host country. This is usually driven
by the availability of low-cost labour and
is often export-oriented. This type of FDI
is also attracted to countries with abundant
natural resources such as oil and gas.
The third type of FDI is the efficiencyseeking type where the firms gain from
common governance of geographically
dispersed activities in the presence of
economies of scale and scope. The idea
here is to take advantage of special
features such as labour costs, skills of the
labour force and quality of infrastructure.
We next examine the evolution of FDI
flows in Kenya and how it has affected
economic growth in Kenya.
The hope of vision 2030 has apparently
not been fulfilled and Kenya’s share in the
regional market, both in EAC and the
wider COMESA is still less than 15%.
However, it still appears that the economic
growth of a developing country may well
depend on among other things on an
opportunity to make profitable investments
and accumulate capital. It is similarly true
that one of the ways of achieving this
objective is through the attraction of
foreign capital and investments which
allows a country to exploit opportunities
that would otherwise not be available
(OECD, 2002).
Evolution of FDI and Kenya’s Economic
Growth
Kenya has had a long history with foreign
firms. From independence of 1963 through
the 1970s and part of the 1980s it was one
of the most favoured destinations of FDI in
the Eastern Africa. FDI grew steadily
through the 1970s as Kenya was the prime
choice for foreign investors seeking to
establish a presence in Eastern and
Southern Africa. In the 1970’s Kenya was
the most favoured destination for FDI in
East Africa. However, over the years she
has lost her appeal to foreign investors a
situation that has continued to the present.
In 2008, Kenya launched vision 2030 with
the objective of among other things to
achieve global competitiveness for FDI
and gain economic prosperity. This
initiative has seen a renewed commitment
to attract FDI to assist in achieving higher
economic growth rates. Kenya has had
inconsistent trends of FDI inflows starting
with the 1970-1980 period .The then
relatively high level of development, good
infrastructure, market size, growth and
openness to FDI at a time when other
countries in the region had relatively
closed regimes all contributed to the
multinational companies (MNCs) choosing
Kenya as their regional hub. There was
also relative political stability and security
during the period. FDI started at a low of
around US$ 10 million a year in the early
1970s before peaking at US$ 60 million by
1979-80. The country received relatively
large capital inflows partly driven by rapid
expansion in the agricultural sector,
expansionary fiscal and monetary policies,
sustainable budget deficit and the import
substitution industrialisation (ISI) strategy.
This involved overvalued exchange rates,
import tariffs, quantitative restrictions and
import licensing (Ikiara et al, 2003). Other
factors included large and favourable
regional markets from the original East
African community (EAC) which attracted
FDI into the country (World Bank, 2010).
However, after the 1980s, Kenya’s
economy
was
characterized
by
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deterioration in economic performance,
corruption
and
bad
governance.
Inconsistency in the implementation of
economic policies and structural reform
measures as well as the deterioration of
public service and infrastructure ensured
decades of low level of FDI inflows. FDI
inflows in the period 1981-1999 averaged
only US$ 22 million per annum. It is noted
that although Kenya was the leading
destination of FDI in the East African
region in the 1970s and 1980s the relative
level of flows was never high even by
developing countries’ standards. This can
be seem by looking at the stock of FDI
which was only 7.5% of the GDP in 2003,
compared to 25.3% for Africa as a whole
and 31.5% for developing countries
(UNCTAD, 2005). Kenya’s regional
leadership in attracting FDI also
disappeared as soon as Tanzania and
Uganda started reforming their economies
and opening up to foreign investors in the
early 1990s. FDI flows in the 1996-2003
period averaged some US$ 29 million
annually while flows to Tanzania and
Uganda surged to US$ 280 million and
US$ 220 million respectively from
negligible levels in the 1980s (see
UNCTAD, 2005). In relative terms,
Kenya’s case was even worse since its
economy was about 30% larger than
Tanzania’s and twice as big as Uganda’s in
2002. It is notable that developing
countries as a whole attracted an annual
average of US$ 41 of FDI per capita in
1996-2003 when Kenya only managed
inflows of US$ 1.3 per capita. Kenya’s
share of FDI inward stock was 55% among
the East African countries in the mid
1990s but this declined to 18% by the end
of 2003. The biggest beneficiary of this
loss was Tanzania who’s share rose by
34% share, rising to 46% by the year 2003.
The same scenario was repeated in the
period 2003-2009 where the average FDI
flows into Kenya was US$ 106 million per
annum compared to US$ 456 and US$ 521
million for Tanzania and Uganda
respectively (World Bank, 2010). Kenya
now attracts about one third of what each
of her neighbours attracts in terms of FDI
inflows. This situation has persisted
despite the Kenya government’s attempts
to implement a series of measures aimed at
attracting foreign investors into Kenya
since 1988, especially with respect to
export platforms such as Export
Processing Zones (EPZs). Nevertheless,
these export platforms have themselves
been disappointing in performance; with
exports from EPZs accounting for about
3.5% of total manufacturing exports while
employment in these firms accounted for
barely 1% of total manufacturing
employment by 1997 (see Glenday and
Ndii, 1997). This rose somewhat due to the
effects of the African Growth and
Opportunity Act (AGOA) after 2001.
Kenya also missed out in the global surge
in FDI that was experienced in most parts
of the world in the 1990s and beyond.
While the average FDI inflows to Kenya
doubled in the 1981-85 and 1996-2003
periods, the average inflow into African
countries increased sixfold and the average
inflows into developing countries as a
whole increased almost tenfold. It seems
clear that Kenya’s poor performance in
attracting FDI at a time of global surge of
inflows and with similar economic
structures must be found mainly within the
country.
