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Inter-industry Analysis of Structure and Performance: Evidence from New Zealand
AbstractWe investigate the relationship between industry structure and industry performance using Structure-Conduct-Performance (SCP) model for a large panel data set of industries in New Zealand. We used a system of linear equations that allow us not only to determine the two-way cause-and-effect relationship among the SCP variables but also to take into account endogeneity of the SCP variables. The trend analysis carried out in this paper indicates a higher industry concentration in most of the industries in New Zealand. Our analysis reveals declining levels of industry concentration only in the Financial sector and Utilities sector between 2010 and 2015. Our empirical estimation results show that there is a significant positive two-way causal relationship between market structure and conduct, and a significant two-way causal positive relationship between market structure and performance. We find that process-led and product-led innovation provide the incumbent firms additional product diversification opportunities than geographical diversification scope, which in turn have asymmetric impact on the structure, conduct, and performance of the industries. Thus, our findings imply that in smaller economies such as New Zealand those industries which have a higher level of relative research and development intensities have higher market shares and understanding the unique attributes of such industries is critical to developing policy and regulation to support a dynamic and growing economy.
Keyword: Competition, market conduct, market performance, New Zealand
1. Introduction
The study of industries’ Structure, Conduct, and Performance (SCP) has long been of
interest to scholars for a variety of reasons. As a broad descriptive model rather than a structural
model, the SCP framework allows the identification and understanding of those attributes that
influence industries’ economic performance and the nature of causal links between those
attributes and performance (Manolis, Gassenhiemer, and Winsor, 2004). Traditionally, the
industrial economists assumed a one-way causal relationship between industry structure and
industry performance via industry conduct, treating industry structure as exogenous and
determined by basic market conditions such as technology and demand. More recent studies
suggest a feedback effect in which industry performance affects both conduct and structure, and
conduct, in turn, affects structure (Domney et al., 2005). At the empirical level, this calls for
accounting for endogeneity in the econometric analysis using the SCP framework for better
understanding of the industry-level competition (Resende, 2007).
In this paper, we undertake an investigation of the SCP relationships for a large sample of
New Zealand industries, by means of a simultaneous equations approach, taking into account
endogeneity of the SCP variables in the econometric analysis. The motivation for yet another
study of this type is twofold. First, earlier studies have explored the manufacturing industries in
New Zealand1 and to the best of our knowledge did not examine a large heterogeneous industrial
landscape. Secondly, these studies did not explicitly recognize that industries are increasingly
responding to pressures exerted by global, national, and local environmental regulations. A well-
1 Pickford and Haslett (1999) use market structure-performance model to examine market power and efficiency for New Zealand manufacturing industries using cross-section 1978/1979 Census data. Ratnayake (1998) examined manufacturing sector’s competitiveness in the context of environmental regulation in New Zealand from 1980 to 1993.
2
governed industry takes a long-term view that integrates environmental and social
responsibilities in analysing risks, discovering opportunities and allocating capital in the best
interests of stakeholders. This pragmatic outlook expected of industries claim to provide
sustainable growth opportunities for future competitive advantage (Porter and Kramer, 2006).
This implies that regulations produce heterogeneous impacts on the SCP variables and these
factors are worth examining to uncover industry dynamics. In this context, an important
empirical question arises: Do those industries that complement their cost cutting and
differentiation strategies with more holistic strategies that include sustainability priorities
perform better or otherwise?
The New Zealand government uses a command-and-control approach, in particular, the
Resource Management Act 1991 to regulate industries’ conduct. The Commerce Act 1986
regulates the process of competition in New Zealand. This act covers anti-competitive conduct in
markets within New Zealand, and also overseas business activity insofar as New Zealand
markets are affected. Specific regulations relating to electricity, telecommunication, and dairy
sectors are in the Electricity Industry Reform ACT 1998 and Part 4A of the Commerce Act, the
Telecommunication Act 2001 and Dairy Industry Restructuring Act 2001. These sectors are also
regulated within the generic framework provided by the Commerce Act, with specific
regulations providing additional provisions for the achievement of competition objectives within
these industries.
Despite the overarching influence of the Commerce Act, a number of complex changes
and arrangements have been made in regard to streamlining regulations of some industries, for
instance, in 2002, the government directed the gas industry to develop self-governance measures
to ensure efficient operations of markets. In 2003, the government determined that the electricity
3
industry self-governance model has failed and it established an Electricity Commission to
oversee electricity market. In March 2014, the New Zealand government
introduced an Environmental Reporting Bill in its Parliament with the aim of
creating a national environmental reporting system. In September 2015, the
Environmental Reporting Act 2015 was passed into law. In the same spirit,
the New Zealand Stock exchange has revised its Corporate Governance Code
2017, which will come into effect on the 1st October 2017, it requires an
issuer to include its consideration of material environmental, social, and
governance (ESG) factors and practices in their non-financial reporting. In
this context, the industries in New Zealand are facing exceptional pressures to implement
market-oriented operational strategies that complement the environmental and social
sustainability priorities and be accountable for their actions. The main premise of this paper is
that, regardless of the regulatory nature of the market, the complexity of the environmental
regulations is an additional cost, and just like customer switching costs, it has a significant
implication for the market power and conduct which will have a positive (negative) effect on
industries’ profitability (Kieschnick et al., 2013; Deloof, 2003).
The rest of the paper is organized as follows. Section 2 provides a review of literature
and development of hypotheses. Section 3 explains the sources of data, variables, and
methodology. Section 4 reports the results, and Section 5 provides the conclusions and
suggestions for future research.
2. Literature Review and hypothesis development
New Zealand was a relatively late entrant into the major world markets. As late as 1960,
two thirds of the exports income was earned from the UK and the USA (Akoorie and Enderwick,
4
1992). New Zealand’s economy did not cope well with the oil prices shock in the 1970s, and
later when the UK joined the EU it appeared to have lost one of its major export market for
agricultural products. Several of the New Zealand industries that competed internationally were
export-dependent and resource-based in the sense that their output was much larger than the
domestic demand (Cartwright,1993). The trade negotiations in the 1980s intensified the
international trade and New Zealand's exports to the European Community (EC) rose to 21 per
cent. This was relatively higher than exports to Australia and Japan with 17 per cent each, and
the USA at 15 per cent (Akoorie et al., 1993). By the mid 1980's, however, the New Zealand’s
economy was growing at a rate significantly below the OECD average (OECD, 2015), and was
weighed down by the State sector, absorbing 20% of gross investment and 12% of the Gross
Domestic Product. As an illustration, 10% of the national income in 1986 was spent on the Post
Office, the Lands and Survey Department, the New Zealand Forest Service and the Ministry of
Energy, with the post-tax return on investment being negligible.
