TIK WORKING PAPERS

on Innovation Studies

No. 20161014 http://ideas.repec.org/s/tik/inowpp.html

Senter for teknologi, innovasjon og kultur Universitetet i Oslo

TIK

Centre for technology, innovation and culture P.O. BOX 1108 Blindern

N-0317 OSLO Norway

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http://www.sv.uio.no/tik/

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Do ‘green’ employment effects vary across industries?

Implications for green growth

Christine Mee Lie TIK Center for Technology, Innovation and Culture, University of Oslo Moltke Moes vei 31, 0851 OSLO, Norway

Corresponding author: c.m.lie@tik.uio.no

[This version: October 2016]

Abstract This article investigates the impact of green innovation on employment growth, employing

firm-level survey data from South Korea. We focus especially on the industry-dimension,

investigating whether displacement or compensation effects vary across industries and

according to subtypes of green process innovations. Results demonstrate that both green and

non-green product innovations are associated with significant employment increases: a 1%

increase in sales growth from new products is associated with a less than 1% increase in

employment. Finally results are found to vary across industries, especially when

simultaneously accounting for subtypes of green process innovations.

Key words: Green innovation; eco-innovation; employment growth; innovation survey;

industries; sectors; South Korea

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1. Introduction By now a strong consensus on the infeasibility of continuous growth at all levels of

development, without accounting for the environmental impacts, is clearly established.

Tackling climate change and securing green growth have emerged as a top global policy

priority, and green innovation has been put at the center of this pursuit. “Green innovation” –

(or eco-innovation) – is defined as “the production, assimilation or exploration of a product,

production process, service or management or business method that is novel to the

organization (developing or adopting it) and which results, throughout its life cycle, in a

reduction of environmental risk, pollution and other negative impacts of resources use

(including energy use) compared to relevant alternatives” (Kemp and Pearson, 2007: 7). Thus

green innovation refers to a broad and complex phenomenon, encompassing a diverse set of

innovation subtypes.

Due to this a topic of increasing importance both within policy making and academic

literature, is the employment effects of green innovation. In a policy making perspective, a

main concern is the potential increase in job creation as new industries emerge around green

technologies, and how policy can help facilitate this. Within the currently small empirical

literature however, there is still not a consensus as to whether green innovation entails

positive compensation effects or negative displacement effects. Positive effects are typically

found for green innovation overall, whereas for both product- and process innovations more

specifically, the results are mixed and ambiguous. Further, very little evidence exists on

whether different subtypes of green innovations effect employment differently, or how these

effects vary across industries. Albeit one of the major findings in innovation literature is that

industries matter (Malerba, 2005): firms differ in terms of innovation strategy and

technological performance shaping firm’s opportunity sets ,constraints and overall innovation

activities (Dosi, 1982; Pavitt, 1984).

The specific objective of our study is to contribute to this emerging literature by

exploring how green product and process innovations, and types of process innovations (e.g.

CO2-reducing, energy-reducing and pollution-reducing), relate to employment growth – as

well as whether these effects differ across industries. We do this by applying the structural

model developed by Harrison et al. (2008; 2014) and further extended to fit the framework of

green innovation by Licht and Peters (2013, 2014). Although this approach entails several

caveats (as will be discussed) it enables us to explicitly identify different theoretical channels

of how process and product innovations affect employment related to displacement and

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compensation effects, as well as the opportunity to separate between effects of green and

non-green innovations. As this approach is previously applied to European countries only (in

a green innovation setting), the study also contributes to expanding the geographical scope by

providing evidence for South Korean manufacturing firms.

As high and increasing unemployment is emerging as a prevalent issue in several

countries alongside the existing issues of climate change and growth, unlocking the black box

and better understanding these channels could have large consequences for policy and policy

design. It should however be noted that an in depth analysis of all the casual mechanisms, in

which subtypes of green innovation and the industry dimension relates to employment, is

outside the scope of the current paper.

The results demonstrate that both green and non-green product innovations are

associated with significant increases in employment growth. More specifically, a 1% increase

in sales growth from new products is associated with a less than 1% increase in employment.

Thus we find evidence of new products being produced more efficiently than old. Process

innovations on the other hand matters little for employment growth overall, implying that the

feared trade-off due to productivity improvements remains unconfirmed. Finally, when

including the subtype and industry classifications in the analysis, we confirm that these

dimension indeed matter for the innovation-employment link. Especially reduction of energy-

and material usage, as well as CO2-reduction in regards the production process, stands out as

generating some additional changes in employment growth (either positive or negative). This

suggests that empirical and theoretical works, as well as policy design, in the future should

attempt to better account for such dimensions.

The article is structured as follows. Section 2 offers a review of the relevant literature

creating an overall context, as well as presents the theoretical and conceptual model. Section

3 presents the methodology applied and the econometric specification, as well as discusses

measurement and endogeneity issues. Section 4 introduces the dataset and descriptive

statistics, and section 5 discusses the empirical results. Finally section 6 summarizes main

overall findings and implications.

2. Literature review The literature on the green innovation-employment link is nested within the broader more

general debate about the relation between innovation and technological development, and

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employment (for a quick overview of this broader literature and the different strands

developed, see Table 1, page 784 in Gagliardi et al., 2016). Despite large scholarly efforts

made however, the literature remains without a widespread consensus on both direction and

magnitude. In addition to differences in level and scope of analysis, such as firm- versus

country-level (e.g. Gagliardi et al., 2016), this dispersion is typically linked to the typology of

innovation used in the analysis. This holds especially with respect to product and process

innovations, and their different impact on employment growth.

Mainly two effects on employment can occur: a displacement effect where innovation

destroys existing jobs and a compensation effect where new jobs are created. The main

channels for this, in a theoretical perspective, are as summarized in Table 1.

< Table 1 here >

Process innovations are closely related to productivity improvements i.e. reduced costs.

The absolute size of this will vary according to existing production technology and degree of

technological change. Such productivity improvements are commonly associated with a

decrease in employment i.e. less labor per output produced. On the other hand, reduced costs

could in a more dynamic perspective cause firms to reduce their prices, thus increasing

demand and further employment growth depending on e.g. the magnitude of the price

decrease, the price elasticity or the competitive structure in the market. Product innovations

on the other hand, mainly effect employment via increased demand: typically due to an

overall market expansion or business stealing effects at the expense of the firm’s competitors.

The magnitude of the effects is determined based on e.g. demand elasticity, number of

relevant substitutes in the market or the strategic response of competitors. Thus whether

process or product innovations lead to displacement or compensation effects is a priori

unclear.

Empirical studies at the firm-level have so far reached an overwhelmingly “positive

biased” stock of findings: the overall implication is that innovations in general have positive

effects (Vivarelli, 2014 and Chennells and Van Reenen, 2002 for an early survey). Further

the specific effects of product innovations are usually found to be positive, whereas the

effects of process innovations are more ambiguous, ranging from significantly negative

effects to significantly positive (see e.g. König et al., 1995; Van Reenen, 1997; Spieza and

Vivarelli, 2002; Garcia et al., 2004; Hall et al., 2008; Harrison et al., 2008, 2014 and

Lachenmeier and Rottman, 2011).

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In the case of the green innovation-employment link this lack of consensus holds to an

even larger degree as this is a much less voluminous and recently emerged strand of research.

Empirical findings are still rather scarce, especially due to data availabilities and

measurement issues (as will be further discussed in section 3.2). Recent increases in

empirical research has however been fostered, in particular, by the inclusion of a special

module on green innovation in the 2008-wave of the European Community Innovation

Surveys (CIS2008) - making it possible to empirically measure different types of green

innovation and relevant determinants. In general most of these studies identify positive

effects of green innovations overall (see e.g. Bijman and Nijkamp, 1988; Pfeiffer and

Rennings, 2001; Rennings and Zwick, 2002; Harabi, 2000; Rennings, 2003; Kunapatarawong

and Martinèz-Ros, 2016), while the results for green product innovations are mixed (see e.g.

Horbach, 2010; Horbach and Rennings, 2013). The same holds for green process innovations.

Concerning the ladder an emerging finding is that the type of green process innovation

seems to matter: thus representing a new dimension not commonly accounted for in extant

research. The studies by Pfeiffer and Rennings (2001), Rennings et al. (2004) and Horbach

and Rennings (2013) however, explore this, and their results highlight the difference between

end-of-pipe process innovations and cleaner technology innovations. Referring to the logic

applied in Horbach and Rennings (2013), the positive and negative effects are as follows: in

the case of end-of-pipe innovations the introduction may require additional staff (positive

effects) or bring about higher costs due to implementation processes (negative effects). In the

case of clean technologies, these might lead to cost-savings due to reduced energy or material

usage, or reduced CO2-emissions, and will thus be related to competitiveness and demand

(positive effects). On the other hand the introduction could also lead to efficiency

improvements and less labor input per output produced (negative effects).

Another dimension underexplored is whether employment effects from green

innovations differ across industries. Related to regular innovation some studies exist such as

e.g. Bogliacino and Pianta (2010), investigating employment effects within the scope of a

revised Pavitt taxonomy (Pavitt, 1984). In the case of green innovation however, most studies

control for industry differences with industry dummies without incorporating these effects in

the formal analysis or performing subsample analysis. Albeit one of the major findings in

innovation literature is that industries matter (Malerba, 2005): firms differ in terms of

resource use, innovation strategy and technological performance due to different industry

contexts, further shaping firm’s opportunity sets, constraints and thus overall innovation

activities (Dosi, 1982; Pavitt, 1984). Thus arguably, one would imagine that the industry

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dimension will play a role in the green innovation-employment link, and that industries

indeed produce different development patterns as they approach a greener economy.