Studies on Kenya’s inability to attract FDI
despite it having been the prime
destination of FDI in the 1970s and 1980s
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have
identified
such
factors
as
macroeconomic instability, corruption and
bad governance, inconsistencies in
economic policies, deteriorating public
service and poor infrastructure as some of
the factors responsible for the low FDI
inflows. These studies also highlight
market size, low economic growth, lack of
policy transparency and rising cost of
electricity and labour. The studies include
Kinaro, 2006; Opolot et al, 2008 and
UNCTAD, 2005 among others.
The
deterioration
of
Kenya’s
infrastructure, particularly at a time of
major improvement in infrastructure in
other parts of the developing world have
induced many foreign investors already
established in the manufacturing sector to
divest or consolidate their operations out
of Kenya in recent years.
The trend of FDI in Kenya has shown that
foreign investors are moving out of Kenya
with few new investors coming in or even
existing investors planning significant
expansion. Kenya’s Vision 2030 asserts
that the country intends to attract at least
10 large strategic investors in key agroprocessing industries and raise its market
share in the regional market from 7% to
15% by the year 2012. Exports can affect
the economy as a whole through
productivity enhancing externalities such
as technology spillovers and therefore if
FDI is found to promote exports, FDI can
enhance economic growth. Numerous
studies have concluded that exporting is
crucial to growth and foreign direct
investments can play a role in enhancing
the export capability of a country (see
Bernard et al, 2000; Bernard and Jensen,
2001; Bigsten et al, 1999, 2002; Girma et
al, 2005, and Kneller et al, 2004).
The Investment Promotion Act enacted in
2004 is a key policy initiative aimed at
promoting foreign direct investment in the
country. It provides incentives and
promotes foreign direct investments that
earn
foreign
exchange,
provide
employment and promote backward and
forward linkages and transfer technology.
The Act, however, took away some of the
benefits through imposing compulsory
investment certificates and high minimum
capital requirements, thus creating a legal
barrier to and administrative burden for
FDI thereby discouraging both domestic
and foreign investment (UNCTAD, 2005).
These general restrictions of the Act are
contrary to practice in many other
countries in Africa and elsewhere in the
world that adopt more liberal entry
regimes and / or more precisely targeted
policies to regulate FDI entry. Tanzania
for example does not impose minimum
capital requirements for FDI entry in
general, but makes special incentives
conditional upon holding an investment
license and investing a minimum of US$
300,000 (compared to Kenya’s US$
500,000). Uganda does not require foreign
investors to invest minimum amounts but
offers the facilitation support of its
Investment Authority when investments
exceed US$ 100,000. Additionally, the
minimum capital requirement does not
effectively grant protection to national
investors in sensitive areas and maximize
the benefits of FDI. The size of investment
is by no means an indicator of
“seriousness” and benefits to the economy
since at times large foreign investments
may crowd out small national investors’ as
much as more modest foreign investments
(UNCTAD, 2005). The UNCTAD(2011)
data shows that Tanzania and Uganda have
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made tremendous improvements in their
attractiveness for FDI since 1994.
Tanzania’s FDI increase has been
attributed to the mining sector especially
uranium and tanzanite, gas and oil
discoveries as well as favourable policies
that liberalized both local and foreign
investments (Kajara 2010). Uganda’s FDI
increase has been attributed to a wide
range of tax incentives to businesses as
well as its own discovery of oil and gas
reserves (Ngowi, 2005). This suggests that
FDI increases with increases in discovery
of natural resources. Furthermore, the
UNCTAD (2011) figures suggest that
economic growth rate of Tanzania and
Uganda has exceeded that of Kenya since
1994 when their FDI begun to increase.
This may lend credence to the hypothesis
that increases in FDI leads to increases in
economic growth, but these are not
proportional suggesting that other factors
also affect growth.
Table 5: Flows into Kenya and Tanzania (in selected years, in US$ million)
Item
Kenya
Kenya’s FDI as % of
Gross Fixed Capital
Formation (GFCF)
Tanzania
Tanzania’s FDI as %
of GFCF
2005-2007 (annual average)
267
6.1
2009
115
-
2010
178
2.7
2011
335
4.9
2012
259
2.9
640
15.3
953
-
1813
24.6
1229
15.6
1706
17
Source: UNCTAD (2013).
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Table 6: FDI stock in Kenya and Tanzania (selected years, in US$ million)
Item
Kenya
Kenya’s FDI stock as % of
GDP
Tanzania
Tanzania’s FDI stock as % of
GDP
1995
732
6.3
2009
2104
-
2010
2282
7.1
2011
2617
7.7
2012
2876
7.0
620
10.2
8066
-
8762
37.1
9278
38.1
10984
38.2
Source: UNCTAD (2013).
UNCTAD (2005) had argued that Kenya’s
inability to attract FDI is a result of
corruption,
poor
governance,
inconsistencies in economic policies and
structural reforms, deteriorating public
service and poor infrastructure all of which
are being addressed. Despite massive
efforts by the government to implement
reforms such as trade reforms, the country
continues to loose its competitiveness for
FDI to Uganda and Tanzania. However,
Sims (2013) indicates that inflows of FDI
to Kenya could match those of Tanzania
and Uganda beginning 2014 aided by
opportunities created by the discovery of
oil deposits in Turkana. The FDI inflows
are projected to average US $ 1.3 billion
annually for the period 2013-2018 ,
placing it at par with Tanzania and Uganda
who had over the years attracted more
investors due to their vast natural
resources such as gas , oil and other
minerals.
The UNCTAD (2011) figures show that
Uganda and Tanzania have overtaken
Kenya in terms of growth rates due to their
rising FDI inflows , but Kenya is still the
regional business leader ,is it that FDI is a
key factor in driving economic growth or
are other factors equally important ?.
Some of these shortcomings have been
recognized by the government and it has
sought to amend the Investment Promotion
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Act by making investment certificates
optional for all investors .The special
incentives remain conditional upon
holding a certificate, though the minimum
capital requirement to qualify for one
would be lowered to US$ 100,000 for
foreign investors and US $ 13,000 for
national investors.
