OECD workshop on measuring the link between public procurement, R&D and innovation. "Demand side vs....

Demand side vs. supply side technology policy:hidden treatment and new empirical evidence on

the technology policy mix

Marco Guerzoni and Emilio Raiteri

Department of Economics and Statistics “Cognetti de Martiis”, University of Turin

OECD Expert Workshop, Paris, Dec 5-6

Marco Guerzoni and Emilio Raiteri Innovative public procurement and R&D Subsidies

Introduction

AimsIn this work we aim at analyzing the simultaneous effect of technologypolicies (public R&D subsidies, tax credits, and innovative publicprocurement) on firms’ innovative behavior.

Results

Strong result: without controlling for simultaneity of other policies,the impact of a single policy is overestimated.

Weak result: the impact of a mix of policies is stronger than thesum of the effects of the policy in isolation.


Introduction

AimsIn this work we aim at analyzing the simultaneous effect of technologypolicies (public R&D subsidies, tax credits, and innovative publicprocurement) on firms’ innovative behavior.

Results

Strong result: without controlling for simultaneity of other policies,the impact of a single policy is overestimated.

Weak result: the impact of a mix of policies is stronger than thesum of the effects of the policy in isolation.


The impact of public R&D subsidies

Do public subsidies displace private efforts in R&D, simply add to themor favor their increase?

Long deabated question in the literature with no conclusive answer

Some evidence of a crowding out effect (Shrieves, 1978; Carmichael,1981; Higgins, 1981)

Some evidence of a reinforcing effect (Holemans, 1988; Link, 1982;Antonelli, 1989 ).

Reviews of the econometric evidence

Garcia-Quevedo(2004) does a review of 74 results: Complementarityvs. substitutability: 38-17 (19 insignificant).

David et al. (2000) discuss methodological issues: different source ofendogeneity.


The impact of public R&D subsidies II

Selection biasSelection bias may have a dual cause:

Public institutions may cherry-pick winners on the basis of somepeculiar characteristics (e.g. more innovative firms)

Firms may possess information/search capability advantage (e.g.larger firms may have devoted staff)

Literature addressing the selection bias issue:

Almus and Czarnitzki (2003) design a quasi-experimental setting,applying propensity score matching to reduce selection bias

Rejects the crowding out hypothesis and provides some evidence ofa positive impact of R&D policies on private investments.

Thereafter numerous works applied the same methodology.


The impact of public R&D subsidies III

Possible drawback in this literature:”hidden treatment” as a confoundingfactor

Well known problem in clinical research:

Various compounds are administered to a patient at the same time,as a cure fore the same disease.

Is it then possible to evaluate the effect of a specific treatmentwithout considering the others?

In our case a hidden treatment can arise from the presence, in asystem of innovation, of other technology policies designed tostimulate private R&D.

Suitable suspects → R&D tax credits and Innovative publicprocurement (IPP)


Innovative Public Procurement

IPP as a technology policy

Def: The purchase of new technologies, innovative products andservices by public institutions

Geroski (1990), Dalpe (1994), Edler & Georghiou (2007),Edquist&Zabala(2012): need for demand oriented innovation policy

Barcelona Strategy, Europe 2020 Flagship Initiative: InnovationUnion (European Commission, 2010), Oecd (2011)

Empirical evidence:

Lichtenberg (1988): public procurement has a positive effect on afirm’s propensity to engage in R&D.

Aschhoff and Sofka (2009) find robust impact of IPP and no impactfor R&D grants on innovation output


Research questions

Summing up:

Literature investigating the impact of R&D subsidies on privateinvestments in R&D, controlling for selection bias, finds areinforcement effect (Almus and Czarnitzki, 2003).

Theoretical and limited empirical literature on IPP suggest a robustand positive impact of IPP on private investments in R&D.

Our goals:

To test the robustness of the results on the impact of R&D subsidieswhen also innovative public procurement is taken into account.

To provide new empirical evidence on the effects of innovativeprocurement and of its interactions with other technology policies.


Research questions

Summing up:

Literature investigating the impact of R&D subsidies on privateinvestments in R&D, controlling for selection bias, finds areinforcement effect (Almus and Czarnitzki, 2003).

Theoretical and limited empirical literature on IPP suggest a robustand positive impact of IPP on private investments in R&D.

Our goals:

To test the robustness of the results on the impact of R&D subsidieswhen also innovative public procurement is taken into account.

To provide new empirical evidence on the effects of innovativeprocurement and of its interactions with other technology policies.


Data and method

Data

Cross-sectional dataset from Innobarometer on Strategic trends ininnovation 2006-2008 survey, conducted in the 27 Member States ofthe EU, Norway and Switzerland.

The project surveyed 5238 companies with more than 20 employeesin a large selection of sectors.

Senior company managers responsible for strategic decisions makingof 5238 company were interviewed

Firms have been asked about any public procurement contracts theyhave been awarded and whether this procurement was innovative ornot.


Empirical strategy

Quasi-experimental framework:

R&D public subsidies, tax credits, and IPP as treatment variables.

Increase in private R&D expenditures as the measured outcome.

Propensity score matching to mitigate the endogeneity problem.

The average treatment effect will be recovered by taking thedifference in averages between treatment and control group.

Data problems.


