Research about Green Entrepreneurship. Data, methods and ... · De nition of GEDataDrivers and...

Definition of GE Data Drivers and barriers to GE GE and RIS3

Research about Green Entrepreneurship.Data, methods and research perspectives

Giovanni Marin1,2

1IRCrES-CNR, Milano, Italy2SEEDS Sustainability Environmental Economics and Dynamics Studies, Italy

Workshop ‘Born to be Green’Winchester, May 2015

Giovanni Marin Research about Green Entrepreneurship


Definition of Green Entrepreneurship (if any...)Defining ‘green’ (entrepreneurship)

Data to support research on Green EntrepreneurshipMeasuring Green EntrepreneurshipOther useful data sourcesMethodologies

Drivers and barriers to GEBarriers to adopting EI in SMEsSkills needs for green occupations

Green Entrepreneurship and Research and Innovation Strategies forSmart Specialization

RIS3 in briefRIS3 and Green Entrepreneurship


Definition of GE Data Drivers and barriers to GE GE and RIS3 Defining ‘green’ (entrepreneurship)

DEFINITION OF GREEN ENTREPRENEURSHIP(if any...)



One possible definition of Green Entrepreneurship

“A green entrepreneur can be either making her businessgreen or simply entering a green business. In other words,green entrepreneurship could be defined in terms of thetechnology used for production in any sector of theeconomy, or in terms of the sectors firms are active in, inwhich case our attention is restricted to parts of the economyproducing specific types of output. The former is sometimesreferred to as a process approach in defining green business,while the latter as an output approach.”



How to define ‘green’ sectors (and consequently GE)▸ Regulation No. 691/2011 describes the environmental goods and

services sector as the production activities of a national economythat generate environmental products. Environmental productsare products that have been produced for the purpose ofenvironmental protection and resource management.

▸ Few industries are fully green (e.g. Nace rev. 2, E38 ‘Wastecollection, treatment and disposal activities; materials recovery’)

▸ Some industry produces environmental goods but it is pollutionand material intensive (e.g. manufacturing of wind turbines orphotovoltaic panels)

▸ Other industries have experienced radical changes and reducedsubstantial their environmental impacts even though they do notproduce environmental products ⇒ should ‘environmentalefficient’ entrepreneurs in these sectors considered greenentrepreneurs?

⇒ The definition of green entrepreneurship depends substantially onhow green sectors are defined



Beyond the sectoral approach

▸ With the exception of some specific sectors, ‘green firms’ arespread across many different sectors

▸ Very limited availability of data (e.g. business entry/exit) fornarrow sectors (e.g. 4-digit NACE)

⇒ Research should ideally move from ‘firms in green sectors’ to ‘greenbusinesses’

▸ Not easy as green businesses can be identified according to avariety of criteria

▸ Adoption and development of green technologies andorganizational practices

▸ Production of green goods and services▸ ...



Why Green Entrepreneurship is different?

▸ Linked to yesterday’s talk by Prof. Mazzanti, the role of policy iscrucial when dealing with green activities

▸ Under-provision of green products in absence of the ‘right’ priceson externalities and resources

▸ Asymmetric information about the ‘greenness’ of product andservices between entrepreneurs and consumers ⇒ labelling andcertification

▸ Trade-offs exist among the various dimensions of ‘greenness’ due toconflicts between different environmental goals and possiblerebound effects ⇒ back to the definition of ‘green entrepreneurship’



OPEN RQDevelop novel ways of identifying green entrepreneurs



DATA TO SUPPORT RESEARCH ON GREENENTREPRENEURSHIP


Definition of GE Data Drivers and barriers to GE GE and RIS3 Measuring GE Other data Methodologies

Measuring ‘entrepreneurship’

▸ It depends on how entrepreneurship is defined (rememberyesterday’s discussion by Prof. Audretsch)

1. How the firm is organized ⇒ SMEs, new firms and start-ups, etc2. How the firm behaves ⇒ opportunity (recognition, creation,

evaluation, exploitation)3. Performance of the firm ⇒ innovation and growth

▸ 1 and 3 are easier to identify (but not trivial) based on rather‘objective’ criteria, while for 2 a more qualitative assessment isneeded



