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.

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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

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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,

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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

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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

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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,

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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.

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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

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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.,

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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

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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

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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.

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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.

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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

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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.

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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.

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