public research organisations (pro) – industry interactions in developing countries: insights...

Public Research Organisations (PRO) – Industry interactions in developing

countries:

Valeria Arza & Andrés LópezFundación CENIT

Insights from the literature and empirical evidence of Argentina

OPEN SESSION

PRO-I interactions in ArgentinaOrganisation

I. Historical roots of PRO-I linkages in the world

II. Conceptual framework to analyse PRO-I linkages in developing countries

III. Case study on an interaction in biotechnology

IV. Research design for the analysis of firms’ survey

V. Determinants of linking and linking actively

VI. Average effect of linking

VII. Average effect of linking actively

VIII. Conclusions

I. Historical developments of PRO-I interactions

Linear model of innovation (separation of S&T) became questioned Changes in the production of scientific

knowledge, especially in some fields of research Geopolitical, economic and ideological changes

about the role of the StateIn developing countries: liberal ideology

Market should guide any inquireBudget constraints to public researchPRO took the place of the state in local/regional development

II. Conceptual framework of PRO-I linkages

Mode 2, Triple Helix, Sabato’s triangle, NIS literature, etc. -> provided a general context to understand PRO-I linkages - no methodologies or tools to derive hypotheses

No framework on PRO-I in developing countries, which differ from developed countries in: policy strategies and needs, historical evolution of S&T institutions structure and behaviour of the private sector

II. PRO-I linkagesRisks & Benefits Benefits

Contribution to the stock of public K (science learn from applications)

Funding for public research Solution of concrete problems Increase the potential for frontier-moving innovations Spillovers – local development

Risks Adequacy of PRO’s research and timing Privatisation of research outputs Interference in lines of research Opportunity costs (of time forgone in interactions) Conflict of interests between primary functions of public research

(teaching and research) and secondary functions (interactions)

II. Motivations and modes of interactions

Motivations Universities

Intellectual imperatives Financial imperatives

Firms Long-term innovative strategies fed by external sources of

knowledge Short-term problem solving strategies

Modes of interaction Bi-directional: shared intellectual resources and

outputs (e.g. joint research) Unidirectional: unilateral provision of intellectual

resources in exchange for money (e.g. consultancy, training, testing, etc.)

II. Motivations & Modes =>Risks & Benefits Main claims

When PRO-I are motivated by intellectual imperatives and long-term strategies => benefits>risks Usually instrumented by bidirectional modes Sophisticate knowledge bases in both ends

When PRO-I are motivated by financial imperatives and short-term strategies => benefits<risks Usually instrumented by unidirectional modes Firms’ supplement their knowledge bases with K from PRO

In LA first types of linkages are scarce Production structure do not draw from fields of research of the

learn heavily from application Use of mature technology Less sophisticate demand from industry Uncertainty + inconsistent S&T policy => short-term demand from I

II. Risks & Benefits Main goals

Given the likelihood of risks in PRO-I in developing countries (LA), we must investigate further which behaviours, practices, and characteristics of PRO & I that prioritises benefits over risks

This projects aims at exploring benefits and risks and both ends by analysing Survey on firms Survey on research In-depth case studies

III Case study on PRO-I interaction in biotechnology Actors: Biosidus, leader firm in biotechnology, and Institute of

Antarctic Research (IIA) Goal: to discover native bacteria which have not been described

before => mission oriented research on the IIA traditional lines of research

Potential application: enzymes from bacteria adapted to extremely low temperatures may substitute enzymes that need high temperatures to be effective very much used in the food and textile industry => potential reduction of the use of energy. 15 million USD market only in Argentina => seeking radical innovations

Outcome: new bacteria was discovered, described and published. The bacterium’s genome was fully described for the first time in Argentina. Several further interactions followed-up with other public and private institutes.

