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Research
Policy
46
(2017)
280–291
Contents
lists
available
at
ScienceDirect
Research
Policy
jo
ur
nal
ho
me
p
age:
www.elsevier.com/locate/respol
Institutions,
resources
and
innovation
in
East
Africa:
A
firm
level
approach
Laura
Barasaa,b,∗,
Joris
Knobenb,
Patrick
Vermeulenb,
Peter
Kimuyua,
Bethuel
Kinyanjuia
aUniversity
of
Nairobi,
School
of
Economics,
P.O.
Box
30197,
Nairobi,
Kenya
bRadboud
University,
Institute
for
Management
Research,
P.O.
Box
9108,
6500
HK
Nijmegen,
The
Netherlands
a
r
t
i
c
l
e
i
n
f
o
Article
history:
Received
20
April
2015
Received
in
revised
form
9
November
2016
Accepted
25
November
2016
Keywords:
Firm-level
resources
Regional
institutional
quality
Innovation
East
Africa
Multilevel
logistic
model
a
b
s
t
r
a
c
t
This
study
examines
how
firm-level
resources
interact
with
regional
institutional
quality
to
explain
inno-
vation
in
East
Africa.
We
hypothesize
that
the
institutional
environment
within
which
the
firm
operates
moderates
the
effect
of
firm-level
resources
on
innovative
output.
We
examine
the
moderating
role
of
institutions
with
regards
to
the
transformation
of
firm-level
resources
including
internal
research
and
development,
human
capital
and
managerial
experience
into
innovative
output
using
firm-level
data
from
the
World
Bank
Enterprise
Survey
and
the
Innovation
Follow-up
Survey
for
three
countries
in
East
Africa
including
Kenya,
Tanzania
and
Uganda.
We
test
our
hypotheses
using
a
clustered
robust
standard
errors
logistic
model.
We
find
that
the
effects
of
firm-level
resources
vary
depending
on
the
institutional
environment
and
that
regional
institutional
quality
positively
moderates
the
effects
of
the
firm-level
resources.
©
2016
The
Authors.
Published
by
Elsevier
B.V.
This
is
an
open
access
article
under
the
CC
BY
license
(http://creativecommons.org/licenses/by/4.0/).
1.
Introduction
Innovation
has
been
considered
a
key
driver
for
economic
growth,
enhancing
competitive
advantage
and
stimulating
the
pro-
ductivity
of
firms
(Schumpeter,
1934)
in
developed
and
developing
countries
alike
(Chudnovsky
et
al.,
2006;
Crespi
and
Zuniga,
2011).
Our
study
focuses
on
product
innovation,
which
is
defined
as
the
introduction
of
a
new
good
or
service
or
the
significant
improve-
ment
of
an
existing
product
with
respect
to
its
characteristics
and
intended
use
(Oslo
Manual,
2005;
Ayyagari
et
al.,
2012;
Chadee
and
Roxas,
2013).
Although
firms
in
developing
countries
oper-
ate
below
the
technology
frontier
with
lower
levels
of
managerial
and
production
skills
(Goedhuys,
2007;
Goedhuys
and
Sleuwaegen,
2010),
individual
firms
play
a
key
role
in
developing
innovations.
While
progress
has
been
made
in
developing
countries
to
improve
the
general
business
climate,
in
terms
of
property
rights,
access
to
finance
and
enhanced
human
capital
(Alvarez
and
Barney,
2014),
firms
in
developing
countries
continue
to
face
a
specific
set
of
challenges
that
influence
their
innovation
activity
and
the
results
∗Corresponding
author
at:
University
of
Nairobi,
School
of
Economics,
P.O.
Box
30197,
Nairobi,
Kenya.
E-mail
addresses:
l.barasa@fm.ru.nl
(L.
Barasa),
j.knoben@fm.ru.nl
(J.
Knoben),
p.vermeulen@fm.ru.nl
(P.
Vermeulen),
pkimuyu@uonbi.ac.ke
(P.
Kimuyu),
bkinuthia@uonbi.ac.ke
(B.
Kinyanjui).
thereof
(Bradley
et
al.,
2012).
These
largely
pertain
to
two
dominant
factors.
The
first
factor
is
related
to
specific
firm-level
resources
and
capabilities.
As
indicated
in
previous
research,
firm
resources
are
directly
related
to
“the
search
for,
absorption
of
and
generation
of
new
technology”
(Srholec,
2011:
1545).
Firm-level
resources
allow
firms
to
distinguish
themselves
from
their
competitors
and
develop
a
competitive
advantage.
According
to
the
resource-based
view
(RBV)
of
the
firm,
this
is
only
possible,
however,
when
resources
are
valuable,
rare,
inimitable
and
non-substitutable
(Barney,
1991).
The
main
problem
for
competitors
in
imitating
a
successful
resource
base
is
the
time
it
takes
to
create
and
develop
such
resources
and
the
causal
ambiguity
surrounding
these
resources,
which
makes
it
difficult
to
identify
exactly
what
resources
lead
to
competi-
tive
advantage
(Peteraf,
1993).
Also
in
developing
countries,
firms
require
resources,
competencies
and
skills,
which
can
be
build
up
through
RandD
or
training,
to
become
innovative
and
competi-
tive
(Goedhuys
et
al.,
2014).
However,
possessing
such
resources
does
not
automatically
lead
to
the
creation
of
value
(Sirmon
et
al.,
2007;
Ndofor
et
al.,
2015).
Firms
must
accumulate,
combine
and
exploit
resources
in
order
to
extract
value
from
them
(Grant,
1991).
However,
Barney
(2001)
argued
that
the
value
of
these
firm
resources
must
be
understood
in
the
broader
context
in
which
the
firm
is
embedded.
In
other
words,
even
if
a
firm
possesses
and
uses
valuable,
rare,
inimitable
and
non-substitutable
resources
more
‘astutely’
than
competitors
(Eisenhardt
and
Martin,
2000),
the
extent
to
which
it
can
actually
extract
value
from
them
is
likely
to
http://dx.doi.org/10.1016/j.respol.2016.11.008
0048-7333/©
2016
The
Authors.
Published
by
Elsevier
B.V.
This
is
an
open
access
article
under
the
CC
BY
license
(http://creativecommons.org/licenses/by/4.0/).
L.
Barasa
et
al.
/
Research
Policy
46
(2017)
280–291
281
also
depend
on
the
environment
of
the
firm
(Sirmon
et
al.,
2007).
Hence,
merely
possessing
and
using
firm
resources
is
not
enough
to
extract
value
from
them
and,
in
our
case,
develop
new
innovative
products.
This
brings
us
to
the
second
challenge
firms
in
developing
countries
face.
The
second
challenge
is
the
role
of
institutions
(Acemoglu
and
Robinson,
2008).
Properly
designed
institutions
can
stimulate
pro-
ductive
behaviours
(Dollar
and
Kraay,
2003),
yet
weak
institutions
often
lead
to
unproductive
behaviours
(Greif,
2006).
Institutions
can
reduce
transaction
costs
and
uncertainty
and
ease
coordination
between
economic
agents
(Alonso
and
Garcimartín,
2013).
Institu-
tional
quality
encompasses
(1)
the
process
by
which
a
government
is
selected,
monitored
and
replaced
(2)
a
government’s
capacity
to
effectively
formulate
and
implement
sound
policies
and
(3)
the
economic
and
social
interactions
between
citizens
and
the
state
are
governed
(Kaufmann
et
al.,
2011).
As
such,
the
institutional
environment
can
influence
the
propensity
of
firms
to
innovate
in
a
variety
of
ways
(North,
1990).
For
instance,
weak
enforcement
of
regulations
and
the
absence
of
intellectual
property
rights
may
hin-
der
innovation.
Compared
to
countries
in
Latin
America,
Southeast
Asia
and
Middle
East
and
North
Africa,
countries
in
sub-Saharan
Africa
perform
poorly
in
upholding
the
rule
of
law,
regulatory
qual-
ity,
control
of
corruption
and
government
effectiveness
(Alence,
2004).
In
our
study,
we
focus
on
the
regional
institutional
environment
within
which
the
firm
is
embedded.
Notwithstanding
the
impor-
tance
of
country-level
institutions,
we
argue
that
the
quality
of
institutions
will
also
significantly
differ
across
regions
in
a
country.
Regions
can
be
characterized
by
a
specific
set
of
formal
(laws,
rules
and
regulations)
and
informal
institutions
(norms
and
values)
(cf.
North,
1990)
that
function
as
durable
structures
specific
to
the
terri-
tory
(Boschma
and
Frenken,
2009).
Regions
in
developing
countries
are
often
culturally,
politically
and
economically
heterogeneous.
In
addition,
within-country
variation
in
the
implementation
of
formal
institutions
is
also
likely
to
exist
in
large
and
complex
countries
(Shi
et
al.,
2012).
In
line
with
Laursen
et
al.
(2012)
we
contend
that
the
regional
environment
affects
the
ability
of
firms
to
introduce
new
innovations.
Yet,
perhaps
more
importantly,
we
argue
that
poor
regional
institutional
quality
within
a
focal
country
makes
it
more
difficult
to
extract
value
from
a
firm’s
resources
that
are
needed
to
innovate
(cf.
Zhu
et
al.,
2015).