From the year 2000, the Kenya
government has implemented a number of
initiatives to improve both economic
performance and stimulate foreign direct
investments. The government joined the
Free Trade Area of the Common Market
for Eastern and Southern Africa
(COMESA) in 2000; negotiated for the
resumption of donor aid by the
International Monetary Fund (IMF);
adopted the United Nations Millennium
Development Goals (MDGs) in 2002 and
resolved to reduce poverty levels by half
by the year 2015; implemented the
Economic Recovery Strategy for wealth
and employment creation (ERS) in 2003 to
stimulate private investment to generate
wealth and reduce poverty; implemented
Kenya’s Vision 2030 in 2008 and
promulgated
a
new
constitutional
dispensation in 2010. The Vision,
implemented in successive five-year
medium term plans, with the first plan
covering the period 2008-2012, is
expected to encourage FDI, achieve high
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average Gross Domestic Product growth
rate (of 10% per annum beginning 2012)
and boost investments. It is also expected
to enhance macroeconomic stability, raise
national savings (from 17% in 2006 to
30% by 2012). These measures did see
Kenya’s growth rise from 0.6% in 2000 to
7% in 2007. The growth rate however fell
to 1.6% in 2008 but has been rising since
to 4.7% in 2012. The fall in GDP growth
was due to global financial crisis; fall in
commodity prices and post-election chaos
that followed the December 2007 general
elections in Kenya.
Despite the recent impressive economic
growth, FDI flows to Kenya average
below US$ 39 per capita between 2003
and 2006 compared to US$ 418 and US$
310 for Tanzania and Uganda respectively.
By 2009, Kenya’s net FDI flows stood at
US$ 116 million while Tanzania’s and
Uganda’s US$ 415 and US$ 789
respectively (World Bank, 2012). This is
despite
the
Kenyan
governments
implementing a series of measures to
attract foreign investors that included
among others Manufacturing Under Bond
(MUB) in 1987, Export Processing Zones
(1990) and accession to the African
Growth and Opportunity Act (AGOA) in
2001 (World Bank, 2012). The last
measure however led to significant FDI
inflows from Asia whose investors used
Kenya as a platform for quota-hopping to
access the otherwise restricted US market,
particularly for clothing manufactures
(UNCTAD, 2005).
The Study Objectives
It has been argued in numerous studies that
FDIs contribute positively to economic
growth in the host economies. This is
particularly true where FDIs bring in
investible financial resources and fill the
gap between desired investment and
domestically mobilized savings, facilitate
entry into export markets, and strengthen
the export capabilities of the host country
resulting in productivity gains, technology
transfer, introduction of new processes,
managerial skill s and knowhow in the
domestic markets, employee
training,
international production networks and
access to markets (Caves 1998; Ayanwale,
2007; Borensztein et al, 1998. Findlay
(1978) also makes a case for the increase
in the rate of technical progress in the host
country through a “contagion effect” from
the more advanced technology as a result
of FDIs. FDIs have also been credited with
increase in tax revenues and improvement
in management and labour skills in host
countries (Todaro and Smith, 2003;
Hayami, 2001). Employment creations,
human capital development, contribution
to international investments are some of
the positive effects of FDIs (Jenkins and
Thomas, 2002; World Bank, 2002).
To the contrary, despite the important role
played by FDI in economic growth in host
countries, the level of FDI in Kenya has
been low and stagnant over the past couple
of decades as alluded to above. It is
equally clear that FDI flows and GDP
growth rates fell in the 1980s and 1990s.
After 2000, rising economic growth rates
contrasted with low and stagnant FDI
flows. Kenya’s experience also contrasts
with both Uganda and Tanzania where
both FDI flows and economic growth have
been on a steady rise since the early 1990s.
There are few studies that analyze the
empirical relationship between FDI and
economic growth in Kenya; these include
Kinaro, 2006; Opolot et al, 2008, and
Mwega and Ngugi, 2007. Though it can be
important in informing government
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investment policy in the host country, the
empirical linkage between FDI and
economic growth in Kenya is not clear.
On the basis of the foregoing arguments
the study will raise two main questions;
namely what is the empirical relationship
between Foreign Direct Investment and
economic growth in Kenya? And what
factors determine the FDI flows to Kenya?
The objective of the study is to empirically
investigate the relationship between
foreign direct investment and economic
growth in Kenya and to examine and
quantify the factors that drive foreign
direct investment flows into Kenya.
Specifically the study will seek to use
Kenyan FDI flows and gross domestic
product data to establish the empirical
relationship between foreign direct
investment and economic growth in Kenya
with a view to quantify the relationship.
The study also seeks to determine and
empirically quantify the factors that drive
FDI flows into Kenya and to suggest
policy options that can be implemented to
increase both FDI inflows into Kenya and
hence increase economic growth in the
economy based on the results of the study.
The rest of the paper is organized as
follows. Following this introduction, the
next section briefly reviews the literature
on the FDI and economic growth and their
relationship as well as providing the
theoretical foundation of the study; this is
followed by a brief presentation of the
methodology and a theoretical framework
to be used in the study and includes a
model to be estimated. The section also
briefly discusses the types and sources of
the data used in the study. The last section
discusses the results and policy
implications based on the results of the
study.
Literature Review
In this section we briefly review some of
the theoretical and empirical literature on
foreign direct investments and economic
growth. The section is divided into three
parts comprising theoretical literature
review, empirical review and an overview
of the literature.
Theoretical Foundation
It has been argued that foreign direct
investment can either positively or
negatively affect economic growth in the
host economies. There are many channels
through which FDI can impact on growth.
