Innovation and Development
ISSN: (Print) (Online) Journal homepage: https://www.tandfonline.com/loi/riad20
What factors hamper innovation amongst SMEs in
Kenya?
Emmanuel Abbey & Emmanuel Adu-Danso
To cite this article: Emmanuel Abbey & Emmanuel Adu-Danso (2022): What factors
hamper innovation amongst SMEs in Kenya?, Innovation and Development, DOI:
10.1080/2157930X.2022.2030890
To link to this article:  https://doi.org/10.1080/2157930X.2022.2030890
Published online: 24 Jan 2022.
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INNOVATION AND DEVELOPMENT
https://doi.org/10.1080/2157930X.2022.2030890
What factors hamper innovation amongst SMEs in Kenya?
Emmanuel Abbey and Emmanuel Adu-Danso
Department of Economics, University of Ghana, Accra, Ghana
ABSTRACT KEYWORDS
While much of the evidence about innovation amongst SMEs in Kenya; firm innovation;
many developing countries have sought to examine the factors obstacles
that enhance the propensity of a firm to innovate, far less effort
has gone into assessing the factors that hamper it. This paper
follows the latter literature to provide an explanation of firm
innovation failure amongst SMEs in Kenya by focusing exclusively
on external obstacles. By relying on the World Bank Enterprise
Surveys and defining innovation as involving product and process
innovation, we showed that factors such as political instability
and infrastructure, measured as access to electricity, can be
critical to firm innovation. We also found that the effects could be
context-specific, as the results differ based on various firm
characteristics, including firm type, sector, age and size. Our
findings provide important policy implications about firm
innovation in Kenya as well as the understanding that providing
a more conducive business environment is not only critical to
enhancing various firm activities but also enhancing innovation
performance.
1. Introduction
The importance of innovation for various firm activities has been well established. For
instance, it has been shown that innovation is essential for employment growth
(Aghion and Howitt 1997; Garcia, Jaumandreu, and Rodriguez 2004; Harrison et al.
2014), firm productivity (Crépon, Duguet, and Mairesse 1998; Comin 2008; Criscuolo
2009; Mohnen and Hall 2013), firm exporting activities (Roper and Love 2002;
Kirbach and Schmiedeberg 2008; Caldera 2010) and wage increases (Van Reenen
1996; Castillo et al. 2014). It has also been shown that innovation is crucial in enhancing
firm survival and firm competitiveness (Brüderl, Preisendörfer, and Ziegler 1992; Leenen
2005).
As a result, various studies have committed to understanding the factors that account
for firm innovation success. Much of the evidence thus far have focused on internal
factors such as research and development (R&D) (Acs and Audretsch 1988; Acs and
Audretsch 2003; Becheikh, Landry, and Amara 2006; de Jong and Vermeulen 2006),
firm size (Pavitt, Robson, and Townsend 1987; Acs and Audretsch 1988; Acs and
Audretsch 2003), firm age (Schumpeter 1942; Sørensen and Stuart 2000) training
(Cohen and Levinthal 1990), access to finance (Hall 2002) and various firm information
CONTACT Emmanuel Abbey enabbey1@gmail.com
© 2022 Informa UK Limited, trading as Taylor & Francis Group
2 E. ABBEY AND E. ADU-DANSO
sources (Acs 2000; Foray 2000; Dosi 1982). Other studies have focused on technological
opportunities, the degree of competition, the existence of appropriability instruments
and a firm’s capacity to benefit from innovation (Cohen 1995; Encaoua et al. 2000; Klein-
knecht and Mohnen 2001). These studies typically encompass evidence for many devel-
oped economies.
What is probably less studied in the literature, and specifically for Africa, are the
factors that hamper firm innovation. There are several advantages that may accrue
from establishing this knowledge. First, evidence on the factors that hamper innovation
can be context-specific in that the results from developing countries may not be entirely
applicable to the case of Africa. Second, knowledge of the factors that hamper firm inno-
vation can provide complementary explanations for firm innovation success. Studies
such as Amara et al. (2016), D’Este et al. (2012), Savignac (2008), Chaminade and
Edquist (2006), Woolthuis (2005) and Galia and Legros (2004), Mohnen and Röller
(2005), Baldwin and Lin (2002) and Mohnen and Rosa (2001) have generally shown
that identifying the factors that hamper innovation is important in explaining why
some firms either do not innovate at all or do not innovate in more matured activities.
From a policy point of view, the identification of such factors (i.e. the various barriers
or obstacles) could be critical in shaping public policy towards a more holistic response
to addressing issues of firm innovation failure in many economies.
Quite often, and within the extant literature, the factors or obstacles that have been
identified to hamper firm innovation is classified variously. For instance, there are
some obstacles that are peculiar to a firm, and to some extent within the firm’s immediate
control (internal obstacles) such as inadequate access to finance, low use of technology,
poor understanding of market needs, lack of skilled workers, insufficient and ineffective
interaction, and organizational inertia (Mohnen and Röller 2005; D’Este et al. 2012;
Amara et al. 2016; D’Este, Amara, and Olmos-Peñuela 2016; and Baldwin and Lin
2002). There are some other obstacles that are beyond the firm’s control (external
obstacles) and include informal competition, the lack of skilled manpower (Mendi and
Costamagna 2017; Mendi and Mudida 2018) and lack of appropriability instruments
(D’Este et al. 2012). D’Este et al. (2012) also distinguished between revealed and deterring
barriers or obstacles to firm innovation. While the revealed barriers refer to the firm’s
awareness of the difficulties arising from its engagement in innovation activities (point-
ing to a ‘disclosing’ or ‘learning’ outcome based on direct experience), the deterring bar-
riers refer to factors regarded by firms as being insurmountable. Baldwin and Lin (2002)
also classified innovation obstacles by aggregating them based on cost, institution, labour,
organization, and information.
In this paper, we extend the literature by exploring factors that are beyond a firm’s
control (external obstacles) and more particularly political instability and infrastructure
(which we measure using access to electricity). The effects of these variables on a firm’s
innovation propensity have not been adequately explored. It is quite often the case that
owing to the lack of sufficient context-specific empirical evidence, innovation policies in
developing economies may not be evidence-based and tend to gravitate towards one-size-
fits-all approaches. The study of these factors, therefore, remain critical especially within
the context of Africa for a number of reasons. First, political instability is a common
phenomenon in many African countries and its effects on innovation is unclear. Political
instability may deter innovation propensity because of the fear or uncertainty associated
INNOVATION AND DEVELOPMENT 3
with investment outturns. Conversely, firmsmay realize an increase in their innovation pro-
pensity during periods of instability owing to the ties they have with political leaders and
their access to privileged information that may be useful for innovation. Again, Africa
generally performs low when relying on various indicators of infrastructure. With
regards to our measure of infrastructure (access to electricity), it is a key input that
affects innovation propensity drastically. The lack or inadequate access to electricity
delays the production process, discourage the introduction of new products and under-
mines investor confidence.
Particularly for our paper, Kenya presents a unique context for exploring these issues
with potential lessons for developing economies within and outside Africa. The country
has experienced its fair share of highs and tragic lows in political activities, most of which
have been tied to some economic or ethnic interest. Over time, these events have left in
their wake a winner-takes-all mindset, having some implications for economic activities
and access to resources. There is a growing dominance of a few elite business people, who
are often linked to some political leaders. With regards to electricity, Kenya happens to be
one of the relatively few countries in Africa with a very high share of the population with
access to grid energy. Based on data from the World Development Indicators, close to 70
percent of the population have access to electricity and this is higher than the average of
45 percent for sub-Saharan Africa (SSA). In terms of innovation, Kenya is amongst the
few countries in SSA that rank highest in firm innovation based on data from the Global
Competitiveness Index of the World Economic Forum. Yet, there are some heterogene-
ities in this evidence; most small/medium firms are amongst the firms with the lowest
innovation propensities (Cirera 2015). There are also substantial differences in the use
of innovation by manufacturing and services firms (Cirera 2015). Besides, firm inno-
vation in Kenya is generally labelled incremental and not radical (Cirera 2015). It
would therefore be interesting to explore the extent to which external obstacles such
as political instability and access to electricity have hampered or enabled innovation
propensity.
In exploring the factors that hamper innovation in Kenya, we utilize the 2018
World Bank Enterprise Survey for the country. We also attempt to account for
the differences that may exist between sectors, the size of a firm, and the newness
of firms (start-ups versus incumbent firms). Given that the innovation profile of
various firm classifications differs, we argue that recognizing these differences
remain key to improving innovation policy. By defining innovation as involving
product and process innovation, we find that factors such as political instability
and access to electricity remain critical in enhancing firm innovation success or
otherwise. We also find other external obstacles to be having interesting and
differing effects on firm innovation. Our findings provide important insights for
policy on firm innovation success in Kenya and potentially other developing econ-
omies with similar characteristics. The results of our paper suggest that building a
more conducive business environment is not only critical to enhancing various
firm activities but also their innovation tendencies.
The rest of the paper is structured as follows. The next section provides a conceptual
background of the paper. This is followed by a discussion on the methods used for the
estimation as well as the main results. The last section provides some concluding
remarks and implications for policy.