Poor
institutional
quality,
or
the
presence
of
weak
institutions,
has
been
reported
to
under-
mine
the
functioning
of
factor
markets,
increase
transaction
costs
and
magnify
information
asymmetries
(Meyer
et
al.,
2009),
which
has
a
negative
effect
on
the
possibilities
to
extract
value
from
cur-
rent
resources.
Regional
institutional
quality
refers
to
a
situation
in
which
there
is
low
corruption,
a
strong
rule
of
law
and
a
high
degree
of
regulatory
quality
within
a
region.
As
such,
we
infer
that
the
extent
to
which
firms
can
successfully
use
their
resources
to
innovate
is
likely
to
differ
between
regions
due
to
differences
in
regional
institutional
quality.
Thus,
it
is
critical
that
we
understand
how
the
regional
institutional
environment
of
a
firm
influences
the
transformation
of
firm-level
resources
into
innovative
output
for
firms
in
developing
countries
(Martin-de
Castro
et
al.,
2013).
Moreover,
it
has
been
argued
that
the
linkage
between
macro-
institutional
frameworks
of
national
and
regional
innovation
systems
is
of
paramount
importance
in
shaping
firms’
innovation
processes
(Cooke
et
al.,
1998;
Asheim
and
Coenen,
2006).
Regional
innovation
systems
relate
to
the
creation
of
policy
frameworks
that
aim
at
the
systematic
promotion
of
learning
processes
for
inno-
vation
and
competitive
advantage
in
regional
economies
(Cooke
et
al.,
1998).
Regions
are
important
mediums
of
governance
and
economic
coordination
at
the
meso-level
(Lundvall
and
Borrás,
1997).
More
importantly,
exploring
the
role
of
governance
struc-
tures
including
regional
regulatory
and
institutional
frameworks
is
vital
for
deepening
the
understanding
of
the
innovation
pro-
cess
(Ekman
et
al.,
2011).
In
addition,
geographical
clustering
of
firms
gives
rise
to
non-pecuniary
knowledge
spillovers
that
cre-
ates
a
highly
innovative
environment
influencing
territorial
growth
(Garavaglia
and
Breschi,
2009).
Hence,
entrepreneurial
activity
in
a
geographical
area
provides
a
means
by
which
firms
exploit
positive
external
spillovers
for
innovation
in
a
region
(Cooke
et
al.,
1998).
All
of
these
insights
underline
the
salience
of
studying
innovation
in
its
regional
institutional
context.
While
there
are
numerous
studies
examining
innovation,
most
investigate
the
determinants
of
innovation
in
the
context
of
advanced
economies
(De
Jong
and
Vermeulen,
2006;
McAdam
et
al.,
2014).
The
findings
of
these
studies
have
limited
implications
for
innovation
in
developing
economies
due
to
the
different
nature
of
innovation
in
developing
countries
(e.g.
Bradley
et
al.,
2012)
and
disparities
in
institutional
quality
at
the
regional-level.
There
are
virtually
no
empirical
studies
examining
how
regional
institutional
quality
moderates
the
relationship
between
firm-level
resources
and
innovative
output
in
East
Africa.
This
may
be
attributed
to
the
fact
that
data
on
innovation
in
developing
countries
has
been
unavailable
only
until
recently
or
was
not
collected
in
a
systematic
manner
(Ayyagari
et
al.,
2012;
Goedhuys
and
Veugelers,
2012).
This
warrants
an
investigation
into
how
regional
institutional
quality
influences
the
ability
of
firms
to
extract
value
from
their
resources.
In
our
case,
value
extraction
is
represented
by
the
innovative
output
of
firms.
The
rationale
behind
our
choice
of
the
three
countries
in
East
Africa
is
their
geographical
and
institutional
proximity,
which
have
been
suggested
as
vital
for
innovation
(Boschma,
2005).
Addi-
tionally,
these
three
countries
embody
common
characteristics
of
countries
in
the
East
African
region
particularly
with
regards
to
striking
disparities
in
regional
institutional
quality
encompassing
differences
in
the
levels
of
corruption,
regulatory
quality,
govern-
ment
effectiveness
and
rule
of
law
(Alence,
2004).
Our
study
makes
two
contributions.
First,
it
sheds
light
on
the
micro
level
relation
between
firm-level
resources
and
innovation
in
developing
coun-
tries,
an
area
of
study
that
has
only
received
scarce
attention
for
a
long
time
due
to
the
absence
of
firm
level
data
(e.g.
Goedhuys
et
al.,
2014).
Second,
this
study
deepens
the
understanding
of
how
the
regional
institutional
environment
interacts
with
firm-level
resources
to
explain
the
innovative
output
of
firms
in
developing
countries.
We
argue
that
regional
heterogeneity
within
countries
gives
rise
to
variation
in
regional
institutional
quality
(cf.
Picard
et
al.,
2006).
Taking
into
account
the
different
cultures
and
gover-
nance
systems,
we
expect
that
the
variation
in
regional
institutional
quality
is
likely
to
influence
the
relation
between
firm
resources
and
innovation.
As
such,
our
study
empirically
investigates
how
the
regional
institutional
environment
influences
the
extent
to
which
firms
are
able
to
extract
value
from
their
resources
for
innovative
output.
2.
Theoretical
background
Firm-level
resources,
defined
as
the
tangible
and
intangible
assets
a
firm
uses
(Barney
and
Arikan,
2001),
form
the
basis
of
differential
performance
between
firms
in
terms
of
value
creation
(Ireland
et
al.,
2003).
From
the
perspective
of
the
RBV,
firm-specific
resources
need
to
be
effectively
managed
to
create
and
extract
value
from
them
(Mahoney,
1995;
Ireland
et
al.,
2003;
Sirmon
et
al.,
2007).
Hence,
the
managerial
ability
to
manage
the
resource
portfo-
lio
into
bundles
of
unique
capabilities
that
can
be
leveraged
within
a
certain
competitive
environment
is
critical
for
extracting
value
from
firm-level
resources
(Ireland
et
al.,
2003:
977).
Firm-level
resources
that
are
known
to
drive
innovation
include
internal
R&D,
training,
information
search,
communication
facilities,
human
cap-
ital
and
a
variety
of
input
factors
(e.g.
Tybout,
2000;
Goedhuys,
282
L.
Barasa
et
al.
/
Research
Policy
46
(2017)
280–291
2007;
Goedhuys
and
Sleuwaegen,
2010;
Srholec,
2011;
Crespi
and
Zuniga,
2011;
Bradley
et
al.,
2012).
Our
study
focuses
on
three
firm-level
resources
that
have
received
much
attention
in
prior
studies
on
innovation
in
devel-
oping
countries:
internal
R&D,
human
capital
and
managerial
experience.
R&D
expenditures,
frequently
used
as
a
measure
for
innovation
input
(Arundel
et
al.,
2007)
are
crucial
for
innovation
at
the
firm
level
(Levin
et
al.,
1987).
The
relation
between
internal
R&D
and
innovation
is
mixed
for
developing
countries
(see
Crespi
and
Zuniga,
2011).
While
several
studies
report
a
positive
associa-
tion
between
R&D
and
innovation
in
Asia
(see
Lee
and
Kang,
2007;
Wang
and
Lin,
2013),
evidence
from
Chile
and
Mexico
does
not
sup-
port
this
finding
(Crespi
and
Zuniga,
2011).
For
African
countries,
Goedhuys
(2007)
shows
a
positive
relation
between
R&D
and
prod-
uct
innovation
in
Tanzania.
In
addition,
Kamau
and
Munandi
(2009)
argue
that
R&D
is
an
important
component
of
innovation-based
strategy
for
clothing
and
textile
manufacturers
in
Kenya.
McGuirk
and
Lenihan
(2013)
argue
that
the
role
of
individuals
and
the
significance
of
their
contribution
to
innovation
activities
is
now
widely
recognized.
Human
capital,
comprising
formal
educa-
tion
and
on-the-job
training
(Romer,
1990),
is
viewed
as
a
principal
source
of
innovation
(Al-Laham
et
al.,
2011).
In
fact,
more
highly
educated
and
more
highly
skilled
workers
have
been
found
to
be
a
direct
source
of
innovation
arising
from
an
increase
in
a
firm’s
absorptive
capacity
(Roper
and
Love,
2006).
The
importance
of
education
for
innovation
has
been
demonstrated
for
developing
countries
as
well
For
instance,
Robson
et
al.
(2009)
find
a
pos-
itive
relation
between
education
level
and
innovation
in
Ghana.
Moreover,
Kamau
and
Munandi
(2009)
report
that
clothing
and
textile
manufacturers
in
Kenya
prefer
hiring
individuals
with
sec-
ondary
school
as
opposed
to
those
with
only
primary
education
because
such
employees
easily
absorb
knowledge,
which
is
crucial
for
innovation.
Moreover,
high
levels
of
literacy
are
an
indication
of
a
highly
skilled
labor
force
(Goedhuys
and
Veugelers,
2012).
Formal
training,
on
the
other
hand,
enhances
a
worker’s
skills
set
thereby
increasing
their
ability
to
innovate
(Blundell
et
al.,
1999).
Innovation
is
a
high
risk
and
resource
intensive
activity
that
heavily
draws
on
managerial
resources.
Managers
rely
on
skills
and
experience
that
have
been
built
over
time
for
decision
mak-
ing
in
identifying
innovation
opportunities
(Li
and
Atuahene-Gima,
2001).