Blomstrom et al (1994) argue that FDI
exerts a positive effect on growth but there
is a threshold level of income above which
FDI has a positive effect on economic
growth and below which it does not or is
insignificant. Borensztein et al (1998) is of
the opinion that the interaction of FDI and
the quality of human capital has important
effect on economic growth and suggests
that the differences in the technological
absorptive ability may explain the
variation in growth effects of FDI across
countries .They point out that countries
may need a minimum threshold of human
capital to experience positive effects of
FDI on economic growth. It is similarly
suggested by Oloffsdotter (1998) that the
beneficial effects of FDI are stronger in the
countries which have a higher level of
institutional capability.
In another study Alfaro et al (2003) find
little support that FDI has an exogenous
positive effect on economic growth,
however, their findings suggest that local
conditions such as the level of education
and the development of local financial
markets play an important role in allowing
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the positive effects of FDI to materialize in
the economy.
Many studies exist concerning FDI and its
determinants, where the main factors
include the rate of investment return,
market size, macroeconomic variables
(especially their stability), quality of
labour, infrastructure, property rights and
the effects of globalisation which has led
to FDIs in LDCs to shift from market
seeking and resource seeking to the more
efficiency seeking FDIs.
The literature indicates that there is no
conclusive
argument
on
factors
determining FDI and its subsequent effect
on economic growth. Causality between
FDI and growth is still unclear. The
direction of FDI might be associated with
domestic policy variables. The direction of
the relevant causalities between FDI and
growth may well depend on the
determinants of FDI. If the determinants
have strong links with growth in the host
country, growth may be found to cause
FDI, and output may grow faster when
FDI takes place in other circumstances like
the case of oil discovery.
The understanding of the impact of
specific categories of foreign capital
inflows has important policy implications.
Most studies on the growth of specific
types of foreign capital flows focus on
FDI. However, the empirical evidence on
FDI and its impact on host countries
growth are ambiguous at both the micro
and macro level.
The positive effects on the growth of the
host economy can come from investible
financial resources filling the gap between
investment and domestically mobilized
savings, facilitation of entry into export
markets
and
strengthening
export
capabilities of the recipient country. Caves
(1998) has postulated that other positive
effects of FDI include productivity gains,
technology transfer, new processes,
managerial skills, employee training,
international production networks and
access to new markets. Borensztein et al
(1998) see FDI as an important channel for
transfer of technology and contributing to
growth in larger measure than domestic
investment. Findlay (1978) postulates that
FDI increases the rate of technical
progress in the host country through what
he calls a “contagion effect” from the more
advanced
technology,
superior
management practices used by the foreign
firms. Todaro and Smith (2003) and
Hayami (2001) noted that FDI may also
increase
tax
revenue,
improve
management, technology as well as labour
skills in host countries. Many other studies
have noted the benefits of FDI to include
new technology, employment creation,
human capital development, international
trade integration, enhancing domestic
investment, and increased revenue
(Jenkins and Thomas, 2002; World Bank,
2000). FDI is seen as being a positive
contributor to the economic growth of the
host country.
However, it has also been argued that
foreign direct investments can also have
adverse effects on the economy of the host
country. Reis (2001) has advanced the
argument that opening up a country to FDI
in the research and development sector
may replace the domestic firms and
decrease welfare due to the transfer of
capital returns to foreign firms.
Firebaugh (1992) points out that the
foreign firms may fail to encourage local
entrepreneurship, reinvest profits, develop
linkages with domestic firms or fail to use
appropriate technology. FDI can be
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detrimental if it “crowds out” domestic
businesses and engenders inappropriate
consumption patterns or reduce domestic
savings and investment rates by stifling
competition through exclusive production
agreements with the host country. FDI
may also lead to less than optimal
corporate taxes where they are provided
with liberal tax concessions and excess
investment
allowances
and
other
incentives. In a distorted market, FDI can
lead to negative value-added at world
prices coupled with repatriation of profits
and dividends (Mwega and Ngugi, 2007).
The study is based on the theory of profit
maximisation, such that the country’s GDP
can be increased by the input of the FDI
inflows which enhances its productivity as
it helps local firms to be more productive
through the infusion of capital and more
modern and efficient technologies that it
brings as well as fostering competition
both locally and in the country’s foreign
markets.
It is generally believed however, that FDI
provides net benefits to the host country.
This explains why the importance of FDI
in economic performance has been
extensively discussed in the economic
literature.
Empirical Studies
Several studies have been conducted on
the empirical relationship between FDI’s
and economic growth. Some of these
studies have shown that FDIs positively
influence economic growth in the host
countries. Dees (1998) in a study on the
determinants and effects of foreign direct
investments in China found that FDI has
been important in explaining China’s
economic growth. Similarly, de Mello
(1997) also presents a positive correlation
between FDIs and economic growth of
selected Latin American countries. Barrel
and Pain (1999) explored the benefits of
FDI of U.S multinational in four European
Union countries and find that FDI may
affect the host country’s performance
positively in the case where there are
transfers of technology and knowledge
through the FDI to the host economy.
Firm-level studies of specific countries
provide contradictory evidence on the role
played by FDI in economic growth. For
example Wilmore (1986) examining a
sample of 282 pairs of firms from 80
industries in Brazil found that FDI had a
beneficial impact on growth since foreign
firms are more efficient than domestic
ones. Moreover, Blomstrom (1986) found
that FDI enhances productivity growth of
Mexican firms. FDI spillovers that occur
when the entry or presence of a foreign
investment firm(s) contribute to the
productivity or efficiency benefits of
indigenous firms are critical in defining
the impact of FDI on the growth of host
nations.
The literature identifies competition,
linkages, labour mobility, skills and
imitation as the main channels of
technological spillovers from FDI
to
indigenous firms (Blomstrom and Kokko,
1998). We however, note that FDI
spillover may either be positive or
negative on their impact on economic
growth in the host countries. Some
empirical studies show positive effects of
FDI spillovers on economic growth in the
host countries.