Most of the empirical literature, as emphasized in the recent study by Gagliardi et al.

(2016) is subject to several limitations. In particular most analyses have only weak or

insufficient identification of whether, and through which channels, green innovation relative

to regular innovation effects employment. Little evidence is also found as to whether green

innovations and technologies require more resources, and thus is more expensive to pursue.

As for the more methodological side, the authors highlight the weakness of applying green

innovation variables identified via the definition used in survey data: as these might be highly

discretional and suffer from several types of structural bias (also highlighted by e.g. Horbach

and Rennings, 2013). Lastly the time frame of the data applied, especially referring to

CIS2008, is limited, making it plausible that more medium or long-term effects of green

innovation are not identified.

Responding to this, the empirical approach attempts to disentangle some differences in

effects stemming from green and non-green product and process innovations. This is done by

applying a more structural model than a purely econometric specification. This has the

disadvantage that we are “locked” to using only survey data – making us incapable of

responding to the second major limitation. Following this we are also not able to cope with

the time-frame issue. However, as this is the case for European studies as well, we are still

able to present results comparable for the case of South Korean firms. Additionally we also

incorporate the subtype dimension related to green process innovations as well as types of

industries: both currently underdeveloped topics.

2.1 Theoretical model The empirical analysis is based on the firm-level model applied in Licht and Peters (2013;

2014): an extended version of the framework developed by Harrison et al. (2008; 2014) and

adapted to incorporate green innovations explicitly. The virtue of the approach is the ability

to disentangle some of the employment effects predicted by theory (as discussed in section 1)

and distinguish them as byproducts of green and non-green innovations. The original model

uses data for the UK, Spain, France and Germany, but the framework has also been applied to

other countries – see e.g. Benavente and Lauterbach (2007) for Chile, Hall et al. (2008) for

Italy and Mairesse et al. (2011) for China. Licht and Peters (2013; 2014) however, are the

only studies so far (to the best of our knowledge) applying this model focusing on green

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innovation specifically (related to European countries and Germany, respectively). For a

detailed description of the formal model and theoretical considerations, see Harrison et al.

(2008; 2014). In the following we will give a briefer presentation.

The formal model is based on a simple multi-product framework where each firm, j,

can produce different products, i, over two periods, t, as defined by the production function:

(1) 𝑌𝑌𝑖𝑖𝑖𝑖 = 𝜃𝜃𝑖𝑖𝑖𝑖𝐹𝐹(𝐶𝐶𝑖𝑖𝑖𝑖 , 𝐿𝐿𝑖𝑖𝑖𝑖 ,𝑀𝑀𝑖𝑖𝑖𝑖)𝑒𝑒𝜂𝜂+ 𝜔𝜔𝑖𝑖𝑖𝑖 , where i = 1, 2 and t = 1, 2

The conventional function F is linear and homogenous in the three inputs labor, Lit,

capital, Cit and materials, Mit. Further, knowledge capital is considered a non-rival input that

drives efficiency related to the production process of both goods, at each point in time, and

thus raises marginal productivity captured by θit. We then have that in period t1 the firm

produces one or more products conceptually aggregated to one, labelled as the “old product”

(i=1), and between period t1 and t2 the firm chooses to introduce one or more new products

(i=2) partially or fully replacing the old if the products are considered substitutes, or

increasing demand if they are considered complements. Output of the new product is then Y21

= 0 in period t1 and Y22 > 0 in period t2, if a new product is introduced. Output of the old

product is Y11 and Y12, the change between periods being ΔY1 = Y12 – Y11.

Based on this and assuming that input and production decisions are derived from

typical cost-minimization, Harrison et al. (2008; 2014) arrives at the following decomposed

employment growth equation:

(2) 𝑙𝑙 = −(𝑙𝑙𝑙𝑙𝜃𝜃12 − 𝑙𝑙𝑙𝑙𝜃𝜃11) + (𝑙𝑙𝑙𝑙𝑌𝑌12 − 𝑙𝑙𝑙𝑙𝑌𝑌11) + �𝜃𝜃11𝜃𝜃22� ∗ �𝑌𝑌22

𝑌𝑌11� − (𝜔𝜔12 − 𝜔𝜔11)

Based on (2) we can infer that the employment growth rate, l, depends on three

different sources in addition to the error term (IV) representing productivity shocks. (I) Refers

to efficiency gain in production of old products assumed to negatively affect labor demand;

(II) refers to rate of change in demand for old products, resulting in differences in real output

produced – either positive or negative depending on the circumstances: own cannibalization

if the new product is a substitute or increase if the product is a complement, business stealing

effects in favor or disfavor depending on competitors, or via demand shifts due to e.g.

business cycle effects or changes in preferences that are autonomous to the firm; (III) refers

(I) (II) (III) (VI)

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to the start of production of the new product assumed to positively affect employment growth

determined by the efficiency ratio between new and old production technologies and output

growth related to new products.

3. Methodology As highlighted in section 2, the empirical analysis of firm-level survey data, brings along

several methodological challenges related to e.g. measurement of green innovation and the

time-frame of the dataset (i.e. endogeneity bias). As these issues affect the validity of the

study and thus the implications we can draw based on the derived results, it is important to

address this. First however, we will introduce the econometric specification based on the

theoretical model derived in section 2.1.

3.1 Econometric specification In a simplified manner equation (2) can be reduced to the below mentioned equation (3), with

α representing (I), y1 (II) and βy2 (III) (μ is the error term). Equation (3) is thus the

econometric specification that will be used in the econometric analysis:

(3) 𝑙𝑙 = 𝛼𝛼 + 𝑦𝑦1 + 𝛽𝛽𝑦𝑦2 + 𝜇𝜇

As for extensions from this benchmark equation, especially three are made. First, a

distinction is made that separates the efficiency gains related to production of old products as

stemming from process or non-process innovators: as it is likely that the increase in

efficiency differ across these groups, Harrison et al. (2008; 2014) suggests making an explicit

distinction where (3) is extended to (4), and pc refers to process-only innovators:

(4) 𝑙𝑙 = 𝛼𝛼0 + 𝛼𝛼1𝑝𝑝𝑝𝑝 + 𝑦𝑦1 + 𝛽𝛽𝑦𝑦2 + 𝜇𝜇

Second, in line with Licht and Peters (2013; 2014) we further extend (4) to incorporate

the difference between green and non-green innovations, represented as pcENV and pcNE, and

y2,ENV and y2,NE (respectively). The employment growth function is then:

(5) 𝑙𝑙 = 𝛼𝛼0 + 𝛼𝛼1𝑝𝑝𝑝𝑝𝐸𝐸𝐸𝐸𝐸𝐸 + 𝛼𝛼2𝑝𝑝𝑝𝑝𝐸𝐸𝐸𝐸 + 𝑦𝑦1 + 𝛽𝛽𝐸𝐸𝐸𝐸𝐸𝐸𝑦𝑦2,𝐸𝐸𝐸𝐸𝐸𝐸 + 𝛽𝛽𝐸𝐸𝐸𝐸𝑦𝑦2,𝐸𝐸𝐸𝐸 + 𝜇𝜇

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Here α0 measures efficiency gain of old products for firms with no process innovations,

α1 related to green process innovations and α2 to non-green process innovations. βENV

measures the efficiency ratio of technologies related to production of old and new green

products, and a value less than 1 thus indicates that new green products are produced with

higher efficiency than old (i.e. less labor). The same logic applies to βNE in the case of non-

green products. As y2,ENV and y2,NE are non-observable, these are replaced by g2,ENV and g2,NE ;

that is the nominal output growth rates related to green and non-green products. We therefore

have that g1 = y1 + π1, g2,ENV = y2,ENV + π2,ENV * y2,ENV and g2,NE = y2,NE + π2,NE * y2,NE.

Finally as π1, that is the price growth at the firm level, is unobservable, we use 𝜋𝜋�1 for

representing the price growth at the industry level as a proxy. (𝑔𝑔1 − 𝜋𝜋�1) thus proxies y1,

leaving us with the final equation:

(6) 𝑙𝑙 − (𝑔𝑔1 − 𝜋𝜋�1) = 𝛼𝛼0 + 𝛼𝛼1𝑝𝑝𝑝𝑝𝐸𝐸𝐸𝐸𝐸𝐸 + 𝛼𝛼2𝑝𝑝𝑝𝑝𝐸𝐸𝐸𝐸 + 𝛽𝛽𝐸𝐸𝐸𝐸𝐸𝐸𝑔𝑔2,𝐸𝐸𝐸𝐸𝐸𝐸 + 𝛽𝛽𝐸𝐸𝐸𝐸𝑔𝑔2,𝐸𝐸𝐸𝐸 + 𝜐𝜐 ,

where 𝝊𝝊 = −𝑬𝑬(𝝅𝝅𝟏𝟏 − 𝝅𝝅�𝟏𝟏) − 𝜷𝜷𝑬𝑬𝑬𝑬𝑬𝑬𝝅𝝅𝟐𝟐,𝑬𝑬𝑬𝑬𝑬𝑬𝒚𝒚𝟐𝟐,𝑬𝑬𝑬𝑬𝑬𝑬 + 𝜷𝜷𝑬𝑬𝑬𝑬𝝅𝝅𝟐𝟐,𝑬𝑬𝑬𝑬𝒚𝒚𝟐𝟐,𝑬𝑬𝑬𝑬 + 𝝁𝝁 .