Useful data sources

▸ Ad-hoc or existing surveys

▸ Business registries

▸ Labelling and certification

▸ Patents and trademarks

▸ Aggregated data

▸ Other useful data



Surveys

▸ Existing surveys:▸ CIS2008 (which includes questions on eco-innovation)▸ Flash Eurobarometer #315 “Attitudes of European entrepreneurs

towards eco-innovation” (on SMEs)

▸ Ad-hoc surveys▸ Expensive and generally ‘one-shot’ but...▸ ...tailored to the specific research design▸ Understand ‘how green’ is the green entrepreneur is of crucial

importance (i.e. try to go beyond dichotomous definitions)



Business registries (not specifically green)

▸ Italy (for example)▸ ASIA (Archivio Statistico Imprese Attive, Istat)▸ STARNET Unioncamere (Chamber of Commerce)

▸ Microdata from national statistical offices (CASD in France,ADELE in Italy, etc)

▸ ORBIS, AMADEUS and other BvD databases ⇒ balance sheet,income statement and other ‘demographic’ information (mostlymedium and big firms)



Labelling and certification▸ Beyond ‘compulsory’ environmental regulations, ‘soft’ and

voluntary measures represent other motivations that induce firmsto be ‘green’

▸ Certification and labelling contribute to signaling the ‘sustainable’nature of the firm to interested customers that are willing to pay apremium for green(er) products and services

▸ Many (fragmented) databases on CSR, certification and labelling:▸ EMAS and ISO14001 certifications (that certify organizational

practices)▸ Eco labels (that certify life-cycle environmental pressures)



Patents▸ Another way to go beyond the definition of ‘green sectors’ is to

focus on technologies▸ Patents contain a wealth of information on the characteristics of

the invention (very detailed technology classes, full text andabstract)

▸ They also contain very useful (but ‘dirty’) information on inventorsand applicants ⇒ useful for example to ‘follow’ the career of‘green inventors’ across different applicants/entrepreneurs

▸ And more and more (e.g. citations)

▸ Green technology classes (based on IPC and CPC classes)▸ OECD EnvTech Indicator▸ Y02 CPC classes▸ Green Inventory (WIPO)

▸ Patent databases▸ Espacenet (http://worldwide.espacenet.com) ⇒ free▸ OECD Regpat, Han, Citations, TPF ⇒ free (but ‘easy’ request is

needed)▸ Thomson Reuters, Patstat and others ⇒ subscription is needed



...but

▸ Patenting is expensive and it is mostly used by big incumbentfirms rather than by SMEs and start-ups

▸ Sometimes start-ups are created to exploit commercially theblueprints protected by one or more existing patents developed byan inventor or a group of inventors ⇒ the applicant of the patent(s)will not be the start-up (that did not exist yet...) ⇒ little publicinformation on licensing of patents

▸ However, the possibility to follow inventors (entrepreneurs?) acrossdifferent experiences, beside the issue of ‘name disambiguation’, is anice feature of patent data



Trademarks

▸ Patents mostly focus on inventions and technological innovations

▸ The emergence of new ‘products or services’ can be tracked bymonitoring Trademarks

but I do not know much about that...



Aggregated data on entrepreneurship (not specificallygreen)

▸ Eurostat Structural Business Statistics (by sector and size)

▸ OECD Structural and Demographic Business Statistics (SDBS)



Other useful data - skill composition▸ EU Labour Force Survey ⇒ EU-harmonized information on labour

force composition (by skill, occupation, age, gender) of EU sectorsand regions from the 90s

▸ Skills content of occupations



Methodologies to study GE


Definition of GE Data Drivers and barriers to GE GE and RIS3 Barriers to adopting EI in SMEs Skills needs for green occupations

DRIVERS AND BARRIERS TO GREENENTREPRENEURSHIP (2 EXAMPLES):- Barriers to eco-innovation in EU SMEs

- Skills needs for green occupations



Motivation and aim

▸ Transition from a policy-driven to market-driven eco-innovation:green and strategic dimension, source of competitive advantage

▸ Barriers determine (hamper) both policy and business strategies.Unfortunately, too little is known about barriers to eco-innovation(insights from innovation studies)