Some empirical studies show positive
effects of FDI spillovers on economic
growth (Caves, 1974) on Australia and
Kokko (1994) for Mexico. However,
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Haddad and Harrison (1993) find no
evidence of positive spillovers from FDI in
Morocco. The study by Aitken and
Harrison (1999) for Venezuelan firms in
the period 1979-1989 and Djankov and
Hoekman (2000) for the Czech Republic
firms report negative spillovers. Hanson
(2001) concludes that the evidence that
FDI generates positive spillovers for host
countries is weak. Microeconomic studies
report positive effects of FDI and
productivity spillovers, these include
studies by Lipsey and Sjöholm, 2004;
Black
and
Gertler,
2008.
Most
macroeconomic studies generally suggest
that FDI exerts a positive impact on
economic growth in particular contexts.
Balasubramannyam et al (1996) and
Zhang (2001) find that effects on growth
of FDI are more significant in the presence
of trade openness and where host country
adopted liberalisation. Borensztein et al
(1998) argue that FDI is an important
channel for the transfer of technology and
contributes to economic growth when the
country has a highly educated workforce.
Blomstrom et al (1994) found that among
developing countries, the positive impact
of FDI on growth is larger in those
countries that exhibit higher levels of per
capita income. FDI is also beneficial for
economic growth when the country has
sufficiently developed and sophisticated
financial markets (Alfaro et al, 2004). The
other factors that enable FDI to positively
impact on growth include political and
economic stability as well as the quality of
institutions and infrastructure which
complements FDI (see Oloffsdotter, 1998;
Hall and Jones, 1999; Rodrik et al, 2002;
Aschauer, 1989 and Tondl and Prüfer,
2007). The literature therefore suggests
that openness to trade, human capital,
financial market development, public
infrastructure and quality of institutions
affects a host country’s ability to absorb
FDI spillover.
The literature on FDI shows that its impact
on economic growth can either be direct or
indirect. The indirect impact or spillovers
are dependent on the host country’s
conditions. Specifically this depends as per
capita income, human capital stock,
financial sectors level of sophistication,
the level of development and quality of
public infrastructure, the quality of
institutions,
trade
openness
and
macroeconomic stability. The empirical
evidence however shows that the
relationship between FDI and growth is
uncertain and varies across host countries.
This paper proposes to use Kenyan data to
find out whether FDI enhances economic
growth in Kenya.
Using panel data for 25 central and Eastern
European and former Soviet transition
economies, Campos and Kinoshita (2003)
examined the effects of FDI on growth for
the period 1990-1998. Their main results
indicated that FDI has a significant
positive effect on economic growth of
each country. Focusing on the factors that
explain growth in developing countries,
Blomström et al (1994) found that foreign
direct investments exerts a positive effect
on economic growth but that there seems
to be a threshold level of income above
which FDI has positive effect on economic
growth and below which it does not. The
explanation is that only those countries
that have reached a certain income level
can absorb new technologies and benefit
from technology diffusion, and thus reap
the extra advantages that FDI offer. They
concur with other studies that suggest
human capital as one of the reasons for the
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differential response to FDI at different
levels of income (Borensztein et al, 1998;
Bengos and Sanchez-Robles, 2003).
However, some studies have found that
FDI may not influence long-run economic
growth. In a study on the interaction
between foreign direct investment,
economic freedom and growth, Bengos
and Sanchez-Robles (2003) estimated the
relationship between FDI and economic
growth using panel data for eighteen Latin
American countries over the period 19701999. They show that FDI had positive
and significant impact on economic
growth. However, they also found that the
host country requires adequate human
capital, political and economic stability
and liberalized market environment so as
to gain from long-term FDI inflows. It has
also been shown by Ang (2008) that better
developed financial systems allow an
economy to exploit the benefits of foreign
direct investment more efficiently. The
author used Thailand as a case study to
examine the role of FDI and financial
development in the process of economic
development. The estimation uses an
unrestricted error-correction model to
avoid omitted lagged variable bias, and an
instrumental variable to correct for
endogeneity bias. Using annual firm series
data from 1970-2004 the results show that
financial
development
stimulates
economic development whereas foreign
direct investment impacts negatively on
output expansion in the long-run.
However, an increased level of financial
development enables Thailand to gain
more from FDI, suggesting that the impact
of FDI on output growth can be enhanced
through financial development.
Some studies indicate that the relationship
between the FDI and economic growth is
weak and insignificant. Ayanwale (2007)
investigating the empirical relationship
between non-extractive FDI and economic
growth in Nigeria using annual time series
and ordinary least squares technique found
the relationship between FDI and
economic growth to be positive but not
significant.
Overview of The Literature
It is clear from this brief review that the
effects of FDI on growth are dependent on
the characteristics of the host country and
the sectors where the FDI is directed.
Large market size and high incomes may
attract market seeking FDI as opposed to
small and low income economies. Marketseeking FDI is therefore induced by
market access to host countries for
efficient utilization of resources and
exploitation of economies of scale. FDI
complements growth when directed
towards highly productive sectors of the
host economy. Investment in development
of good quality infrastructure, low cost and
highly skilled human capital and
innovations and technological progress
increase productivity and promote growth
in the long-run. The empirical evidence
shows that the relationship between FDI
and growth and the expected significance
of the determinants of FDI varies across
host countries.
The empirical evidence shows that the
relationship between FDI and growth and
the expected significance of the
determinants of FDI varies across host
countries. Some studies show positive
effect of FDI on economic growth while
others show negative impact; whereas
some
studies
exhibit
insignificant
relationship between the two variables.
The differences in empirical results may
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be attributable to differing economic
institutional and technological conditions
in the recipient countries. The few country
specific studies also indicate that there
may be endogenous relationship between
FDI and growth which may have to be
taken into account if the results are to be
robust.