4 E. ABBEY AND E. ADU-DANSO
2. Related literature
The extant literature exploring the obstacles to firm innovation is varied. Appendix 1
provides a conceptual understanding of how these obstacles affect the innovation
decision-making process. This notwithstanding, different classifications of these
obstacles can be found in the literature. Amara et al. (2016), for instance, distinguished
between internal and external obstacles. While the internal obstacles are generally associ-
ated with the difficulties in implementing internal changes such as difficulties with man-
agerial or organizational practices (e.g. lack of skilled personnel, lack of management
training and cultural rigidness), the external obstacles are beyond the firm’s control
and this includes factors such as lack of finance, informal competition and the lack of
appropriability instruments to protect innovative products. D’Este et al. (2012) also dis-
tinguished between revealed and deterring barriers. While the former refers to the firm’s
awareness of the difficulties associated with engaging in innovation activities (pointing to
a ‘disclosing’ or ‘learning’ outcome based on direct experience), the latter refers to a
barrier that is regarded by firms as being insurmountable. Baldwin and Lin (2002) also
classified innovation obstacles by aggregating them based on cost, institution, labour,
organization, and information.
We extend the extant literature on external obstacles by exploring potential barriers
that have been relatively less explored in the literature particularly within the context
of developing economies in Africa. These factors include political instability and infra-
structure (access to electricity).
2.1. Role of political instability and infrastructure
Political instability presents an interesting situation for firm innovation as it may act both
as an obstacle and an enabler. As an obstacle, political instability may discourage the
attraction of foreign investments, which often serve as an important source of innovation
in many developing countries that are not able to invest in R&D, when compared to the
case of advanced economies. Political instability can also discourage the human capital
needed to develop domestic technological capabilities (Allard, Martinez, and Williams
2012). Political instability may potentially discourage firm innovation that requires sig-
nificant financial outflow because of the fear that the outturn could be zero. Wu, Rui,
and Cumming (2014) have argued that during periods of political instability, banks
may be discouraged from funding firms and their innovative activities because of the
risk of default. Political instability can also undermine public policies intended to encou-
rage firm innovation (Courvisanos 2009).
As an enabler, political instability can serve as a ‘helping hand’ in increasing innova-
tive activities especially for firms that are politically connected (Díaz-Díaz, López-Itur-
riaga, and Santana-Martín 2021). Typically, such firms have access to some form of
information or resources that may be useful for innovation but may not be easily acces-
sible or available to other firms. They also have access to networks of political decision-
makers that can influence political decisions and actions in their favour. Díaz-Díaz,
López-Iturriaga, and Santana-Martín (2021) showed that political connectedness can
be more useful for the acquisition of patents and not necessarily the usual corporate
investments into new products and services. Díaz-Díaz, López-Iturriaga, and Santana-
INNOVATION AND DEVELOPMENT 5
Martín (2021) explained this by suggesting that politically connected corporate firms are
less likely to list on the stock market nor seek external finance to avoid scrutiny.
While the evidence from Díaz-Díaz, López-Iturriaga, and Santana-Martín (2021) is
instructive, it is overly focused on corporate firms. Typically, the evidence for small
and medium enterprises that face less scrutiny and are largely informal will be
different. Such firms are less likely to invest in innovative activities because of the fear
of losing their investments outturns in a polarized political environment. Consistent
with this view, we hypothesize that political instability may deter firm innovation, but
the effect may be minimal for smaller firms as well as new firms since their investment
outlays may typically be marginal.
With regards to infrastructure, we focus our argument on access to electricity. Electri-
city, as energy infrastructure, remains a key input to a firm’s production process. Like
other infrastructural inputs, the lack of reliable and efficient energy sources poses a sig-
nificant drawback to firms’ product and process innovation as firms may be compelled to
pursue more labour-intensive innovations which may be relatively less efficient rather
than the more efficient capital-intensive types, especially when access becomes an
issue. For firms that decide to seek alternatives, there are significant cost implications.
Usually, within the context of developing countries, these alternative sources may be rela-
tively more expensive and, therefore, debilitating to the innovation activities of firms.
Studies that highlight the role of infrastructure in the form of access to electricity as an
obstacle to firm innovation activities are quite scarce. The extant literature has focused
largely on other types, particularly, public infrastructure like transport, IT and
financial infrastructure (OECD 2010; Sivak, Caplanova, and Hudson 2011). It has been
shown that access to public infrastructure is critical for innovation (OECD 2010;
Sivak, Caplanova, and Hudson 2011). In line with this view, we hypothesize that the
lack of electricity access can be an essential obstacle to firm innovation, both in the
form of the introduction of new products or processes.
2.2. Role of other external obstacles
Although the focus of this study is on the role of political instability and infrastructure
(i.e. access to electricity) as external obstacles for firm innovation, we account for
other external factors such as the lack of skilled manpower, financial constraints, incom-
patibility with existing technology, and informal competition in our analysis and this
session explores the extent to firm such factors can be an obstacle to firm innovation.
The recognition of the lack of skilled manpower as an obstacle to firm innovation has
long been recognized in the literature. Schumpeter (1939, 1962) in his formulation of the
theory of innovation emphasized the centrality of individual entrepreneurs who discover
new, commercially untried ideas and introduce them to the market. Although Schump-
eter’s argument focused on radical innovations, the relevance of skilled manpower for
firms involved in incremental innovation is still recognized. Skilled manpower is impor-
tant in addressing several internal changes in a firm, and this includes managerial, organ-
izational, and technical practices. Some studies have argued that skilled manpower is
important in knowledge creation and in undertaking a firm’s R&D activities (D’Este,
Rentocchini, and Vega-Jurado 2014, 2016). Several studies have also found the lack of
skilled labour to hamper firm innovation, some of which include Amara et al. (2016)
6 E. ABBEY AND E. ADU-DANSO
for Canada, D’Este, Amara, and Olmos-Peñuela (2016) for Spain, D’Este, Rentocchini,
and Vega-Jurado (2014) for the UK and Galia and Legros (2004) for France. Consistent
with these findings, we hypothesize that the lack of skilled manpower can be detrimental
to firm innovation, be it incremental or radical. Although a few studies have sought to
assess the role of skilled labour based on various firm characteristics, we envisage the
phenomenon to be more precarious for smaller firms, newer firms (or younger firms)
and manufacturing firms. The reason being that small and newer firms may not have
the capacity to attract and maintain highly skilled labour, and in manufacturing firms,
the level of sophistication in adopted technologies implies less skilled labour may be
needed.
Again, financial constraints are one of the most researched innovation obstacles given
that it also influences a firm’s acquisition of a variety of other resources, including capital
and R&D activities, required for innovation. Mancusi and Vezzulli (2010) identified
financial constraints to be a negative factor affecting firm R&D decisions in Italy.
Tiwari et al. (2008) relied on data from Denmark to show that the presence of
financial constraints presents key obstacles to firm innovations. Savignac (2008) finds
similar evidence in France. Hall (2002) established that financial difficulties are severe
for innovative projects because their specific features increase the risk and the informa-
tional problems that a firm may have with external investors. We hypothesize that
financial constraints are barriers for innovation and for process innovation which
tends to be capital intensive.
Although incompatibility with existing technology can be a constraint to firm inno-
vation, the literature on it is quite scarce. While Keller (1996) describes it mostly from
the perspective of skills compatibility (i.e. technology is only implementable as the
labour force has built up the corresponding skills), Nieto and Quevedo (2005) defined
it from an organizational perspective to include the lack of managerial capacity. This not-
withstanding, we define incompatibility with existing technology as desirable innovations
that firms wish to adopt, but the existing organizational structures and processes do not
make it feasible. Typically, incompatibility with existing technology can truncate a firm’s
learning curve, thereby discouraging additional investments in R&D or other related
activities that can increase a firm’s innovation propensity. It may also not allow firms
to maintain their competitiveness as they are likely to pursue low-valued innovations
and not the radical types that can be key to the sustainability of their business operations.
Based on this, we hypothesize that incompatibility with existing technology can be detri-
mental for firm innovation as it constrains firms from moving beyond their current tech-
nological frontiers.
Finally, while informal firms can be important sources of information for innovation
(Prahalad 2005; Bhattacharyya et al. 2010; Radjou, Prabhu, and Ahuja 2012) or important
innovation collaborators (von Hippel 2005; George, McGahan, and Prabhu 2012), infor-
mal competition may deter firm innovation. Informal competition may distort the
process of skills accumulation since informal firms require low skills (Mendi and Costa-
magna 2017). It can also discourage the introduction of new or significantly improved
products since the entry cost for informal firms may be lower, and they are less
affected by regulatory requirements (Djankov et al. 2002; McKenzie and Seynabou
Sakho 2010). Emphasizing the positive spillover effects, Mendi and Costamagna (2017)
argued that informal competition can force firms to differentiate further and to escape
INNOVATION AND DEVELOPMENT 7
the competition effect by being more innovative. While studies such as Galdon-Sanchez
and Schmitz (2002), Symeonidis (2002) and Blundell, Griffith, and van Reenen (1999)
find that informal competition has a positive effect on innovation, others including
Avenyo, Konte and Mohnen (2021), Spulber (2013), Grossman and Helpman (1991)
and Aghion and Howitt (1992) find a negative effect. In this study, we hypothesize
that informal competition could likely deter firm innovation within the context of
Kenya, because of the role it has in distorting skills accumulation. This is because the
type of innovative activities in many African countries tends to be incremental.
3. Data and estimation strategy
3.1. Data
As indicated earlier, we relied on the 2018 World Bank Enterprise Survey for Kenya for
the analysis. The Kenyan Enterprise Surveys collected several firm-level information
across the following: primary information about firms, infrastructure and services,
sales and supplies, degree of competition, capacity, land and permits, finance, mobile
money use, labour, business environment and performance. Unlike the surveys for
many other developing economies, the Kenyan dataset had some unique questions on
various obstacles to innovation as well as the general business environment. We relied
mostly on these innovation obstacles in our analysis. Table 1 presents the distribution
of firms in our analytical sample by rm size,1fi sector (manufacturing or services), the
newness of firms (i.e. whether start-up or an incumbent firm2) and the type of innovative
activities (product/process).
