Cost-Benefit Approach to Public Support of Private R

1
Cost-Benefit Approach to Public Support of
Private RD Activity
Bettina Peters Centre for European Economic
Research (ZEW) b.peters_at_zew.de
DIMETIC Doctoral European Summer School Pecs,
July 14, 2010
2
Part IIEconometrics of Evaluation of Public
Funding Programmes
3
Motivation

• Public support of private RD activity is not
  without cost either crowding-out may occur!
• Once subsidies are available, companies have an
  incentive to apply for any project (even for the
  ones which are also privately profitable) as
  subsidy comes at marginal cost equal to zero.
• Subsidies may not only stimulate the projects
  with high social return.
• In the worst case (total crowding out), private
  funding is simply replaced with public funding.
• growing literature about evaluation of RD
  programmes

4
The Evaluation Problem

• The aim of quantitative methods of evaluation is
  the measurement of effects generated by policy
  interventions on certain target variables
• We are interested in the causal effect of a
  treatment 1 relative to another treatment 0 on
  the outcome variable Y.
• In case of public RD support
• What is the effect of an RD subsidy on the
  subsidized firms RD expenses (input)? or
• Or the impact on other variables like patent
  applications, firm growth, employment etc.
  (output)

5
Different Effects of RD subsidies
Self-assessment by companies (259 subsidized
German companies in 2001)
Source Czarnitzki et al. (2001)
6
The Evaluation Problem

• In most cases, we are interested in the average
  treatment effect on the treated (TT)
• TT the difference between the actual observed
  value of the subsidzed firms and the
  counterfactual situation
• Which average value of RD expenditure would
  the treated firms have shown if they had not
  been treated

S Status of group, 1 Treatment group 0
Non-treated firms YT outcome in case of
treatment YC outcome of the treated firm in
the case it would not have received the subsidy
(counterfactual situation)
7
The Evaluation Problem

• Actual outcome E(YTS 1) can be estimated by
  the sample mean of Y in the group of treated
  (subsidized) firms
• Problem The counterfactual situation E(YCS 1)
  is never observable and has to be estimated!
• ? How to do estimate the counterfactual?

8
The Evaluation Problem

• Naive estimator for ATT Use the average RD
  expenditure of non-subsidized firms assuming that
• Assumption is justified in an experiment where
  subsidies are given randomly to firms.
• In real life, however, it is likely that funded
  firms are typically not a random sample, but are
  the result of an underlying selection process
• Subsidized firms differ from non-subsidized firms
• It is likely that the Subsidized firms differ
  from non-subsidized firms and that the subsidized
  companies would have spent more on RD than the
  non-subsidized companies even without the subsidy
  program.

9
The Evaluation Problem

• Policy makers want to maximize the probability of
  success and thus try to cherry-pick firms with
  considerable RD expertise,
• i.e. firms with high high RD in the past,
  professional RD management, good success with
  their other RD projects or experienced in
  applying for public funding will be preferably
  selected.
• Selection bias in the estimation of the treatment
  effect.
• We cannot use a random sample of non-treated
  without any adjustment.
• As the highest expected success is correlated
  with current RD spending, subsidy becomes an
  endogenous variable (depending on the firms
  characteristics).
• Solution in non-experimental settings
  Microeconometric evaluation methods (surveys of
  Heckman et al., 1999 Blundell and Costa-Dias,
  2000, 2002).

10
Microeconometric Evaluation Methods

• Before-after comparison panel data
• Difference-in-difference estimator (DiD) panel
  data
• Instrumental Variables estimator (IV)
  cross-sectional data
• Selection models cross-sectional data
• Matching methods cross-sectional data
• Mixed Method Conditional difference-in-difference
  combines the DiD estimator and matching methods
  panel data

11
Before-After Comparison

• Suppose firm i got funding in period t, and we
  observe RD expenses in t and t-1.
• ATT could be estimated based on the average
  difference of RD of treated firms in t (Yit) and
  the RD of the same firm in the previous period
  where it did NOT receive a treatment Yi,t-1.
• Requires panel data
• Allows to control for individual fixed effects,
  but not for macroeconomic shocks