Empirical strategy

Procedural confounding

To tackle this issue we design 10 different treatments:subsidies, taxcredits, IPP, subsidies only, credits only, IPP only, 3 treatments forany combination of two of them, all of them.

First 3 treatments do not take into account potential policiessimultaneity → procedural confounding. Each has different controlgroup.

The other 7 treatments take into account possible interactions →eliminate (reduce) potential confounding. One control group.

Research questions

Are estimations retrieved for the first three treatments going in thesame direction along the evidence provided so far?

If yes, is there a significant difference in the estimates recoveredwhen reducing the procedural confounding?


Treatment and controls

Tabelle : Loss of observations due to common support and caliperrequirement

Treated group Control group Loss & Caliper (%)Treatment vulnerable to confounding

Policy Subsidies 1141 3863 81 (7.0)

Policy Procurement 573 4453 44 (7.6)

Policy Tax Credits 1113 3789 91 (8.1)


Treatment and controls

Tabelle : cont.Treated group Control group Loss & Caliper (%)

Treatment in isolation

Policy Subsidies only 473 2804 29 (6.1)

Policy Procurement only 284 2804 21 (7.3)

Policy Tax credit only 492 2804 41 (8.3)

Simultaneous treatments

Policy Sub Tax 414 2804 29 (7.0)

Policy Sub IPP 86 2804 7 (8.1)

Policy IPP Tax 75 2804 5 (6.6)

Policy All 86 2804 6 (6.9)


Propensity score matching estimator results

Difference in the % of firms increasing R&D

Sample R&D grants T.credit IPP R&D Only T.credit only IPP only

ATT 0.069*** 0.064*** 0.112*** 0.029 0.043 0.066*Unmatched 0.088*** 0.107*** 0.155*** 0.35 0.07*** 0.084***

Sample R&D + t. credits IPP + t. credits R&D + IPP all policies

ATT 0.107*** 0.267*** 0.173*** 0.147**Unmatched 0.112*** 0.310*** 0.257*** 0.270***





ATT 0.069*** 0.064*** 0.112*** 0.029 0.043 0.066*Unmatched 0.088*** 0.107*** 0.155*** 0.35 0.07*** 0.084***


ATT 0.107*** 0.267*** 0.173*** 0.147**Unmatched 0.112*** 0.310*** 0.257*** 0.270***





ATT 0.069*** 0.064*** 0.112*** 0.029 0.043 0.066*Unmatched 0.088*** 0.107*** 0.155*** 0.35 0.07*** 0.084***


ATT 0.107*** 0.267*** 0.173*** 0.147**Unmatched 0.112*** 0.310*** 0.257*** 0.270***





ATT 0.069*** 0.064*** 0.112*** 0.029 0.043 0.066*Unmatched 0.088*** 0.107*** 0.155*** 0.35 0.07*** 0.084***


ATT 0.107*** 0.267*** 0.173*** 0.147**Unmatched 0.112*** 0.310*** 0.257*** 0.270***


Main findings

Main findings:

Results retrieved from the treatments vulnerable to confoundingeffect are coherent with the evidence provided in the literature.

When we consider policy tool in isolation, the positive impact ofR&D subsidies on R&D investments reduces and ceases to besignificant → IPP proves to be a crucial confounding factor and tohave robust impact on innovative inputs.

If consider in isolation, IPP is still significant.

The convexity of the policy tools ? → evidence points in thatdirection but possible selection bias


Conclusions

Policy perspective:

Past results have been overestimated.

Our results recommend to carefully consider the interaction amongdifferent tools in composing technology policy mixes.

Data collection for evaluation should consider this issue.

IPP may represent an effective way to foster firms’ innovativeactivities and to reinforce positive effects of R&D subsidies,stimulating additional expenses in R&D.

Push and Pull strategies should be considered at the same time.


Thank you for your attention![[email protected]]


Propensity score distributions before and after matching


Empirical strategy II

Selection bias

To reduce the selection bias we follow Almus and Czarnitzki (2003)who brought in innovation policy studies non-parametric matching.

We apply propensity score matching (Rosenbaum and Rubin, 1983)to find a group of non-treated individuals that are similar to thetreated ones in all relevant pre-treatment characteristics.

Once the matching is implemented treated and control units shouldbe on average observationally identical. An unbiased estimation ofthe average treatment effect should be retrieved by:

PsmATT = Ep(x)|T{E [Y T |T , Pr(X)]− E [Y C |C , Pr(X)]} (1)


Propensity score specification

Estimation of the propensity scores

To recover the propensity score for every treatment we run differentprobit and multinomial logistic regressions of the treatmentvariables on a set of relevant covariates.

Following Caliendo and Kopeinig (2008) the following variables areincluded: Size of the firm, young firm dummy, in house R&Ddepartment dummy, location of the firm’s core market, country oforigin of the firm, sector dummies.

Once the propensity scores are recovered, we use these measures toproceed to matching and then to PSM estimations.

The most straightforward way to asses the quality of the matchingis through a graphic analysis of PS density distributions before andafter the matching


Propensity score distributions before and after matching

Policy Procurement Policy R&D Policy Procurement Only Policy R&D Only


Date post:	29-Nov-2014
Category:	Government & Nonprofit
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OECD workshop on measuring the link between public procurement, R&D and innovation. "Demand side vs....

Government & Nonprofit