Table 1. Distribution of firms in our sample.
Frequency Percent
Firm Size
Micro 13 1.34
Small 422 43.42
Medium 357 36.73
Large 180 18.52
Region
Mombasa 93 9.57
Kilifi 69 7.10
Machakos 59 6.07
Kirinyaga 75 7.72
Kiambu 96 9.88
Trans Nzoia 39 4.01
Uasin Gishu 68 7.00
Nakuru 99 10.19
Kisumu 71 7.30
Nairobi 303 31.17
Sector
Services 536 55.14
Manufacturing 436 44.86
Newness
Start-up Firm 340 34.98
Incumbent Firm (small) 230 23.66
Incumbent Firm (large) 402 41.36
Innovation
Product Innovation 258 26.54
Process Innovation 451 46.40
8 E. ABBEY AND E. ADU-DANSO
Based on firm size, we observe that about one percent of the firms in our analytical
sample are micro firms, 43 percent are small-sized firms, 38 percent are medium-sized
firms, and 19 percent are large-sized firms. Geographically, most of the firms are situated
in the capital Nairobi (31%), and the least number of firms are in largely agrarian Trans
Nzoia County (4%). In terms of the economic sector, while about 55 percent are services
firms, 45 percent are manufacturing firms. Based on the age of firms and membership of
an enterprise group, we observe that about 35 percent of the firms are start-ups (i.e. aged
5 years or less), 24 percent are small-sized incumbent firms and 41 percent are large-sized
incumbent firms. Finally, regarding the type of innovation undertaken, about 27 percent
were classified as product innovators, while 46 percent were classified as process
innovators.
In the survey, firms were asked to identify the extent to which certain factors were
regarded as obstacles either for innovation or for doing business more generally (on a
Likert scale of 0–4).3 Based on the data, we identified the following to be amongst the
pool of external obstacles that are likely to hamper firm innovation (i.e. the introduction
of new or significantly improved products or services as well as the techniques of pro-
duction): (1) political instability, (2) electricity, (3) lack of skilled manpower, (4)
financial constraints, (5) incompatibility with existing technology, and (6) informal com-
petition. We present in Tables 2 and 3 the firm’s perceptions of these obstacles by relying
on a severity index.4 This index describes the relative importance of the above factors in
affecting the innovativeness of the sampled firms. The results are presented based on the
economic sector of operation and firm size (Table 2) and then based on newness and type
of innovator (Table 3).
More generally, we observe in Tables 2 and 3 that political instability ranked highest as
the most severe obstacle. This is consistent irrespective of firm size, sector, newness, and
innovator type. The second most severe obstacle is financial constraints, across all
definitions of firms, except for services firms and large firms. For these two types of
firms, informal competition and electricity ranked second. The obstacle of least impor-
tance is incompatibility with existing technology for almost all firm types except
medium-sized firms, small incumbent firms and product innovators.
3.2. Estimation strategy
In deciding on the most appropriate estimation strategy for the estimations, we explored
various binary choice models because of the nature of our measures of innovation in the
survey data. We relied on product and process innovation. Product innovation, in the
Table 2. Obstacle severity index (full sample, sector and firm size).
Economic sector Firm size
Constraint Full sample Services Manufacturing Small Medium Large
Political instability 2.129 (1) 2.198(1) 2.044 (1) 2.090 (1) 2.151 (1) 2.178 (1)
Financial constraints 1.830 (2) 1.800 (3) 1.867 (2) 1.890 (2) 1.852 (2) 1.644 (3)
Informal competition 1.776 (3) 1.851 (2) 1.683 (4) 1.860 (3) 1.824 (3) 1.478 (4)
Lack of skilled manpower 1.167 (5) 1.146 (5) 1.193 (5) 1.103 (5) 1.190 (4) 1.272 (5)
Electricity 1.620 (4) 1.509 (4) 1.757 (3) 1.593 (4) 0.000 (6) 1.783 (2)
Incompatibility with existing technology 1.035 (6) 1.007 (6) 1.069 (6) 1.099 (6) 1.000 (5) 0.950 (6)
Note: Rank in parenthesis.
INNOVATION AND DEVELOPMENT 9
Table 3. Obstacle severity index (newness and innovators).
Firm age Innovator type
Start- Incumbent Incumbent Product Process All
Obstacles ups (Small) (Large) Innovators Innovators Innovators
Political Instability 2.2 (1) 2.0 (1) 2.1 (1) 2.3 (1) 2.1 (1) 2.2 (1)
Financial Constraints 1.9 (2) 1.9 (2) 1.7 (2) 2.0 (2) 1.9 (2) 1.9 (2)
Informal Competition 1.9 (2) 1.9 (2) 1.6 (3) 1.8 (3) 1.7 (3) 1.8 (3)
Lack of Skilled Manpower 1.3 (4) 1.0 (5) 1.2 (5) 1.4 (4) 1.3 (5) 1.3 (5)
Electricity 1.6 (3) 1.4 (3) 1.5 (4) 1.8 (3) 1.6 (4) 1.7 (4)
Incompatibility with 1.1 (5) 1.1 (4) 1.0 (6) 1.1 (5) 1.1 (6) 1.1 (6)
existing Technology
Note: Rank in parenthesis.
survey, was measured by asking firms if they had introduced any innovative product or
service. The response to this question was binary (1 = Yes, 2 = No). For process inno-
vation, the firms were asked if they had introduced any innovative methods of manufac-
turing products or offering services. The response to this question was also binary (1 =
Yes, 2 = No). We thus relied on the Probit estimator5 to identify the constraint factors,
which we specify is as follows:
Yi =b1skilled mani + b2finan consti + b3incompatibilityi + b4informal compi
+ b5political instai + b6electricityi + b7controli + b7regioni + 1i
where Yi ( j = 1, 2) represents the innovation measures (i.e. product, process) by the ith
firm, skilled_mani is a dummy for the lack of skilled manpower, finan_consti is a
dummy for financial constraints, incompatibilityi is a dummy for incompatibility with
existing technology, informal_compi is a dummy for informal competition,
political_instai is a dummy for political instability, and electricityi is a dummy for electri-
city. As a robustness check, we apply a complementary technique (the bivariate Probit
estimator) since product and process innovation can be complementary.
In our estimations, we include other controls such as the firm’s use of research and
development (R&D), appropriability instruments, the firm being a member of an enter-
prise group, foreign ownership, female ownership, exporting, training, the experience of
a top manager, firm size, firms age, innovation cooperation, information sources of inno-
vation, location and sector.6 The choice of these controls is based on theory.
For instance, Becheikh, Landry, and Amara (2006), de Jong and Vermeulen (2006),
Acs and Audretsch (2003), Acs and Audretsch (1988) discussed the importance of
R&D in a firm’s knowledge production function in resolving production problems as
well as in building upon the firm’s previous knowledge in the use of various techniques.
Also, a firm is more likely to benefit from the innovations of other firms that it is in the
same enterprise group (Amsden and Hikino 1994; Chang, Chung, and Mahmood 2006).
Again, foreign ownership can enhance firm innovation through the transfer of R&D,
skilled personnel and other inputs or the ‘cherry-picking’ of the most productive firms
(Love, Roper, and Du 2009; Choi, Lee, and Williams 2011; Guadalupe, Kuzmina, and
Thomas 2012); conversely, foreign ownership can mar firm innovation, if local firms
do not have the capacity to absorb the superior foreign technologies (Malecki 1980;
Howells 1984; Kleinknecht and Poot 1992; Love, Roper, and Du 2009). Furthermore,
Dessler (2001) and Bagshaw (2004) noted the importance of women in the innovation
10 E. ABBEY AND E. ADU-DANSO
process as they improve the management skills of teams as well as their decision-making.
Moreover, Caldera (2010) discussed the importance of exporting for innovating firms
based on the characteristics of the exporting market (requires high-quality goods).
Additionally, innovation-related training serves as an important complement in building
human capital specific for innovative activity. Moreso, the experience of a top manager, is
an important human capital that has been found to be critical for innovation (Daellen-
bach, McCarthy, and Schoenecker 1999).
With respect to firm size and firm age, Schumpeter (1942) argued that larger/older
firms have some monopoly powers that they can exploit for their innovations. More
recent literature has also argued that large firms can afford the high fixed cost of innovat-
ing due to their high economies of scale and their ability to manage associated risks (Acs
and Audretsch 2003). A competing view is that smaller firms can be more innovative
because they skirt the bureaucratic organizational structure of large firms (Pavitt,
Robson, and Townsend 1987). The same applies to younger firms. Innovation
cooperation has been extensively discussed in the strategic alliance literature and has
been shown to be a complementary asset to technological know-how as well as access
to various resources for firm innovation (Bougrain and Haudeville 2002; McGill and
Santoro 2009). Similarly, various information sources of innovation have been acknowl-
edged in the literature. Information sources for innovation are important since inno-
vations are not entirely outputs of discrete events, but also from the problem-solving
initiatives of firms that can emerge from customers, suppliers and business associations
(Dosi 1982; Foray 2000).