12

• Difference-in-Difference Estimator

The DiD estimator is based on a
before-and-after comparison of subsidized firms
and a non-subsidized control group.
Advantage - no functional form for outcome
equation required - not even a regressor is
needed - controls for common macroeconomic
trends - controls for constant
individual-specific unobserved effects NOTE
when covariates should be included, one can
estimate an OLS model in first differences. (but
functional form assumption necessary!) Disadvantag
e - strategic behavior of firms to enter
programs would lead to biased estimates
(Ashenfelters dip) - panel data required
including observations BEFORE AND AFTER (or
WHILE) treatment - biased if reaction to
macroeconomic changes differs between groups
(1) and (0) - problem to construct data if RD
subsidies show high persistence
13
Instrumental Variables (IV) Estimators

• Suppose y b0 b1 x1 u
• We think that x1 is endogenous, i.e. COV(x,u)!0.
• e.g. wage equation
• wage may depend on education and ability.
• But we only observe x1 education.
• Then, u v b2ability (where v is a new error
  term, b2 is the coefficient of ability)
• OLS would be inconsistent as it relies on
  COV(x,u)0.
• Suppose we have an instrument w, that fullfils
  two requirements
• w is uncorrelated with the error term u ?
  COV(w,u)0, i.e.(i.e. z should have no partial
  effect on y once we control for x1)
• and w is correlated with the endogenous variable
  x, i.e. COV(w,x)!0.
• IV estimator

14
Instrumental Variables (IV) Estimators

• The recent utilization of IV estimators in
  context of evaluation goes back to Imbens/Angrist
  (1994) and Angrist et al. (1996) who invent the
  Local Average Treatment Effect (LATE).
• IV estimators have the advantage over selection
  models that one does not have to model the
  selection process and to impose distributional
  assumptions.
• Main disadvantage need of an instrument, whose
  requirement are more demanding than those for the
  exclusion restriction in selection models.
• Instruments can be
• other variables (external instruments, often
  hard to find and justify)
• lagged values of endogenous variables (requires
  panel data)
• In the case of RD it is very difficult to find
  valid instruments.

15
Selection models

• control function approach
• Selection models are based on a two step
  procedure (based on Heckmans work, 1974, 1976,
  1979)
• estimate the propensity to get an RD subsidy for
  all firms
• estimate outcome equation for participants and
  non-participants including acorrection for a
  possible selection mechanism,

16
Selection models

• Under the assumption of joint normality, we can
  estimate
• Madalla (1983) ATT is determined by subtracting
  the estimated RD expenditure of publicly funded
  firms, which they would have conducted if they
  had not received public RD funding, from the
  expected RD expenditure of funded firms. The
  difference is augmented by the selection
  correction

17
Selection models

• Advantage
• Controls for unobserved characteristics (entering
  the first- and second-step equation).
• Root-N-consistency
• Disadvantage
• Restrictive distributional assumption on the
  error terms (joint normality).
• An exclusion restriction is needed which is
  included in the selection equation but not in the
  structural equation to identify the treatment
  effects.
• A fully parametric model for the selection and
  for the structural equation has to be defined.

18
Semiparametric Selection Models

• Semiparametric estimators Gallant and Nychka
  (1987), Cosslett (1991), Newey (1999), or
  Robinson's (1988) partial linear model.
• Semiparametric estimators identify only the slope
  parameters of the outcome equation. Intercept in
  outcome equation is no longer identified, but
  required for deriving ATT
• An additional estimator for the intercept is
  needed to identify the treatment effects, e.g.
  Heckman (1990),Andrews and Schafgans (1998).
• See Hussinger (2008) for applications of such
  estimators for the evaluation of innovation
  policy.

19
Matching

• Ex post mimic an experiment by constructing a
  suitable control group by matching treated and
  non-treated firms
• Selected control group is as similar as possible
  to treatment group in terms of observable
  characteristics.
• Matching is a nonparametric method to identify
  the treatment effect

20
Matching

• A1 Conditional independence assumption (CIA)
  (Rubin 1974, 1977) All the relevant differences
  between the treated and non-treated firms are
  captured in their observable characteristics
• gt For each treated firm, search for twins in the
  potential control group having the same
  characteristics, X, as the subsidized firms.
• A2 We observe treated and non-treated firms with
  the same characteristics (common support)
• Under these assumptions, the ATT can be
  calculated as

21
Matching

• Treatment effect for firm i
• Two common matching estimators
• Nearest Neighbor wij1 for the most similar
  firm, zero otherwise gt only one control
  observation is used
• Kernel-based entire control group is used for
  each treated firm, weights wij are determined
  by a kernel that downweights distant
  observations from Xi.