In New Zealand, the industrial sector changed significantly as a result of a large-scale
deregulation and economic reforms that started in 1984. The Government's initial approach to
reform the State sector was based on the concepts of corporatization (or commercialization),
deregulation, and privatization. The 1986 State-Owned Enterprises (SOEs) and Companies Act
and the State-Owned Enterprises Act 1987 consolidated the incorporation of 14 SOEs, to be run
as commercial enterprises with minimal political intervention (Williams, 1992). Deregulation of
various industries began with the financial, broadcasting and transport sectors being amongst the
first to feel the effects of free market economic policies. Among the SOEs deregulated were
electricity generation and distribution, postal and the telecommunications services. The main
objective was to enhance contestability and commercial efficiency of these SOEs while the
5
government continued its control via regulations (Williams, 1992). For example, the New
Zealand government sold 51.6% of Auckland Airport shares by public floatation, and 66% of
Wellington Airport to a private entity, Infratil NZ Ltd. These were also followed by a large scale
of privatization in the Energy sector (Gerald, 1999; Domney et al., 2005). Hamilton (1991)
report that concentration of business assets increased rapidly from 1979 through 1989.
However, the most controversial piece of the government regulation was the removing of
the courts’ power to ruminate rates charged by a natural monopoly, and placed them in the hands
of the Minister of Commerce, giving him the sole power to step in to regulate all monopoly
profits. As such, starting from 1986, the takings of monopoly profits were legal in New Zealand
unless the government of the day stepped in to regulate a specific offender. For instance, until the
1990s, no legal barriers prevented the owners of an infrastructure facility from raising the prices,
and hence the value of the businesses. It seems that whether the regulatory risk metamorphosed
into a political risk or not, depended upon the ruling politicians on whether they were on their
side. As such, a company’s management could raise prices, profits and assets values with
impunity and customers would have no recourse except to wait for the next general election.
According to Williams (1992) even after deregulation, SOEs often enjoy a de facto dominant
position in the markets in which they operate. For example, Telecom (now known as Spark)- the
biggest telecommunication company in New Zealand, continues to enjoy a monopoly in the local
loops of its Public Switched Telephone Network, which endows it with considerable strategic
leverage in complementary market segments. Nillesen and Pollitt (2011) provide a
comprehensive review of ownership unbundling and its consequences in the Electricity sector in
New Zealand. They report that ownership bundling did not achieve its objectives of facilitating
greater competition in the electricity supply industry, but it did lead to lower costs and higher
6
quality service. Indeed, Chan et al. (2017) report that state ownership in New Zealand is
negatively associated with firm profitability compared to private ownership using both cross-
sectional and time series approach.
With regard to access to external financing of the Industries, Bertram (2004) documents
interesting changes in the makeup of sharemarket in New Zealand. The agriculture, primary
processing, manufacturing, engineering, and construction sectors that represented 75% of total
sharemarket capitalization in the 1980s reduced their industrial representation in the benchmark
index to only 21% by 2000. In contrast, the finance and investment companies’ shares surged
from 5% of the sharemarket capitalization in 1980 to 28% in 1990 fuelled by the wave of
takeovers that swept through the New Zealand economy. From only 5% in 1980 and 6% in 1990,
the utilities and transport sectors’ shares of sharemarket capitalization increased to 44% by 2000.
Despite these changes, Smith et al. (2012) report that the industries’ use of debt financing
(instead of equity financing) led to an increase in relative-to-industry sales growth but a decrease
in relative-to-industry economic performance. They conclude that New Zealand firms’ use of
debt indicated their aggressive approach in competing in their product markets, even though this
strategy came at the cost of lower relative-to industry profitability. In the next section, we
explain the SCP model and its usefulness in exploring the changes in the industries’ underlying
forces in New Zealand.
2.1 Structure, Conduct, Performance (SCP) Model
The SCP framework was introduced by Bain (1968). It is a commonly used theoretical
framework in the industrial organization literature, for an understanding of the impact of
industry’s competition on firms’ market power, conduct and performance (Lee, 2012; Resende,
7
2007; Delome et al., 2002). It is not a structural model for inter-industry analysis per se.
According to Panagiotou (2006), the SCP framework assumes that there exists a stable causal
relationship between three variables: structure, conduct, and performance. The term structure
refers to the external environment, which includes a number of firms operating in an entire
industry (buyers and sellers). The term conduct refers to product, pricing, R&D, advertising and
promotion, and innovation strategy of the firms serving industry. Lastly, the term performance
refers to the quantity (output) and performance (profitability) of the firms serving an industry
(Nabieu, 2013; Lee, 2012)2.
Intuitively, market conditions influence industry structure and firms would have to adjust
their conduct accordingly to survive in the tough competitive conditions, which would, in turn,
affect their economic performance. Market structure influences the firms’ conduct with regard to
their pricing, selling and advertising, employees and supplier relations, and investment policies
(e.g., capital intensive or labour-intensive methods of production) that in turn affect the top-line
(sales) and bottom-line (net profit) of firms. Heterogeneity in firms’ conduct given market
structure, directly and indirectly, affects the industry level performance. A feedback effect
ensues as market participants learn about other firms’ competitive behaviour suggesting that
industry performance affects both conduct and structure. Hence, there is a three-way cause-and-
effect relationship among structure, conduct, and performance variables.
According to industrial organization literature, in an oligopolistic market structure, there
are a few firms in the industry, and their monopoly power and profitability depend on how these
firms interact. If the firms’ interaction is more cooperative than competitive, firms could charge
2 Despite the critique of the SCP approach (see e.g., Baumol et al., 1982), most of the authors concur with the idea that the SCP approach is not a model but it is a useful device for capturing the essential relationships between the three variables. The main categories of variables under the SCP can easily be identified, and since the approach is not industry-specific, it has been applied to many industries so making inter-industry comparison possible. The majority of studies considered a system comprising three equations referring to concentration, advertising, and profitability.
8
prices well above marginal cost and earn very large profits. In some oligopolistic industries,
firms do cooperate but in others they compete aggressively even though this means lower
profitability. For instance, in the capital intensive industrial sectors, the scale economies may
make it unprofitable for a few firm to coexist in the market and they create entry barriers
preventing new firms from entering such an industry sector. Because fewer firms can survive in
such a competitive industry, the larger more dominant firms can exert their market power to
influence the price or the quantity and drive out the smaller and weaker firms from the industry.