3.2 Measurement issues As highlighted by Gagliardi et al. (2016), measurement of green innovation and the dominant

use of innovation survey data for this, within the green innovation-employment literature,

entail some caveats. Regarding the application in empirical analysis this refers especially to

the limited year coverage (generally 2-3), and the fact that both innovation and employment

variables (thus) are created based on data for the same time period. As most studies are based

on CIS or equivalent surveys however, this caveat is difficult to overcome given that survey

data is initially chosen: only the 2008-wave asked questions directly concerning firm’s green

innovation activities. Due to this, Gagliardi et al. (2016) in their analysis of Italian

manufacturing firms, adopt a different route applying environmental patent data from EPO

linked with firm level data from the AIDA (Bureay van Dijk) database. They are then able to

overcome this issue, as well as better account for endogeneity amongst variables. By

applying a thorough and robust instrumental variable (IV) approach the authors thus develops

an alternative route to the survey data approach, which dominates existing literature.

Patent data however, also includes some caveats. Besides the typical critiques, a

specific point is that the focus by definition is moved to the impact of “creation” or invention

of green technologies, whereas the CIS-based studies more specifically look at the actual

adoption of green innovations and how this relates to employment growth. Additionally the

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use of patent data makes it impossible to distinguish between the impacts of green process

and product innovations, a dimension that arguably are found important in existing both

empirical and theoretical literature. On the other hand, patent data facilitates a more explicit

separation of effects from green versus non-green technologies, which in itself is an

underexplored topic.

Our choice of innovation survey data in this paper are based upon the following. Firstly

we want to provide research that helps broaden the geographical scope with evidence from

South Korea, comparable to the studies using the equivalent survey for European economies.

Secondly we are more interested in the adoption effects of these green innovations on

employment, especially since we aim to explore different subtypes of green process

innovations, thereby exploiting the details available in the survey questionnaire to a larger

degree than what has typically been done. Thirdly we are also interested in shedding light on

whether these employment effects differ across types of industries, as this might have

implications for industrial structure and the competiveness landscape as we enter a green

economy. The link to industries is arguably more straight forward in the case of survey data.

By doing this, as pointed out by Gagliardi et al. (2016), we make some sacrifices related to

time period and robustness towards endogeneity.

3.3 Endogeneity issues and estimation strategy An additional possible caveat related to the time period of the innovation surveys typically

applied, is the fact that they capture the years of the financial crisis. The European data

covers the years 2006-2008 while the South Korean covers 2007-2009. Whether this actually

impacted the survey data however, is difficult to know.

< Figure 1 here >

Looking at the employment situation in South Korea, Figure 1 compares the

development of the unemployment rate for the last 10 years relative to Japan, the US, EU27

and OECD. From this we can draw especially two conclusions: one that the financial crisis

significantly increased unemployment in the US, EU27 and OECD (overall) – with the EU27

rate remaining high even today. Thus the potential impact of the financial crisis seems to be

much larger for studies of European economies; second that Japanese and South Korean rates

were relatively unaffected by the financial crisis and with rates of around 4% or less the

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whole 10-year period. This aggregate trend is also confirmed in our survey data (see Table 3

and 4 in section 4.2.1 and 4.2.2)

Moving to the discussion of endogeneity more generally speaking, we note that besides

innovation, employment growth is likely affected by other variables as well not specifically a

part of the structural model presented in section 3.1. However, as the model is formulated in

growth rates, the impact of both observable and unobservable time-constant firm-specific

characteristics on employment, have already been accounted for.

Further equation (6) is estimated using an Instrument variable (IV) approach to account

for remaining endogeneity issues, as highlighted by Harrison et al. (2008; 2014) and Licht

and Peters (2013; 2014). The IV-estimator tries to correct for the fact that 𝑔𝑔2,𝐸𝐸𝐸𝐸𝐸𝐸 and 𝑔𝑔2,𝐸𝐸𝐸𝐸 ,

due to measurement error, should be considered endogenous to sales growth of new products.

In order to achieve this we need instruments that are correlated to 𝑔𝑔2,𝐸𝐸𝐸𝐸𝐸𝐸 and 𝑔𝑔2,𝐸𝐸𝐸𝐸 (i.e. to

“innovation success”), but uncorrelated to the error term i.e. the instruments cannot be

correlated to the relative price difference of new to old products.

In our IV-regressions we apply 3 instruments typically used in previous analysis and

proven valid (see e.g. Peters, 2008; Hall et al., 2008; Peters et al.., 2013; Dachs and Peters,

2014; Harrison et al.., 2008; 2014) and 3 additional related to green innovation activities as

used in Licht and Peters (2013, 2014). The instrument RANGE measures whether the

introduced product innovation was aimed at increasing the product range i.e. this gives an

indication of the extent in which firms are associated with horizontal as opposed to vertical

product differentiation. Horizontal differentiation is further expected to be correlated with

increased sales, but not to any particular direction of changes in prices (Harrison et al., 2008;

2014) or unanticipated productivity shocks. Similar overall logic related to independence

from direction in price changes, is applied in the identification of the other instruments:

CLIENT and RD, and ENV_REG, ENV_DEM and ENV_AGREE (for a description see

Table 2).

As argued for by Gagliardi et al. (2016) this approach does still not guarantee that

endogeneity can be ruled out, and they especially find the application of such instruments

questionable as they are taken from the same survey as all other variables (both dependent

and controls). Albeit, given our application of similar dataset and the aim to provide

comparable evidence for South Korea, we choose this route although aware of the caveats.

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Additionally we control for industries in the total sample, firm size and ownership

structure in all regressions. For a description of the instruments and the other main variables

used in the regressions, see Table 2.

< Table 2 here >

4. Data and descriptive statistics In this section we will start by introducing the survey data to be used, before presenting some

relevant descriptive statistics.

4.1 Korea Innovation Survey 2010 (KIS2010) The paper employs data for manufacturing firms from Korea Innovation Survey 2010

(KIS2010)[1], following the Oslo Manual. KIS2010 is the equivalent to the 6th wave of the

European Community Innovation Survey (CIS2008), the only wave that includes questions

related to the introduction of green innovations specifically. Although being optional the

module was included by all EU27-countries with the exception of Denmark, Greece, Spain,

Slovenia and the UK. This thus represents the only set of survey-based data on green

innovation that are comparable across several countries (although some variations in the level

of detail exist).

The definition of green innovation in the survey-questions is largely in line with the one

presented by Kemp and Pearson (2007) as part of the EU-project on “Measuring Eco-

Innovation”. Based on this the survey asks whether any of nine listed environmental benefits

have been realized: six referring to process innovations and three to product innovations. The

questions are as follows.

Environmental benefits from the production of goods and services within your

enterprise, i.e. green process innovations:

• reduced energy use per unit of output produced (ECOEN)

• reduced material use per unit of output produced (ECOMAT)

• reduced CO2 “footprint” (total CO2) by firm (overall) (ECOCO2)

• replaced materials with less polluting or hazardous substitutes (ECOSUB)

• reduced soil, water, noise or air pollution related to production (ECOPOL)

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• recycled waste, water or materials related to production (ECOREC)

Environmental benefits from the aftersales use of a good or service by end-users, i.e.

green product innovations:

• reduced energy use from aftersales usage, by end-user (ECOENU)

• reduced soil, water, noise or air pollution from aftersales usage, by end-user

(ECOPOS)

• improved recycling of product aftersales usage, by end-user (ECOREA)

The survey had a response rate of 51 percent, and a total of 3,925 firms responded

(corresponding to a population of 41,485 firms). Out of these, newly established firms

reporting no sales or employment numbers in 2007 had to be dropped, together with firms

with incomplete data on important variables. Additionally to account for outliers and

potential bias generated by this, we deleted all firms in which employment or sales growth

was below the 5% or above the 95% percentile.[2] This leaves us with a sample of 3,060

firms.

4.2 Descriptive statistics 4.2.1 Differences across types of innovator groups

As seen in Table 3 the share of innovators overall is 57,12%: that is over half of all firms in

the sample conducts some type of regular innovation (product, process, organizational or

marketing). Out of these 45,65% conducts product innovation and only 7,71% process

innovations only. All three groups of innovators show higher employment and sales growth

than non-innovators, although this pattern is less explicit in the case of employment growth,

especially related to process only innovators.

< Table 3 here >

In the case of green innovation 45,20% of firms had conducted any type: 32,81%

related to product innovation and 3,20% to process innovation only. Employment and sales

growth rates are lower than for regular innovators across all three groups (green-, green

process only- and green product innovators). Relative to the non-innovators, overall green-

and green product innovators show higher sales growth, whereas green process only

14

innovators slightly lower. In the case of employment however, all groups of green innovators

show lower growth. Especially process only innovators have a significantly smaller rate with

1,45% versus 3,89% for non-innovators. This implies that all types of green innovations are

seemingly associated with lower employment growth than firms not conduction any types of

innovations, while the opposite is true for regular innovators. Also process innovators, both

regular and green, are associated with the lowest growth rates amongst groups of innovators.