4. Descriptive statistics
We present in Table 4, descriptive statistics of the variables used for the estimations. In
addition to the earlier discussion about the distribution of the innovation obstacles in
Section 3.1, we observed that 19 percent of the firms had undertaken research and devel-
opment (R&D), 8 percent belonged to an enterprise group, 14 percent had foreign own-
ership and 45 percent had a female owner. We again observed that 30 percent of the firms
are exporting firms, and about 47 percent have undertaken some form of innovation-
related training. The average years of experience of a top manager is 3, while the
average firm size and firm age are 73 and 16 respectively. The most dominant source
of innovation cooperation is through collaborations/co-development (14%), while the
most dominant information source is from customers (29%) (see Table 4).
When comparing the descriptive statistics between product and process innovators,
we did not observe very major differences between product and process innovating
firms, as both firms had a fairly equal proportion of all variables. This notwithstanding,
product innovators, had a slightly higher proportion for the following variables: lack of
skilled manpower, enterprise group, foreign ownership, and all information sources of
innovation (customers, suppliers and business associations). We again observe that a
fair amount of the firms undertook both product and process innovation.
We calculated the correlation between product and process innovating firms, and this
was around 0.3, suggesting a fairly weak positive correlation. The correlations of the rest
of the variables are presented in Appendix 3. The intention is to show: (1) the extent of
association between the variables being used in the analysis; (2) to check and control for
INNOVATION AND DEVELOPMENT 11
Table 4. Descriptive statistics of variables
Product Process
Full sample innovation innovation
Variable Mean Std. Dev. Mean Std. Dev. Mean Std. Dev. F-Value
Innovation type
Process innovation 0.265 0.442 0.399 0.490 1.000 0.000 –
Product innovation 0.464 0.499 1.000 0.000 0.698 0.460 –
Constraints
Lack of Skilled Manpower 0.642 0.480 0.707 0.455 0.698 0.460 4.23***
Financial Constraints 0.825 0.380 0.845 0.363 0.849 0.359 1.00
Incompatibility with existing Technology 0.614 0.487 0.643 0.480 0.643 0.480 0.24
Informal Competition 0.777 0.417 0.785 0.411 0.779 0.416 2.08
Political Instability 0.842 0.365 0.851 0.356 0.864 0.343 3.17**
Electricity 0.824 0.381 0.856 0.352 0.860 0.347 1.05
Research and Development 0.193 0.395 0.297 0.457 0.372 0.484 6.70***
Innovation cooperation
Strategic Alliance 0.117 0.322 0.211 0.408 0.256 0.437 13.17***
Collaboration/Co-development 0.140 0.347 0.253 0.435 0.341 0.475 17.57***
Acquisition 0.042 0.201 0.073 0.261 0.109 0.312 5.80***
Information source for innovation
Customers 0.287 0.453 0.257 0.438 0.252 0.435 1.42
Suppliers 0.122 0.328 0.129 0.335 0.112 0.316 1.14
Business Associations 0.111 0.314 0.102 0.303 0.097 0.296 1.46
Other firm characteristics
Enterprise group 0.088 0.284 0.106 0.309 0.085 0.280 0.66
Foreign Ownership 0.140 0.347 0.153 0.360 0.132 0.339 2.79*
Female Dummy 0.452 0.498 0.503 0.501 0.543 0.499 1.32
Exporting Firms 0.303 0.460 0.353 0.478 0.395 0.490 1.12
Innovation-related Training 0.474 0.500 0.621 0.486 0.686 0.465 8.29***
Experience of Top Manager 2.511 0.818 2.591 0.801 2.591 0.807 2.20***
Log of Firm Size 3.155 1.334 3.371 1.375 3.371 1.502 1.50***
Log of Firm Age 2.076 1.375 2.169 1.340 2.222 1.339 1.56***
Note: F-Value is a Pearson two-way test of homogeneity between product and process innovators.
possible multicollinearity amongst the variables. We find that most of our main explana-
tory variables are positively correlated with product and process innovation. In addition,
none of the correlation coefficients is above the suggested threshold of 0.7, which would
have indicated the possible presence of multicollinearity. We further provide tolerance
statistics to show that there is no problem with multicollinearity between the explanatory
variables.
5. Estimation results
We present in Tables 5 and 6, the main results of the Probit estimator. In Table 5, where
the results for the propensity that a firm introduces product innovation are presented, we
observe that access to electricity remains a major constraint to product innovation. This
finding is consistent for services firms, small-sized firms, start-ups and incumbent firms
that are small-sized. Electricity is not an obstacle for product innovation amongst man-
ufacturing and large-sized firms, although we lose precision on the significance. These
findings, however, underscore the point that electricity remains a key obstacle to inno-
vation activities. Given that firms in developing countries are heavily dependent on pub-
licly provided electricity and their production process may be very sensitive to electricity
access, there are some implications of this finding for policy. Although, it is difficult to
find studies explicitly exploring the role of electricity as an obstacle to innovation,
12 E. ABBEY AND E. ADU-DANSO
Table 5. Marginal effects for product innovation and obstacles.
Economic sector Firm size Firm age
Services Manufacturing Small-sized Medium-sized Large-size Incumbent Incumbent
Variables Full sample firms firms firms firms firms Start-ups (small) (large)
Obstacles
Political Instability 0.067 0.075 −0.058 0.082 −0.055 −0.042 −0.000 0.165*** −0.154**
(0.060) (0.069) (0.061) (0.066) (0.083) (0.078) (0.094) (0.044) (0.063)
Electricity −0.086 −0.132** 0.128* −0.209*** −0.047 0.123* −0.133* −0.165*** 0.023
(0.055) (0.058) (0.066) (0.065) (0.074) (0.072) (0.073) (0.058) (0.061)
Lack of Skilled Manpower 0.112** 0.129*** −0.051 0.154*** −0.078 −0.288*** 0.128** 0.205*** −0.097*
(0.044) (0.048) (0.063) (0.048) (0.062) (0.069) (0.062) (0.042) (0.055)
Financial Constraints 0.011 0.033 0.030 −0.015 0.069 0.036 0.044 −0.089 −0.002
(0.063) (0.067) (0.059) (0.077) (0.073) (0.068) (0.095) (0.055) (0.059)
Incompatibility with existing 0.056 0.101** 0.029 0.075 0.197*** 0.231*** 0.096 0.061 0.112**
Tech. (0.044) (0.049) (0.055) (0.051) (0.058) (0.062) (0.059) (0.046) (0.050)
Informal Competition −0.081 −0.125* 0.026 −0.042 −0.065 0.012 0.068 −0.120* −0.038
(0.059) (0.068) (0.057) (0.068) (0.059) (0.070) (0.073) (0.064) (0.051)
Innovation cooperation
Strategic Alliance 0.233*** 0.360*** 0.016 0.368*** 0.130 0.096 0.323*** 0.203** 0.111
(0.070) (0.101) (0.085) (0.096) (0.101) (0.117) (0.107) (0.080) (0.080)
Collaboration/Co- 0.264*** 0.342*** 0.151* 0.430*** 0.306*** −0.012 0.201** 0.332*** 0.236***
development (0.065) (0.094) (0.086) (0.109) (0.077) (0.111) (0.101) (0.121) (0.059)
Acquisition of other 0.223** 0.191* 0.313*** 0.031 0.252** – 0.386*** −0.427** 0.327***
Companies (0.092) (0.114) (0.103) (0.137) (0.126) (0.128) (0.163) (0.101)
Information sources
Customers 0.038 0.037 0.042 0.072 0.031 0.101 −0.067 0.123*** 0.003
(0.050) (0.053) (0.057) (0.055) (0.070) (0.063) (0.068) (0.042) (0.056)
Suppliers 0.118* 0.149** −0.142* 0.151* 0.153* 0.061 0.138 0.010 0.291***
(0.067) (0.072) (0.085) (0.080) (0.080) (0.111) (0.100) (0.054) (0.076)
Business Association 0.100* 0.106* −0.022 0.138** 0.080 0.004 0.051 0.100 0.054
(0.054) (0.064) (0.064) (0.063) (0.092) (0.072) (0.107) (0.073) (0.057)
Other control variables
Research and Development 0.100* 0.079 0.087 0.109 0.246*** 0.263*** 0.116 0.121** 0.169***
(0.055) (0.064) (0.061) (0.067) (0.066) (0.073) (0.078) (0.048) (0.060)
Enterprise Group −0.038 −0.053 −0.006 0.000 0.099 −0.087 −0.039 0.023 0.185**
(0.072) (0.075) (0.071) (0.083) (0.077) (0.121) (0.088) (0.057) (0.075)
Foreign Ownership 0.029 0.014 0.061 −0.191 −0.056 0.141** 0.049 −0.422*** −0.018
(0.065) (0.073) (0.055) (0.117) (0.067) (0.057) (0.091) (0.116) (0.058)
Female Ownership 0.082** 0.099** 0.009 0.133*** 0.010 −0.074 0.130** −0.063 −0.015
(0.039) (0.044) (0.043) (0.045) (0.057) (0.059) (0.055) (0.041) (0.046)
INNOVATION AND DEVELOPMENT 13
Exporting Firms −0.086* −0.075 −0.132** −0.155** −0.048 0.063 −0.133 −0.043 0.035
(0.051) (0.059) (0.056) (0.066) (0.055) (0.057) (0.081) (0.051) (0.049)
Innovation-related Training 0.172*** 0.194*** 0.115** 0.173*** 0.123** 0.051 0.134* 0.135*** 0.176***
(0.042) (0.044) (0.047) (0.048) (0.054) (0.058) (0.071) (0.044) (0.043)
Experience of Top Manager 0.067** 0.073** 0.007 0.108*** −0.002 −0.036 0.112*** 0.038 −0.011
(0.029) (0.033) (0.039) (0.029) (0.043) (0.031) (0.039) (0.029) (0.034)
Log of Firm Size 0.045** 0.036 0.083*** 0.057 −0.020 −0.028 0.065* 0.042 0.023
(0.021) (0.026) (0.018) (0.043) (0.044) (0.029) (0.035) (0.045) (0.027)
Log of Age 0.007 −0.004 0.031 −0.008 −0.023 0.021 −0.055 0.034 −0.068**
(0.017) (0.020) (0.023) (0.018) (0.025) (0.021) (0.051) (0.023) (0.031)
Location Yes Yes Yes Yes Yes Yes Yes Yes Yes
Sector Yes Yes Yes Yes Yes Yes Yes Yes Yes
Observations 972 536 436 435 357 180 340 230 402
Standard errors in parentheses.