Thus, in an oligopolistic structure, pricing, output, advertising and investment decisions involve
important strategic consideration, which could be complex. It is expected that the market
concentration will decrease the cost of collusion between firms and results in an increase in the
market shares and higher net profits for a few firms however because of the strategic
considerations their impact on market conduct is not clear.
In an industry characterised as monopolistic competitive, there are many firms that sell
highly differentiated products that differ in quality, appearance, and reputation. It is relatively
easier for other firms to introduce new brands which might limit the profitability of existing
firms if the profits were very large. The new firms spend money on R&D, and selling and
promotion to introduce new brands of their own, which reduce the market share and profitability.
According to the efficient-structure hypothesis (EFS), only a few efficient firms are able to
increase their size and market share because of their ability to generate higher profits, which
usually leads to higher market concentration. It could be the result of lower costs achieved
through either superior management or production processes (Goldberg and Rai, 1996). Thus, it
is expected that there will be a three-way cause and effect relationship among the SCP variables.
In the light of above discussion, we test the following hypotheses:
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Hypothesis 1: Structure has a positive effect on the conduct and financial performance.
Hypothesis 2: Conduct has a positive effect on the structure and financial performance.
Hypothesis 3: Financial performance has a positive effect on the structure and conduct.
2.2. Impact of Environmental regulations on the SCP
According to industrial organization literature, environmental regulations decrease the
strength of a firm’s economic position, and as a result, corporation would behave less
philanthropically in more competitive markets (Fernandez-Kranz and Santalo, 2010). There are
two major views on the relationship between environmental regulations and competitiveness.
First, it is claimed that stringent environmental regulations impose significant costs on the
domestic firms and industries may lose their international competitiveness in terms of declining
exports, increasing imports compared with those from the countries which have lower
environmental standards and regulations. Second, an entirely opposite view is that environmental
regulations may lead to innovations and productivity improvements that would be able to create
comparative advantage in environmentally sensitive industries (Ratnayake, 1998). The industrial
competition theorists posit that scope and intensity of regulations influence industries’
performance. For firms operating in a monopolistic industry under minimal regulations, their
profitability is related to their market power. In contrast, firms operating in an oligopolistic
industry under a highly regulated environment, their profitability is affected by the scope and
intensity of the regulations. Such regulations constrain their market power but do not impede
their efficiency.
We propose that there are two main effects of environmental regulations: the rent
dissipation effect and the escape competition effect. According to the rent dissipation effect, the
10
cost disadvantage associated with the compliance with stringent regulatory requirements would
reduce the profit margin, and this would reduce the market share of the incumbent firms over
time. The escape competition effect, in contrast, predicts that there is a positive effect as new
firms would be able to find innovative solutions to environmental protection issues using new
technology and processes giving them a head start compared to old firms in an industry reliant on
their legacy systems. In this context, being a manufacturer of environmentally friendly products
and a responsible employer would bestow competitive advantage which may increase market
share. In a Bertrand competition type of model, any business strategy that leads to a small
decrease in price could translate into a higher market share because consumers would switch to
the firms that offer a better deal, e.g., environmentally friendly and/or green, ethical
products/services (McWilliams and Siegel, 2001) given that firms produce homogenous
products/services.
For an illustration purpose, to meet environmental compliance goals, a substantial
expenditure on the EMS in a milk processing plant could act as an entry barrier and consolidate
the market position of only a few larger firms. In the absence of such an investment, the
inadequate environmental management could negatively impact the quality of products, reduce
the market share and profitability (Turban and Greening, 1997). Furthermore, environmental
regulations in one region may cause one or more plants in that region to shut down and transfer
their production plants to other regions. Each entering firm incurs a sunk fixed cost such as
research & development and a plant-specific fixed cost for each plant it opens. Thus, a firm’s
decision to serve a region is a consideration of a trade-off between high fixed costs option of a
foreign branch plant or high variable cost option of exporting to that market (Markusen et al.,
11
1993). Thus, we believe that capital expenditure on pollution prevention, energy efficiency, and
minimization of waste would have an impact on market structure, conduct, and performance.
There are very few studies that have examined the impact of environmental costs and/or
corporate social responsibility (CSR) on industry structure and conduct. CSR has a positive
impact on organizational strategy. Businesses could use CSR as a means to pre-empt costlier
regulatory actions to avoid taxes and even influence regulations in such a way that their
competitors face higher costs than the firms practicing CSR (McWilliams, 2002). Advertising
enhances firms’ CSR positive attributes. In more competitive industries, CSR participation could
either be higher because firms want to differentiate themselves from others in the industry or
lower because increased competition could help constraint firms from incurring non-essential
expenses. Erhemjamts et al. (2012) report that CSR leads to higher levels of advertising and
selling expenses. CSR is a resourceful tool for companies to enhance or improve their
competitive advantage, a good CSR-Strategy fit leads to highly differentiated products/services,
access to new markets, and a better fit between the firm’s products/services and consumer
profiles. Fernandez-Kranz and Santalo (2010) report a positive association between within-
industry CSR variations and the intensity of product market competition. Peters and Mullen
(2009) argue that time-series data analysis provides a robust evidence
compared to a cross-sectional analysis of the cumulative positive effects of
CSR on financial performance. In the next section, we explore these ideas further by
creating a typology of innovation strategies and their impact on the SCP.
2.3 Impact of Product-led and Process-led innovations on the SCP
Theoretically, process-led and product-led strategies would reduce environmental
costs, thereby increasing environmental and social performance that yields higher market share,
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competitive advantage and economic performance (Ameer and Othman, 2014). We posit that
process-led and product-led differentiation strategies offer new growth opportunities in the
environmental and social sphere of business operations, however substantial capital expenditure,
such as the Environmental Management System (EMS), research and development,
and quality assurance costs (e.g., ISO 14000) might also constrain companies’ ability
to gain from such new opportunities. Nevertheless, strategic investments influence
stakeholders’ perceptions about the impact of non-financial risks (Levi and
Newton, 2015; Junkus and Berry, 2015; Boasson et al., 2006) and act as an
‘insurance’ against future potential environmental liabilities. Burton et al. (2011)
report negative impacts of environmental regulatory costs on market structure in pulp and paper
industries.