3.2.2 Differences across industries

Table 4 looks at similar statistics as discussed above, but with a special emphasis on the

industry dimension. More specifically we separate amongst three types of industry

classifications: the first (TYPE 1) based on the 23 2-digit KSIC-industries (Korea Standard

Industry Classification[3]) given by the dataset and aggregated according to the same logic as

is done in Licht and Peters (2013) for European data; the second (TYPE 2) based on typical

technology-dimensions – here also following Licht and Peters (2013); and the third (TYPE 3)

based on taxonomies like Pavitt (1984) and Castellacci (2008), as commonly used within

general innovation literature (for details about the classifications see Table A1 in the

APPENDIX).

< Table 4 here >

Related to TYPE 1 we see that the most innovative industries are chemicals and

pharmaceuticals, machinery and electronics, with shares of 81,53%, 79,62% and 71,63%

respectively. The least innovative industries were wood and paper and textile and clothing

(29,13% and 39,18%). Chemicals and pharmaceuticals however has a small share of process

only innovators compared to their share of innovation in general (9,06%). Plastic and rubber

and food and beverages on the other hand have the largest shares with 11,76% and 11,35%,

closely followed by basic metals and machinery, with shares of 10,82% and 10,19%. When it

comes to green innovation the most innovative industries are chemicals and pharmaceuticals,

plastic and rubber, non-metallic minerals, machinery, electronics and motor vehicles, all with

shares of above 45% (with the first three all typically categorized as energy-intensive

industries). All shares however, are smaller than their non-green counterparts.

In the case of employment growth basic metals has the highest rate of 5,22%, and

plastic and rubber and chemicals and pharmaceuticals both rates above 4,90%. This is about

1-2 percentage points higher than most other industries, with the exception of non-metallic

15

minerals that has an employment growth rate of less than 1%. As for sales, chemicals and

pharmaceuticals, food and beverages and plastic and rubber have the highest growth rates:

31,19%, 26,15% and 25,76%. This is significantly higher than most other industries. Basic

metals, the industry with the highest employment growth rate however, has the second lowest

sales growth rate.

Looking at the low- versus mid- and high-tech dimension (TYPE 2), the patterns are as

expected: low-tech firms conduct both less regular and green innovation. The difference

however are twice as large in the case of regular innovation i.e. the distribution of green

innovators are much more similar across classifications. Low-tech firms also experience

lower employment growth, whereas the average sales growth seems to be the same (20,02%

and 20,26%),

Lastly following the classification of TYPE 3, it is clear that science based (SB)

industries have the highest share of innovators and supplier dominated (SD) ones, the lowest

- the difference being almost 30%. As is the case for TYPE 2 however, the difference is much

smaller in the case of green innovation. Further whereas the share of innovators is around 7%

higher for science based (SD) than for specialized suppliers (SS), no such difference is found

related to green innovators. Scale intensive firms (SI) have the highest share of process only

innovators, both in the case of regular and green innovations. When it comes to employment,

science based (SB) and specialized suppliers (SS) stand out with the highest growth rates,

4,76% and 4,92%. The difference however, is not that large in size: scale intensive (SI) with

the lowest share has a value of 3,49%. The similar patterns are found in the case of sales

growth.

As deducted from the statistics in Table 4 then, industry differences seem to exist

across all types of industry classifications. Differences in shares of green innovators however,

seems to in general be lower in magnitude than for regular innovation. This implies that

although being relevant also in the green case, the typical industry dimensions often found

highly important when analyzing differences in regular innovation activities might matter

relatively less. Whether this pattern is found also in more formal analyses, is therefore

interesting to investigate and helps shed light on the role of industries in the pursuit of more

aggregated green growth. Especially it explores the idea that different industries within the

green economy will contribute differently to overall growth, as the pursuit of green

innovations within their respective technological trajectory generates different output effects.

Such dynamics will further shape the future green competitiveness landscape and likely

change the role in which different industries act as “growth engines” in national economies.

16

5. Results and discussion We here present the main results from the estimation of equation (6) as presented in section

3.1. We start with the basic specification and further extend to incorporate the industry

dimensions (section 5.2) and lastly the process innovation subtypes (section 5.3). The last

section finally summarizes the main implications in an overall manner, pointing especially to

the policy implications for the pursuit of green growth in national economies.

5.1 Basic specification Table 5 presents the main results from the base specifications: (1) refers to the baseline

investigating the link between innovation and employment without distinguishing between

green and non-green innovations; (2) extends by distinguishing between effects from green

and non-green process innovations; and finally the full specification (3) additionally separates

between green and non-green product innovations.

< Table 5 here >

Before discussing the main results we make some general comments about the

empirical strategy related to endogeneity. As mentioned in section 2 an instrument variable

estimation technique has been pursued due to potential endogeneity bias to secure consistent

estimates. In order for this to work, our 6 chosen instruments have to be correlated with sales

growth due to new products, but uncorrelated with the error term. Although not reported here,

correlation holds for all specifications as evident in the first-stage regressions presented in

Table A2 in the APPENDIX. Additionally we conducted several tests as reported in the

ladder parts of Table 5 on the appropriateness of these instruments: testing for weakness,

under-identification, validity and endogeneity. For all but the latter the tests hold. For the

endogeneity test however, we find only significant p-value related to specification (1) and (2).

This implies that for specification (3) there is no severe endogeneity (the same applies to the

service sector sample in Licht and Peters, 2014). However, as the respective IV-estimates are

still valid and consistent, only less efficient, we report these since they are comparable to

previous studies (e.g. Harrison et al. 2008; 2014 and Licht and Peters (2013; 2014) (the

equivalent OLS-regressions to the ones presented in Table 5 are found in Table A3 in the

APPENDIX. The overall implications are the same).

17

The econometric results demonstrate that higher sales growth of new products is indeed

associated with significant increases in employment growth i.e. successfully introducing new

product innovations spurs employment. This holds for all specifications, thus also when

distinguishing between green and non-green product innovations. Further as explained in

section 2 the β-coefficients measures the efficiency differences between new and old products

(green and non-green). A coefficient of less than 1 implies that new product innovations are

produced with a higher efficiency i.e. with a lower labor input requirement than the old. Thus

a value of more than 1 corresponds to a higher labor requirement, while a value of 1 imply

that old and new products are produced with the same efficiency.

Looking at the β-coefficients in Table 5 we see that the values are all less than 1: 0,847

for specification (1) and (2) in the case of new products in general, and 0,876 for new green

products and 0,819 for non-green, in specification (3). This is also confirmed by the

significant Wald-tests as the null-hypotheses of coefficients equal to 1 are rejected at the one

percent level. Thus an increase in new products in general corresponds to an on average less

than 1 percent increase in employment growth. Although the coefficient for green products is

higher than for non-green, indicating that the efficiency effects are larger for non-green

product innovations, the Wald-test cannot confirm that the two effects are indeed different in

magnitude.

When it comes to employment effects related to process innovations we find only weak

evidence of productivity gains in the case of non-green innovations, and thus displacement of

labor: the coefficient is only -0,0392 and significant at the 15%-level. The lack of

significance in the case of green process innovations on the other hand, indicates no signs of

downsizing in labor. Hence in line with results in Licht and Peters (2013) we find no strong

evidence of the feared negative impacts from these process innovations on employment

growth. Thus at least within this limited time period no significant trade-off between

employment and green process innovations are found.

Looking at the industry dummies included (TYPE 1), we find supporting evidence that

industry differences also exists in the case of the innovation-employment link. Especially

food and beverages, wood and paper, non-metallic minerals and basic metals show additional

positive and significant employment effects. Chemicals and pharmaceuticals and electronics

on the other hand, have significantly negative effects.

In the following subsections we will explore the industry dimension further by running

subsample regressions according to industry specification TYPE 2 and TYPE 3 (as described

in section 3). Only specification (3) from Table 5 is reported.

18

5.2 Industry subsamples 5.2.1 Low-tech versus mid- and high-tech (TYPE 2)

Looking at Table 6 we see that the results overall are very similar to the ones for the whole

sample. For low-tech firms the positive effect on employment growth from green products

however, is not significantly different from 1 (as confirmed by Wald-tests). For mid- and

high-tech firms coefficients are substantially lower in value than what is seen in Table 5,

indicating that they achieve a much higher productivity gain from introducing product

innovations in general: the coefficients for green and non-green products are 0,771 and 0,709

respectively. As such firms are associated with more sophisticated technologies, production

methods and R&D investments: the fact that they also introduce products that are

significantly more cost-efficient is not surprising.

Further, as is the case in Table 5, no significant employment destruction effects are

observed due to the introduction of green process innovations or non-green process

innovations conducted by mid- and high-tech firms. Small and significant effects are found in

the case of low-tech firms and non-green process innovations: -0,0793.

< Table 6 here >

5.2.2 Pavitt taxonomy (TYPE 3)

Table 7 indicates more variation in results when applying a Pavitt-taxonomy separation. As is

the case for TYPE 2 (section 4.2.1) and the total sample however, the coefficients are all

positive indicating employment growth effects. With values of less than 1 this indicates

efficiency improvements for new relative to old products. This holds for both green and non-

green products for all four subsamples, although coefficients for green products in the case of

science based (SB) and supplier dominated (SD) firms are not significantly different than 1

(insignificant Wald-tests), and the coefficients for non-green products in the case of scale

intensive (SI) and supplier dominated (SD) firms are only significant at the 15%-level.