22
Kernel-Based Matching

• Weights are the kernel density at Xj - Xi
  (rescaled that they sum up to 1)
• Often the Gaussian kernel or the Epanechnikov
  kernel is used,
• Calculation of counterfactual requires
  kernel-regression (e.g. Nadaraya-Watson
  estimator)
• locally weighted average of the entire control
  group (for each treated firm)

23
Kernel-Based Matching

• Bandwith h may be chosen according to Silvermans
  rule of thumb
• with k number of arguments in the matching
  function
• If you want to include more than a single X in
  the matching function, you can use the
  Mahalanobis distance

24
Propensity Score

• Usually X contains many variables which make it
  almost impossible to find control observations
  that exactly fit those characteristics of the
  subsidized firm.
• Rosenbaum and Rubin (1983) showed that it is
  possible to reduce X to a single index - the
  propensity score P - and match on this index.
• It is possible to impose further restrictions on
  the control group, e.g.that a control
  observations belongs to the same industry or same
  region etc.

25
A NN Matching Procedure

• Specify and estimate probit model to obtain
  propensity scores
• Restrict sample to common support
• Delete all observations on treated firms with
  propensity scores larger than the maximum and
  smaller than the minimum in the potential control
  group.
• Do the same step for other variables that are
  possibly used in addition to the propensity score
  as matching argument.
• Choose one observation from sub sample of treated
  firms and delete it from that pool
• Calculate the Mahalanobis distance between this
  treated firm and all non-subsidized firms in
  order to find the most similar control
  observation.
• Z contains the matching arguments (propensity
  score and/or additional variables such as e.g
  industry or size classes)
• O is the empirical covariance matrix of the
  matching arguments based on the sample of
  potential controls

26
A NN Matching Procedure

• Select observation with minimum distance from
  potential control group as twin for the treated
  firm
• NN matching with replacement selected controls
  are not deleted from the set of potential control
  group so that they can be used again
• NN matching without replacement selected
  controls are deleted from the set of potential
  control group so that they cannot be used again
• Repeat steps 3 to 5 for all observations on
  subsidized firms
• The average effect on the treated mean
  difference of matched samples
• With YC_hat being the counterfactual for firm i
  and nT is the sample size of treated firms.
• Sampling with replacement ? ordinary t-statistic
  on mean differences is biased (neglects
  appearance of repeated observations) ? correct
  standard errors Lechner (2001) ? estimator for
  an asymptotic approximation of the standard
  errors

27
Matching in Stata

• Psmatch2.ado
• Software and documentation from Barbara Sianesi
  and Edwin Leuven, IFS Londonhttp//www.ifs.org.uk
  /publications.php?publication_id2684
  http//ideas.repec.org/c/boc/bocode/s432001.html

28
Disadvantages of Matching

• It only allows controlling for observed
  heterogeneity among treated and untreated firms
  (in observable cahracteristics in X)
• Common support is necessary, that is, the range
  of the propensity score of the control group must
  cover the treatment group.
• If the common support is rather small in your
  data, matching is not applicable

29
Mixed method Conditional Difference-in-Differenc
e

• Conditional difference-in-difference (DiD) method
  for repeated cross-sections, which combines
  ordinary DiD estimation with matching
• The Conditional DiD estimator consists of
  matching firms i and j with the same observable
  characteristics X_i,t0 X_j,t0 where i receives
  treatment in t1 but not in t0 and j is a
  non-treated firm in both periods.
• Heckman et al. (1998) show that CDiD based on
  non-parametric matching proved to be a very
  effective tool in controlling for both selection
  on observables and unobservables.

30
Microeconometric Evaluation Methods

• Before-after comparison panel data
• Difference-in-difference estimator (DiD) panel
  data
• Instrumental Variables estimator (IV)
  cross-sectional data
• Selection models cross-sectional data
• Matching methods cross-sectional data
• Mixed Method Conditional difference-in-difference
  combines the DiD estimator and matching methods
  panel data

31
Which method to use?

• The econometric method that you can apply heavily
  depends on the data you have
• Panel or cross-section?
• Is the treatment variable a binary indicator
  (yes/no) or is it a continuous treatment
  variable?
• Do I have candidates for instrumental variables?
• Do I want to make functional form assumptions of
  my RD investment equation?
• Do I want to specify a structural model or
  simultaneous equation system?