▸ A taxonomy/clustering of SMEs (relevant for policy andtransition towards green economy) based on barriers toeco-innovation and EI investments is proposed



Data and methods

▸ CIS 2008: info on eco-innovation, but no question on barriers.Previous CIS waves: no info on eco-innovation, but questions onbarriers to innovation

▸ 2011 Flash Eurobaromenter ‘Attitudes of European entrepreneurstowards eco-innovation’: questions on barriers to eco-innovation andinvestment in environmental innovation activities (% of totalinnovation investment)

▸ Sample: 2,308 SMEs in EU27, manufacturing and ‘green’ industries(i.e. water supply, sewerage and waste management)

▸ Cluster analysis



Variables

▸ Cost barriers:▸ Insufficient internal funding; insufficient external funding; uncertain

return to eco-innovation; insufficient access to subsidies andfinancial incentives

▸ Knowledge barriers:▸ Lack of qualified personnel and technological capabilities; lack of

external info; lack of suitable business partners; lack ofcollaboration with research organizations; technological lock-ins

▸ Market barriers:▸ Uncertain demand; lack of incentives to reducing material use;

lack of incentives to reducing energy use; dominated market;existing regulations not providing incentives to eco-innovate

▸ EI investment:▸ Normalized ordinal variable reflecting the % of investment in

eco-innovation over the total of innovation expenditures



Results

Revealed Deterring Cost Market Green ANOVAbarriers barriers deterred deterred Non EI champions Total (F test)

(N=447) (N=434) (N=408) (N=463) (N=331) (N=225) (N=2,308)

Cost barriers 2.404 2.443 2.094 1.24 0.459 1.304 1.737 1119.04***Knowledge barriers 2.023 2.042 1.147 1.205 0.364 0.96 1.366 703.66***

Market barriers 2.2 2.18 1.233 1.619 0.558 1.311 1.587 815.67***EI investment 2.566 0.691 1.216 1.037 0.934 3.569 1.532 888.69***

▸ Revealed barriers: high barriers and high EI invest

▸ Deterring barriers: high barriers but very low EI invest

▸ Cost deterred: high opportunity costs, failure of the Porterhypothesis from the supply side

▸ Market deterred: EI opportunity is perceived, but failure of thePorter hypothesis from the demand side

▸ Non eco-innovators: low barriers and EI invest. Lack of incentives,interest and ‘non innovators’

▸ Green champions: ‘only’ medium barriers but highest EI invest. EIopportunities and capabilities are core for the business



External profiling: clusters and EI adoption

Revealed Deterring Cost Market Green ANOVAbarriers barriers deterred deterred Non EI champions Total (F test)

(N=447) (N=434) (N=408) (N=463) (N=331) (N=225) (N=2,308)

Product or process EI 0.619 0.26 0.399 0.387 0.231 0.731 0.422 57.53***Product EI 0.407 0.163 0.216 0.176 0.097 0.498 0.246 44.98***Process EI 0.492 0.175 0.295 0.311 0.182 0.604 0.328 45.99***

▸ Top: Green Champions & Revealed Barriers; Medium: Cost &Market deterred; Low: Deterring barriers & Non eco-innovators

▸ ‘Green champions’ invest more than ‘revealed barriers’, but theinnovative outcome is not different ⇒ Learning from barriers?

▸ ‘Deterring barriers’ SMEs invest less than ‘non eco-innovators’but they achieve the same rate of EI adoption ⇒ Learning frombarriers (in Second Division)?

▸ No matter the EI investment, pecuniary barriers, either cost ormarket ones, tend to have the same effect on EI adoption

▸ Disinterest and lack of orientation towards EI make ‘noneco-innovators’ firms having a significantly worse performancethan ‘market deterred’ ones, despite the same EI invest



Relation between clustering and country/sector belonging

1 2 3 4 5 6

Revealed Deterring Cost Market Greenbarriers barriers deterred deterred Non EI champions