< Table 7 here >

Comparing coefficients across the four subtypes we see that whereas the efficiency gain

from non-green products are similar to the general sample values for science based (SD),

scale intensive (SI) and supplier dominated (SD) firms, the coefficient for specialized

suppliers (SS) is significantly lower: 0,605. This indicates that these firms experience

19

especially large efficiency improvements when introducing new products relative to old. This

also holds for green products, although not to the same degree (coefficient equal to 0,702).

Scale intensive (SI) firms have the highest efficiency improvement related to the introduction

of green products: coefficient of 0,668. For these firms then, the beneficial cost-effects of

green products are the highest. Although not significantly different than 1 the equivalent

values for science based (SI) and supplier dominated (SD) firms are 0,920 and 0,942

respectively.

In the case of process innovations the findings are consistent with what is found in both

Table 5 and 6. The only significant finding is the negative coefficient of -0,129 for

specialized suppliers (SI), indicating productivity gains and reduction in labor due to green

process innovations. This group of firms thus stands out as reporting the highest efficiency

effects related to new green and non-green product innovations as well as productivity

increase related to green process innovations.

5.3 Exploring effects of subtypes For the main specification (3) we also explore whether differentiating among different

subtypes of green process innovations effects the overall results, and whether clear patterns

could be identified along the end-of-pipe and clean technology dimension as discusses in

section 1.

< Table 8 here >

Table 8 presents the results for the total sample and the low-tech and mid- and high-

tech subsamples. The first observation is that the overall results do not change: coefficients

related to green and non-product innovations remain the same. The same goes for the

coefficients for non-green process innovations.

When we disaggregate the green process variable into the 6 subtypes as indicated in

section 4.1 however, some interesting differences appear. Firstly related to the total sample, a

significantly negative coefficient of -0,165 is found for firms introducing process innovations

that reduce material usage in the production process. This implies that such innovations are

associated with productivity gains and labor displacement effects. This holds to an even

stronger degree for the mid- and high-tech firms (coefficient of -0,185) but is not confirmed

in the case of low-tech firms. The ladder group does however experience a positive and

20

significant effect from introducing CO2-reducing process innovations (coefficient equal to

0,199).

< Table 9 here >

In Table 9 we do the exact same exercise as in Table 8 with the TYPE 3 subsamples.

Again the overall results are the same. For the subtypes specialized suppliers (SS) and scale

intensive (SI) firms we similarly find negative and significant coefficients for material

reducing process innovations, but coefficients are stronger than indicated in Table 8: -0,383

and -0,328 respectively. Negative values are also found related to pollution reducing process

innovations in the case of specialized suppliers (SS). Positive and significant effects on the

other hand are found related to energy-reducing innovations amongst science based (SB)

firms, CO2-reducing innovations amongst scale intensive (SS) and pollution-reducing and

material substituting amongst supplier dominated (SD). These coefficients thus imply

productivity effects and either labor displacement or compensation effects respectively.

Overall then, only mixed evidence are found supporting the end-of-pipe and cleaner

technology dichotomy in thinking about employment growth effects. An important

conclusion is rather that interesting differences in effects seem to lay both at the subtype and

industry dimension. The results especially imply that industries might experience different

employment effects from implementation of different subtypes of green process innovations.

Thus what types of green innovations that are most common or called for within different

industries might shape the employment growth patterns. Linking the employment literature to

studies of drivers for different subtypes of green innovations (see e.g. a new taxonomy of

green innovators in Castellacci and Lie, 2016) could thus enrich the understanding of how

external factors like policy could specifically target industries more optimally.

6. Concluding comments and implications The study has employed innovation survey data for South Korean firms to explore how green

innovation and different subtypes of green innovations effects employment growth, and

whether this differs across industries. The empirical work is based on the structural model

presented by Harrison et al. (2008; 2014) and later extended and applied by Licht and Peters

(2013; 2014).

21

The findings demonstrate that both green and non-green product innovations are

associated with significant increases in employment growth across several specifications.

More specifically, a 1% increase in sales growth from new products is associated with a less

than 1% increase in employment. Thus new products are produced more efficiently than old,

differing from the findings in Licht and Peters (2013; 2014) for European countries and

Germany. Further although the coefficient for non-green product innovations are less, we

cannot confirm that the efficiency is actually different from green innovations in magnitude.

Process innovations are further found to matter little for employment growth overall,

implying that the feared trade-off due to productivity improvements remains unconfirmed –

in line with Licht and Peters (2013; 2014). Finally when incorporating subtypes and industry

classifications in the analysis, it is confirmed that these dimension indeed seem to matter for

the innovation-employment link.

Further it might be likely that the employment effects are underestimated due to the

short time period under investigation: even though it is sensible to assume that displacement

effects appear quite immediately without large lags in time, the compensation effects, in

particular related to process innovations, might occur with a certain delay in time (causing

underestimated positive effects). This applies to both green and non-green innovations,

although it is hard to a priori assess whether the effects would differ. We see however that

Gagliardi et al. (2016), looking at a longer time period albeit also using different data, finds

larger positive effects in magnitude.

The findings are relevant for better understanding green growth and how the pursuit of

this might change the competitiveness landscape and create new “growth engines” as

different industries go green by pursuing a variety of subtypes of green innovations. Further

the findings support that both green and non-green innovations help boost employment and

that one cannot say that green innovations contributes less: i.e. no direct evidence is found

implying that policies promoting green innovations, even at the expense of regular

innovations, would bring about displacement effects or effect employment any differently

than regular innovation. The findings are thus supporting of the idea that both industrial and

climate policy promoting green innovations do not necessarily imply a break in future growth

path. The current paper has also brought new evidence on the relevant case of South Korea,

broadening the geographical scope and highlighting the need to carry our similar analyses for

other countries at all levels of development.

Finally, the analysis points to new directions of research. First subtypes of green

process innovations and their effect on employment should be further explored, and the

22

subtype disaggregation applied to the case of green product innovations as well. This will

likely require new work both in theoretical and empirical strands. Second the industry

dimension found important in innovation literature in general, should be more explicitly

accounted for and incorporated. As illustrated in the current paper the employment effects

might differ, providing industries with different opportunity sets in a future green economy

driven by a variety of green innovations.

Acknowledgements Data collection for this work was supported by Korea Foundation via grant of their “Field

Research Scholarship 2015”. We are thankful for useful comments from participants at the

ASIALICS 2016 conference in Bangkok, Thailand.

Footnotes [1] A separate survey is conducted for manufacturing and service industry in South Korea.

KIS2010 for service industries was not available. The KIS surveys are only available in

South Korean language.

[2] This is in line with what was done in Licht and Peters, 2014.

[3] KSIC is constructed based on ISIC, and KSIC rev.9 is equivalent to ISIC rev.3. The

classification used in Lich and Peters (2013) is NACE rev.2 and the classification is

transferred with help of concordance tables.

23

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26

Table 1: Firm-level effects on employment from green innovation

Displacement effects Compensation effects

Product innovation

Productivity effect:

New products require less (or more) labor input

Indirect demand effect:

Decrease in demand of existing substitute products

Direct demand effect:

New products increase overall demand

Indirect demand effect: Increase in demand of existing

complementary products

Process innovation

Productivity effect: Less labor input for a given output

produce

Price effect: Cost reduction passed on to prices

expands demand

NOTE: Table mostly taken from Dachs and Peters (2014) as reprinted in Licht and Peters (2013). Some adaptions are made.

Figure 1: Unemployment rates, 2005-2015

NOTE: Data taken from OECD (2016), Unemployment rate (indicator) (Accessed on 25 July 2016).

*Defined by OECD as: “the number of unemployed people as a percentage of the labor force, where the latter consists of the unemployed plus those in paid or self-employment. Unemployed people are those who report that they are without work, that they are available for work and that they have taken active steps to find work in the last four weeks. When unemployment is high, some people become discouraged and stop looking for work; they are then excluded from the labor force. This implies that the unemployment rate may fall, or stop rising, even though there has been no underlying improvement in the labor market”.