The next section presents the methodology
used in the study.
Theory And Empirical Model
In this section we present the methodology
used in the study. The methods are
outlined before the model is specified.
Section 3.2 briefly presents the types,
sources and measurement of the data used.
The relationship between FDI and
economic growth is situated in growth
theory that pronounces the role of
improved technology, efficiency and
productivity in promoting growth (Lim,
2001). However, the potential contribution
of FDI to growth is dependent strictly on
the on the circumstances in the recipient or
host countries. Certain host country
conditions are necessary to facilitate the
spill-over effects.
In this study, we assume in line with
standard economic theory that foreign
capital inflows into a recipient country will
increase its stock of capital and level of
technology and lead to better economic
performance. Foreign direct investment
will affect economic growth positively
through improved technology, efficiency
and increased productivity (Lim, 2001).
However, as noted in the review of
literature, the potential contribution of FDI
to growth is strictly dependent on the
circumstances
in the recipient or host
country. Theoretically a country’s GDP is
influenced by a variety of factors. RGDP
is measured as nominal GDP deflated by
the GDP deflator, (base 2000 s=100).
We hypothesise FDI to positively
influence growth. FDI promotes economic
growth in host countries by providing
external capital (O’hearn, 1990). Apart
from increasing tax revenues, improving
managerial and labour skills it also creates
employment in the host country. The gross
fixed domestic investment
(GFDI) is
proxied in this study by the share of the
gross domestic capital formation to GDP
less net FDI flows. Increased investments
rates promote productivity in a country as
argued by Grossman and Helpman (1991).
We expect a positive relationship between
this variable and GDP. The level of human
capital measured here by the secondary
school enrolment rate should have a
positive impact on GDP. It can be argued
that widespread availability of cheap and
highly skilled labour force tends to attract
private
investment
and
enhances
productivity in a country (Akinlo 2004,
Barro and Lee, 1994). The rate of inflation
measured here by the annual percentage
change in consumer price index is a
reflection of macroeconomic stability. A
low and stable inflation rate implies a
more reliable economic environment
enabling investors to benefit from existing
opportunities (see Larrain and Vergara,
1993; Serven and Solimano, 1993). A
negative relationship exists between
inflation and GDP.
The size of Government (GOVSIZE) is
measured in this study by the share of total
government consumption to GDP.
Anyanwale (2007) points out that higher
level of general final government
consumption provide social capital that
encourages production and growth. Trade
openness (OPEN) on the other hand
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promotes economic growth through
increasing competitiveness and providing
access to markets for finished products,
(Balassa, 1978). It also enables the
importation of raw materials and capital
goods and facilitates access to new
technologies and skills. Large export
markets encourage inward market-seeking
FDI and foreign capital inflows (Kinaro,
2006; Ajayi 2007).
There are evidently other influences on
GDP with varying degrees of importance
are captured by the error term (e) in our
specification.
The Model
As explained in the section above,
equation 1 below shows the hypothesized
relationship between economic growth and
its determinants.
We hypothesize economic growth to be
influenced by several factors as shown in
the model below
RGDP  f ( FDI , GFDI ,UMCAP, INFL, GOVSIZE , OPEN
Where
RGDP = Real Gross Domestic Product
FDI = Foreign Direct Investment
GFDI = Gross Fixed Domestic Investment
HUMCAP = Level of Human Capital
INFL = Rate of Inflation
GOVSIZE = Government consumption
The equation which relates the real gross
domestic product to various factors that
influence it can be elaborated as
RGDP  g 0  g1 FDI  g 2 HUMCAP  g 3 INFL  g 4 GOVSIZE  g 5 OPEN  g 6   )................(2)
Where g1,....., g6 are the coefficients to be
estimated and g0 is the constant. ε is the
error term.
Equation (2) above shows the growth
model to be estimated.
When estimating the growth equation it is
possible that some of the variables could
be correlated to the error term resulting in
a problem of endogeneity and could give
rise to biased estimated coefficients. If
this were to happen appropriate
instruments will be searched and used in a
2 stage least squares (2SLS) estimation.
Since we also wish to quantify the factors
that drive direct foreign investment (DFI)
inflow into the country we shall estimate
an FDI equation. We hypothesize that
direct foreign investment is influenced by
a variety of factors as shown below:
FDI   0  1 RGDP   2 INFRAC  3OPEN   4 GR  5 RLIR   6TDS   7 ROI  ....(3)
Where
FDI = Foreign Direct Investment
measured share of FDI to GDP
RGDP = Real Gross Domestic Product
(nominal GDP deflated by the GDP
deflator
HUMCAP = Level of Human Capital
(proxied by the secondary school
enrolment rate)
GR = Market size (measured by annual %
change in real GDP)
RLIR = Real Interest rate (measured by
the difference between the nominal
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lending interest rate and the rate of
inflation
GFDI = Gross Fixed Domestic Investment
(proxied by the share of the gross domestic
capital formation to GDP less net FDI
inflows)
OPEN = Openness of the economy
(measured by the ratio of trade exports +
imports to GDP)
INFRAC = Infrastructure (proxied by the
electric
power
transmission
and
distribution losses as a % of the total
output)
INFL = Inflation rate measured by the
annual % change in consumer price index
TDS = Total debt service to GDP ratio
measured by the share of total external
debt service to GDP
CPI = Corruption Perception Index
ROI = Return on Investment proxied by
long-term US interest rates
GOVSIZE = Government consumption
measured by the share of the total
government consumption to GDP
µ = The Stochastic error term
The coefficients  0 ............. 7 are to be
estimated.