Indeed,
empirical
studies
have
shown
that
experienced
managers
are
better
able
to
understand
the
nuances
of
their
com-
petitive
environment,
which
has
a
positive
effect
on
the
innovative
performance
of
firms
(McGee
and
Dowling,
1994).
Similarly,
Bantel
and
Jackson
(1989)
showed
that
more
innovative
banks
benefit-
ted
from
the
experience
of
their
management
team.
In
developing
countries,
the
work
experience
of
small
business
owners
has
also
been
found
to
positively
affect
the
growth
potential
of
firms
(Nichter
and
Goldmark,
2009).
Our
study
also
includes
the
broader
institutional
environment
in
which
firms
are
embedded
for
exploring
the
relationship
between
firm-resources
and
innovation
in
developing
countries.
Poor
gov-
ernance
characterizes
a
majority
of
developing
countries,
implying
the
existence
of
institutions
that
are
not
well-functioning
(Abed
and
Gupta,
2002).
Olson
et
al.
(2000)
argue
that
differences
in
the
quality
of
governance
have
led
to
varied
growth
rates
in
develop-
ing
countries.
Other
empirical
studies
also
point
at
the
critical
role
of
institutions
for
economic
growth
and
development
in
develop-
ing
countries
(Glaeser
et
al.,
2004;
Acemoglu
and
Robinson,
2008).
Acemoglu
et
al.
(2003)
show
that
countries
with
weak
institutions
report
slow
growth.
In
particular,
such
countries
exhibit
a
high
degree
of
political
instability,
widespread
corruption,
weak
pro-
tection
of
property
rights
and
weak
functioning
markets
(see
also
Bräutigam
and
Knack,
2004).
According
to
Oyelaran-Oyeyinka
(2004),
strong
institutions
are
imperative
for
innovation
because
of
two
reasons.
First,
institu-
tions
mitigate
the
uncertainty
that
surrounds
innovation
activities
by
providing
regulations
that
govern
economic
agents
and
by
enforcing
contractual
obligations.
Secondly,
institutions
mediate
intellectual
property
rights
(IPRs)
and
patent
laws
that
govern
innovation
activities.
Oyelaran-Oyeyinka
(2006)
demonstrates
that
several
countries
in
Africa
adopted
the
industrialization
model
of
developed
countries
but
were
less
than
successful
at
achieving
technological
progress
due
to
weak
institutions
and
inadequate
human
capital.
Oluwatobi
et
al.
(2015)
examine
the
effect
of
insti-
tutional
quality
on
innovation
in
40
African
countries.
The
authors
suggest
that
control
of
corruption
and
improvement
of
regulatory
quality
result
in
higher
rates
of
innovation
in
Africa.
The
key
argument
that
we
develop
in
our
paper
is
that
firms
will
be
less
capable
of
extracting
value
from
the
resources
needed
to
develop
new
innovative
products
depending
on
the
function-
ing
of
institutions.
Well-functioning
institutions
are
imperative
for
entrepreneurial
activity
and
innovation
(Tebaldi
and
Elmslie,
2013).
We
include
three
institutions
that
have
been
reported
to
affect
entrepreneurial
activity
and
innovation:
corruption,
rule
of
law
and
regulatory
quality
(cf.
Chadee
and
Roxas,
2013).
Whereas
these
formal
institutions
may
not
differentiate
at
the
level
of
regions
within
a
country,
we
argue
that
the
actual
implementa-
tion
or
enforcement
of
these
institutions
does
vary
across
regions
within
a
country,
due
to
local
experiences
with
corruption,
the
rule
of
law
and
regulatory
quality
(cf.
Asiedu
and
Freeman,
2009).
3.
Hypotheses
As
indicated
in
the
foregoing
discussion,
we
argue
that
poor
regional
institutional
quality
within
a
focal
country
makes
it
more
difficult
for
a
firm
to
extract
value
from
resources
needed
for
innovation.1As
such,
we
infer
that
the
extent
to
which
firms
can
successfully
use
their
resources
for
innovation
is
dependent
on
the
regional
institutional
environment.
Following
this
line
of
thought,
we
hypothesize
that
stronger
regional
institutional
qual-
ity
enhances
the
transformation
of
firm-level
resources,
including
internal
R&D,
educated
employees,
investments
in
formal
train-
ing
for
skilled
labor
and
managerial
experience
into
innovation
We
elaborate
our
four
interaction
effects
in
the
following
sections.
3.1.
Internal
R&D
and
regional
institutional
quality
The
relation
between
internal
R&D
and
innovative
output
has
been
established
in
previous
research.
It
is
well
known
that
firms
that
invest
in
R&D
extend
their
scientific
and/or
technical
knowledge
base,
which
allows
them
to
design
and
develop
new
innovative
products
or
services.
However,
the
extent
to
which
firms
are
able
to
extract
value
from
their
internal
R&D
efforts
to
develop
innovative
output
(Martin-de
Castro
et
al.,
2013)
depends
on
regional
institutional
quality.
Firms
in
poor
institutional
envi-
ronments
are
less
likely
to
conduct
and,
of
specific
relevance
to
our
study,
benefit
from
R&D
(Zhao,
2006).
Such
environments
are
often
resembled
by
poor
protection
of
intellectual
property
rights,
which
means
that
firms
cannot
extract
value
from
their
R&D
investments.
When
knowledge
is
not
protected
(for
instance
through
patents)
it
is
easily
imitated
and
more
difficult
for
a
firm
to
appropriate
value
from
it
(Barney,
1991).
Hence,
in
institutional
environments
where
few
imitation
restrictions
exist,
it
is
likely
that
firms
will
be
1Even
though
we
expect
that
the
firm-level
resources
individually
have
direct
effects
on
innovative
output,
we
are
mainly
interested
in
how
these
resources
inter-
act
with
regional
institutional
quality
to
explain
innovative
output
in
developing
countries
(see
McCann
and
Folta,
2011).
As
such,
we
do
not
formulate
hypotheses
for
the
main
effects.
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46
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283
unsuccessful
in
transforming
their
R&D
investments
into
innova-
tive
output.
Moreover,
corrupt
environments
reduce
the
magnitude
of
the
possibility
for
firms
to
invest
in
R&D
and
subsequently
profit
from
innovation
(Anokhin
and
Schulze,
2009:
475).
Corruption
is
believed
to
discourage
economic
activities,
including
innovation
and
entrepreneurship
(Estrin
et
al.,
2013).
Innovators
are
often
sub-
ject
to
extortion
from
government
officials,
because
they
require
licenses
and
permits.
Refraining
from
obtaining
these
licenses
reduces
a
firm’s
potential
to
invest
in
R&D
and
develop
innovative
new
products.
Alternatively,
curbing
the
abuse
of
tax
credits
by
firms,
as
well
as
reigning
in
corruption
by
tax
officials,
enhance
the
effect
of
R&D
spending
on
innovation
at
the
firm-level
(cf.
Bardhan,
1997).
Thus,
in
our
study
we
argue
that
the
value
firms
can
extract
from
their
internal
R&D
is
higher
in
an
environment
with
a
high
degree
of
regional
institutional
quality,
which
will
have
a
positive
effect
on
innovative
output.
Thus,
we
hypothesize
that:
H1:
The
level
of
regional
institutional
quality
positively
moder-
ates
the
effect
of
internal
R&D
on
innovative
output.
3.2.
Human
capital
and
regional
institutional
quality
Absorptive
capacity,
a
vital
component
of
innovation,
consti-
tutes
the
ability
to
identify,
assimilate
and
exploit
knowledge
from
the
external
environment
(Cohen
and
Levinthal,
1989).
The
degree
of
absorptive
capacity
depends
on
human
capital,
which
is
crucial
for
creating
new
knowledge
(Griffith
et
al.,
2004).
Well-educated
employees
and
a
skilled
labor
force
are
therefore
conducive
to
inno-
vation
(see
Liu
and
Buck,
2007).
Yet,
we
argue
that
the
institutional
environment
plays
a
central
role
in
the
transformation
of
a
firm’s
absorptive
capacity
into
innovation.
For
instance,
an
educational
system
that
is
based
on
privilege
rather
than
achievement
is
likely
to
seriously
hamper
the
effect
of
human
capital
on
innovation
(cf.
Heyneman,
2004),
because
employees
will
lack
the
necessary
skills
to
identify
and
understand
new
knowledge
and
transform
this
into
new
products.
As
such,
poorly
governed
educational
systems
do
not
allow
firms
to
extract
the
full
potential
of
human
capital
for
innovation.
It
is
also
possible
that
regional
institutional
quality
influences
the
relation
between
human
capital
and
innovation
through
the
rate
of
enrollment
in
schools
and
the
quality
of
education
that
is
provided
(cf.
Heyneman,
2004).
It
is
well
known
that
teach-
ers
in
developing
countries
are
frequently
absent
or
compensate
their
limited
wages
by
having
bribes
built
into
their
pay
structure
(Biswal,
1999).
As
such,
the
actual
skills
conducive
to
innovation
possessed
by
skilled
labor
are
likely
to
be
relatively
low
in
regions
with
low
regional
institutional
quality.
Varsakelis
(2006)
argued
that
improving
regulatory
quality
could
lead
to
the
adoption
of
a
science
oriented
educational
system,
which
in
turn
would
stimulate
the
innovative
productivity
of
a
country.