N 2299 2308 2298 2308 2308 2298

Chi sq (country dummies) 103.4*** 113.9*** 42.37** 124.6*** 108.5*** 65.86***p-value (country dummies) 3.47E-11 5.76E-13 0.0164 7.73E-15 4.79E-12 0.0000156Chi sq (sectoral dummies) 16.59 12.55 19.04 20.07 10.7 54.94***

p-value (sectoral dummies) 0.279 0.637 0.212 0.169 0.774 0.00000183

▸ We regress the probability of belonging to a cluster on a set ofcountry and sector dummies. Then we test the the jointsignificance of the dummies

▸ The nationality of the firm seems to predict to which cluster itbelongs, contrary to what happens for sectors. Our taxonomy‘overcomes’ the sectoral ones (based on NACE, R&D or emissionintensities)



Prevalent cluster by country



Conclusion

▸ 6 statistically robust clusters based on the combination ofdifferent types of perceived barriers (cost, market, knowledge) andthe engagement in EI investment

▸ Strong correspondence with the adoption of eco-innovation

▸ A guide for policy tayloring (one size, as usual, does not fit all)!

▸ Sectoral policy seems to us quite ineffective: our barrier-basedclusters imply the need of different initiatives



Questions, questions, questions....

▸ Does the Green Economy require a specific kind of skills?

▸ Are these skills similar to the ones used in existing non-greensectors? Are they similar to currently available skills?

▸ Is there an overlap with the set of skills demanded by recent‘technological revolutions’ (e.g. ICTs)?

▸ Is Europe ready to evaluate potential skill gaps and intervene tofill these gaps?



Background

▸ Vast theoretical and empirical literature documenting the changesin the demand for skills due to the ICT revolution

▸ Many policy reports (CEDEFOP, ILO, OECD, UNEP, etc) haveemphasized the fact that the green economy is likely to inducechanges in the demand for specific skills

▸ Mostly based on ad-hoc case studies and qualitative assessments



The Green Economy Initiative by O*NET

Dierdorff et al (2009) Greening of the World of Work: Implications forO*NET-SOC and New and Emerging Occupations

▸ Three different categories of green occupations:

▸ ‘Increased demand’ occupations (with no specific ‘green tasks’) ⇒their demand is likely to increase thanks to the ‘green economy’with no expected change in the skill content of these occupations

▸ ‘Enhanced skills’ occupations ⇒ the ‘green economy’ is likely toaffect the skill and task contents of these existing occupations

▸ ‘New and emerging’ occupations ⇒ the ‘green economy’ requiresnew occupations that did not exist before

▸ After identifying these occupations, recent updates of the O*NETcatalogue have assessed the green specific tasks of ‘Enhancedskills’ and ‘New and emerging’ occupations



Which is the profile of Green Occupations?

▸ Are green occupations different from similar non-greenoccupations with respect to:

▸ Skills content (routine vs non-routine; cognitive vs manual)▸ Education, experience and training requirements

NR cognitive NR interactive R cognitive R manual NR manual RTI index

Green emerging 0.0293 -0.00737 -0.0320* -0.0152 -0.00291 -0.0692(0.0187) (0.0205) (0.0192) (0.0149) (0.0364) (0.0476)

Green enhanced skills 0.0297** 0.00404 -0.0198* -0.00508 0.0152 -0.0583**(0.0130) (0.0145) (0.0108) (0.0155) (0.0162) (0.0269)

log(years of educ) log(years of exp) log(years of train)

Green emerging 0.0205 -0.0515 0.168*(0.0221) (0.124) (0.0998)

Green enhanced skills 0.0191** 0.357*** 0.341***(0.00861) (0.113) (0.129)

N=465. OLS estimates weighted by employment share. Robust standard errors in parenthesis. * p<0.1, ** p<0.05, *** p<0.01.SOC 3-digit dummies included. Occupations in SOC 3-digit categories with no green occupation have been excluded.



Green Skills?