The micro perspective of innovation views the product innovativeness as something new
to the firm or new to the firms’ customers, and such innovations are contingent upon a firm’s
existing capabilities and competencies (Garcia and Calantone, 2002). For illustration, let’s
compare IBM and General Motors (GM) entry into electric automobile market. For IBM, a giant
electronics manufacturer market entry would be a disruptive and discontinuance of its existing
innovation trajectory, but for the GM it would not be considered as a discontinuous. The process
innovation, on the other hand, relates to improvements, it does not matter whether small or large,
that targets output and productivity. A product process is a system of process equipment, work
force, task specification, material inputs, work and information flows, and so forth that are
employed to produce a product or service (Utterback and Albernathy, 1975). The primary
purpose of the process innovation is the efficiency improvement of the production process for
product innovations. Irrespective of the regulatory environment, an increase in environmental
13
and social consciousness of the internal and external stakeholders have serious implications for
the present and future operational strategies of industries. Green product innovation is a
multidimensional process that focuses on material, energy, and pollution (Dangelico and Pujari,
2010) as these elements relate to a product’s life cycle and its impact on the environment at
various stages. Vachon and Klassen (2006) pointed out that, by interacting with their suppliers
and their customers, organizations could potentially develop and implement more effective
solutions to environmental challenges they are facing.
In the light of above environmental management and strategy literature, we foresee that
some industries in New Zealand, for instance, Industrial, Basic materials, and Utilities, besides
using a cost differentiation strategy would supplement their strategies, with incremental
improvement in process technology to improve energy use, recycling and waste management.
while the firms in the Consumer goods sector, besides using a differentiation strategy will
supplement their strategies with regular innovations build on technical and production
competencies targeted to existing markets and customers. Thus, the former sectors would more
likely to emphasize process-led innovation and the latter, product-led innovation operational
strategies. Thus, we test the following two hypotheses:
Hypothesis 4: Process led innovation strategies positively affect the structure, conduct, and financial performance.
Hypothesis 5: Product led innovation strategies positive affect the structure, conduct and financial performance.
3. Data and methodology 3.1 Data
We selected 10 industries in New Zealand for the SCP analysis. These industries are:
classified using the Thomson Reuters Business Economic Sector Classification as follows: Basic
Materials, Consumer cyclicals, Consumer non-cyclicals, Energy, Financials, Healthcare,
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Industrials, Technology, Telecommunication, and Utilities. Unlike other studies that have
analysed only one industry, we examine a cross-section of industries for the first time in the New
Zealand context. For this purpose, we use the NZX All Index which is considered to be the total
market indicator for the New Zealand equity market. It comprises all eligible securities quoted
on the NZX Main Board (NZSX). The NZSX’s constituents’ historical financial data, as per
above classification of industries, was downloaded from the Thomson Reuters database, to
calculate the SCP variables used in the previous studies. In lines with earlier studies on the SCP,
any industry with fewer than five firms was dropped from consideration. The annual financial
statements data of the remaining firms in those industries was considered and the industry
averages constructed for the SCP variables. This approach left led to a final sample of 6
industries. One major limitation of this approach is, companies that are privately owned in these
industries are not included.
3.2 Variables
This section describes the variables used for the analysis.
Structure: We calculated the 3-firm concentration ratio which indicates the share of sales of
firms in an industry accounted for by the top 3 firms in an industry in a year t, denoted by CR3,t.
For our purpose, this can be expressed as:
CRk=∑i=1
k
Si(1)
where Si is the share of industry’s sales of a firm i and k denotes the number of firms in the
industry over which the concentration ratio is calculated (here =3). CR3 emphasizes the inequality
between the leading group of firms’ market power and efficiency. The changes in the
15
concentration ratios reflect expansion(contraction) in response to opportunities and threats. We
used it as a proxy for industry structure (S) in our estimation model. We also used the
Herfindahl-Hirschman index (HHI), which is commonly used a measure of an industry
competition. It is defined as the sum of the squared values of each firm’s market share. It is
expressed as follows:
HHI j=∑i=1
n
Si2 10 ,000
n≤HHI≤10 , 000
(2)
where Si is the share of industry’s sales of a firm i and n denotes the number of firms operating in
the industry. Values of the HHI between 1000 and 1800 indicate moderately concentrated
markets and those in excess of 1800 are considered to be concentrated marks (Lam, Yap,
Cullinane, 2007). We evaluate the changes in the values HHI as an indicator of fringe firms’
ability to challenge the leaders in the industry or ability of new rivals to enter the industry (see
also e.g., Acquaah, 2003).
Conduct: Market conduct refers to the policies that affect customers, rivals and suppliers. We
posit that sales and advertising policies that are continually monitored and consistently updated
in regard to the business conditions create market entry barriers. We used the ratio of the total
advertising, sales and promotion expenses to total sales, denoted by ADV, as a proxy measure of
an industry conduct (C).
Performance: We used the total net profit after tax divided by sales, denoted by NPM, as a proxy
for an industry performance (P).
Process-led and Product-led innovation strategies: According to O’Brien (2003:416) the
appropriate proxy for the strategic importance of innovativeness to the firm is not the intensity of
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investment in R&D but rather the firm’s relative intensity of investment in R&D, i.e., relative to
other firms competing in the same industry. Large expenditure on R&D do no guarantee that a
firm will be an effective innovator. However a firm that invests in R&D at a much higher rate
than their competitors are most likely trying to compete on the basis of innovativeness3. We
adapted O’Brien’s (2003) RD intensity variable as follows: first, we calculated the ratio of the
research and development expenses to total capital expenditure for each firm in an industry
relative to its peer, and percentile score was assigned. The industries that were placed in the
bottom percentile were classified as process-led strategies and those that were placed in the top
percentile were classified as product-led innovation strategies4. We acknowledge that a major
limitation of this approach was that firms with no R&D data in some industries were
automatically excluded and 5 industries that met were included only.
Control variables:
Diversification: We used two measures of diversification – product level and geographical level.
Product diversification and geographic diversification denoted by GeoDiv and ProdDiv. These
two are calculated using Palepu (1985) entropy index described below:
GeoDivi , t=∑i
[ Pi , t . ln(1/P i , t )](3)
Pr odDiv i , t=∑i
[P i , t . ln (1/Pi , t )](4)
where pi is the proportion of sales made in the geographic segment (product segment) of industry
i and ln(1/pi) is the natural logarithm of the inverse of the sales.
3 See O Brien (2003) for more details and useful illustration on this point.4 Resende’s (2007) approach of using a dummy variable that assume value 1 if an incremental product innovation (and similarly process innovation) took place during a sample period and 0 otherwise because such information is not available at the individual firm or at aggregate industry-level.