Taking
into
account
that
absorptive
capacity
is
principally
driven
by
human
capital
(Vinding,
2006),
we
expect
that
firms
with
a
strong
human
capital
base
com-
prising
highly
educated
and
highly
skilled
workers
will
be
more
innovative
(Franco
et
al.,
2012),
and
this
effect
will
be
strength-
ened
when
regional
institutional
quality
is
high
(Roper
and
Love,
2006).
Thus,
we
formulate
our
hypothesis
as
follows:
H2:
The
level
of
regional
institutional
quality
positively
moder-
ates
the
effect
of
employee
level
of
education
on
innovative
output.
H3:
The
level
of
regional
institutional
quality
positively
moder-
ates
the
effect
of
skilled
labor
on
innovative
output.
3.3.
Managerial
experience
and
regional
institutional
quality
Managerial
experience
is
generally
considered
to
be
an
impor-
tant
input
for
successful
innovation
(Schilirò,
2010).
For
example,
managers
possessing
more
experience
are
likely
to
explore
more,
and
more
varied,
innovation
projects.
In
that
regard
managerial
experience
reflects
an
important
tacit
skill
required
to
select
the
most
promising
innovation
projects
(Custódio
et
al.,
2014).
It
seems
likely,
however,
that
the
relation
between
managerial
experience
and
innovation
will
be
influenced
by
the
institutional
environment
This
is
because
decision
making
at
the
managerial
level
involves
an
assessment
of
internal
and
external
factors
that
may
work
against
or
support
particular
innovation
projects.
Excessive
requirements
imposed
by
government
regulation
or
corruption
increases
the
time
senior
management
spend
in
dealing
with
government
reg-
ulations
and
administrators.
(Tybout,
2000).
As
such,
low
levels
of
regional
institutional
quality
could
lead
to
a
displacement
of
the
attention
of
senior
managers
away
from
innovation
activities
resulting
in
lower
levels
of
innovation.
In
high
regional
institutional
quality
environments
on
the
other
hand,
experienced
managers
can
direct
their
attention
towards
finding
and
selecting
new
oppor-
tunities
and
markets
for
their
firms
resulting
in
higher
levels
of
innovation.
In
addition,
managers
need
to
be
able
to
understand
the
broader
institutional
environment.
When
there
is
low
institutional
quality,
government
officials
may
be
inclined
to
delay
project
approval
or
decline
permits.
Dealing
with
such
barriers
to
innovation
requires
more
patience,
political
will
and
experience
from
managers
(Austin,
2002).
Hence,
we
suggest
that
a
strong
institutional
environment
reinforces
the
effect
of
managerial
experience
on
innovative
out-
put
because
firms
will
be
more
capable
of
extracting
value
from
a
manager’s
experience.
We
formulate
the
following
hypothesis:
H4:
The
level
of
regional
institutional
quality
positively
moder-
ates
the
effect
of
managerial
experience
on
innovative
output.
4.
Data
and
methods
4.1.
Data
We
test
our
hypotheses
using
firm-level
data
from
the
World
Bank
Enterprise
Survey
(ES)
and
the
Innovation
Follow-up
Survey
(IFS)
module
covering
the
period
2010–2012
for
Kenya,
Tanza-
nia
and
Uganda.2The
ES
collects
data
focusing
on
an
economy’s
business
environment
and
investment
climate
encompassing,
cor-
ruption,
competition,
access
to
finance
and
performance
measures.
The
World
Bank
has
conducted
firm-level
surveys
since
the
1990s,
however,
since
2005
data
collection
efforts
have
been
centralized
and
instruments
standardized
for
establishing
comparability
of
data
across
countries.
The
IFS,
launched
in
2011,
specifically
focuses
on
innovation
and
innovation-related
activities
within
firms.
The
ES
involves
administering
firm-level
surveys
to
a
representative
sam-
ple
of
firms
in
the
non-agricultural
formal
sector
in
an
economy
comprising
firms
in
the
manufacturing,
retail
and
service
sector.
In
addition,
ES
are
stratified
according
to
the
sector
of
activity,
firm
size
and
geographical
location
of
the
firm.
The
ES
respondents
comprise
business
owners
and
top
managers
from
713
firms
in
Kenya,
723
firms
in
Tanzania
and
640
firms
from
Uganda.
Similarly,
respondents
for
the
IFS
include
business
owners
and
top
managers
from
549
firms
in
Kenya,
543
firms
in
Tanzania
and
449
firms
from
Uganda.
IFS
respondents
are
a
subset
of
the
original
ES
and
were
randomly
selected
to
form
a
sample
of
75%
of
the
ES
respondents
(www.enterprisesurveys.org).
Considering
that
the
datasets
for
the
ES
and
the
IFS
comprise
the
same
firms,
our
study
merges
these
two
2Even
though
both
surveys
include
sampling
weights
we
refrained
from
using
these.
We
did
so
as
we
merged
data
from
these
two
surveys
and
not
all
firms
in
the
ES
appear
in
the
IFS
as
the
IFS
targets
a
subset
of
the
ES
sample.
This
makes
applying
weights
problematic
as
the
weights
differ
between
the
two
surveys.
Additionally,
not
all
the
firms
present
in
the
merged
sample
are
in
our
final
analyses
due
to
(limited)
missing
data
issues,
rendering
the
use
of
weights
impractical.
284
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280–291
datasets
using
the
unique
firm
identifiers
for
each
country
to
create
a
rich
dataset
for
our
empirical
analysis.
4.2.
Dependent
variable
Our
measure
of
innovative
output
in
firms
relates
to
product
and
service
innovation.
Specifically,
the
survey
asks
respondents
whether
the
firm
introduced
any
new
or
significantly
improved
product
or
service
in
the
last
three
years.
The
IFS
further
provides
that
the
innovative
product
or
service
can
be
new
to
the
firm
or
new
to
the
market.
We
use
a
dummy
variable
that
takes
the
value
of
“1”
if
a
firm
has
introduced
any
new
or
significantly
improved
innovative
product
or
service
and
“0”
if
otherwise.
This
measure
of
innovation
has
been
used
in
previous
studies
(Ayyagari
et
al.,
2012;
Chadee
and
Roxas,
2013).
4.3.
Independent
variables
4.3.1.
Firm-level
resources
4.3.1.1.
R&D.
The
IFS
asks
respondents
if
their
firm
conducted
internal
R&D
from
fiscal
year
2010
through
2012.
To
measure
R&D,
we
use
a
dummy
variable
that
takes
a
value
of
“1”
if
the
response
is
yes
and
“0”
if
otherwise.
4.3.1.2.
Employee
level
of
education.
The
ES
data
provides
informa-
tion
on
the
level
of
education
attained
by
employees.
We
use
the
percentage
of
employees
who
have
completed
secondary
school
education
as
a
measure
of
the
level
of
education
attained
by
employees.
4.3.1.3.
Skilled
labor.
The
IFS
contains
an
item
that
asks
respon-
dents
whether
the
firm
provided
employees
with
formal
training
for
the
development
or
production
of
innovative
products
or
ser-
vices,
we
use
a
dummy
variable
that
takes
a
value
of
“1”
in
the
case
a
firm
did
so
and
“0”
if
otherwise.
4.3.1.4.
Managerial
experience.
For
our
study,
managerial
experi-
ence
is
the
number
of
years
the
top
manager
or
business
owner
has
worked
in
the
sector.
Following
Ayyagari
et
al.
(2012)
we
use
a
dummy
variable
for
representing
managerial
experience
that
takes
a
value
of
“1”
where
a
business
manager’s
experience
in
the
sector
is
greater
than
10
years
and
“0”
if
otherwise.
4.3.2.
Regional
institutional
quality
Even
though
measures
of
institutional
quality
constructed
from
perceptions-based
data
are
inherently
subjective,
the
availability
of
a
large
array
of
institutional
development
indicators
allows
the
construction
of
composite
measures
of
institutional
quality
that
are
reliable
and
can
be
calculated
at
the
regional
(i.e.
sub-national
level)
(Hajra,
2005).
In
addition,
perceptions-based
data
have
reli-
ably
reported
governance
outcomes
very
similar
to
more
objective
measures
based
on
formal
rules
(Kaufmann
et
al.,
2007).
Moreover,
Kuncic
(2014)
argues
that
finding
a
single
measure
of
institu-
tional
quality
is
difficult
because
institutions
are
latent
factors
in
an
economic
system.
Hence
he
proposes
that
using
a
composite
measure
combining
information
from
several
measures
of
insti-
tutions
offers
a
better
solution
for
measuring
institutional
quality
This
study,
therefore
uses
a
composite
measure
of
firm-level
per-
ceptions
of
governance
at
the
regional
level
for
measuring
regional
institutional
quality.
This
measure
is
constructed
from
firm-level
perceptions
of
corruption,
rule
of
law
and
regulatory
quality
that
are
aggregated
to
the
regional
level.
More
specifically,
following
previous
studies
(Fogel
et
al.,
2006;
Chadee
and
Roxas,
2013),
various
items
from
the
ES
are
used
to
generate
a
composite
measure
of
corruption,
rule
of
law
and
reg-
ulatory
quality.
We
use
two
items
for
generating
a
measure
of
corruption.