32
Empirical Studies

• Busom (2000), 154 obs., Spanish manufacturing,
  parametric selection model
• Wallsten (2000), 479 obs., US SBIR program,
  simultaneousequations model, 3SLS (incl. amount
  of funding)
• Czarnitzki (2001), 640 obs., Eastern German
  manufacturing, NN-Matching
• Czarnitzki/Fier (2002), 1,084 obs., German
  service sector, NN-Matching
• Fier (2002), 3,136 obs., German manufacturing
  (specific program), NN-Matching
• Lach (2002), 134 obs. Israeli manufacturing, DiD
  and dynamic panel models
• Almus/Czarnitzki (2003), 925 obs., Eastern German
  mf., NN-matching
• Gonzales et al. (2006), 2.214 obs. Spanish
  manufacturing, simultaneousequations model with
  thresholds
• Hussinger (2008), 3744 obs., German manufacturing
  sector 1992-2000, parametric and semiparametric
  selection models
• Schmidt and Aerts (2008), Germany and Flanders,
  CIS34, NN matching and CDID
• Surveys David et al. (2000 survey on
  crowding-out effects), Klette et al. (2000,
  including output analyzes like firm growth, firm
  value, patents etc.), Parsons and Phillips
  (2007), Aerts et al. (2007)

33
Example for Effect of RD subsidies on RD
Expenditure Using Matching Estimators

• Schmidt and Aerts (2008), Two for the price of
  one? Additionality effects of RD subsidies A
  comparison between Flanders and Germany, Research
  Policy 37 (5), 806-822
• Data German and Flemish Community Innovation
  Surveys (3 and 4)
• 2 methods
• Matching estimator and
• conditional DiD

34
Mean Comparison Before Matching
35
Mean Comparison Before Matching
36
Probit Estimations and Marginal Effects
37
Mean Comparison After Matching
38
Mean Comparison After Matching
39
Average Treatment Effects of the Treated
Companies
40
To sum up Does public funding stimulate or crowd
out private RD expenditure?

• Nearly all empirical studies reject the
  hypothesis of a total crowding out (i.e. no
  change in total private RD expenditure due to
  public funding).
• Exception Wallsten (2000) for US SBIR program
• Hypothesis of partial crowding out is also often
  rejected.
• David et al. (2000) At the macro level, only 2
  out of 14 studies yield a substitute relationship
  of public and private RD investment. At the firm
  level 9 out of 19.
• Czarnitzki et al. (2002) average multiplier
  effect of 1 which can be higher for specific
  groups

41
To sum up Does public funding stimulate or crowd
out private RD expenditure?

• Crowding in effects Public RD subsidies
  stimulates net RD expenditure (total RD exp.
  minus subsidy)
• Gonzales et al. (2006) multiplier effect for
  Spanish firms in 1990-1999 slightly above 1
• Fier et al. (2004) multiplier effect of 1,14 for
  German firms in 1990-2000 (varies according to
  technology fields)
• Hussinger and Czarnitzki (2004) multiplier
  effect of 1.44
• Parsons and Phillips (2007) average multiplier
  effect of 1.29 for surveyed studies
• Large variation in estimated multiplier effect,
  not surprising because funding schemes are
  different and have to be taken into account.

42
Extensions

• Heterogeneous treatments
• Effects on innovation output
• Effects on innovation behaviour

43
Heterogeneous Treatments

• So far, simply binary indicator (funded yes/no)
• Heterogeneous Treatments, e.g.
• Countinuous treatment
• Categorial treatment
• Countinuous treatment
• Hirano and Imbens (2005)
• different subsidies levels
• generalized propensity score (GPS) method for the
  estimation of so called dose-response functions.
• Categorial treatment
• Imbens (2000), Gerfin and Lechner (2002)
• divide treated firms in different groups, e.g.
  low subsidy and high subsidy
• distinguish between different policy programs.