17
R&D: We used the ratio of research and development expenses to total sales ratio to calculate the
research intensity of the companies.
Growth: We calculated a firm’s growth, by using the ratio of net sales lagged one year, and net
sales lagged two years. This variable aims to control for the impact of forward looking growth
opportunities.
3.3 Methodology
In this section, we discuss a simultaneous equations framework that takes into account
the endogenous nature of the SCP variables to investigate the relationship between structure,
conduct, and performance of industries. The endogenous variables are determined within the
system of simultaneous equations. In spirit of the earlier SCP studies, our system of equations
includes three equations, one each for structure (S), conduct (C) and performance (P) as a
function of the other two variables: S=f (C, P), C=f (S, P) and P=f (S, C) as below:
S j , t=γ 0 +γ1C j ,t +γ2 P j , t +Z i , j ,t−1+υ1 ,i ,t (5)
C j , t=γ 0 +γ1 S j ,t +γ2 P j , t +Z i , j ,t−1+υ2i , t (6)
P j , t=γ 0 +γ 1 S j , t+γ2 C j , t +Z i , j ,t−1+υ3 ,i , t (7)
where for an industry j at time t the SCP variables on the left-hand side affect the SCP variables
on the right-hand side contemporaneously5. The system of equations includes a vector of control
variables denoted by Z, which are predetermined variables and their values are not determined in
the system. Although these variables are not strictly exogenous such as R&Dt and Growtht but
5 We follow the bulk of earlier SCP studies by considering contemporaneous relationships. Our model specifications match the set of SCP variables considered in these studies except that we did not introduce lagged values of SCP as attempted by Delorme et a. (2002). We concur with Resende (2007) regarding the difficulty in choosing a lag structure and the resulting difficulty associated with data availability over a long period.
18
following Delorme et al., (2002) we include them in the system of equations because these
variables cause the movement of endogenous variables. We also included two diversification
variables GeoDivt and ProdDivt because the differences in company scope of operations in terms
of geographical coverage, i.e., national, regional, or local differentiate the companies in the
industry. Furthermore, the extent of diversification of companies of companies and the path of
diversification, i.e., related diversification vs. non-related diversification affect the level of R&D,
capital investment, marketing and promotional expenses.
We used Seemingly unrelated regression (SUR) approach to estimate the system of linear
equations. In the presence of endogenous variables, the ordinary least square (OLS) variables
will generate biased and inconsistent estimators. The SUR method involves generalized least
square estimation and achieves an improvement in efficiency by taking into account explicit the
fact that cross-equation error correlation may not be zero. Although earlier studies have used the
Two-stage least square (2SLS) and instrumental variable estimators that yield consistent
parameter estimates when equations are simultaneous but each estimation techniques inefficient
estimates because these techniques apply only to a single equation within the system of
equations. Thus, they do not take into account the fact that one or more predetermined variables
are omitted from the equation to be estimated, but they do not take into account the fact that
there may be predetermined variables equations omitted from equations as well (Pindyck and
Rubinfeld,1998). In either case, the problem of loss of efficiency can be resolved using any of
several methods of estimating systems of equations in which parameters for all equations are
determined in a single equation, one of such method is the SUR.
4. Results
4.1 Descriptive statistics
19
This section reports the trend of market structure variables, HHI and CR3 (see Table 1,
Panel A and B) and diversification variables, GeoDiv and ProdDiv (see Panel C and D) over an
interval of the five-years, starting in the year 2000 and ending in the year 2015. We observe that
for the Financial sector and Utilities sector, a decrease in the values of two market structure
variables that could be attributed to entry of new companies after privatization. These results are
similar to the market concentration statistics reported by the Electricity Authority New Zealand
reinforcing our conjecture that the Utilities sector has become more competitive after
privatization. The HHI and CR3 have remained relatively stable for the Consumer non-cyclicals
sector and Healthcare sector. All industry sectors except the Financials and Utilities are
moderately concentrated exhibiting the monopolistic competition while others are highly
concentrated in New Zealand exhibiting oligopolistic structure. The upward trend in the mean
GeoDiv for Consumer non-cyclical, Industrials, and Utilities sector shows that these sectors
achieved more regional expansion than other sectors in New Zealand over the period of 15 years.
The Finance, Healthcare and Consumer cyclical sector have also shown a steady trend. We
observe a downward trend in the geographical diversification of the Consumer cyclical sector
during the entire period. There is an upward trend in the mean ProdDiv for the Utilities sector
only while other sectors show a stable pattern (see Table 1, Panel C).
[Insert Table 1 about here]
Table 2 presents the descriptive statistics. The mean(median) values of HHI varies from a
high of 6613(6867) for the Consumer non-cyclical sector to a low of 2056 (1562) for the
Financial sector, which seems to suggest that former is a highly concentrated sector compared to
latter. The mean(median) of NPM -0.3008 (-0.2341) are the lowest for the Financial sector (and
for the Utilities sector as well), while the mean(median) of ADV 0.7507(0.5295) is the highest for
20
the Industrial sector. The mean values of GeoDiv and ProdDiv obtained in this study are
relatively similar to those reported by Sun et al. (2017) for the Chinese companies over the
period of 2001-2011, but lower than the total diversification values reported by Chen et al.
(2009) for the Australian companies.
[Insert Table 2 about here]
Table 3 presents the correlation results between variables used in the system of equations.
For the main SCP variables, we find that market structure and conduct are positively correlated
as well as market structure and performance however not at any significant level. We find
evidence of a significant positive correlation between the two measures of diversification and
performance. Indeed, Hitt et al., (2016) forcefully argue that diversification is positively related
to both innovation and firm performance. There is a statistically significant positive correlation
between the conduct and RD as well as between the market structure and RD.
[Insert Table 3 about here]
4.2 Empirical results
The empirical results are presented in Table 4 using the sequence of S-C-P. The positive
sign on the S coefficients support H1 indicating a positive impact of the market structure on the
conduct and performance for the Consumer cyclicals, Industrials, and Utilities sector in New
Zealand. The relative size of the structure coefficients implies that increase in the firms’ sales
concentration ratio in their respective markets has some sort of accelerator effect, ranging from
3.41 for the Consumer cyclical sector to 5.8 for the Utilities sector in New Zealand. Firms
respond to growing demand by expanding production and making fuller use of their production
capacity and spending more on the advertising and sales promotion to consolidate their position
21
in the market. The relative size of the coefficients implies a stronger influence of the market
concentration on market conduct than contemporaneous financial performance. As per industrial
economics literature, a positive relationship between market concentration, market conduct, and
profitability is an evidence of market power in oligopolistic market structure. When firms gain
high market share, they are able to exercise market power, become price setter, and enhance their
bargaining power to get inputs from suppliers at lower costs and exploit the market channel
members to increase their size and market share.