III Case study on PRO-I interaction in biotechnology Modes of interaction: during the first four years => general

agreement; when concrete results were foreseen => specific agreements, stating rights and obligations Rights on patents and commercialisation for the firm, which

would pay royalties to the institute Further publications / cooperation need the consensus of both

parts Conclusion: Benefits were clear: the success of the research is explained by:

Curiosity-led / frontier moving motivations (long-term) Instrumented by bi-directional cooperation Leader actors in their fields / strong knowledge bases

Yet the case raises concerns about issues of privatisation of public knowledge and potential conflict of interests regarding publications => may they be overcome by S&T regulation?

IV Research design on firms’ surveysGoals of research

1. To compare the characteristics, behaviour and performance of firms that connect and firms that do not connect to PRO.

2. To compare the characteristics, behaviour and performance of firms that connect to PRO with bi-directional linkages and those that connect with unidirectional ones.

IV Research design on firms’ surveysData

Especial section added to the National Innovation Survey (fieldwork 2007) Firms that connected to PRO in 2005: 590 answered by

354 Firms that did not connect to PRO in 2005 (sub-sample

that resembles sector & size of linked group): 384 answered by 238

Data refers to 2005 Broad definition of interactions with PRO, including

active cooperation and information exchange

IV Research design on firms’ surveysMethodology

Propensity score matching techniques Construct valid counterfactual groups against which to

compare outcomes of the treated (e.g linked) group It finds a twin for every linked firm in the unlinked group.

Three steps Probit to calculate the propensity score (i.e. the probability

of linking to PRO) Matching methods on the propensity scores (i.e. matching

linked with unlinked firms with similar probabilities of linking) Four methods used: Nearest neighbour, Caliper, Kernel, Radius

Calculation of the average treatment effect (i.e. difference in an outcome variable -e.g. innovative expenditures- between linked firms and their twins) for different matching methods

V Linking actively We identify four modes of interactions that involve

active participation of firms (bi-directional linkages)

Average importance 0-1 scale

% of linked firms with importance

>0.5

Informal Exchange 0.58 51%

Publications 0.56 47%

Conferences 0.54 46%

Hiring graduates 0.44 27%

Consultancies 0.44 26%

Research contracts 0.42 26%

Joint R&D 0.42 25%

Licences 0.38 16%

Networks 0.37 15%

Patentes 0.37 15%

Scientific parks 0.35 12%

Internships 0.34 10%

Incubators 0.30 5%

University owned firms 0.27 3%

Spin off 0.27 2%

Any active mode 0.36 37%

37% of linked firms considered at least one of these modes as moderately or highly important

V Determinants of linking actively

marginal

effects 0.355*

Skills Professionals over total employment [0.192]

0.022** Size

Deciles based on employment for the full sample [0.010]

0.819 IA_sector

Innovative activities over sales for the sector full sample [2.323]

0.006** Linked_act_sector

Quantity of actively linked firms in the sector [0.002]

0.085 Link_vert Vertical linkage

[0.060] 0.018

Link_int Linkage within the firm network [0.058]

Observations 354 Pseudo R-squared 0.04 Wald 18.51***

Robust standard errors in brackets * significant at 10%; ** significant at 5%; *** significant at 1%

Source: The Survey 2005 & The Survey 2006

Firms that link actively have more sophisticate knowledge bases

VI Average effect of linkingOutcome variables

Variable group Variable Name Type of dataIA_sales Ratioimaq_sales Ratioinhouse_sales Ratioinn_prod Dummyinn_proc Dummy

c) Appropriability behaviour

patent1-4 scale normalised to 0-1

fin_int Percentage

fin_pro Percentage

import_pro_pub import_pro_protimport_pro_techimport_pro_lab

1-4 scale normalised to 0-1

role_pro_edurole_pro_resrole_pro_socrole_pro_entr


a) Innovative behaviour

b) Innovative outcome

d) Sources of financing innovation

e) Perception of PRO (outcomes and roles)

VII Average effect of linking activelyOutcome variables

The same outcome variables plus:

Variable group Variable Name Type of data

f) Goals of the interaction

goal_absgoal_contrgoal_supplgoal_hrgoal_capgoal_k


g) Firms' payment for linking

pay_abspay_contrpay_supplpay_hrpay_cappay_k

Dummies

VII Average effect of linking activelya) Innovative behaviour

Actively linked firms (ALF) invest 1.4% of sales in in-house innovative activities, which is significantly more than Non-actively linked firms (NALF)

They invest as much in in-house as in machinery

ATTNearest Neighbour 0.0258 0.0211 0.0316 0.0105

Kernel (normal) 0.0258 0.0237 0.0316 0.0079Radius 0.0258 0.0223 0.0321 0.0098Caliper 0.0258 0.0212 0.0321 0.0109

Nearest Neighbour 0.0161 0.0129 0.0133 0.0004Kernel (normal) 0.0161 0.0149 0.0133 -0.0016

Radius 0.0161 0.0136 0.0136 0.00005Caliper 0.0161 0.0133 0.0136 0.0004

Nearest Neighbour 0.0060 0.0064 0.0139 0.0075*Kernel (normal) 0.0060 0.0056 0.0139 0.0083**

Radius 0.0060 0.0057 0.0143 0.0086**Caliper 0.0060 0.0062 0.0143 0.0081*

AI_vtas

inhouse_vtas

imaq_vtas

a) Innovative behaviour

Variables Weight methods

All non-active firms before

matching (NALF)

NALF after matching (control

group)

Actively linked firms (ALF)

Difference of means of ALF - NALF firms

Means

VII Average effect of linking activelyb) Innovative outcome

No difference was found.

Data about linkages and innovation refer both to 2005. Time lags

ATTNearest Neighbour 0.5000 0.5615 0.6077 0.0462

Kernel (normal) 0.5000 0.5367 0.6077 0.0710Radius 0.5000 0.5320 0.6032 0.0712Caliper 0.5000 0.5635 0.6032 0.0397

Nearest Neighbour 0.5357 0.5308 0.5923 0.0615Kernel (normal) 0.5357 0.5642 0.5923 0.0281

Radius 0.5357 0.5549 0.5794 0.0245Caliper 0.5357 0.5317 0.5794 0.0476

inn_prod

inn_proc



Means



matching (NALF)


group)

b) Innovative outcome

VII Average effect of linking activelyc) Appropriability behaviour

ALF are more prone to patenting. 8% of ALF obtained at least one patent, while only around 1.5%-3.5% of twins obtain patents.

ALF are in a privileged position to access public K. Concerns about privatisation of K (Nelson 2004)

ATTNearest Neighbour 0.0357 0.0154 0.0769 0.0615**

Kernel (normal) 0.0357 0.0355 0.0769 0.0414Radius 0.0357 0.0295 0.0794 0.0498*Caliper 0.0357 0.0159 0.0794 0.0635*

c) Appropriability behaviour

Variable Weight methods


matching (NALF)


group)



Means

patent

VII Average effect of linking activelye) Goals of the interaction

ALF consider all goals more important than NALF

The difference is the most important for goals_k and goals_contr

The difference is the least important for goals_cap and goals_hr

This confirm the active nature of ALF: they value K of PRO and they link to contribute rather than to supplement

ATTNearest Neighbour 0.3549 0.3585 0.4942 0.1357***

Kernel (normal) 0.3549 0.3577 0.4942 0.1365***Radius 0.3549 0.3567 0.5000 0.1433***Caliper 0.3549 0.3620 0.5000 0.138***

Nearest Neighbour 0.3359 0.3547 0.5543 0.1996***Kernel (normal) 0.3359 0.3448 0.5543 0.2095***

Radius 0.3359 0.3628 0.5500 0.1872***Caliper 0.3359 0.3540 0.5500 0.196***


Radius 0.3158 0.3244 0.4760 0.1516***Caliper 0.3158 0.3280 0.4760 0.148***


Radius 0.4012 0.4200 0.5660 0.1460***Caliper 0.4012 0.4550 0.5660 0.111***


Radius 0.4397 0.4580 0.5907 0.1327***Caliper 0.4397 0.4673 0.5907 0.1233***


Radius 0.3304 0.3361 0.5370 0.2009***Caliper 0.3304 0.3450 0.5370 0.192***

goals_cap

goals_k

goals_abs

goals_contr

goals_suppl

goals_hr

e) Goals of interaction



matching (NALF)


group)