*** p < 0.01, ** p < 0.05, * p < 0.1.
14 E. ABBEY AND E. ADU-DANSO
Table 6. Marginal effects for process innovation and obstacles.
Economic Sector Firm Size Firm age
Services Manufacturing Small-sized Medium-sized Large-Size Incumbent Incumbent
Variables Full Sample Firms Firms Firms Firms Firms Start-ups (Small) (Large)
Obstacles
Political Instability 0.157*** 0.194*** 0.039 0.261*** 0.010 −0.001 0.137 0.347*** 0.056
(0.053) (0.070) (0.048) (0.074) (0.042) (0.058) (0.086) (0.087) (0.040)
Electricity −0.020 −0.020 0.038 −0.094 0.031 0.003 −0.104 −0.061 0.031
(0.064) (0.071) (0.057) (0.076) (0.054) (0.056) (0.087) (0.063) (0.042)
Lack of Skilled Manpower 0.037 0.056 −0.042 0.030 −0.023 0.074 −0.002 0.135** −0.076*
(0.054) (0.060) (0.054) (0.063) (0.054) (0.049) (0.068) (0.060) (0.043)
Financial Constraints −0.014 −0.007 −0.069 0.048 0.161*** −0.004 0.115 0.009 0.031
(0.057) (0.069) (0.060) (0.083) (0.053) (0.058) (0.090) (0.071) (0.039)
Incompatibility with −0.007 −0.009 −0.001 −0.005 −0.024 0.106** −0.025 0.018 −0.019
existing Tech. (0.048) (0.057) (0.046) (0.060) (0.044) (0.049) (0.069) (0.054) (0.037)
Informal Competition 0.049 0.032 0.114** 0.127 0.053 −0.041 0.089 0.064 0.000
(0.060) (0.070) (0.052) (0.077) (0.052) (0.060) (0.095) (0.069) (0.037)
Innovation cooperation
Strategic Alliance 0.160** 0.192** 0.031 0.127 −0.014 0.051 −0.145 −0.043 0.060
(0.076) (0.085) (0.077) (0.100) (0.068) (0.081) (0.101) (0.122) (0.050)
Collaboration/Co- 0.047 0.004 0.330*** 0.036 0.136** 0.147* 0.401*** 0.307*** 0.167***
development (0.073) (0.082) (0.071) (0.110) (0.068) (0.082) (0.130) (0.092) (0.043)
Acquisition of other 0.379*** 0.414*** −0.055 1.157*** 0.004 0.241*** – 0.163 0.034
Companies (0.099) (0.110) (0.115) (0.258) (0.070) (0.088) (0.118) (0.051)
Information sources
Customers −0.006 −0.015 0.015 −0.038 −0.076* 0.120* 0.036 −0.036 −0.060
(0.051) (0.058) (0.052) (0.067) (0.042) (0.063) (0.057) (0.066) (0.041)
Suppliers 0.023 0.004 0.082 0.023 0.040 −0.018 0.009 0.027 0.015
(0.071) (0.079) (0.060) (0.089) (0.054) (0.081) (0.091) (0.077) (0.041)
Business Association 0.100 0.106 −0.049 0.177* −0.144** 0.067 0.034 0.278*** 0.010
(0.076) (0.086) (0.054) (0.090) (0.068) (0.062) (0.116) (0.078) (0.048)
Other control variables
Research and Development 0.108* 0.088 0.169*** 0.060 0.134*** 0.048 0.019 0.453*** 0.042
(0.065) (0.073) (0.047) (0.095) (0.043) (0.054) (0.083) (0.113) (0.042)
Enterprise Group −0.091 −0.091 −0.092 −0.028 −0.106* −0.104 −0.167 0.160 −0.115**
(0.094) (0.108) (0.081) (0.127) (0.061) (0.124) (0.136) (0.100) (0.055)
Foreign Ownership −0.144** −0.229** 0.082 −0.156 −0.098 0.035 −0.072 −0.205* −0.101*
(0.067) (0.096) (0.053) (0.140) (0.070) (0.051) (0.090) (0.111) (0.054)
Female Ownership 0.024 0.008 0.085** 0.070 −0.014 −0.031 −0.002 0.003 0.080**
(0.042) (0.049) (0.041) (0.058) (0.043) (0.048) (0.054) (0.057) (0.035)
INNOVATION AND DEVELOPMENT 15
Exporting Firms −0.078 −0.075 −0.148*** −0.084 −0.115*** −0.053 −0.231*** 0.051 −0.048
(0.053) (0.065) (0.046) (0.077) (0.043) (0.049) (0.076) (0.068) (0.034)
Innovation-related Training 0.072 0.080 0.039 0.022 0.122*** 0.038 0.081 −0.046 0.043
(0.054) (0.062) (0.040) (0.067) (0.036) (0.052) (0.071) (0.063) (0.033)
Experience of Top Manager −0.014 −0.028 0.053* 0.025 −0.049** 0.106*** −0.037 0.184*** −0.049**
(0.033) (0.038) (0.030) (0.040) (0.024) (0.034) (0.037) (0.052) (0.023)
Log of Firm Size −0.002 −0.009 0.024 0.043 0.093*** 0.099*** 0.139*** −0.091* 0.042**
(0.020) (0.026) (0.016) (0.046) (0.034) (0.024) (0.027) (0.052) (0.017)
Log of Age −0.001 0.004 −0.028 0.009 −0.025 −0.032 −0.114* −0.049 −0.006
(0.019) (0.022) (0.019) (0.023) (0.017) (0.022) (0.060) (0.034) (0.021)
Location Yes Yes Yes Yes Yes Yes Yes Yes Yes
Sector Yes Yes Yes Yes Yes Yes Yes Yes Yes
Observations 972 536 436 435 357 180 340 230 402
Standard errors in parentheses.
*** p < 0.01, ** p < 0.05, * p < 0.
16 E. ABBEY AND E. ADU-DANSO
Sivak, Caplanova, and Hudson (2011) showed the extent to which public infrastructure
remains critical for innovation and entrepreneurship and how its absence or lack of
access can be a deterring factor, even though their emphasis was mostly on transport,
IT and financial infrastructure.
Our finding of the effect of political instability is mixed. Depending on the age of the
firm, we found for the case of small incumbent firms that political instability enables
product innovation, but for large incumbent firms, Political instability acts as a
barrier. Díaz-Díaz, López-Iturriaga, and Santana-Martín (2021), argued that political
instability may not be associated with corporate investments into new products and ser-
vices, even for politically connected firms due to the inefficiencies with political decisions
and other constraints. For the case of Kenya, it is possible that incumbent firms, that are
large-sized, may not be investing in the introduction of new products because of their
knowledge of the inefficiencies of political decisions and the fear of losing out on their
investments. For small firms, the results seem to confirm the findings of Díaz-Díaz,
López-Iturriaga, and Santana-Martín (2021) that political instability may indeed lend a
‘helping hand’ for product innovation. Such firms through their political connectedness
may exploit access to networks of political decision-makers to their exclusive advantage
and where possible, to the disadvantage of larger firms. Moreover, the large-sized firms in
our analytical sample are largely concentrated in the food, chemical as well as plastics and
rubber sectors with a sizeable proportion having some form of foreign ownership. It is
therefore not difficult to see why political instability can be an obstacle for such
businesses in introducing new or significantly improved products.
Again, we observe that the lack of skilled manpower is mostly an obstacle to product
innovation amongst large-sized firms. Given the important role skilled manpower play in
firms through knowledge creation and R&D activities (D’Este, Rentocchini, and Vega-
Jurado 2014; D’Este, Amara, and Olmos-Peñuela 2016), it is fairly easy to comprehend
why the lack thereof will affect large firms disproportionately. In a different spectrum,
informal competition is observed to be an obstacle for product innovation amongst ser-
vices firms and small incumbent firms although the effect is not very strong. The finding
is, however, consistent with Avenyo, Konte andMohnen (2021), which finds that that the
competitive behaviour of informal sector enterprises in the product market is one of the
main barriers to innovations in formal sector enterprises.
Also, incompatibility with existing technology does not appear to be an obstacle for
product innovation amongst services firms, as well as medium and incumbent large-
sized firms. We did not find evidence to suggest that financial constraints affect the pro-
pensity to undertake product innovation. While R&D was observed to be important for
innovation in medium-sized and large-sized firms, foreign ownership was found to be
important for large-sized firms. Female ownership was essential for the full sample, ser-
vices firms, small-sized firms and start-ups. Exporting does not seem to enhance product
innovation. Training, the experience of a top manager, firm size, all innovation
cooperation measures and information from suppliers and business associations were
important for product innovation at varying degrees and across different firm categories.