The
first
item
asks
respondents
whether
they
perceive
the
court
system
as
fair,
impartial
and
uncorrupted
with
responses
being
measured
using
a
four-point
scale
(1
=
strongly
disagree,
to
4
=
strongly
agree).
The
second
item
asks
respondents
to
what
degree
they
perceive
corruption
as
an
obstacle
to
the
current
oper-
ations
of
the
firm.
The
respondents’
perceptions
of
the
degree
of
corruption
are
captured
using
a
five-point
scale
(0
=
not
an
obsta-
cle,
4
=
very
severe
obstacle).
We
also
develop
a
composite
measure
of
the
rule
of
law
using
three
items
that
relate
to
how
respon-
dents
perceive
the
degree
to
which
courts,
political
instability
and
crime,
theft
and
disorder
are
obstacles
to
their
business
opera-
tions
and
are
measured
using
a
five-point
scale
(0
=
not
an
obstacle,
to
4
=
very
severe
obstacle).
Lastly,
we
measure
regulatory
quality
using
a
composite
measure
of
four
items.
These
items
ask
respon-
dents
to
indicate
on
a
five-point
scale
(0
=
not
an
obstacle,
to
4
=
very
severe
obstacle)
to
what
degree
they
perceive
tax
rates,
tax
admin-
istration,
customs
and
trade
regulations,
and
business
permits
and
licensing
as
obstacles
to
their
business
operations.
Subsequently,
we
generated
regional
measures
of
corruption,
rule
of
law
and
regu-
latory
quality
by
standardizing
the
individual
items
and
calculating
the
mean
firm-level
scores
within
each
region.
However,
the
three
resulting
variables
are
highly
correlated
(correlations
between
0.73
and
0.88).
We
therefore
ultimately
calculated
our
composite
mea-
sure
of
institutional
quality
as
the
mean
of
the
scores
for
the
three
pillars
of
regional
institutional
quality
for
each
region.
Due
to
the
standardization
of
the
items,
scores
below
zero
reflect
below
aver-
age
regional
institutional
quality
whereas
scores
above
0
reflect
above
average
regional
institutional
quality.
4.4.
Control
variables
4.4.1.
Firm
age
This
study
uses
firm
age
as
a
control
variable
since
previous
studies
support
the
finding
that
firm
age
is
inversely
related
to
inno-
vative
output
(Ayyagari
et
al.,
2012).
Younger
firms
are
more
likely
to
introduce
new
products
and
processes
as
compared
to
older
firms.
We
use
the
difference
between
the
year
of
the
survey
and
the
year
the
firm
was
established
to
compute
the
firm
age.
4.4.2.
Firm
size
This
study
also
controls
for
firm
size
as
previous
studies
have
found
a
positive
relation
between
firm
size
and
innovation
(Jiménez-Jiménez
and
Sanz-Valle,
2011;
Ayyagari
et
al.,
2012).
Moreover,
medium-sized
(20
≤
employees
≤
99)
and
larger
firms
(employees
≥
100)
have
been
found
to
be
more
innovative
in
comparison
to
smaller
firms
(Ayyagari
et
al.,
2012).
The
authors
conclude
that
larger
firms
are
in
a
position
to
provide
economies
of
scale
in
innovation
just
as
in
production.
We
use
the
number
of
full-time
permanent
employees
as
our
measure
of
firm
size.
We
use
a
dummy
variable
to
measure
firm
size
with
firms
with
greater
than
20
employees
taking
a
value
of
“1”
and
“0”
if
otherwise.
4.4.3.
Legal
status
Ayyagari
et
al.
(2012)
demonstrates
that
ownership
and
legal
organization
play
a
significant
role
for
innovation.
The
authors
show
that
firms
organized
as
corporations
report
greater
innova-
tion
activity
in
comparison
to
unincorporated
forms
of
business
(cooperatives,
sole
proprietorships
or
partnerships).
The
measure
for
legal
status
emanates
from
respondents
being
the
asked
to
pro-
vide
the
legal
organization
of
the
firm.
Legal
status
is
a
dummy
variable
taking
the
value
of
“1”
if
the
firm
is
organized
as
a
cor-
poration
(shareholding
company
with
publicly
traded
shares
and
shareholding
company
with
non-traded
or
privately
traded
shares),
and
“0”
if
the
firm
is
legally
organized
as
a
sole
proprietorship,
partnership,
limited
partnership
or
has
another
form.
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Research
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46
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280–291
285
4.4.4.
External
financing
The
IFS
module
asks
managers
to
provide
estimates
of
the
pro-
portion
of
working
capital
financed
by
various
sources
for
the
previous
fiscal
year.
Following
Ayyagari
et
al.
(2012),
the
differ-
ent
sources
of
external
financing
are
expressed
in
percentage
form.
The
sources
of
external
financing
include
banks,
non-bank
financial
institutions,
purchases
on
credit
from
suppliers
and
advances
from
customers
and
other
sources.
We
measure
external
financing
as
the
percentage
of
working
capital
obtained
from
external
sources.
4.4.5.
Technology
licensed
from
a
foreign-owned
company
This
variable
is
captured
by
an
item
in
the
ES
that
seeks
to
find
out
whether
firms
use
technology
licensed
from
foreign-owned
companies
in
their
operations.
We
expect
that
use
of
foreign
tech-
nology
may
suppress
innovation
in
a
firm.
We
use
a
dummy
variable
that
takes
a
value
of
“1”
where
a
firm
uses
technology
licensed
from
a
foreign-owned
company
and
“0”
if
otherwise
4.4.6.
Sector
dummy
variables
The
sample
comprises
firms
from
the
manufacturing,
retail
and
service
sector.
Our
study
controls
for
sector
heterogeneity
since
sector
specific
effects
may
influence
innovation.
We
use
two
sector
dummies
taking
a
value
of
“1”
where
a
firm
belongs
to
the
man-
ufacturing
sector
or
retail
sector
respectively
and
“0”
if
otherwise.
This
setup
implies
that
the
service
sector
is
the
reference
category.
4.4.7.
Country
dummy
variables
This
study
controls
for
differences
across
the
three
countries
by
means
of
country
dummies
taking
a
value
of
“1”
where
a
firm
is
located
in
Tanzania
or
Uganda
respectively
and
“0”
if
otherwise.
Kenya
is
taken
as
the
reference
country.
4.5.
Analysis
A
logistic
regression
model
is
used
for
analyzing
the
data
due
to
the
binary
nature
of
the
dependent
variable.
Because
our
study
employs
clustered
data
where
firms
are
nested
within
regions
our
data
violate
the
assumption
of
independence
of
all
observations
and
therefore
residuals
at
the
firm
level
are
expected
to
be
corre-
lated
with
the
regional
level.
We
utilize
clustering
of
the
standard
errors
at
the
regional
level
to
account
for
this
dependency
between
observations.
We
opt
to
use
this
approach
over
estimation
multi-
level
models
due
to
the
relatively
small
number
of
regions
in
our
dataset
(i.e.
16)
to
which
multilevel
estimation
is
relatively
sen-
sitive.
However,
we
do
report
the
results
of
such
an
analysis
as
a
robustness
test
(see
Section
5.1)
Thus,
the
study
examines
innovation
in
firms
taking
into
con-
sideration
the
effect
of
the
two
levels.
The
firm
(level
1
unit)
and
the
region
(level
2
unit)
explain
the
variation
in
innovation
in
firms.
The
general
form
of
the
logistic
regression
is:
Pr (Yi=1|X)=eb0+b1X+b2Z+b3XZ+ε
1
+
eb0+b1X+b2Z+b3XZ+ε(1)
Transforming
equation
one
and
formulating
a
2-level
model
yields
the
following:
Log Y
1
−
Y=
b0+
b1X
+
b2Z
+
b3XZ
+
εi,j (2)
where
Y,
the
dependent
variable,
represents
innovation,
X
repre-
sents
firm
level
resources,
Z
represents
the
regional
institutional
quality
and
XZ
is
the
interaction
of
firm
level
resources
and
regional
institutional
quality.
Apart
from
reporting
on
the
significance
and
the
signs
of
the
logit
coefficients,
it
is
more
meaningful
to
examine
the
marginal
effects
of
the
variables
and
provide
graphical
interpretation
of
the
interaction
effects
(Bowen
and
Wiersema,
2004).
We
follow
the
common
practice
of
showing
the
marginal
effects
of
a
variable
at
it
means
as
well
as
one
standard
deviation
above
and
below
the
mean
(Hoetker,
2007).
In
addition,
we
use
the
likelihood
ratio
test
to
assess
the
fit
of
our
models
(Long
and
Freese,
2006).
5.
Results
Table
1
illustrates
distinct
variation
in
regional
institutional
quality
for
Kenya,
Tanzania
and
Uganda.
Kenya
has
better
insti-
tutions
in
comparison
to
Tanzania
and
Uganda.
Among
the
three
countries,
Tanzania
has
much
weaker
institutions.
More
impor-
tantly,
we
observe
that
perceptions
of
institutional
quality
are
strikingly
different
not
only
across
the
three
countries
but
also
across
regions
within
these
countries.
Table
2
provides
the
descriptive
statistics
and
correlations
for
our
data.
We
observe
that
36%
of
firms
in
the
sample
have
inno-
vative
output.
In
addition,
only
21%
of
the
firms
conduct
internal
R&D.