In the FDI model equation (3) above, FDI
is measured as the ratio of FDI to GDP and
is the dependent variable. It is
hypothesised that a high real GDP reflects
large market size that attracts further FDI
especially the market seeking ones,
resulting in more demand for products or
services to be provided by FDI (Chunlai,
1997; Mwega and Ngugi, 2007). The
Gross Fixed Domestic investment
increases the rate and efficiency of
domestic capital investment, raising
productivity in a country and thereby
encourages FDIs. We expect a positive
impact on FDI. The measure of openness
is as defined above. This may encourage
exports and hence lead to market seeking
FDIs
Infrastructure is critical for both economic
growth and competitiveness. In this study
it is proxied by
Electric
power
transmission
and
distribution losses as a percentage of total
output. We expect this to have a negative
impact on FDI inflows as it relates to high
cost of production (see Anyanwale, 2007).
The real exchange rate has an important
impact on FDI inflows. A depreciation of
the exchange rate
Encourages higher inflows as it makes
local assets and production costs cheaper.
An appreciation of the exchange rate has
the opposite effect.
The choice of variables included in the
model specifications has been guided by
the theories of economic growth and the
determinants of FDI inflows discussed in
the literature review above.
The following section elaborates the
sources and types of data used in the study.
Data Sources, Types and Measurement
The study covers the period 1970-2010;
and therefore includes the period which
Kenya was the preferred FDI destination
in East Africa as well as the period in
which she was overtaken by both Tanzania
and Uganda as the main FDI destinations
in the region.
The data is annual time series data
obtained from secondary sources. The
sources include the Central Bank’s annual
economic reviews, Republic of Kenya
Statistical Abstracts and economic surveys
produced by the Kenya National Bureau of
Statistics (KNBS). Other sources of data
included the World Bank’s World
Development Indicators and Global
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Development Finance reports. The
UNCTADs World Investment reports and
the IMF’s International Financial Statistics
have also been used. Due to difficulties in
obtaining certain qualitative data on such
variables like corruption it was left out of
the analysis even though we are acutely
aware that corruption levels in a country
can have a major negative impact on the
inward flow of FDIs.
Time Series Properties and
Estimation Tests
Given the time series nature of our data, it
was imperative to carry out estimation
tests to be sure that our data is not nonstationary so that we avoid the problem of
spurious regression results. We therefore
conducted stationarity tests for the series
using the Augmented Dickens Fuller
(ADF) test. The ADF assumes that the
error terms are independently and
identically distributed. A time series data
is said to be stationary if its mean,
variances and autocovariance remain the
same no matter at what point we measure
them.
Unit root test for stationarity results
We used the Augmented Dickens Fuller
test to test for stationarity in our data. The
test indicates whether or not the variables
are stationary. The null hypothesis is that
of non-stationarity while the alternative
hypothesis is that of stationarity. The test
statistic is then compared with the tcritical. If the t-statistic is less than tcritical we reject the null hypothesis of
non-stationarity and therefore the series is
stationary. On the other hand, if the tstatistic is more than the critical we accept
the null hypothesis and non-stationarity
and the series is therefore non-stationary
and prone to spurious regression. The table
below shows that our data was stationary
and we do not face the possibility of
spurious
regression
results.
Table 7: Unit Root Test using ADF
VARIABLE ADF
1%
STATISTIC CRITICAL
VALUE
RGDP
-6.900
-3.648
GR
-5.015
-3.648
FDI
-6.941
-3.648
HUMCAP
-6.426
-3.648
OPEN
-6.303
-3.648
INFRAC
-5.139
-3.648
GOVSIZE
-5.190
-3.648
TDS
-6.008
-3.648
INFL
-6.352
-3.648
5%
CRITICAL
VALUE
-2.958
-2.958
-2.958
-2.958
-2.958
-2.958
-2.958
-2.958
-2.958
10%
CRITICAL
VALUE
-2.612
-2.612
-2.612
-2.612
-2.612
-2.612
-2.612
-2.612
-2.612
NATURE
RLIR
ROI
-2.958
-2.958
-2.612
-2.612
STATIONARY
STATIONARY
-5.772
-6.761
-3.648
-3.648
STATIONARY
STATIONARY
STATIONARY
STATIONARY
STATIONARY
STATIONARY
STATIONARY
STATIONARY
STATIONARY
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Results in table 5 above show that the
ADF test statistics are less than the tcritical at the 1%, 5% and 10% and we
therefore reject the null hypothesis of nonstationarity and accept that the series are
stationarity and our OLS regression could
be conducted since the results would not
be spurious (The OLS results are shown in
tables 4 and 5).
The study hopes to determine the
important variables that may be important
in encouraging the inflow of direct foreign
investment to Kenya and recommend
policies that can enhance the inflow of FDI
into Kenya. The study also hopes to
determine the empirical relationship
between economic growth and foreign
direct investment in Kenya with the view
to both boost inflow of direct foreign
investment and economic growth.
Having elaborated the hypothesised
relationships between our dependent
variables and the independent variables in
the two models we estimated the
relationships using time series data and the
Table 8: Determinants of RGDP
Variables
fdi
humcap
infla
govsize
open
_cons
Coefficient
1949.496
822.911
9.835928
3523.373
585.8971
-45026.6
results are discussed in the following
section.
Results and Discussion
We set out to empirically investigate the
relationship between foreign direct
investment and economic growth in Kenya
by examining the factors that drive foreign
investment flows into Kenya. The
objective was to establish the empirical
relationship between FDI and economic
growth in Kenya.
Regression Results
The growth equation
From our results in table 1 we see that the
growth in GDP is positively influenced by
human capital and the variable is
significant at the 1% level (t-value=4.96)
and a P- value=0.000. The results also
show that the government expenditure
(GOVSIZE) is a significant determinant of
the real GDP.T he variable is significant at
the 1% level with a t-value of 3.17 and a pvalue of 0.003.