Most of these findings are consistent with those in the extant literature. Given that in
most developing countries, innovation is at best described as marginal or incremental,
the mixed evidence on the effect of these external obstacles on the propensity to under-
take product innovation is not surprising.
INNOVATION AND DEVELOPMENT 17
With regards to process innovation (in Table 6), we did not find evidence of either
political instability or electricity access acting as obstacles. The results suggest that pol-
itical instability is an enabler of process innovation in the full sample, as well as for ser-
vices firms, small-sized firms and incumbent firms that are small-sized. It is important to
indicate that this result is quite unexpected and surprising. This finding on small-sized
firms regarding process innovation is consistent with our observations for small firms
regarding product innovation. Also, most of the services firms in our analytical sample
are into retail services, hotels and restaurants. The relative ease with which services
firms can alter their production techniques relative to non-services firms may explain
why political instability does not appear to be an obstacle to process innovation.
With regards to the other external obstacles, we find that the lack of skilled manpower
hampered process innovation in incumbent firms that are large-sized, even though the
effect was not very strong while in incumbent firms that are small, the lack of skilled man-
power was not a constraint. The other factors such as financial constraints, incompatibil-
ity with existing technology and informal competition were found to increase the
propensity to undertake process innovation. In particular, while financial constraints
was not an obstacle for undertaking process innovation by medium-sized firms, incom-
patibility with existing technology was not an obstacle for process innovation for large-
sized firms. Also, informal competition was not an obstacle for undertaking process
innovation for manufacturing firms.
While these latter results do not align with what can mostly be found in the literature,
it is appropriate that the issue of context is discussed. For instance, studies such as
Mancusi and Vezzulli (2010) and Hall (2002) found financial constraints to be obstacles
to R&D and innovation. Our finding of a positive effect could hint of medium-sized firms
relying on other sources (e.g. foreign partnerships) to enhance process innovation apart
from the fact that innovation is undertaken in developing economies may be more of the
incremental kind and not radical thereby requiring substantial funding. The firms are,
therefore, less likely to be constrained by finance.
Again, the finding that incompatibility with existing technology increases the propen-
sity for a firm to undertake process innovation could be because such innovations are at
best incremental and not radical. It is also important to note that a substantial proportion
of firms in many developing countries are gradually gravitating towards the services
sector where most innovations tend to depend on the customer, and incompatibility
with existing technology may not entirely be an obstacle to such firms undertaking
matured process innovations.
Mendi and Costamagna (2017) find informal competition to hinder innovation
among firms in Kenya. While this contradicts our finding for manufacturing firms, it
is again crucial to note that Galdon-Sanchez and Schmitz (2002), Symeonidis (2002)
and Blundell, Griffith, and van Reenen (1999) find that informal competition has a posi-
tive effect on innovation and rely on the escape competition effect to explain their result –
the fact that informal competition induces firms to further differentiate to escape com-
petitive pressure. We foresee a similar effect in the case of manufacturing firms in
Kenya based on our findings.
For most of the remaining control variables, their signs and significance were consist-
ent with earlier results for the propensity to undertake product innovation. The only
exceptions were foreign ownership which does not enhance process innovation in the
18 E. ABBEY AND E. ADU-DANSO
full sample and for services firms, and the experience of a top manager, which is also not a
factor that enhances process innovation in medium-sized firms. Lastly, business associ-
ations and customers are not important sources of information for process innovation in
medium-sized firms.
6. Robustness check
The analysis was carried out again relying on the biprobit estimator (See Appendix 4 and
5). The use of the biprobit estimator is based on evidence that process and product inno-
vation may complement each other. The results from the probit estimator were generally
similar to those found in Tables 5 and 6. The results reinforce our argument about the
critical role of political instability and access to electricity for innovation in Kenya.
Most of the other variables maintained their signs and significance.
7. Discussion and conclusion
The main aim of this paper was to identify the factors that hamper firm innovation in
Kenya. While the overwhelming emphasis of the innovation literature has been to ident-
ify the factors that drive firm innovation, studies that assess the factors that potentially
hamper firm innovation, particularly in developing economies, remain limited in the lit-
erature. This study sought to provide some evidence on the barriers to firm innovation
from the perspective of a developing African economy, which potentially holds impor-
tant lessons for policy on factors that can undermine efforts aimed at encouraging inno-
vation among firms in developing economies. Of particular interest was the effect of
political instability and infrastructure in the form of access to electricity. These obstacles
are fairly common to many developing economies. The study relied on data from the
World Bank’s Enterprise Survey on Kenya, which in several ways reflects the develop-
mental aspirations and challenges of a typical developing country context.
By defining firm innovation as product and process innovations and estimating a
Probit model, we find that political instability and access to electricity act as both enablers
and barriers to innovation activities depending on the characteristics of firms. On the one
hand, while political instability was found to enable product innovation in mature firms
which are small, it acts as an obstacle for mature firms which are large. Regarding process
innovation, on the other hand, political instability was observed as an enabler in the full
sample, small firms, matured firms that are small and services firms. These findings share
some similarities with the extant literature, largely agreeing with those identifying inno-
vation obstacles.
Furthermore, we find strong evidence suggesting that challenges with electricity access
act as a barrier to product innovation among services firms, small-sized firms and
matured firms that are small. We, however, find no evidence to suggest that electricity
access acts as a barrier or enabler to process innovation among firms in Kenya. The
results on product innovation align with findings in studies that show the role of the pro-
vision of public infrastructure for firm innovation (Sivak, Caplanova, and Hudson 2011).
Regarding the other external obstacles, we explored heterogeneities in the results when
we conducted the analysis for the different categorisations of firms and find the lack of
skilled manpower, incompatibility with existing technology, informal competition, and
INNOVATION AND DEVELOPMENT 19
electricity to be additional factors that hamper product innovation for different firm
types. We also found the lack of skilled manpower, financial constraints, incompatibility
with existing technology and informal competition to be additional factors that hamper
process innovation for different firm types.
Some of these findings were supported in the literature. For instance, the finding of the
lack of skilled manpower hampers innovation is consistent with the studies of Amara
et al. (2016) for Canada, D’Este, Amara, and Olmos-Peñuela (2016) for Spain, D’Este,
Rentocchini, and Vega-Jurado (2014) for the UK and Galia and Legros (2004) for
France. The finding of financial constraint also being an obstacle to firm innovation is
consistent with the studies of Savignac (2008) for France, Mohnen and Röller (2005)
for some European countries and Mohnen and Rosa (2001) for Canada. Mendi and
Mudida (2018) find in the case of Kenya that informality is a constraint to firm inno-
vation. Incompatibility with existing technology shares some characteristics with
studies that relied on the absence of technology-related knowledge factors in undermin-
ing innovation (D’Este et al. 2012) and organizational inertia variables (Baldwin and Lin
2002).
Two main conclusions can be drawn from our study. First, the impact of political
instability on innovation, product or process, depends largely on the characteristics of
firms. Secondly, challenges associated with access to key public infrastructure (electricity
access) remains deleterious to innovation activity, irrespective of the characteristics of
firms. Both findings are intuitive and provide compelling reasons for policy measures
to attenuate their effects on innovation within the context of a developing economy.
Also, given that the factors that hamper innovation may be varied and, in some cases,
not limited to the two factors emphasized in this study, policymakers need to account
for firm categorisations in tailoring policy interventions for encouraging firm innovation.
This is because in some cases, a factor that is an obstacle to firm innovation for a particu-
lar type of firm may promote innovation in another. This suggests that not only is a
diversified set of policies needed to attenuate the deleterious impact of various innovation
obstacles, but an attempt must be made to minimize the ‘over-flow’ effects on firms that
may be adversely impacted by a particular initiative or event.
It is important to indicate that our results may be considered preliminary since they
are based on cross-sectional data. We are unable to address issues of endogeneity in the
obstacles as well as complementarities. Particularly for the latter, it implies that a policy
measure that seeks to address a particular factor can also improve another. For now, we
only find some strong correlations between political instability and infrastructure.
Notes
1. In theWorld Bank Enterprise Survey, Firm size is defined using the number of employees as
follows: Micro = 0–4, Small = 5–19, Medium = 20–99, Large> = 100
2. Startups and incumbent firms were defined based on firm age and evidence of the firm being
a subsidiary or not. Startups are firms below five years and are not subsidiaries of any form.
Incumbent firms are those above five years.
3. The definition of the Likert Scale was as follows: 0 = No Obstacle, 1 =Minor Obstacle, 2 =
Moderate Obstacle, 3 =Major obstacle, 4 = Severe obstacle.
4. We followed Biggs and Srivastava (1996) to compute the severity indexes using the formulae
as follows SI = {sum [(class frequency× score of rating)]}/(total sample).
20 E. ABBEY AND E. ADU-DANSO
5. The Probit estimator provided more intuitive results. The definitions of the variables used in
the model are provided in Appendix 2.
6. The definitions of the variables are provided in the Appendix.
Disclosure statement
No potential conflict of interest was reported by the authors.
ORCID
Emmanuel Abbey http://orcid.org/0000-0001-8404-2010
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24 E. ABBEY AND E. ADU-DANSO
Appendices
Appendix 1. Conceptual framework
Motivated by the Conceptual Framework of Vagnani, Gatti, and Proietti (2019)
This conceptual framework is motivated by the innovation adoption decision framework of
Vagnani, Gatti, and Proietti (2019) that views the entire process to be an organizational choice
influenced by various sources including the attributes of the innovation (relative advantage, com-
patibility, and complexity), behavioural preferences of decision-makers (managers) and the
sufficiency of organizational resources. We argue that for the firm to make a positive decision
about innovation, the firm needs to overcome some external obstacles (which includes the political
climate, availability of the needed infrastructure etc.). We also argue that how a firm goes about
making such decisions depend on its economic sector, size and age.