South Korea

JapanUSA

OECD (total)

EU28

0%

2%

4%

6%

8%

10%

12%

2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

Une

mpl

oym

ent r

ate*

27

Table 2: Definition of key variables

Variables Description DEPENDENT VARIABLE

EMP This is used as dependent variable, and is, as following from equation (6) defined as: 𝑙𝑙 − (𝑔𝑔1 − 𝜋𝜋�1) (for the period 2007-2009)

l Employment growth rate (head counts) from 2007 to 2009

𝑔𝑔1 = SGR_OLDPD Sales growth rate due to old products, calculated as total sales growth rate g, minus the sales growth rate due to new products, 𝑔𝑔2 for the period 2007-2009

𝜋𝜋�1 = PRICE_GROWTH Price growth rate for existing products. Data at the industry level taken from Statistics Korea (KOSTAT) for the years 2007-2009 at the 2-digit KSIC industry level. An average for the period is used per industry

MAIN EXPLANATORY VARIABLE

PD_ENV Dummy equal to 1 if the firm conducts green product innovation in the years 2007-2009. Zero otherwise

PC_ENV Dummy equal to 1 if the firm conducts green process innovation in the years 2007-2009. Zero otherwise

𝑔𝑔2 = SGR_NEWPD Sales growth due to new products, calculated by multiplying the share of sales due to new products introduced in the period 2007-2009 (this is taken directly from a question in the survey) with the ratio of sales in 2009 over 2007

𝑔𝑔2,𝐸𝐸𝐸𝐸𝐸𝐸 = SGR_NEWPD_ENV Sales growth rate due to new green products introduced, calculated as SGR_NEWPD * PD_ENV in the years 2007-2009

𝑔𝑔2,𝐸𝐸𝐸𝐸 = SGR_NEWPD_NE Sales growth rate due to new non-green products introduced, calculated as SGR_NEWPD * (1- PD_ENV) in the years 2007-2009

PCONLY Dummy variable equal to 1 if only process innovation and no product innovation has been introduced in the years 2007-2009. Zero otherwise

PCONLY_ENV Dummy variable equal to 1 if only green process innovation and no green product innovation has been introduced, calculated as PCONLY * PC_ENV in the years 2007-2009. Zero otherwise

PCONLY_NE Dummy variable equal to 1 if only non-green process innovation and no non-green product innovation has been introduced, calculated as PCONLY * (1- PC_ENV) in the years 2007-2009. Zero otherwise

CONTROL VARIABLES INDUSTRY A set of dummy variables, referring to TYPE 1 as described in section 3

OWNERSHIP Two dummy variables indicating whether the firm belongs to a company group with domestic or foreign ownership. Unaffiliated firms are used as reference group

SIZE A set of dummy variables for each size class based on data for the year 2007. Firms with 10-49 employees are used as reference group. The other groups had employees of 50-249 or 250 and more

INSTRUMENT VARIABLES

RANGE Dummy variable equal to 1 if the product innovation was aimed at increasing the product range to a medium-high or high degree. Zero otherwise

CLIENT Dummy variable equal to 1 if clients have been of high or medium-high importance as informational source related to innovation activities. Zero otherwise

RD Dummy variable equal to 1 if R&D was continuously carried out. Zero otherwise

ENV_REG Dummy variable equal to 1 if the introduction of green innovations was in response to environmental regulation or taxes. Zero otherwise

ENV_DEM Dummy variable equal to 1 if the introduction of green innovations was in response to market demand. Zero otherwise

ENV_AGREE Dummy variable equal to 1 if the introduction of green innovations was in response to voluntary codes or agreements for environmental good practice (within own sector). Zero otherwise

NOTE: All variables except the price growth data is taken from the KIS2010 survey directly.

28

Table 3: Descriptive statistics by type of innovators

# of firms (%) Employment

growth (%) Sales growth

(%) Mean Median Mean Median All firms* 3060 100 4.20 0 20.14 14.49 Non-innovators 1312 42.88 3.89 0 17.35 10.00 Innovators 1748 57.12 5.44 0.98 22.24 17.44 Process innovators only 236 7.71 3.95 0 20.19 16.76 Product innovators 1397 45.65 4.66 1.52 23.12 18.27 Green innovators 1383 45.20 3.64 0 20.06 14.94 Green process innovators only 98 3.20 1.45 0 15.40 9.26 Green product innovators 1004 32.81 3.76 0 20.53 15.56 NOTE: *n= 3060

Table 4: Descriptive statistics by type of industry classification

Industry # of firms Innovators (%) Green

innovators (%) Employment growth (%)

Sales growth (%)

* * Mean Median Mean Median TYPE 1 Food & beverages 229 59.83 11.35 43.67 4.37 3.81 0 26.15 25.50 Textiles & clothing 319 39.18 3.76 34.48 1.88 3.29 0 17.39 8.53 Wood & paper 357 29.13 6.72 36.41 2.80 4.55 0 17.81 12.81 Chemicals & pharmaceuticals 287 81.53 9.06 61.32 4.18 4.90 1.43 31.19 26.09

Plastic & rubber 187 62.03 11.76 49.73 4.28 4.93 0 25.76 18.15 Non-metallic minerals 175 54.29 6.29 52.00 2.29 0.93 0 22.14 15.66 Basic metals 342 53.51 10.82 41.52 4.68 5.22 0 14.28 8.50 Machinery 157 79.62 10.19 57.32 5.73 4.83 3.12 21.13 15.00 Electronics 490 71.63 6.53 48.78 2.40 4.37 0 18.44 13.14 Motor vehicles 278 58.99 7.91 46.40 2.88 4.28 0.12 13.39 5.79 Manufacturing n.e.c. 202 49.00 3.47 33.17 0.10 3.90 0 21.16 13.11 TYPE 2 Low-tech 1107 42.00 6.23 36.76 2.53 3.91 0 20.02 13.44 Mid- & High-tech 2032 65.45 8.61 49.16 3.54 4.41 0 20.26 15.00 TYPE 3 Science based (SB) 602 75.42 7.48 52.16 3.16 4.76 0 23.58 18.55 Specialized suppliers (SS) 428 68.22 8.65 52.10 3.50 4.92 2.66 23.24 17.40 Scale Intensive (SI) 967 53.46 9.00 46.85 4.03 3.49 0 17.06 11.11 Supplier dominated (SD) 1063 45.63 6.30 36.97 2.35 4.25 0 19.74 13.33 NOTE: *Statistics for process innovators only

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Table 5: Main IV-regression results by industry TYPE 1

Dependent variable: Employment growth (EMP = l – g1) (1) (2) (3) Variable Coef. t-stat Coef. t-stat Coef. t-stat Sales growth: New products (β) .8470753 22.23*** .8466677 22.22*** - - - New green products (βENV) - - - - .8760769 15.92*** - New non-green products (βNE) - - - - .819188 14.55*** Process innovation only (α) -.0244319 -1.11 - - - - - Green process (α1) - - -.00439 -0.13 -.0042062 -0.12 - Non-green process (α2) - - -.0392865 -1.56+ -.0392351 -1.55+ 1: Food & beverages .0657502 2.19** .0658699 2.19** .0658979 2.20** 2: Textiles & clothing .0369227 1.30 .0366684 1.29 .0360778 1.26 3: Wood & paper .1009834 3.69*** .1008058 3.68*** .0993206 3.62*** 4: Chemicals & pharmaceuticals -.0839891 -2.90*** -.0841423 -2.91*** -.0855006 -2.96***

5: Plastic & rubber .036851 1.13 .0368704 1.13 .0363451 1.12 6: Non-metallic minerals .0882109 2.84*** .0882736 2.84*** .0878555 2.84*** 7: Basic metals .1296683 4.79*** .1295604 4.79*** .128952 4.77*** 8: Machinery .0476113 1.35 .0471949 1.34 .0480077 1.37 9: Electronics -.0646644 -2.43** -.0645767 -2.43** -.0663263 -2.49** 10: Motor vehicles .0211816 0.71 .021346 0.72 .0201286 0.68 SIZE DUMMIES .0102636 1.32 .0101653 1.31 .0090689 1.17 OWNERSHIP DUMMIES .0150568 1.36 .0149521 1.35 .0138616 1.23 Constant -.1012193 -3.79*** -.1008009 -3.77*** -.0968774 -3.55*** Adjusted R-Square .4268 0.4270 0.4283 Root MSE .2934 .29334 .29301 Wald-test: β = 1 .0001*** 0.0001*** - Wald-test: βENV = 1 - - 0.0244** Wald-test: βNE = 1 - - 0.0013*** Wald-test: βENV = βNE - - 0.4854 Wald-test: α1 = α2 - 0.73 0.3910 Test on exogeneitya .0472** 0.0488** 0.1602 Test on instrument validityb: Sargan/Hansen J-test .9374 0.9318 0.9541

Test on underidentificationc: Kleibergen-Paap LM test 544.84*** 544.955*** 291.550***

Test on weak instrumentsd: Cragg-Donald F-test 263.54*** 263.588*** 67.124***

Test on weak instrumentsd: Kleibergen-Paap F-test 293.77*** 294.006*** 52.314***

Test on weak instrumentsd: Anderson-Rubin Wald-test 361.13*** 360.76*** 364.62***

Test on weak instrumentsd: Stock-Wright LM-test 299.73*** 299.62*** 301.25***

NOTE: n= 2975. Significance levels: *** 1%, ** 5%, * 10% and +15%.Coefficients and standard errors are robust to heteroscedasticity. INDUSTRY TYPE 1 with INDUSTRY11: Manufacturing n.e.c. is used as reference category. Instruments for (1) and (2) are RANGE, CLIENT and RD. For (3) we add ENV_REG, ENV_AGREE and ENV_DEM. P-value reported for all Wald-tests. a: Test run using the stata command estat endogenous that reports Wooldridge’s score test with H0 that variables are exogenous i.e. significance indicates endogeneity and thus a need for IV (NB: If not significant, IV-results

30

are still consistent). P-value is reported. b: The test is run using the stata command estat overid that reports Wooldridge’s score test with H0 that the instruments are either invalid or the structural equation incorrectly specified i.e. insignificance indicates validity of instruments. The p-value is reported. c: The test is run automatically when using the stata command ivreg2. Significance indicates rejection of H0 i.e. that the equation is identified; meaning that the excluded instruments are relevant i.e. correlated with the endogenous regressors.The relevant statistic is reported. d: These tests are run automatically when using the stata command ivreg2. Significance indicates rejection of H0 that instruments are weak. The relevant statistic is reported.