Also
interesting
is
the
fact
that
about
60%
of
the
employees
have
attained
secondary
school
education.
Finally,
the
correlations
between
the
firm-level
resources
and
innovation
output
have
the
expected
positive
signs.
We
test
our
hypotheses
by
estimating
Eq.
(2)
using
a
clustered
robust
standard
errors
model.
The
results
of
our
estimation
are
summarized
in
Table
3,
which
contains
six
models.
Model
1
is
the
baseline
model,
which
contains
results
of
the
main
effects
of
firm
resources
variables,
the
regional
institutional
quality
variables
and
Table
1
Regional
institutional
quality.
Country
Region
Regulatory
Quality
Rule
of
Law
Corruption
Regional
Institutional
Quality
Kenya Central
0.49
0.21
0.19
0.30
Nyanza
0.39
0.13
0.31
0.28
Mombasa
0.37
0.31
0.23
0.30
Nairobi
0.32
0.21
0.17
0.23
Nakuru
0.45
0.29
0.44
0.40
Tanzania Arusha
−0.01
0.03
0.21
0.07
Dar-es-Salaam
−0.51
−0.37
−0.49
−0.46
Mbeya
0.20
0.54
0.59
0.45
Mwanza
−0.09
−0.41
−0.37
−0.29
Zanzibar
−0.76
−0.40
−0.70
−0.62
Uganda Kampala
−0.22
0.13
0.17
0.03
Jinja
0.04
−0.21
−0.31
−0.16
Lira
0.07
−0.24
−0.28
−0.15
Mbale
0.52
0.27
0.28
0.35
Mbarara
0.04
−0.26
0.19
−0.01
Wakiso
−0.16
−0.04
0.11
−0.03
286
L.
Barasa
et
al.
/
Research
Policy
46
(2017)
280–291
Table
2
Descriptive
statistics
and
correlation
matrix
(n
=
1541).
Mean
Std.
Dev. Min
Max
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
1
Innovation
0.36
0.48
0.00
1.00
–
2
Age
(log) 2.58
0.79
0.00
4.67
0.01
–
3
Size
(log)
2.84
1.31
0.00
8.61
0.11
0.23
–
4
Legal
status
0.10
0.31
0.00
1.00
0.09
0.13
0.15
–
5
External
financing
30.08
32.04
0.00
100.00
0.04
0.07
0.07
0.09
–
6
Foreign
technology
licensing
0.09
0.29
0.00
1.00
0.13
0.13
0.21
0.12
0.08
–
7
Manufacturing
sector 0.49
0.50
0.00
1.00
0.05
0.23
0.13
0.05
0.10
0.32
–
8
Retail
sector
0.22
0.41
0.00
1.00
−0.03
−0.14
−0.13
−0.02
−0.02
−0.17
−0.52
–
9
Service
sector 0.29
0.45
0.00
1.00
−0.03 −0.13
−0.02
−0.03
−0.09
−0.20
−0.63
−0.34
–
10
Kenya
0.36
0.48
0.00
1.00
0.09
0.20
0.17
0.16
0.19
0.09
0.03
0.01
−0.04
–
11
Tanzania
0.35
0.48
0.00
1.00
−0.31 −0.07 −0.04 −0.13 −0.11
−0.15
0.01
−0.05
0.03
−0.55
–
12
Uganda
0.29
0.45
0.00
1.00
0.22
−0.14
−0.13
−0.03
−0.09
0.07
−0.04
0.04
0.01
−0.48
−0.47
–
13
Internal
R&D
0.21
0.41
0.00
1.00
0.19
0.07
0.15
0.03
0.06
0.08
0.08
−0.06
−0.04
0.07
0.03
−0.10
–
14
Education
level
of
staff
59.81
34.83
0.00
100.00
0.10
0.05
0.18
0.08
−0.03
0.06
−0.07
0.06
0.03
0.38
−0.22
−0.16
0.06
–
15
Skilled
labor
0.28
0.45
0.00
1.00
0.18
0.11
0.18
0.06
0.08
0.12
0.11
−0.09
−0.04
0.13
−0.06
−0.07
0.37
0.09
–
16
Managerial
experience
0.62
0.49
0.00
1.00
0.06
0.41
0.18
0.08
0.01
0.06
0.13
−0.08
−0.07
0.12
−0.04
−0.09
0.11
0.08
0.07
–
17
Regional
institutional
quality
0.00
0.32
−0.62
0.45
0.14
0.08
0.05
0.11
0.09
0.10
0.03
0.06
−0.10
0.65
−0.66
0.01
−0.09
0.22
0.00
−0.01
control
variables.
In
addition
to
reporting
the
results
of
the
main
effects
of
control
variables
and
the
independent
variables,
mod-
els
2–5
also
separately
report
the
results
of
the
interaction
effects
between
regional
institutional
quality
and
internal
R&D,
employee
level
education,
skilled
labor
and
managerial
experience
respec-
tively.
Model
6,
which
offers
a
superior
model
fit
in
comparison
to
models
2–5,
provides
the
results
of
the
full
model
with
main
effects
and
interaction
effects
including
the
control
variables,
inde-
pendent
variables
and
the
interaction
of
the
firm-level
resources
and
the
regional
institutional
quality.
In
addition
to
reporting
the
marginal
effects
of
the
multi-level
logistic
regression
for
the
full
model,
we
also
provide
interaction
plots
for
exploring
the
form
of
the
interaction
of
firm-level
resource
and
regional
institutional
quality.
The
coefficients
of
the
independent
variables
including
inter-
nal
R&D,
employee
level
of
education,
skilled
labor
and
managerial
experience
are
positive
and
statistically
significant
as
expected.
Marginal
effects
analyses
reveal
that
internal
R&D
has
a
strong
pos-
itive
effect
on
innovation.
The
likelihood
of
innovation
was
about
19%
higher
for
firms
conducting
internal
R&D
in
comparison
to
firms
not
conducting
internal
R&D
(32
vs.
51%).
Employee
level
of
education
has
a
very
small
positive
effect
on
innovation
with
the
likelihood
of
innovation
being
approximately
0.06%
higher
for
a
1%
increase
in
the
percentage
of
employees
with
secondary
school
education.
The
likelihood
of
innovation
was
about
11%
higher
for
firms
that
provided
their
workers
with
formal
training
for
the
intro-
duction
or
development
of
innovative
products
or
services
(44%
vs.
33%).
The
effect
of
managerial
experience
on
innovation
was
about
3%
higher
for
managers
with
more
than
ten
years
of
experi-
ence.
The
coefficient
of
the
context
variable,
regional
institutional
quality,
was
negative
and
statistically
significant.
An
important
observation
regarding
the
coefficients
of
the
inter-
action
of
the
firm-level
resources
with
regional
institutional
quality
is
that
with
the
exception
of
managerial
experience,
the
internal
R&D,
employee
level
of
education
and
skilled
labor
were
found
to
be
positive
and
statistically
significant.
To
a
large
extent,
our
results
support
our
hypotheses
that
institutions
reinforce
the
effect
of
firm-level
resources
on
the
likelihood
of
innovation.
The
subse-
quent
discussion
explains
the
interaction
terms
in
the
full
model
by
means
of
marginal
effects
plots.
We
examine
the
form
of
interaction
of
firm-level
resources
with
regional
institutional
quality
beginning
with
internal
R&D,
followed
by
employee
level
of
education
and
skilled
labor
and
managerial
experience
respectively.
The
margin
plots
indicate
the
form
of
interaction
of
firm-level
resources
with
different
levels
of
regional
institutional
quality
including
when
it
is
at
(1)
the
minimum
value,
(2)
a
low
degree
(1
standard
deviation
below
the
mean),
(3)
the
mean
value,
(4)
a
high
degree
(1
standard
deviation
above
the
mean),
and
(5)
the
maximum.
Fig.
1
displays
the
form
of
the
interaction
of
internal
R&D
and
regional
institutional
quality.
Indeed,
the
effect
of
conducting
inter-
nal
R&D
varies
for
different
levels
of
regional
institutional
quality.
We
observe
that
when
regional
institutional
quality
is
at
its
min-
imum,
the
effect
of
conducting
internal
R&D
on
innovation
is
negligible.
It
is
also
evident
that
with
a
low
degree
of
regional
institutional
quality
(1
standard
deviation
below
the
mean),
the
effect
of
conducting
internal
R&D
is
still
relatively
small.
However,
a
high
degree
of
regional
institutional
quality
(1
standard
deviation
above
the
mean)
amplifies
the
effect
of
conducting
internal
R&D.
Similarly,
maximum
values
of
regional
institutional
quality
have
a
strong
amplifying
effect
on
the
effect
of
internal
R&D.
Thus,
we
see
a
sizeable
positive
effect
in
this
interaction
signaling
that
the
institutional
environment
within
which
firms
operate
is
imperative
for
successful
transformation
of
firm-level
resources
into
innova-
tive
output.
This
finding
offers
very
strong
support
for
hypothesis
1
where
we
propose
that
internal
R&D
in
combination
with
a
high
L.
Barasa
et
al.
/
Research
Policy
46
(2017)
280–291
287
Table
3
Multivariate
logistic
regression
coefficients
with
clustered
robust
standard
errors
(n
=
1541).