Our estimation results provide a partial support for H2 in that industry conduct, proxied
by advertising, is affected by industry structure because the sign on the C coefficients is
significantly positive for the Consumer cyclical, Industrials, and Utilities sector in the structure
equation (S), and the sign on the C coefficient is significantly negative for all of the sectors in the
financial performance (P) equation implying that advertising and market concentration are
inversely related. Our findings are similar to Delorme et al (2002) who also found that industry
conduct to have no effect on financial performance.
Lastly, we find a partial support for H3 as positive sign on the P coefficient suggest that
the contemporaneous financial performance has a positive impact on the market structure in the
Consumer cyclicals and Utilities sector. In contrast, negative sign on the P coefficient suggest
that it has a negative impact on market structure in the Financial and Healthcare sector
respectively. This suggests that current profitability creates future barriers to entry in the
Consumer cyclicals and Utilities sector only. The coefficient of performance (P) is significantly
negative in the conduct (C) equation for all of the sectors (except Healthcare). In sum, our
empirical results seem to support the SCP framework, i.e., the market structure positively affects
the conduct and vice versa, and market structure positively affects the performance and vice
22
versa. Our findings for the relationship between structure and conduct are similar to Resende’s
(2007) results for the manufacturing industries in Brazil and Delorme et al. (2002) for the
manufacturing industries in the US.
Among the control variables, we find that the ProdDiv has a significant positive impact
on market conduct in the Consumer cyclicals sector, and GeoDiv has a significant positive
impact on market conduct in the Industrial sector. The lagged growth has a statistically
significant positive impact on conduct and performance for the Industrial sector only, which
seems to offer support to an industry life-cycle effects on advertising. Such pattern seems to
indicate that firms’ spending on the advertising and promotion activities could be regarded as a
forward looking strategic variable in that firms do not wait to accumulate cash reserves with
which to fund future advertising campaigns (Delorme et al., 2002). And the lagged RD has a
significant positive impact on the structure only for Healthcare and Utilities sectors respectively.
[Insert Table 4 here]
Table 5 presents the estimation results by segregating sample into two mutually exclusive
industry innovativeness groups: product-led innovation and process-led innovation. In the
structure (S) and conduct equation (C) respectively, our estimation results show that industries’
product-led innovativeness has a significantly positive impact on industry concentration and
behaviour. However product-led innovativeness does not seem to have a significant positive
impact on performance. In contrast, industries’ process-led innovativeness has a significantly
negative impact on industry concentration and behaviour. In the conduct equation (C) and
performance equation (P) respectively, the coefficient of ProdDiv has a significant positive
impact on conduct and performance. The coefficient of ProdDiv can be interpreted to mean that
a $1 investment in product-led innovative strategy (i.e., R&D Relative intensity) will lead to a
23
$4.83 increase in financial performance. Our results partially support hypothesis 4 and 5. These
findings seem to indicate that industry specific process-led and product-led innovation provides
the incumbent firms varied geographical and product diversification opportunities that have
asymmetric impact on the structure, conduct and profitability. i.e., firms that spend on product-
led innovations have higher profitability compared to those firms that spend on process-
efficiency innovations.
[Insert Table 5 about here]
5. Conclusion
Our paper expands on the earlier SCP studies by implementing a simultaneous equations
model for a sample of Industries in New Zealand over a period of 15 years. Our main findings
are: (1) there is a significant positive two-way causal relationship between market structure and
conduct, (2) a significant positive two-way causal relationship between market structure and
performance, and (3) industry’s product-led innovativeness has a significantly positive impact on
the structure and conduct. The empirical evidence for the industries in New Zealand is similar to
previous studies for developed countries. In particular, we identify that persistent innovativeness
is indispensable for increase in the industry share through product diversification opportunities.
Our research contributions are twofold. First, we believe it is the first study that has
carried out an extensive market structure and competition analysis of the main industrial sectors
in New Zealand. Of particular importance, is our exposition that most of the industries are highly
concentrated. Second, our findings imply that in smaller economies such as New Zealand those
industries which have a higher level of relative research and development intensities also have
higher market shares, therefore understanding the unique attributes of such industries is critical
24
to developing policy and regulation to support a dynamic and growing economy. As other
international markets saturate while New Zealand boasts of a growing green economy thriving
even in the face of increasing competition. We propose that future research should explore
attributes of these industries to provide additional insight into industries’ competitiveness. Such
research outputs will be useful for government to design an attractive foreign investment policy
to boost local manufacturing and improve the scope of services sectors’ contribution to the
national output. It would have been ideal to parse the data set into two time periods, i.e., pre and
post the Environmental Reporting Act 2015, however the limited data availability
prohibits analysis of pre-post. We strongly believe that this would provide a useful venue for
future research when more annual observations are available. It would like provide us a unique
opportunity to distinguish the ‘good’ from the ‘bad’ industries.
25
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29
Table 1 Industry Concentration, Geographic and Product Diversification Trends.
YearConsumer Cyclicals
Consumer Non-Cyclicals Financials Healthcare Industrials Utilities
Panel A: HHI2000 3881 10,000 6545 7213 6796 33342005 287 7693 1663 3546 3107 20142010 3177 6665 1562 3835 2728 13492015 3061 4987 984 2958 2656 1342
Panel B: CR3 -3-firm concentration ratio2000 0.88 1.00 0.86 0.93 0.97 0.682005 0.72 0.94 0.71 0.93 0.80 0.692010 0.71 0.93 0.63 0.89 0.76 0.482015 0.72 0.93 0.45 0.77 0.74 0.49
Panel C: GeoDiv2000 0.19 0.02 0.02 0.03 0.07 0.052005 0.13 0.02 0.06 0.21 0.11 0.122010 0.15 0.04 0.06 0.22 0.13 0.102015 0.14 0.07 0.09 0.22 0.14 0.13
Panel C: ProdDiv2000 0.24 0.07 0.09 0.12 0.13 0.132005 0.12 0.03 0.08 0.22 0.08 0.162010 0.17 0.06 0.08 0.21 0.09 0.232015 0.17 0.07 0.08 0.19 0.06 0.29
This table presents the trend in the values of the HHI, 3-firm concentration ratio (CR3), Geographical diversification (GeoDiv) and Product diversification (ProdDiv) respectively. CR3, 3-firm concentration ratio which indicates the share of net sales of firms in an industry accounted for by the top 3 firms. GeoDiv and ProdDiv are calculated using Palepu’s entropy index as follows:
GeoDivi , t=∑i
[ Pi , t . ln(1/P i , t )]Pr odDiv i , t=∑
i[P i , t . ln (1/Pi , t )]
where pi is the proportion of net sales made in the geographic segment (product segment) of industry i and ln(1/pi) is the natural logarithm of the inverse of the net sales. Age of a firm is calculated as the number of years since the date of incorporation of the company.