Table 6: Main IV-regression results by industry TYPE2

Dependent variable: Employment growth (EMP = l – g1) Low-tech Mid- & High tech Variable Coef. t-stat Coef. t-stat Sales growth new products: Green (βENV) .857065 7.81*** .7713265 12.07*** Sales growth new products: Non-green (βNE) .7894097 7.02*** .709411 10.88*** Process innovation only: Green (α1) -.0466009 -0.90 .0008163 0.02 Process innovation only: Non-green (α2) -.0792531 -1.74* -.0373005 -1.15 INDUSTRY DUMMIES -.0069654 -2.43** .0001247 0.04 SIZE DUMMIES .0005817 0.04 .0115973 1.21 OWNERSHIP DUMMIES -.0028966 -0.12 .0152686 1.17 Constant .0216723 0.64 -.0700304 -2.08** Adjusted R-Square 0.3506 0.4301 Root MSE .29233 .30234 Wald-test: βENV = 1 0.1928 0.0003*** Wald-test: βNE = 1 0.0613* 0.0000*** Wald-test: βENV = βNE 0.7052 0.5058 Wald-test: α1 = α2 0.6236 0.4808 Test on exogeneity 0.7297 0.9311 Test on instrument validity: Sargan/Hansen J-test 0.9963 0.7190

Test on underidentification: Kleibergen-Paap LM test 77.048*** 208.659***

Test on weak instruments: Cragg-Donald F-test 21.455*** 45.686***

Test on weak instruments: Kleibergen-Paap F-test 13.975*** 37.750***

Test on weak instruments: Anderson-Rubin Wald-test 125.84*** 197.07***

Test on weak instruments: Stock-Wright LM-test 99.54*** 167.94***

NOTE: n= 1084 for Low-tech and 1891 for Mid- & High-tech. Significance levels: *** 1%, ** 5%, * 10% and +15%.Coefficients and standard errors are robust to heteroscedasticity. Regressions equivalent to (3) in Table 5.Tests are as described in the NOTE of Table 5.

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Table 7: Main IV-regression results by industry TYPE3

Dependent variable: Employment growth (EMP = l – g1) Science based (SB) Specialized suppliers (SS) Scale intensive (SI) Supplier dominated (SD) Variable Coef. t-stat Coef. t-stat Coef. t-stat Coef. t-stat Sales growth new products: Green (βENV) .9204573 9.67*** .7019801 6.52*** .6682276 5.20*** .94181 8.44*** Sales growth new products: Non-green (βNE) .7958829 6.98*** .6049943 5.63*** .7986377 6.00*** .848252 8.40*** Process innovation only: Green (α1) -.0985608 -0.97 -.1291515 -1.54+ .0505821 0.97 .0084246 0.14 Process innovation only: Non-green (α2) -.0940547 -1.22 -.0668281 -1.03 .0024517 0.08 -.0472766 -1.01 INDUSTRY DUMMIES .0081399 1.59+ -.1201994 -5.11*** .001271 0.31 -.0022552 -0.83 SIZE DUMMIES .0121481 0.74 .0280785 1.32 .0115852 0.87 .0019762 0.13 OWNERSHIP DUMMIES .0226542 1.13 -.0477678 -1.40 .0284427 1.50+ .0221219 0.94 Constant -.2295154 -3.80*** 1.011883 4.30*** -.0288981 -0.99 -.0318051 -0.90 Adjusted R-Square 0.4995 0.2215 0.3463 0.3858 Root MSE .29523 0.2758 .27664 .300 Wald-test: βENV = 1 0.4033 0.0056*** 0.0098*** 0.6022 Wald-test: βNE = 1 0.0733* 0.0002*** 0.1302+ 0.1331+ Wald-test: βENV = βNE 0.3395 0.5113 0.5308 0.5794 Wald-test: α1 = α2 0.9704 0.5171 0.3924 0.4501 Test on exogeneity 0.4542 0.6852 0.5704 0.3156 Test on instrument validity: Sargan/Hansen J-test 0.4936 0.9365 0.5610 0.9652 Test on underidentification: Kleibergen-Paap LM test 75.55*** 50.28*** 62.64*** 80.08*** Test on weak instruments: Cragg-Donald F-test 18.34*** 12.52*** 14.57*** 21.31*** Test on weak instruments: Kleibergen-Paap F-test 16.11*** 9.66*** 11.19*** 14.28*** Test on weak instruments:Anderson-Rubin Wald-test 80.56*** 50.95*** 66.26*** 155.90*** Test on weak instruments:Stock-Wright LM-test 61.15*** 40.95*** 59.95*** 115.56*** NOTE: n= 593 for SB, 425 for SS, 955 for SI and 1002 for SD. Significance levels: *** 1%, ** 5%, * 10% and +15%.Coefficients and standard errors are robust to heteroscedasticity. Regressions equivalent to (3) in Table 5. Tests are as described in the NOTE of Table 5.

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Table 8: Subtypes: Main IV-regression results by industry TYPE 1 and 2

Dependent variable: Employment growth (EMP = l – g1) Total sample Low-tech Mid- & High-tech Variable Coef. t-stat Coef. t-stat Coef. t-stat Sales growth new products: Green (βENV) .8842255 16.05*** .8293384 7.67*** .7852013 12.35*** Sales growth new products: Non-green (βNE) .8109138 14.64*** .7876934 7.27*** .7047675 10.91***

Process innovation only: Non-green (α2) -.0400555 -1.59+ -.0783958 -1.72* -.0374112 -1.16 Process innovation only: ECOEN .0282963 0.58 .0796844 1.00 .0079857 0.15 Process innovation only: ECOMAT -.164541 -2.52** -.0126905 -0.20 -.1845158 -2.31** Process innovation only: ECOCO2 .0159522 0.30 .1991727 4.20*** -.0296094 -0.48 Process innovation only: ECOSUB .063389 1.37 .0985415 1.24 .0344132 0.58 Process innovation only: ECOPOL -.0274823 -0.40 -.1424234 -1.42 .1042835 1.30 Process innovation only: ECOREC -.0121484 -0.48 .0119891 0.27 -.0133968 -0.43 1: Food & beverages .0683711 2.27** - - - - 2: Textiles & clothing .0371231 1.30 - - - - 3: Wood & paper .1008733 3.65*** - - - - 4: Chemicals & pharmaceuticals -.084182 -2.91*** - - - - 5: Plastic & rubber .0417315 1.29 - - - - 6: Non-metallic minerals .089908 2.89*** - - - - 7: Basic metals .1323955 4.87*** - - - - 8: Machinery .0474738 1.36 - - - - 9: Electronics -.06567 -2.46** - - - - 10: Motor vehicles .0207329 0.70 - - - - INDUSTRY DUMMIES - - -.0072965 -2.52** -.0003612 -0.11 SIZE DUMMIES .0076071 0.98 -.0001568 -0.01 .0098199 1.03 OWNERSHIP DUMMIES .0136944 1.22 -.0020486 -0.08 .0151079 1.16 Constant -.095193 -3.48*** .0220216 0.66 -.0629251 -1.86* Adjusted R-Square 0.4304 0.3548 0.4331 Root MSE .29245 .29138 .30154 Wald-test: βENV = 1 0.0356*** 0.1143+ 0.0007*** Wald-test: βNE = 1 0.0006*** 0.0500** 0.0000*** Wald-test: βENV = βNE 0.3618 0.8072 0.3854 Test on exogeneity 0.1781 0.8968 0.9587 Test on instrument validity: Sargan/Hansen J-test 0.9540 0.9863 0.7426

Test on underidentification: Kleibergen-Paap LM test 294.70*** 75.53*** 214.34***

Test on weak instruments: Cragg-Donald F-test 68.53*** 22.67*** 46.37***

Test on weak instruments: Kleibergen-Paap F-test 53.40*** 13.69*** 39.26***

Test on weak instruments: Anderson-Rubin Wald-test 361.07*** 118.08*** 201.47***

Test on weak instruments: Stock-Wright LM-test 297.91*** 94.12*** 172.01***

NOTE: n= 2975 for the total sample, 1084 for Low-tech and 1891 for Mid- & High-tech.. Significance levels: *** 1%, ** 5%, * 10% and +15%.Coefficients and standard errors are robust to heteroscedasticity. INDUSTRY TYPE 1, with INDUSTRY11: Manufacturing n.e.c. is used as reference category. Tests are as described in the NOTE of Table 5.