Standard Error
2514.297
166.0322
32.06965
1111.173
215.6161
23981.22
t-values
0.78
4.96
0.31
3.17
2.72
-1.88
P-values
0.443
0.000
0.761
0.003
0.001
0.069
No. of obs = 41; F(6, 34)= 21.28; Prob>F = 0.0000; R-Squared = 0.7897; Adj R-Squared = 0.7526; Root MSE = 8237.2
It was argued that the government size
which is measured by the share of total
government consumption in GDP should
influence economic growth in a positive
manner. The higher the level of the general
final government consumption the more
the social capital and this encourages
production and the growth of GDP. The
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results indicate a highly significant
coefficient (t-value=3.17) at the one
percent level of significance. The results
therefore suggest that government
consumption is a major contributor to
GDP growth and should be encouraged. It
appears that government expenditure on
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social services and other amenities is an
important boost to growth.
The openness of the economy to the rest of
the world is similarly shown to be a major
driver of the GDP growth rate. The
variable was measured as the ratio of trade
defined as imports plus exports to the
GDP. The idea here is that trade openness
promotes economic growth through
increasing competitiveness and providing
access to markets both for finished
products and exports as pointed out by
Balassa (1978). By enabling importation
of raw materials and capital goods and
facilitating access to new technologies and
managerial skills it positively impacts on
the growth of GDP. The coefficient is
large and positive and is reported to be
significant at the 1% level (t-value=2.72)
with a p-value of 0.010.
The variables in the growth equation
explain about 80% of the variations in the
GDP growth rate and the overall model
seems to be well specified with an Fstatistic of 21.2. The p-value for the model
as a whole indicates it is fairly well
specified.
The FDI equation
The results in our table 2, shows the
determinants of FDI inflows to Kenya. The
real gross domestic product measured as
defined above is shown to be a major
influence on the FDI inward flow.
Table 9: The determinants of FDI
Variables
rgdp
infrac
open
gr
rlir
tds
roi
_cons
Coefficient
2.69E-05
0.066262
0.017142
-0.00854
-0.00668
0.072952
-0.07517
-0.73934
Standard Error
1.09E-05
0.03405
0.013785
0.022764
0.010635
0.043933
0.05979
1.102412
t-values
2.47
1.95
1.24
-0.37
-0.63
1.66
-1.26
-0.67
P-values
0.019
0.06
0.223
0.71
0.534
0.107
0.218
0.507
No. of obs = 41; F(8, 32)= 1.86; Prob>F = 0.1011; R-Squared = 0.3179; Adj R-Squared = 0.1474; Root MSE = .52994
The theoretical basis for this is that a high
real GDP reflects a large market size that
attracts FDI, especially the market seeking
type. The high GDP leads to higher
demand for the products and services
provided by the foreign firms. A high real
GDP therefore has a positive influence on
the FDI. The results in table 2 show that
the variable is highly significant at the 1%
level and has a t-value of 2.47. The
coefficient is positive as was hypothesized.
The results further show that the
infrastructure is also an important
influence on the FDI inflow. The variable
has a positive coefficient and is significant
at the 10% level with a t-value of 1.95. We
had proxied infrastructure by electric
power transmission and distribution losses
as a percentage of total output. The
expectation was that it would have a
negative impact on FDI inflows due to the
measurement method employed as it
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relates to high cost of production.
However, the results do not support this
hypothesized relationship. It is possible
and even likely that good infrastructure
which
improves
a
country’s
competitiveness may in fact attract FDI
flows due to improved cost conditions in a
country.
The variable on total external debt service
as a ratio of GDP (tds) captures the
liquidity and solvency constraints imposed
by the debt burden and the higher the debt
services ratio the more it deters FDI
inflows. The results indicate that the
coefficient is positive though not
statistically significant. The a priori
expectation was that it would be negative
due to the negative impact of the debt
burden which discourages FDI flows.
These results imply that Kenya can attract
more FDI which are acknowledged to have
potential benefits that can accrue to the
country. FDI is important for economic
growth as it provides the much needed
capital
for
investment,
increases
competition within the country and aids
local firms to become more productive by
adopting more efficient technology or by
investing in human or physical capital.
FDI also contributes to growth in a
substantive manner because it is more
stable than other forms of capital flows.
Conclusion And Policy Implications
The driving objective of this study was to
investigate the empirical relationship
between Foreign Direct Investment and
economic growth in Kenya. The study also
set out to empirically investigate the
factors that drive FDI flows into Kenya,
having established that Kenya’s FDI
inflow record over the recent years has not
been impressive despite her being among
the most favoured FDI destinations in the
1970s in Eastern Africa. The realisation
that Kenya is now among the countries
with very low levels of FDI motivated the
study. By establishing empirically which
factors drive both growth and FDI inflows
to Kenya, it is possible to design policies
that can attract the flows into Kenya.
The study has shown that human capital,
government expenditure and openness of the
economy are vital for the growth of the
economy and therefore policies that can
enhance these factors would be needed.
Furthermore, the drivers of Foreign Direct
Investment have been shown to be the real
GDP growth, low levels of indebtedness and
improved infrastructural facilities. It is clear
that the role of government would be crucial in
encouraging FDI inflow to Kenya. It can also
be argued that most FDI to Kenya is the
market-seeking type and this requires a rapidly
growing real GDP. The formation of regional
blocks, and political stability would also be
crucial. It can be concluded that the main FDI
determinants in Kenya are market size (rgdp),
political stability (d2007) and openness of the
economy as well as infrastructure. The major
impediments to FDI inflow would be political
instability, institutional factors as well as
crime and insecurity. There is little doubt that
the cost of doing business, bureaucratic redtape, high cost of electricity and other utilities
and poor investment code as well as endemic
corruption are some of the factors that hep
explain the low FDI inflows into Kenya in the
recent decades.
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