Appendix 2. Definition of variables
Variables Definition
Outcome variables
Product Innovation 1, if the firm introduced a new or significantly improved product, 0 otherwise
Process Innovation 1, if the firm introduced any innovative methods of manufacturing products or
offering services, 0 otherwise
Obstacles
Lack of Skilled Manpower 1, if the lack of skilled manpower is an obstacle to undertaking an innovation, 0
otherwise
Financial Constraints 1, if financial constraint is an obstacle to undertaking an innovation, 0 otherwise
Incompatibility with existing 1, if incompatibility with existing technology is an obstacle to undertaking an
Technology innovation, 0 otherwise
Informal Competition 1, if informal competition is an obstacle to doing business, 0 otherwise
Political Instability 1, if political instability is an obstacle to doing business, 0 otherwise
Electricity 1, if electricity is an obstacle to doing business, 0 otherwise
Control variables
Research and Development 1, if the firm conducted internal or external Research and Development, 0 otherwise
Enterprise Group 1, if the firm is part of an enterprise group, 0 otherwise
Foreign Ownership 1, if the firm is foreign-owned, 0 otherwise
Female Ownership 1, if there is a female amongst the owners of the firm, 0 otherwise
Exporting Firms 1, if the firm is an exporting firm, 0 otherwise
Innovation-related Training
(Continued )
INNOVATION AND DEVELOPMENT 25
Continued.
Variables Definition
1, if the firm provided formal training to any of its employees specifically for the
development and/or introduction of innovative products or services and processes,
0 otherwise
Experience of Top Manager Number of Years of experience of the top manager
Log of Firm Size The total number of full-time employees
Log of Age The age of the firm
Innovation cooperation
Strategic Alliance 1, if the firm has introduced a product/process innovation through strategic alliance,
0 otherwise
Collaboration/Co-development 1, if the firm has introduced a product/process innovation through collaboration/co-
development, 0 otherwise
Acquisition of other companies 1, if the firm has introduced a product/process innovation through acquisition of
other companies, 0 otherwise
Information sources for innovation
Customers 1, if the main source of innovation for the firm is through customers, 0 otherwise
Suppliers 1, if the main source of innovation for the firm is through suppliers, 0 otherwise
Business Association 1, if the main source of innovation for the firm is through business associations, 0
otherwise
Firm type
Services Firms Categorical variable for the different Services firms
Manufacturing Firms Categorical variable for the different Manufacturing firms
Small-sized firms 0–19 (Number of Employees)
Medium-sized firms 20–99 (Number of Employees)
Large-Sized firms >=100 (Number of Employees)
Start-ups Firms less than five years
Small Incumbent firms Firms younger than five years and employ between 0 and 19
Large incumbent firms Firms older than five years and employ between above 20
26 E. ABBEY AND E. ADU-DANSO
Appendix 3. Correlation matrix
Tolerance
Statistics 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
1. Process – 1.00
Innovation
2. Product – 0.28 1.00
innovation
3. Skilled 0.75 0.07 0.13 1.00
Manpower
4. Financial 0.84 0.04 0.05 0.29 1.00
Constraint
5. Incompatibility 0.77 0.04 0.06 0.42 0.26 1.00
6. Informal 0.88 0.00 0.02 0.16 0.14 0.17 1.00
Competition
7. Political 0.94 0.04 0.03 0.15 0.10 0.12 0.11 1.00
Instability
8. Electricity 0.88 0.06 0.08 0.23 0.19 0.19 0.14 0.13 1.00
9. R&D 0.81 0.27 0.24 0.07 0.09 0.00 −0.05 −0.01 0.08 1.00
10. Enterprise 0.96 −0.01 0.06 0.02 0.02 −0.04 0.05 0.02 −0.02 0.11 1.00
Group
11. Foreign 0.91 −0.01 0.04 −0.01 −0.06 −0.06 −0.03 −0.06 0.01 0.04 0.05 1.00
Ownership
12. Female 0.93 0.11 0.10 0.07 0.10 0.06 −0.01 0.00 0.11 0.10 0.00 −0.10 1.00
Dummy
13. Export Status 0.77 0.12 0.10 0.04 −0.01 −0.01 −0.19 −0.06 0.06 0.22 0.02 0.18 0.13 1.00
14. Training 0.82 0.25 0.27 0.11 0.04 0.06 −0.07 0.00 0.05 0.29 0.03 0.09 0.10 0.20 1.00
15. Top Manager 0.72 0.06 0.09 0.03 0.00 0.00 −0.04 0.00 0.05 0.05 0.00 0.05 0.02 0.17 0.08 1.00
exp.
16. Log of Firm 0.76 0.10 0.15 −0.03 −0.10 −0.07 −0.07 0.02 0.02 0.14 0.01 0.21 −0.03 0.31 0.19 0.22 1.00
Size
17. Log of Age 0.63 0.06 0.06 −0.02 −0.07 −0.03 −0.15 −0.06 −0.06 0.05 0.05 0.07 0.04 0.27 0.16 0.51 0.36 1.00
18. Strategic 0.58 0.26 0.27 0.07 0.08 0.01 0.00 0.01 0.03 0.24 0.07 0.04 0.09 0.12 0.22 0.09 0.09 0.06 1.00
Alliance
19. Collaboration 0.55 0.35 0.30 0.08 0.05 0.02 0.03 0.05 0.05 0.29 0.03 0.04 0.10 0.16 0.26 0.09 0.11 0.08 0.63 1.00
20. Acquisition 0.88 0.20 0.14 0.00 0.00 0.00 −0.01 0.01 −0.02 0.07 0.04 −0.03 0.02 0.03 0.14 0.02 0.07 0.05 0.27 0.30 1.00
21. Customers 0.83 −0.05 −0.06 −0.01 −0.04 −0.01 0.03 −0.04 −0.07 −0.05 0.00 −0.05 −0.06 −0.08 −0.10 −0.10 −0.10 −0.09 −0.05 −0.05 −0.02 1.00
22. Suppliers 0.87 −0.02 0.02 0.02 0.02 0.01 0.03 0.02 −0.03 −0.08 −0.03 −0.06 −0.02 −0.10 −0.05 0.03 −0.07 0.02 0.00 −0.03 −0.03 −0.24 1.00
23. Business 0.89 −0.03 −0.03 −0.06 0.04 −0.04 −0.01 −0.03 −0.05 −0.03 −0.01 0.05 −0.02 −0.01 0.03 0.00 0.06 0.02 −0.04 −0.05 −0.04 −0.22 −0.13 1.00
Association
INNOVATION AND DEVELOPMENT 27
Appendix 4. Conditional probabilities of success for process innovation (Biprobit Model).
Economic sector Firm size Firm age
Small- Large-
Full Services Manufacturing sized Medium-sized sized Incumbent Incumbent
Variables sample firms firms firms firms firms Start-ups (Small) (Large)
Obstacles
Political Instability 0.177*** 0.225** 0.073 0.333*** 0.012 −0.002 0.178* 0.429*** 0.079*
(0.068) (0.089) (0.062) (0.104) (0.047) (0.056) (0.100) (0.150) (0.046)
Electricity −0.002 0.017 −0.001 −0.055 0.030 −0.006 −0.065 −0.026 0.022
(0.079) (0.088) (0.064) (0.170) (0.060) (0.060) (0.098) (0.146) (0.046)
Lack of Skilled Manpower 0.016 0.031 −0.034 −0.017 −0.026 0.110** −0.050 0.091 −0.077
(0.068) (0.077) (0.060) (0.137) (0.062) (0.052) (0.079) (0.140) (0.047)
Financial Constraints −0.016 −0.008 −0.089 0.089 0.190*** 0.011 0.106 0.080 0.050
(0.077) (0.093) (0.071) (0.112) (0.057) (0.062) (0.090) (0.108) (0.044)
Incompatibility with existing −0.021 −0.038 −0.011 −0.032 −0.039 0.080 −0.076 −0.030 −0.030
Tech (0.061) (0.073) (0.052) (0.097) (0.049) (0.050) (0.079) (0.085) (0.042)
Informal Compettition 0.070 0.062 0.126** 0.187* 0.056 −0.058 0.119 0.155 −0.007
(0.075) (0.089) (0.059) (0.104) (0.058) (0.060) (0.106) (0.104) (0.041)
Information sources
Strategic Alliance 0.132 0.134 0.021 0.023 −0.015 0.041 −0.322** −0.092 0.059
(0.095) (0.114) (0.095) (0.260) (0.073) (0.082) (0.143) (0.196) (0.053)
Collaboration/Co-development 0.002 −0.087 0.380*** −0.045 0.118 0.152* 0.475*** 0.224 0.157***
(0.091) (0.106) (0.091) (0.252) (0.075) (0.081) (0.147) (0.248) (0.048)
Acquisition of other Companies 0.425*** 0.487*** −0.178 1.575*** −0.026 −0.102 2.810*** 0.355 −0.013
(0.128) (0.146) (0.135) (0.362) (0.087) (0.177) (0.288) (0.252) (0.060)
Information sources
Customers −0.012 −0.025 0.018 −0.070 −0.086* 0.106* 0.076 −0.103 −0.065
(0.064) (0.073) (0.061) (0.099) (0.048) (0.063) (0.063) (0.109) (0.044)
Suppliers −0.007 −0.057 0.136** −0.033 0.031 −0.019 −0.013 0.028 −0.006
(0.093) (0.110) (0.069) (0.172) (0.058) (0.082) (0.106) (0.114) (0.045)
Business Association 0.092 0.086 −0.053 0.172 −0.167** 0.062 0.017 0.357*** 0.002
(0.092) (0.108) (0.065) (0.163) (0.078) (0.061) (0.130) (0.109) (0.054)
Other control variables
Research and Development 0.103 0.084 0.180*** 0.048 0.131** −0.002 −0.018 0.585*** 0.017
(0.081) (0.093) (0.054) (0.147) (0.051) (0.063) (0.096) (0.174) (0.046)
Enterprise Group −0.112 −0.114 −0.123 −0.034 −0.143* −0.107 −0.205 0.226* −0.152**
(0.116) (0.132) (0.104) (0.162) (0.075) (0.128) (0.165) (0.131) (0.068)
Foreign Ownership
(Continued )
28 E. ABBEY AND E. ADU-DANSO
Continued.