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Table 9: Subtypes: Main IV-regression results by industry TYPE 3

Dependent variable: Employment growth (EMP = l – g1) Science based (SB) Specialized suppliers (SS) Scale intensive (SI) Supplier dominated (SD) Variable Coef. t-stat Coef. t-stat Coef. t-stat Coef. t-stat Sales growth new products: Green (βENV) .9131671 9.39*** .7186411 6.71*** .7323267 5.92*** .9197308 8.31*** Sales growth new products: Non-green (βNE) .8022798 7.08*** .5867276 5.55*** .7842228 6.05*** .8489144 8.68*** Process innovation only: Non-green (α2) -.0908384 -1.19 -.067059 -1.04 .0006755 0.02 -.0469331 -1.01 Process innovation only: ECOEN .1984883 2.56** .0256038 0.30 .0090921 0.11 .0116222 0.14 Process innovation only: ECOMAT -.0419078 -0.39 -.3827957 -4.78*** -.3276681 -2.37** -.0064044 -0.13 Process innovation only: ECOCO2 -.0002151 -0.00 -.0862147 -0.68 .0653375 1.74* .1530273 2.62*** Process innovation only: ECOSUB -.0281944 -0.25 -9.61e-06 -0.00 .0798364 0.96 .1157051 1.86** Process innovation only: ECOPOL .2263609 1.31 -.4023913 -1.50+ -.0052669 -0.07 -.03048 -0.34 Process innovation only: ECOREC -.0482764 -0.88 .0006201 0.01 -.0167086 -0.41 .004256 0.08 INDUSTRY DUMMIES .0074116 1.46+ -.1203509 -5.26*** .0009395 0.23 -.0025461 -0.93 SIZE DUMMIES .0099813 0.60 .0268328 1.24 .0088308 0.66 .0026795 0.18 OWNERSHIP DUMMIES .022467 1.12 -.0520671 -1.50+ .0249754 1.35 .020928 0.91 Constant -.2240397 -3.71*** 1.020967 4.48*** -.0164232 -0.56 -.0307196 -0.87 Adjusted R-Square 0.5037 0.2337 0.3595 0.3885 Root MSE .29397 0.2736 .27383 .2993 Wald-test: βENV = 1 0.3717 0.0086*** 0.0304** 0.4685 Wald-test: βNE = 1 0.0811* 0.0001*** 0.0958* 0.1222+ Wald-test: βENV = βNE 0.4041 0.3794 0.7913 0.6638 Test on exogeneity 0.5058 0.6767 0.8273 0.3156 Test on instrument validity: Sargan/Hansen J-test 0.4319 0.7469 0.4232 0.9467 Test on underidentification: Kleibergen-Paap LM test 88.74*** 51.39*** 66.31*** 78.20*** Test on weak instruments: Cragg-Donald F-test 18.85*** 12.45*** 16.22*** 22.03*** Test on weak instruments: Kleibergen-Paap F-test 16.66*** 9.89*** 11.99*** 13.83*** Test on weak instruments: Anderson-Rubin Wald-test 77.21*** 52.62*** 68.20*** 148.06*** Test on weak instruments:Stock-Wright LM-test 61.60*** 43.20*** 61.52*** 111.31*** NOTE: n= 593 for SB, 425 for SS, 955 for SI and 1002 for SD. Significance levels: *** 1%, ** 5%, * 10% and +15%.Coefficients and standard errors are robust to heteroscedasticity. Tests are as described in the NOTE of Table 5.

34

APPENDIX Table A1: Overview and description of industries (based on KSIC revision 9 2-digit level)

INDUSTRY

AGGREGATION KSIC CODE DESCRIPTION INDUSTRY

TYPE 1 INDUSTRY

TYPE 2

INDUSTRY1: Food & beverages

10 Food products Low-tech Supplier dominated (SD) 11 Beverages Low-tech Supplier dominated (SD)

INDUSTRY2: Textiles & clothing

13 Textiles, except sewn wearing apparel and fur articles Low-tech Supplier dominated (SD)

14 Sewn wearing apparel and fur articles Low-tech Supplier dominated (SD)

15 Tanning and dressing of leather, luggage and footwear Low-tech Supplier dominated (SD)

INDUSTRY3: Wood & paper

16 Wood and products of wood and cork, except furniture, articles of

straw and plaiting materials Low-tech Supplier dominated (SD)

17 Pulp, paper and paper products Low-tech Scale intensive (SI)

18 Publishing, printing and reproduction of recorded media Low-tech Scale intensive (SI)

INDUSTRY4: Chemicals &

pharmaceuticals

20 Chemicals and chemical products except pharmaceuticals, medicinal

chemicals Mid- & high-tech Scale intensive (SI)

21 Pharmaceuticals, medicinal chemicals and botanical products Mid- & high-tech Scale intensive (SI)

INDUSTRY5: Plastic & rubber 22 Rubber and plastic products Mid- & high-tech Scale intensive (SI)

INDUSTRY6: Non-metallic

minerals 23 Other non-metallic mineral

products Mid- & high-tech Scale intensive (SI)

INDUSTRY7: Basic metals

24 Basic metals Mid- & high-tech Scale intensive (SI)

25 Fabricated metal products, exept machinery and furniture Mid- & high-tech Supplier dominated (SD)

INDUSTRY8: Machinery

26 Other machinery and equipment Mid- & high-tech Specialized suppliers (SS)

27 Medical, precision and optical instruments, watches and clocks Mid- & high-tech Science based (SB)

INDUSTRY9: Electronics

28 Electric machinery and apparatuses n.e.c. Mid- & high-tech Specialized suppliers (SS)

29 Electronic components, radio, television and communication

equipment and apparatuses Mid- & high-tech Science based (SB)

INDUSTRY10: Motor vehicles

30 Motor vehichles, trailers and semitrailers Mid- & high-tech Scale intensive (SI)

31 Other transport equipment Mid- & high-tech Specialized suppliers (SS)

INDUSTRY11: Manufacturing

n.e.c.

32 Furniture Low-tech Supplier dominated (SD) 33 Manufacturing of articles n.e.c. Low-tech Supplier dominated (SD)

19 Coke, refined petroleum products and nuclear fuel Mid- & high-tech Supplier dominated (SD)

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Table A2: First stage regression results by industry TYPE 1

Dependent variable: Employment growth (EMP = l – g1) (1) (2) (3) Variable Coef. t-stat Coef. t-stat Coef. t-stat First stage 1: Sales growth: New products/green products

RANGE .1578671 8.43*** .1576471 8.41*** .0815138 5.50*** RD .2254843 17.39*** .2254262 17.39*** .0502454 5.86*** CLIENT .055596 3.31*** .0561139 3.33*** .0332032 2.55*** ENV_REG - - - - .0615939 3.92*** END_AGREE - - - - .0574194 3.78*** ENV_DEM - - - - .1354261 10.14*** F-Statistic of excl. instr. 293.77*** 294.01*** 63.90*** Adjusted R-Square 0.2815 0.2814 0.2123 First stage 2: Sales growth: New non-green products

RANGE - - - - .0755599 4.72*** RD - - - - .1745303 15.37*** CLIENT - - - - .0224147 1.57+ ENV_REG - - - - -.067884 -6.25*** END_AGREE - - - - -.0619721 -5.50*** ENV_DEM - - - - -.1271355 -12.06*** F-Statistic of excl. instr. - - - - 65.13*** Adjusted R-Square - - - - 0.1778 NOTE: n= 2975. Significance levels: *** 1%, ** 5%, * 10% and +15%.Coefficients and standard errors are robust to heteroscedasticity. INDUSTRY, SIZE and OWNERSHIP coefficients not reported here.

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Table A3: Main OLS-regression results by industry TYPE 1

Dependent variable: Employment growth (EMP = l – g1) (1) (2) (3) Variable Coef. t-stat Coef. t-stat Coef. t-stat Sales growth: New products (β) .7799312 39.27*** .780002 39.27*** - - - New green products (βENV) - - - - .8200986 34.55*** - New non-green products (βNE) - - - - .7474663 28.54*** Process innovation only (α) -.0381267 -1.83* - - - - - Green process (α1) - - -.0183895 -0.53 -.0176196 -0.51 - Non-green process (α2) - - -.0525884 -2.17** -.0520142 -2.14** 1: Food & beverages .0620953 2.06** .0622383 2.07** .0624128 2.08** 2: Textiles & clothing .0308696 1.09 .0306627 1.08 .0301383 1.06 3: Wood & paper .0912366 3.37*** .0911308 3.37*** .0896045 3.31*** 4: Chemicals & pharmaceuticals -.0819371 -2.83*** -.0821016 -2.84*** -.0839137 -2.91*** 5: Plastic & rubber .0340884 1.05 .0341267 1.05 .033561 1.03 6: Non-metallic minerals .0844138 2.72*** .0845019 2.72*** .0841123 2.72*** 7: Basic metals .124192 4.59*** .1241245 4.59*** .1235557 4.57*** 8: Machinery .0562799 1.64* .0558111 1.62* .0565194 1.66* 9: Electronics -.0614712 -2.32** -.0614076 -2.31** -.0637625 -2.40** 10: Motor vehicles .0167943 0.57 .0169861 0.57 .0155984 0.53 SIZE DUMMIES .0134971 1.79* .0133782 1.78* .0118554 1.57+ OWNERSHIP DUMMIES .0147883 1.32 .0146876 1.31 .0133054 1.19 Constant -.0909525 -3.4*** -.0906145 -3.43*** -.0859946 -3.24*** R-Square 0.4296 0.4298 0.4310 Root MSE .2934 .29341 .29316 Wald-test: β = 1 0.0000*** 0.0000*** - Wald-test: βENV = 1 - - 0.0000*** Wald-test: βNE = 1 - - 0.0000*** Wald-test: βENV = βNE - - 0.0232** Wald-test: α1 = α2 - 0.4048 NOTE: n= 2975. Significance levels: *** 1%, ** 5%, * 10% and +15%.Coefficients and standard errors are robust to heteroscedasticity. INDUSTRY TYPE 1, with INDUSTRY11: Manufacturing n.e.c. is used as reference category.