▸ O*NET (Occupational Information Network) provides a detailedcatalogue of general skills and tasks (about 400 items) withimportance scores for a detailed list of occupations (more than900)

▸ Based on a data-driven approach we select those general skills thathave systematically and significantly greater importance scores inoccupations with high level of ‘greenness’ (i.e. share of ‘greenspecific tasks)

Task Implk = α + βl ×Greennessk +DSOC 3digitk + εk

▸ A general skill is identified as a Green General Skill (GGS) if thecoefficient βl is positive and statistically significant (1 percent)

▸ The selected GGS are then grouped together with the support of aprincipal component analysis



Green skills - results

Engineering & Technical

2C3b Engineering and Technology2C3c Design2C3d Building and Construction2C3e Mechanical

4A3b2 Drafting, Laying Out, and Specifying Technical Devices, Parts, and Equipment

Science

2C4b Physics2C4d Biology

Operation Management

2B4g Systems Analysis2B4h Systems Evaluation

4A2b3 Updating and Using Relevant Knowledge4A4b6 Provide Consultation and Advice to Others

Monitoring

2C8b Law and Government4A2a3 Evaluating Information to Determine Compliance with Standards



Is environmental regulation driving the demand for GS?▸ We measure the intensity of GGS by industry and state by

attributing importance scores of GGS by occupation to industriesand states according to the labour force composition

▸ We estimate the following equation (by state and industry; year2012):

GGSij = βERij +∑n

ηX nij + µj + φi + εij

▸ ER is proxied by emissions per employee by state and sector4-digit (National Emission Inventory)

Greenness Engineering& Technical

Science OperationManage-

ment

Monitoring Greenspecifictasks

log(SO2/L) -0.00303*** -0.00878*** -0.0110*** -0.0134*** -0.00466*** -0.211***(0.000974) (0.00193) (0.00155) (0.00271) (0.00118) (0.0461)

Hansen test (p-value) 0.241 0.699 0.250 0.648 0.849 0.251

N=3328 industry-state pairs. Standard errors clustered by state and 3-digit NAICS in parenthesis. * p<0.10, **p<0.05, ***p<0.01. Regressions weighted by employment in 2012 at the state and NAICS 4-digit level. Controls not shown: growth rate ofemployees 2002-2012; log average establishment size (employees per establishment) in 2012; log of the count of facilities reportingto the NEI; NAICS 3-digit dummies, state dummies. IVs: log of violation (2000-2009) per employee (2009); log of full inspection(2000-2009) per employee (2012). Partial F of excluded IVs: 112.



Is Europe ready to evaluate potential skill gaps andintervene to fill these gaps?

no



Is Europe ready to evaluate potential skill gaps andintervene to fill these gaps?

▸ No systematic evaluation of skills needs to develop the greensector (broadly defined)

▸ Lack of data when compared to the US ⇒ the gap is going to befilled soon thanks to ESCO



OPEN RQEvaluate the gaps in the availability of crucialcapabilities (skills, competencies, knowledge,

infrastructures, etc) that are needed to support andpromote green entrepreneurship


Definition of GE Data Drivers and barriers to GE GE and RIS3 RIS3 in brief RIS3 and Green Entrepreneurship

GREEN ENTREPRENEURSHIP AND RESEARCHAND INNOVATION STRATEGIES FOR SMART

SPECIALIZATION



Research and Innovation Strategies for SmartSpecialization (RIS3)

Smart specialisation is a new innovation policy conceptdesigned to promote the efficient and effective use of publicinvestment in research. Its goal is to boost regionalinnovation in order to achieve economic growth and prosperity,by enabling regions to focus on their strengths. Smartspecialisation understands that spreading investment toothinly across several frontier technology fields risks limitingthe impact in any one area.

A smart specialisation strategy needs to be built on a soundanalysis of regional assets and technology. It should alsoinclude an analysis of potential partners in other regions andavoid unnecessary duplication. Smart specialisation needs tobe based on a strong partnership between businesses, publicentities and knowledge institutions such partnerships arerecognised as essential for success.



RIS3 and entrepreneurship

▸ Large space to entrepreneurs in the phase of choice of RIS3 areas

▸ Entrepreneurs as central players in the implementation of RIS3



Sustainable innovation

http://s3platform.jrc.ec.europa.eu/home



Eco-innovation Resource efficiency



Smart green & integrated transp systems Sustainable agriculture



Sustainable energy & renewables Sustainable land & water use



Sustainable production & consumption Waste management



OPEN RQEx-ante and ex-post evaluation of ‘green’ RIS3 in termsof barriers to the emergence of green entrepreneurship

and effectiveness of policies to support RIS3



THANK YOU FOR YOUR ATTENTION

[email protected]


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