Variables
Model
1
Model
2
Model
3
Model
4
Model
5
Model
6
Control
variables
Age
(log) −0.159 (0.107)
−0.156
(0.108)
−0.156
(0.108)
−0.162
(0.112)
−0.157
(0.107)
−0.155
(0.110)
Size
(log)
0.115** (0.054)
0.108*(0.057)
0.114** (0.054)
0.109*(0.058)
0.114** (0.054)
0.105*(0.058)
Legal
status
0.278
(0.274)
0.282
(0.283)
0.280
(0.274)
0.266
(0.286)
0.273
(0.277)
0.275
(0.289)
External
financing
0.001
(0.002)
0.001
(0.002)
0.001
(0.002)
0.001
(0.002)
0.001
(0.002)
0.001
(0.002)
Foreign
technology
licensing
0.252
(0.173)
0.200
(0.182)
0.258
(0.172)
0.244
(0.183)
0.249
(0.172)
0.211
(0.186)
Manufacturing
sector
0.128
(0.124)
0.148
(0.129)
0.131
(0.125)
0.150
(0.124)
0.131
(0.124)
0.164
(0.128)
Retail
sector
0.006
(0.218)
0.026
(0.226)
−0.001
(0.218)
0.027
(0.217)
0.010
(0.219)
0.030
(0.225)
Tanzania
−1.362*** (0.418)
−1.329*** (0.457)
−1.288*** (0.434)
−1.324*** (0.453)
−1.354*** (0.424)
−1.231** (0.490)
Uganda
0.700*** (0.263)
0.672** (0.277)
0.760*** (0.269)
0.681** (0.269)
0.704*** (0.264)
0.732** (0.288)
Resources
and
institutions
Internal
R&D
0.866*** (0.289)
0.878*** (0.179)
0.854*** (0.292)
0.898*** (0.290)
0.867*** (0.287)
0.890*** (0.199)
Employee
level
of
education
0.004** (0.002)
0.003** (0.002)
0.004*** (0.001)
0.004** (0.002)
0.004** (0.002)
0.003** (0.001)
Skilled
labor
0.510*** (0.172)
0.532*** (0.169)
0.509*** (0.169)
0.452*** (0.119)
0.512*** (0.171)
0.493*** (0.112)
Managerial
experience
0.180*(0.103)
0.178*(0.102)
0.172*(0.103)
0.182*(0.103)
0.171*(0.098)
0.171*(0.100)
Regional
institutional
quality
−0.350
(0.517)
−0.865
(0.627)
−0.671
(0.517)
−0.866
(0.631)
−0.521
(0.559)
−1.529** (0.708)
Interactions
Internal
R&D*RIQ
(H1) 1.811*** (0.563)
1.441*** (0.554)
Employee
level
of
education*RIQ
(H2)
0.007** (0.003)
0.008** (0.003)
Skilled
labor*RIQ
(H3)
1.500*** (0.436)
1.030*** (0.387)
Managerial
experience*RIQ
(H4)
0.274
(0.172)
0.097
(0.144)
Constant
−1.147*** (0.312)
−1.090*** (0.324)
−1.203*** (0.290)
−1.119*** (0.314)
−1.142*** (0.306)
−1.150*** (0.320)
LR
Chi2
17.09
1.19
13.25
0.42
11.69
Prob
>
chi2
0.000
0.275
0.000
0.516
0.009
Robust
clustered
standard
errors
in
parentheses.
*p
<
0.10.
** p
<
0.05.
*** p
<
0.01.
0.25
0.30
0.35
0.40
0.45
0.50
0.55
0.60
0.65
1=D&R lanretnI0=D&R lanretnI
Pr(Innovation=1)
Minimum RI
QRI
Q - 1 SD Mean
RIQ
RIQ
+ 1 SD
Maximum RIQ
Fig.
1.
Predictive
margins
of
internal
R&D.
degree
of
regional
institutional
quality
strengthens
the
effect
of
internal
R&D
on
innovation.
Fig.
2
illustrates
that
lower
levels
of
regional
institutional
quality
diminish
the
effect
of
employee
level
of
education
on
innovation.
Also,
the
effect
of
employee
level
of
education
on
innovation
is
pos-
itive
but
remains
weak
for
lower
degrees
of
regional
institutional
quality
(1
standard
deviation
below
the
mean).
We
also
observe
that
employee
level
of
education
in
an
environment
with
a
high
degree
of
regional
institutional
quality
(1
standard
deviation
above
the
mean)
has
a
relatively
stronger
positive
effect
on
innovation.
In
addition,
a
high
degree
of
regional
institutional
quality
further
reinforces
the
effect
of
employee
level
of
education
of
innovation.
This
result
offers
support
to
hypothesis
2.
Fig.
3
shows
that
the
effect
of
skilled
labor
on
innovation
is
signif-
icantly
diminished
when
the
regional
institutional
quality
is
at
the
minimum
value.
Similarly,
an
environment
with
weak
institutions
(1
standard
deviation
below
the
mean)
still
exhibits
low
effects
of
skilled
labor
on
innovation.
We
also
note
that
a
high
degree
of
regional
institutional
quality
(1
standard
deviation
above
the
mean)
leads
to
a
strong
positive
effect
of
skilled
labor
on
innova-
tion.
Additionally,
the
effect
of
skilled
labor
on
innovation
is
further
reinforced
when
regional
institutional
quality
is
at
its
maximum
value.
In
line
with
the
findings
for
the
employee
level
of
education
0.25
0.27
0.29
0.31
0.33
0.35
0.37
0.39
0.41
0
10
20
30
40
50
60
70
80
90
100
Pr(Innovation=1)
Empll
oyee
le
vel of
educati
on
Min RIQ RI
Q - 1 SD Mean
RIQ
RIQ
+ 1 SD
Max RIQ
Fig.
2.
Predictive
margins
of
employee
level
of
education.
288
L.
Barasa
et
al.
/
Research
Policy
46
(2017)
280–291
Table
4
Robustness
checks
using
a
clustered
robust
standard
errors
model
(n
=
1541).
Variables
Model
7
Model
8
Control
variables
Age
(log) −0.204*(0.121)
−0.139
(0.109)
Size
(log)
0.055
(0.065)
0.105*(0.058)
Legal
status
0.320
(0.325)
0.274
(0.287)
External
financing
0.001
(0.002)
0.001
(0.002)
Foreign
technology
licensing
0.518** (0.242)
0.289
(0.181)
Manufacturing
sector
0.054
(0.182)
Retail
sector 0.015
(0.233)
Tanzania
−1.211** (0.488)
Uganda
0.735*** (0.280)
Resources
and
institutions
Internal
R&D
0.829*** (0.224)
0.895*** (0.197)
Employee
level
of
education
0.001
(0.002)
0.003** (0.001)
Skilled
labor 0.490*** (0.135)
0.501*** (0.115)
Managerial
experience
0.183*(0.103)
0.180*(0.097)
Regional
institutional
quality
−0.263
(0.633)
−1.481** (0.717)
Interactions
Internal
R&D*RIQ
(H1) 1.611*** (0.560)
1.433*** (0.548)
Employee
level
of
education*RIQ
(H2)
0.005
(0.005)
0.007** (0.003)
Skilled
labor*RIQ
(H3)
1.280*** (0.396)
1.018*** (0.383)
Managerial
experience*RIQ
(H4)
0.149
(0.153)
0.098
(0.142)
Constant
−0.850*(0.487)
−1.121*** (0.304)
Robust
clustered
standard
errors
in
parentheses.
*p
<
0.10.
** p
<
0.05.
*** p
<
0.01.
this
result
strongly
supports
hypothesis
3
that
strong
institutions
positively
moderate
the
effect
of
skilled
labor
on
innovation.
Finally,
our
results
show
no
support
for
hypothesis
4.
The
effect
of
managerial
experience
on
innovation
seems
completely
unaf-
fected
by
the
regional
institutional
quality.
5.1.
Robustness
tests
Table
4
shows
models
7–8
that
test
the
robustness
of
the
results
of
our
full
model
to
excluding
country
dummies
in
model
7
and
excluding
sector
dummies
in
model
8
while
still
using
a
clus-
tered
robust
standard
errors
estimation
technique.
Table
5
shows
models
9–11
that
test
the
robustness
of
our
results
to
using
a
multilevel
estimation
technique.
The
latter
is
an
alternative
to
0.25
0.30
0.35
0.40
0.45
0.50
1=robal dellikS0=robal dellikS
Pr(Innovation=1
Minimum RI
QRI
Q - 1 SD Mean
RIQ
RIQ
+ 1 SD Maximum
RIQ
Fig.
3.
Predictive
margins
of
skilled
labor.
our
main
approach
which
utilizes
robust
standard
errors
as
the
intraclass
correlation
(i.e.
variance
at
the
regional
level)
is
approxi-
mately
22%;
more
than
twice
the
minimum
amount
recommended
when
considering
multilevel
estimations.
We
use
a
two-level
ran-
dom
intercept
intercepts
model
comprising
the
firm
level
and
the
regional
level.
Both
robustness
tests
reveal
that
only
one
of
our
effects
is
sensitive
to
the
inclusion
of
country
dummies
or
the
estimation
technique
used,
namely
the
effect
of
the
employee
level
of
edu-
cation.