30
Table 2 Descriptive statistics
Industry Variable Mean Median Standard deviation
Minimum Maximum Industry Mean Median Standard deviation
Minimum Maximum
Consumer HHI 6613.12 6867.81 2028.46 1199.89 10,000 Healthcare
4119.10 3613.74 1732.98 2785.31 9423.27
Non-cyclicals CR3 0.9261 0.9323 0.0446 0.7970 1.0000 N=8 0.8344 0.8867 14.4308 0.0943 1.000N=13 ADV 0.3225 0.1697 0.4670 0.0000 1.7884 0.6738 0.4678 0.4978 0.0000 2.0532
NPM -0.2824 -0.0432 0.6842 -2.7268 0.0492 0.2680 0.1592 0.5171 -0.7222 1.8596RD 0.0509 0.0027 0.0977 0.0000 0.3024 1.0593 0.6660 1.5971 0.0000 6.9016Growth 1.2610 1.1531 0.5289 0.7677 3.1044 1.3639 1.1607 0.7745 0.4494 4.0903GeoDiv 0.0406 0.0366 0.0331 0.0000 0.1437 0.1933 0.2164 0.0833 0.0000 0.3674ProdDiv 0.0515 0.0536 0.0295 0.0000 0.1394 0.1916 0.2145 0.0662 0.0000 0.2545Age 18.3803 17.769 6.8537 11.000 40.000 19.8988 18.7500 5.4427 13.0000 36.0000Assets (billions) 1.135 1.384 0.521 0.061 1.833 0.499 0.738 0.0006 3.973
Consumer Industrials Cyclicals HHI 2932.40 3061.10 588.93 1356.72 3881.00 N=20 3562.38 3107.10 1624.29 2061.25 8005.13N=16 CR3 0.7402 0.7380 0.0433 0.6536 0.8832 0.8312 0.7857 0.1517 0.7417 1.000
ADV 0.1695 0.2416 0.0982 0.0000 0.2690 0.7507 0.5295 5.4155 0.0000 22.9300NPM 0.2075 0.0724 0.5546 0.0413 2.3568 0.2999 0.0975 1.4049 -1.2797 5.5766RD 0.0014 0.0016 0.0014 0.0000 0.0049 0.0191 0.0069 0.0209 0.0000 0.0536Growth 1.3858 1.0529 1.1699 0.9886 5.7129 2.4572 1.0998 5.0687 0.7525 22.0884GeoDiv 0.1264 0.1250 0.0252 0.0828 0.1989 0.1100 0.1137 0.0255 0.0576 0.1379ProdDiv 0.1561 0.1495 0.0295 0.1237 0.2455 0.0799 0.0822 0.0216 0.0480 0.1386Age 17.5388 17.9286 5.6289 3.0000 30.0000 18.4966 18.3000 5.4236 7.0000 28.0000Assets (billions) 0.758 0.861 0.248 0.232 1.093 0.601 0.552 0.4156 0.075 2.060
Financials HHI 2056.74 1562.05 1341.67 1021.67 6545.15 Utilities 2361.20 1998.90 2047.93 1342.09 10,000N=26 CR3 0.6490 0.6263 0.1451 0.0000 0.8608 N=9 0.6360 0.6846 0.1225 0.4761 1.0000
ADV 0.3774 0.3261 3.5276 0.0000 14.8400 0.3635 0.1066 0.8115 0.0000 3.3795NPM -0.3008 -0.2341 3.6581 -7.8186 11.3878 0.0605 0.0938 1.5707 -4.6575 3.8913RD n-a n-a n-a n-a n-a 0.0002 0.0000 0.0004 0.0000 0.0013Growth 4.8377 1.3461 13.8167 0.9708 58.4070 1.3795 1.1463 0.8919 0.9145 4.7479GeoDiv 0.0655 0.0672 0.0169 0.0219 0.0938 0.1040 0.1107 0.0508 0.0000 0.1965ProdDiv 0.0826 0.0811 0.0129 0.0619 0.1026 0.1846 0.1746 0.0725 0.0000 0.2919Age 10.7546 9.9600 4.0182 4.0000 18.0000 9.0305 9.3750 4.3371 2.0000 16.0000Assets (billions) 0.634 0.591 0.233 0.172 1.386 3.080 3.180 1.606 0.071 4.961
This table presents the descriptive statistics of the variables calculated at industry-level. The variables are defined as follows: CR3, 3-firm concentration ratio which indicates the share of net sales of firms in an industry accounted for by the top 3 firms; ADV is the total advertising, sales, and promotion expenses divided by total net sales ratio; NPM is the total Net Profit After tax divided by total net sales. RD is the total research and development expenses divided by total net sales. GeoDiv and ProdDiv are calculated using Palepu’s entropy index as follows:
GeoDivi , t=∑i
[ Pi , t . ln(1/P i , t )]Pr odDiv i , t=∑
i[P i , t . ln (1/Pi , t )]
where pi is the proportion of net sales made in the geographic segment (product segment) of industry i and ln(1/pi) is the natural logarithm of the inverse of the net sales. Age of a firm is calculated as the number of years since the date of incorporation of the company. Growth is the ratio of net sales lagged one year, and net sales lagged two years. Assets are the total assets. All variables except Age and Growth are calculated as at financial year end. N is the total number of the firms in the industry.
31
Table 3 Correlation coefficientsThis table presents the Pearson correlations coefficients.
NPMt GeoDiv ProdDiv ADVt RDt Growtht CR3
NPMt 1.000
GeoDiv 0.3124*** 1.0000
ProdDiv 0.2785*** 0.6265*** 1.0000
ADVt -0.3793*** 0.0052 -0.0205 1.0000
RDt 0.0937 0.0657 0.1292 0.3654*** 1.0000
Growtht 0.0426 -0.0669 -0.0907 0.0197 -0.0426 1.0000
CR3 0.0457 -0.0272 -0.3532*** 0.1397 0.2334** 0.0246 1.0000*, **, *** shows significant at 10, 5 and 1 percent level of significance respectively.