Means

VII Average effect of linking activelyf) Payment (only important goals)

ALF more often pay for they goals they seek than NALF

This may imply that ALF collaborate more formally with NALF

Exception: using PRO’s infrastructure (service provision), around 60% of both ALF and NALF pay (no sig. diff)

ATTNearest Neighbour 0.1186 0.0986 0.2113 0,1127*

Kernel (normal) 0.1186 0.1044 0.2113 0,1069*Radius 0.1186 0.0790 0.1935 0,1145*Caliper 0.1186 0.0806 0.1935 0.1129

Nearest Neighbour 0.1111 0.1084 0.4096 0,3012***Kernel (normal) 0.1111 0.1196 0.4096 0,2901***

Radius 0.1111 0.1451 0.3836 0,2385***Caliper 0.1111 0.0959 0.3836 0,2877***

Nearest Neighbour 0.1628 0.1364 0.3485 0,2121**Kernel (normal) 0.1628 0.1771 0.3485 0,1714**

Radius 0.1628 0.1926 0.3214 0.1289Caliper 0.1628 0.1607 0.3214 0.1607

Nearest Neighbour 0.2566 0.2427 0.4563 0,2136***Kernel (normal) 0.2566 0.2593 0.4563 0,1970***

Radius 0.2566 0.2506 0.4444 0,1938**Caliper 0.2566 0.2424 0.4444 0,2020***

Nearest Neighbour 0.5664 0.5364 0.6364 0.1Kernel (normal) 0.5664 0.5750 0.6364 0.0614

Radius 0.5664 0.5736 0.6214 0.0478Caliper 0.5664 0.5243 0.6214 0.0971

Nearest Neighbour 0.1250 0.1915 0.3723 0,1809*Kernel (normal) 0.1250 0.1292 0.3723 0,2431***

Radius 0.1250 0.1068 0.3614 0,2546***Caliper 0.1250 0.1084 0.3614 0,2530***

pay_cap

pay_k

pay_abs

pay_contr

pay_suppl

pay_hr

f) Firm's payment for linking



matching (NALF)


group)



Means

VIII) Conclusions2nd goal: to compare the characteristics of bi-directional linkages (actively linked firms - ALF) and unidirectional linkages (NALF)

i. ALF higher intensity of investments in in-house innovative activities than NALF. ALF invest more or less the same proportion in incorporated and in-house technologies.

ii. ALF are particularly prone to protecting their intellectual property. Issues of concern about privatisation of public knowledge

iii. ALF value more largely all goals of cooperation. iv. Low incidence of formal payment for all interactions. ALF are

more likely to pay when interacting with PRO: more formal interactions? Especially, for HR at PRO.

v. There are better opportunities for PRO when interacting with ALF than with NALF because more knowledge value is involved

ALF invest more heavily in in-house innovative activities ALF value particularly knowledge resources exclusively available

in PRO rather than other inputs that could be found in other service providers.

ALF interact with PRO to contribute rather than to supplement innovative activities

VIII) ConclusionsMain findings for policy making Bi-directional interactions are more likely to optimise

risks and benefits of PRO-I linkages There is more knowledge value involved in interactions

with ALF than with NALF There is more learning potential for PRO. They invest their

time more productively: they could reach better research outputs and find sources of inspiration for new lines of research

Incentives should be created to promote bi-directional linkages while restricting unidirectional ones.

Linked firms, and ALF in particular, seem to be more prone to using IPR

Patenting by PRO is cumbersome Issues of concern about the privatisation on publicly

created knowledge. S&T policy must avoid tragedy of the scientific commons (Nelson 2004)

public research organisations (pro) – industry interactions in developing countries: insights...

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