Economic sector Firm size Firm age
Small- Large-
Full Services Manufacturing sized Medium-sized sized Incumbent Incumbent
Variables sample firms firms firms firms firms Start-ups (Small) (Large)
−0.197** −0.308** 0.091 −0.167 −0.113 0.011 −0.114 −0.127 −0.118*
(0.087) (0.125) (0.063) (0.191) (0.083) (0.052) (0.111) (0.307) (0.062)
Female Ownership −0.000 −0.032 0.093* 0.036 −0.020 −0.018 −0.061 0.044 0.095**
(0.054) (0.062) (0.048) (0.122) (0.048) (0.047) (0.057) (0.092) (0.039)
Exporting Firms −0.079 −0.077 −0.154*** −0.071 −0.123** −0.048 −0.247*** 0.067 −0.053
(0.064) (0.082) (0.053) (0.124) (0.048) (0.050) (0.078) (0.096) (0.038)
Innovation-related Training 0.046 0.041 0.011 −0.034 0.119*** 0.029 0.027 −0.123 0.035
(0.070) (0.081) (0.051) (0.140) (0.041) (0.052) (0.085) (0.102) (0.037)
Experience of Top Manager −0.046 −0.076 0.064* −0.009 −0.058** 0.118*** −0.064 0.241*** −0.053**
(0.040) (0.048) (0.033) (0.094) (0.027) (0.034) (0.042) (0.068) (0.026)
Log of Firm Size −0.016 −0.022 −0.000 0.048 0.112*** 0.101*** 0.147*** −0.139* 0.041**
(0.026) (0.034) (0.020) (0.071) (0.038) (0.024) (0.028) (0.075) (0.019)
Log of Age −0.005 0.005 −0.040* 0.010 −0.026 −0.039* −0.111* −0.080 0.005
(0.024) (0.028) (0.021) (0.031) (0.019) (0.022) (0.065) (0.058) (0.024)
Location Yes Yes Yes Yes Yes Yes Yes Yes Yes
Sector Yes Yes Yes Yes Yes Yes Yes Yes Yes
Observations 972 536 436 422 357 180 340 230 402
Standard errors in parentheses.
*** p < 0.01, ** p < 0.05, * p < 0.1.
Appendix 5. Conditional probabilities of success for product innovation (Biprobit Model).
Economic sectors Firm size Firm age
Variables Full sample Services firms Manufacturing firms Small-sized firms Medium-sized firms Large-sized firms Start-ups Incumbent (small) Incumbent (large)
Obstacles
Political Instability 0.007 −0.003 −0.077 −0.034 −0.058 −0.037 −0.009 0.006 −0.144**
(0.065) (0.078) (0.100) (0.081) (0.082) (0.081) (0.847) (0.067) (0.057)
Electricity −0.084 −0.131** 0.140 −0.189*** −0.047 0.076 −0.142 −0.172** 0.014
(0.056) (0.060) (0.094) (0.069) (0.074) (0.093) (0.568) (0.071) (0.053)
Lack of Skilled Manpower
(Continued )
INNOVATION AND DEVELOPMENT 29
Continued.
Economic sectors Firm size Firm age
Variables Full sample Services firms Manufacturing firms Small-sized firms Medium-sized firms Large-sized firms Start-ups Incumbent (small) Incumbent (large)
0.103** 0.112** −0.035 0.163*** −0.066 −0.151 0.149** 0.147** −0.057
(0.044) (0.050) (0.092) (0.053) (0.058) (0.313) (0.068) (0.067) (0.051)
Financial Constraints 0.015 0.032 0.020 −0.041 0.008 0.020 0.057 −0.080 −0.014
(0.068) (0.075) (0.150) (0.085) (0.099) (0.093) (0.631) (0.068) (0.051)
Incompatibility with existing Tech 0.058 0.098** 0.029 0.082 0.204*** 0.167 0.112 0.090 0.097**
(0.044) (0.049) (0.058) (0.051) (0.056) (0.606) (0.200) (0.058) (0.042)
Informal Compettition −0.087 −0.121* 0.020 −0.078 −0.090 0.003 0.104 −0.167** −0.033
(0.059) (0.070) (0.219) (0.070) (0.063) (0.279) (0.745) (0.072) (0.044)
Innovation cooperation
Strategic Alliance 0.171** 0.266** 0.022 0.313*** 0.128 0.101 0.308 0.181 0.064
(0.075) (0.104) (0.096) (0.091) (0.098) (0.338) (1.051) (0.117) (0.065)
Collaboration/Co-development 0.214*** 0.295*** 0.124 0.324*** 0.232*** 0.006 0.250 0.257 0.131*
(0.073) (0.104) (0.677) (0.115) (0.089) (0.725) (2.730) (0.170) (0.078)
Acquisition of other Companies 0.084 0.039 0.345** −0.366* 0.250* 1.401 0.600 −0.551*** 0.270***
(0.096) (0.116) (0.163) (0.200) (0.130) (1.398) (14.652) (0.210) (0.090)
Information sources
Customers 0.036 0.040 0.045 0.081 0.054 0.085 −0.058 0.166*** 0.017
(0.048) (0.052) (0.081) (0.054) (0.073) (0.618) (0.280) (0.049) (0.049)
Suppliers 0.127* 0.176** −0.155 0.171** 0.136* 0.035 0.134 0.045 0.240***
(0.071) (0.078) (0.163) (0.086) (0.081) (0.095) (0.191) (0.061) (0.072)
Business Association 0.073 0.079 −0.015 0.102* 0.108 0.014 0.051 −0.002 0.042
(0.051) (0.061) (0.109) (0.062) (0.104) (0.326) (0.228) (0.078) (0.051)
Other control variables
Research and Development 0.066 0.046 0.091 0.094 0.192** 0.198 0.110 −0.061 0.132**
(0.057) (0.067) (0.344) (0.074) (0.079) (0.260) (0.125) (0.085) (0.051)
Enterprise Group −0.025 −0.045 −0.006 −0.053 0.147 −0.077 −0.046 −0.058 0.187**
(0.069) (0.070) (0.210) (0.069) (0.093) (0.616) (1.070) (0.052) (0.073)
Foreign Ownership 0.098 0.129 0.066 −0.066 −0.025 0.107 0.049 −0.383*** 0.015
(0.069) (0.081) (0.180) (0.121) (0.073) (0.205) (0.477) (0.125) (0.061)
Female Ownership 0.078* 0.102** 0.007 0.120** 0.019 −0.047 0.134 −0.078 −0.035
(0.041) (0.045) (0.158) (0.050) (0.055) (0.151) (0.082) (0.058) (0.049)
Exporting Firms −0.049 −0.035 −0.138 −0.104 −0.008 0.026 −0.153 −0.044 0.042
(0.049) (0.061) (0.318) (0.074) (0.071) (0.199) (1.468) (0.058) (0.044)
Innovation-related Training 0.141*** 0.153*** 0.117 0.159*** 0.078 0.043 0.137 0.156** 0.130***
(0.045) (0.050) (0.090) (0.058) (0.071) (0.204) (0.363) (0.066) (0.046)
Experience of Top Manager
(Continued )
30 E. ABBEY AND E. ADU-DANSO
Continued.
Economic sectors Firm size Firm age
Variables Full sample Services firms Manufacturing firms Small-sized firms Medium-sized firms Large-sized firms Start-ups Incumbent (small) Incumbent (large)
0.082*** 0.102*** 0.007 0.120*** 0.014 −0.002 0.119 −0.051 0.004
(0.028) (0.033) (0.113) (0.031) (0.046) (0.549) (0.120) (0.042) (0.031)
Log of Firm Size 0.047** 0.039 0.090* 0.033 −0.047 0.011 0.090 0.086 0.010
(0.022) (0.028) (0.046) (0.045) (0.056) (0.490) (0.869) (0.055) (0.025)
Log of Age 0.007 −0.007 0.033 −0.010 −0.015 0.006 −0.092 0.073** −0.057**
(0.018) (0.021) (0.055) (0.020) (0.027) (0.182) (0.702) (0.031) (0.027)
Location Yes Yes Yes Yes Yes Yes Yes Yes Yes
Sector Yes Yes Yes Yes Yes Yes Yes Yes Yes
Observations 972 536 436 422 357 180 340 230 402
Standard errors in parentheses.
*** p < 0.01, ** p < 0.05, * p < 0.1.