The
fact
that
this
effect
is
sensitive
to
both
the
inclusion
of
country
dummies
as
well
as
to
the
specification
of
a
multilevel
model
indicates
that
for
this
specific
variable
country
differences
might
account
for
a
lot
of
its
variation.
As
a
result
the,
relatively
small,
moderation
effect
of
regional
institutional
quality
on
the
relationship
between
the
employee
level
of
education
and
inno-
vation
can
only
be
picked
up
once
this
large
national
variation
is
accounted
for.
This
indicates
that,
next
to
regional
variation,
it
remains
important
to
account
for
national
differences
between
countries.
6.
Discussion
Our
findings
support
our
hypotheses
to
a
large
extent.
In
par-
ticular,
the
interaction
of
three
firm-level
resources
(internal
R&D,
employee
level
of
education
and
skilled
labor)
and
regional
insti-
tutional
quality
has
a
positive
and
statistically
significant
effect
across
all
models.
This
implies
that,
while
firm-level
resources
are
pivotal
for
innovation,
investigating
the
interaction
of
firm-
level
resources
with
regional
quality
institutions
provides
better
insight
into
what
resources
matter
for
innovation
given
the
insti-
tutional
context
within
which
the
firms
operate.
Essentially,
our
study
underscores
the
importance
of
institutions
for
innovation
in
developing
countries.
We
find
evidence
that
the
value
of
firm-level
resources
in
terms
or
increasing
the
likelihood
of
innovation
is
conditional
on
the
regional
institutional
environment.
Better
institutional
environ-
ments
increase
the
value
of
firm-level
resources
for
innovation
while
weak
institutions
diminish
the
value
of
firm-level
resources
for
innovation
(Zhao,
2006).
We
argue
that
whilst
firm-level
resources
are
known
to
drive
innovation,
the
moderation
effect
of
institutions
is
imperative
because
institutions
influence
the
extent
to
which
firms
extract
and
appropriate
value
from
firm-level
resources.
Hence,
the
extent
to
which
firms
can
successfully
extract
value
from
resources
for
innovation
is
contingent
on
regional
insti-
tutional
quality.
The
moderating
effect
of
institutions
is
observed
even
with
low
levels
of
institutional
quality.
As
such,
we
suggest
that
incremental
improvements
in
institutional
quality
are
sufficient
for
enhanc-
ing
value
extraction
from
firm-level
resources
for
innovation
in
developing
countries.
We
argue
that
larger
investment
in
firm-level
resources
will
not
necessarily
result
in
higher
levels
of
innovation
since
institutions
influence
how
firms
appropriate
value
from
their
resources.
Thus,
innovation
at
the
firm
level
not
only
depends
on
firm-level
resources
but
also
on
the
institutional
environment
in
which
the
firm
operates.
This
argument
leads
us
to
an
important
theoretical
implication.
According
to
the
RBV,
firms
are
a
bundle
of
resources
and
capabilities,
which
are
combined
and
coordinated
for
competitive
advantage
(Barney,
1991).
While
the
RBV
con-
tends
that
a
firm’s
internal
resources
are
important
in
sustaining
competitive
advantage,
there
is
a
growing
literature
on
resource
utilization
that
suggests
that
value
can
only
be
extracted
from
resources
by
using
them
in
a
smarter
way
than
the
competition
(Eisenhardt
and
Martin,
2000;
Sirmon
et
al.,
2007;
Ndofor
et
al.,
2015).
Notwithstanding
the
value
of
these
studies,
we
show
that
the
value
extraction
potential
of
firm
resources
depends
not
only
on
L.
Barasa
et
al.
/
Research
Policy
46
(2017)
280–291
289
Table
5
Robustness
checks
using
a
multilevel
model
(n
=
1541).
Variables
Model
9
Model
10
Model
11
Control
variables
Age
(log) −0.125
(0.086)
−0.122
(0.087)
−0.107
(0.085)
Size
(log)
0.085*(0.051)
0.081
(0.051)
0.087*(0.051)
Legal
status
0.349*
(0.195)
0.366*(0.196)
0.344*(0.196)
External
financing
−0.000
(0.002)
−0.001
(0.002)
−0.000
(0.002)
Foreign
technology
licensing
0.194
(0.224)
0.208
(0.225)
0.295
(0.212)
High-technology
sector
0.228
(0.154)
0.238
(0.155)
Medium-technology
sector 0.085
(0.172)
0.098
(0.172)
Tanzania
−0.970** (0.454)
−0.942** (0.448)
Uganda
1.131*** (0.358)
1.129*** (0.353)
Resources
and
institutions
Internal
R&D
0.867*** (0.161)
0.849*** (0.161)
0.873*** (0.160)
Employee
level
of
education
0.003
(0.002)
0.003
(0.002)
0.003
(0.002)
Skilled
labor 0.515*** (0.143)
0.522*** (0.144)
0.522*** (0.143)
Managerial
experience
0.217
(0.140)
0.229
(0.141)
0.229
(0.140)
Regional
institutional
quality
−1.468** (0.700)
−0.977
(0.948)
−1.414** (0.693)
Interactions
Internal
R&D*RIQ
(H1) 1.478*** (0.490)
1.529*** (0.491)
1.478*** (0.490)
Employee
level
of
education*RIQ
(H2)
0.009
(0.007)
0.009
(0.007)
0.009
(0.007)
Skilled
labor*RIQ
(H3)
1.094** (0.454)
1.146** (0.454)
1.073** (0.454)
Managerial
experience*RIQ
(H4)
0.127
(0.433)
0.144
(0.429)
0.130
(0.433)
Constant
−1.482*** (0.418)
−1.364*** (0.368)
−1.419*** (0.407)
Random-effects
parameters
Est
(sd
error)
95%
CI
Est
(sd
error)
95%
CI
Est
(sd
error)
95%
CI
Regional
intercept 0.410
(0.110)
0.242–0.694
0.971
(0.196)
0.653–1.443
0.400
(0.108)
0.235–0.681
Standard
errors
in
parentheses.
the
managerial
utilization
of
resources,
but
also
on
the
institutional
environment
of
the
firm.
In
particular,
we
find
that
the
regional
institutional
quality
positively
moderates
the
effect
of
using
certain
resources
on
innovative
output.
Moreover,
the
moderating
effect
varies
across
regions
such
that
there
is
a
stronger
effect
in
regions
with
stronger
institutions.
Thus,
a
major
theoretical
implication
of
using
a
resources-based
perspective
on
innovation
is
that
the
actual
potential
to
extract
value
from
firm-level
resources
depends
heav-
ily
on
the
institutional
quality
of
the
firm’s
environment.
Hence,
we
argue
that
integrating
a
resources-based
perspective
with
an
institutional
perspective
provides
more
insightful
interpretation
of
factors
influencing
innovative
output
at
the
firm-level.
6.1.
Policy
implications
Our
findings
show
that
institutions
play
an
important
role
in
moderating
the
positive
effect
of
firm-level
resources
on
innova-
tion.
Regional
institutional
quality
plays
a
critical
role
regarding
the
extent
to
which
firms
successfully
extract
value
from
resources
into
innovative
output
in
developing
countries.
The
value
of
firm-level
resources
for
innovation
significantly
depends
on
the
institutional
environment
from
which
the
firms
operate.
In
cognizance
of
the
observed
regional
variation
in
institutional
quality,
it
is
impera-
tive
that
policy
makers
focus
on
improving
governance
by
fighting
corruption,
enforcing
the
rule
of
law
and
enhancing
regulatory
quality
not
only
at
the
national
level,
but
at
the
regional
level
too.
Focusing
on
improving
governance
at
the
regional
level
may
serve
to
reduce
disparities
in
innovative
output
in
individual
countries.
Overall,
strengthening
the
institutional
environment
within
which
businesses
operate
provides
a
sound
business
environment
that
promotes
entrepreneurial
activities
and
ultimately
innovation
at
the
firm
level.
As
such,
sound
institutions
serve
to
increase
the
value
of
firm-level
resources
in
relation
to
innovative
output
since
firms
are
better
able
to
appropriate
value
from
resources
into
innovative
output.
Hence,
adding
firm
resources
(in
terms
of
R&D
investments
or
human
capital)
can
only
add
value
to
the
firm
and
its
economic
performance
under
the
condition
of
a
strong
regional
institutional
environment.
Beyond
the
evidence
put
forward
by
our
study,
avenues
for
further
research
include
investigating
the
effect
of
different
cat-
egories
of
higher
educational
attainment
on
innovation,
which
our
study
does
not
accomplish
due
to
unavailability
of
data.
In
addi-
tion,
Mansfield
(1984)
opines
that
the
composition
of
internal
R&D
expenditure
is
crucial
to
understanding
how
internal
R&D
impacts
innovation
in
firms.
As
such
this
forms
an
interesting
area
for
fur-
ther
research.
Last
but
not
least,
given
the
institutional
context
within
which
the
firm
operates,
future
availability
of
panel
data
might
allow
researchers
to
examine
the
causal
effects
of
firm-level
resources
on
innovative
output
in
developing
countries.
Acknowledgements
The
authors
gratefully
acknowledge
the
financial
support
pro-
vided
by
the
UK’s
Department
for
International
Development
(DFID)
under
Grant
number
PO5639.
The
funding
agent
had
no
involvement
in
study
design,
in
the
collection,
analysis
and
inter-
pretation
of
data,
nor
in
the
writing
of
the
paper.
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