32
Table 4 SCP Model Estimation Consumer Non-cyclicals Consumer Cyclicals Financials
S C P S C P S C PIntercept 1.1557***
(0.0000)-1.8477(0.3702)
-4.0374(0.4642)
0.7301***
(0.0000)-2.5055***
(0.0003)-0.4401**
(0.0408)1.4339***
(0.0000)2.1755***
(0.0069)-17.4700*
(0.0567)S - 1.5238
(0.3901)3.3427
(0.4720)- 3.4174***
(0.0002)0.6376**
(0.0280)- -1.4817***
(0.0046)-11.0421*
(0.0723)C 0.0494
(0.3583)- -2.2533***
(0.0016)0.2690***
(0.0008)- -0.1363*
(0.1026)-0.5121***
(0.0045)- -8.5840**
(0.0109)P 0.0386
(0.2057)-0.4019***
(0.0019)- 0.8847**
(0.0271)-2.4923*
(0.1079)- -0.0341*
(0.0723)-0.0767***
(0.0109)-
GeoDiv -3.9462(0.3617)
24.7508(0.2190)
57.4768(0.1887)
-0.3935(0.4206)
0.9068(0.6002)
0.3535(0.3738)
-4.8721**
(0.0578)-7.7963*
(0.0983)-79.2074(0.1015)
ProdDiv -0.7075(0.8749)
-20.5988(0.4027)
-48.5929(0.3875)
-0.4851(0.2386)
2.0767*
(0.1098)0.2087
(0.5539)-4.1521*
(0.0608)-5.6267(0.1804)
-40.1115(0.3778)
RDt-1 -0.3629(0.1349)
- - -0.7688(0.7077)
- - - -
RDt - - -0.3577(0.8842)
- - - - -
Growtht-1 - 0.3591(0.1104)
0.7742(0.1745)
- -0.0029(0.7548)
-0.0069(0.8153)
0.0009(0.5953)
0.0013(0.6511)
0.0158(0.6084)
Adj. R2 0.7482 0.3113 0.2510 0.6275 0.6894 0.0186 0.6167 0.2072 0.0106Healthcare Industrials Utilities
Intercept 1.0951***
(0.0006)4.7245***
(0.0034)0.8517
(0.7385)0.7185***
(0.0000)-3.4257***
(0.0070)-2.4955*
(0.0188)1.0122***
(0.0000)-6.1010(0.1652)
-10.5010(0.1103)
S - -0.3710***
(0.0025)-3.0689*
(0.1035)- 4.8167***
(0.0036)0.5446**
(0.0144)- 5.8729***
(0.1619)9.9652*
(0.1098)C -0.1216**
(0.0421)- 0.3418
(0.4669)0.0377**
(0.0395)- -0.7402***
(0.0002)0.0661**
(0.0261)- 0.1286
(0.5522)P -0.0843*
(0.0700)0.0974
(0.6713)- 0.0316
(0.3685)-1.3961***
(0.0000)- 0.0418**
(0.0316)-0.6576***
(0.0021)-
GeoDiv -0.1707***
(0.7592)-5.1166**
(0.0271)5.9788
(0.1371)0.1444
(0.5637)3.8533*
(0.0984)2.4468
(0.1411)0.0186
(0.9676)0.3693
(0.9575)2.5698
(0.7933)ProdDiv -0.4264
(0.2739)-2.6882*
(0.1235)1.3812
(0.5754)0.0300
(0.9237)-2.4151**
(0.3772)-1.6955(0.3722)
-2.0602**
(0.00001)11.2494(0.2108)
18.9773(0.1464)
RDt-1 0.0228*
(0.0810)- - -0.5611**
(0.0163)- - 4.5452*
(0.0984)- -
RDt - - 0.2683(0.2342)
- - - - --24.3099(0.6286)
Growtht-1 - -0.1200(0.1134)
-0.0313(0.5582)
- 0.0400***
(0.0002)0.0304***
(0.0014)- 0.3568
(0.1704)0.5707**
(0.1277)Adj. R2 0.4795 0.5372 0.3673 0.8202 0.8958 0.6817 0.9173 0.4742 0.4983
This table presents the empirical results for the SCP Model estimated for the panel data of industries over the period of 2000-2015 using the Seemingly Unrelated Regression (SUR) regression approach. The p-values of the two tailed test are shown in the parenthesis. *, **, *** significant at 1, 5, and 10 percent level.
33
Table 5 SCP Model Estimation – Industry Product-led vs. Process-led Innovativeness
S C P S C PProduct-led innovation Process-led innovation
Intercept 0.7231***
(0.0000)-0.2299(0.4639)
-1.7000**
(0.0260)0.9065***
(0.0000)0.331
(0.7449)-1.2866(0.1374)
S - 0.0601(0.8817)
1.0766(0.2215)
- -0.0254(0.9529
1.2191(0.1829)
C 0.0544(0.8817)
- -1.6603***
(0.0000)0.1044
(0.8817)- -1.6566***
(0.0000)P 0.0158
(0.2615)-0.3687***
(0.0000)- 0.0418
(0.4110)-0.3680***
(0.0000)-
Product -led 0.1834***
(0.0000)0.2885**
(0.0220)0.2122
(0.1393)- - -
Process -led - - - -0.1319***
(0.0000)-0.2941***
(0.0270)-0.4132(0.1453)
GeoDivt 0.0289(0.9143)
0.7353(0.4555)
2.4872(0.2304)
0.0351(0.8910)
0.8034(0.4198)
2.5820(0.2186)
ProdDivt -0.5202**
(0.0338)1.7781**
(0.0485)4.8265**
(0.0272)-0.9240***
(0.0000)1.6394
(0.1313)5.0324**
(0.0277)Growtht-1 0.0010
(0.5764)0.0105
(0.1334)0.0233
(0.1165)0.0007
(0.6985)0.0417
(0.1322)0.1843
(0.1164)Adj. R2 0.5042 0.1303 0.1921 0.4546 0.1303 0.1985N 90 90 90 90 90 90
This table presents the empirical results for the SCP Model by classifying the industries into two mutually exclusive groups of the process-led and product-led innovativeness using the Seemingly Unrelated Regression (SUR) regression approach. The p-values of the two tailed test are shown in the parenthesis *, **, *** significant at 1, 5, and 10 percent level.
34
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