44
Effects on Innovation Output (Output
Additionality)

• Subsidies may just increase wages of RD
  employees but not the number of RD personnel. If
  an increase in wages does not go along with
  higher research productivity, subsidies are
  likely to result in higher innovation input, but
  not necessarily in innovation output.
• Subsidized projects may be associated with higher
  risk than privately financed projects. If failure
  rates are higher, subsidies are likely to result
  in higher RD investment, but not necessarily in
  innovation output.
• Czarnitzki and Hussinger (2004) and Czarnitzki
  and Licht (2006) add patent equation to the input
  model. Both purely private RD and publicly
  funded RD increase patenting output. Subsidized
  RD is a little less productive, though.

45
Effects on Innovation Behaviour(Behavioural
Additionality)

• Example Current practice in Europe is to support
  research consortia (firmsfirms / firms
  scientific institutions) rather than giving
  subsidies to individual firms.
• Czarnitzki, Ebersberger and Fier (2007) apply
  Gerfin/Lechner methodology to investigate effects
  of subsidies vs. RD collaborations in Germany
  and Finland
• RD collaboration achieves RD input (and output)
  more than subsidies to individual firms
• AND there is room for fostering collaboration
  especially in Germany (Treatment effect on the
  untreated).

46
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47
Further challenges in research

• Output effect mainly measured in terms of patents
  (patents are an indicator of inventions, not
  necessarily of innovations)
• Alternative innovation out share of sales with
  new products (Hussinger 2008)
• Empirical Does collaborative RD funding result
  in collusion in product market?
• Overall welfare effect might be negative
• Specifities of policy schemes are not exploited
  in current research.
• Ideally, policy makers would like to know if a
  certain program design is more likely to prevent
  crowding-out effects than another.
• Selection equation is a reduced form estimation.
  Decision of the firm to apply and decision of the
  government to support a firm are not separately
  accounted for.
• Solution Structural models (see work by Otto
  Toivanen)

48
More Literature

• NBER Summer Course in Econometrics by Guido
  Imbens and Jeff Wooldridge
• http//www.nber.org/minicourse3.html
• Includes videos of lectures and extensive lecture
  notes
• Survey by Imbens and Wooldridge (2009)
• http//www.economics.harvard.edu/faculty/imbens/fi
  les/recent_developments_econometrics.pdf
• published in Journal of Economic Literature

49
Part IIISimple Cost-Benefit Approach to Public
Support of Private RD Activity
50
State Aid for RDI

• Community Regulation Dec. 30, 2006
• State aid for RDI shall be compatible if the
  aid can be expected to lead to additional RDI
  and if the distortion of competition is not
  considered to be contrary to the common interest,
  which the Commission equates for the purposes of
  this framework with economic efficiency
• To establish rules ensuring that aid measures
  achieve this objective, it is, first of all,
  necessary to identify the market failures
  hampering RDI
• Negative effects of the aid to RDI must be
  limited so that the overall balance is positive.

51
Cost-Benefit Analysis

• Empirical evidence that social returns to RD
  exceed private returns identifies market failure
  and thus provide a central argument in favour of
  direct or indirect public support of private RD
  activities.
• But only necessary but sufficient condition
• Public RD programmes are always associated with
  costs which go beyond the pure size of the
  subsidy
• Cost-Benefit-Analysis
• Evaluation of taxed-based RD support (RD tax
  credits) in Australia (Lattimore 1997),
  Netherlands (Cornet 2001a,b) and Canada (Parsons
  and Phillips 2007)
• Evaluation of direct project-based RD support in
  Germany (Peters, Kladroba, Licht, Crass 2009)

52
Cost-Benefit Analysis

• Basic idea
• Government supports RDI activities of firms in
  period t0 (size of the support P1 )
• No returns to RD in funding period (t0)
• Returns Rt accruing from RD from period t1
  onwards
• Compare net present value of benefits and costs
• A project is beneficial if C0 is larger than 0 or
  equivalently benefit/cost-ratio is larger than 1

53
Benefits

• Returns
• in period t1 R
• in period t2 R(1-d), where d is the
  depreciation rate on knowledge
• What is R?
• Returns R to RD are equal to the actual change
  of private RD expenditure times the average
  social rate of return s.
• Change of private RD expenditure depends on size
  of public support P and multiplier/crowding out
  effects m
• Further account for the fact that a proportion ?
  of the subsidies may just use to increase wages
  of RD employees but not to increase the amount
  of research that is undertaken.

54
Benefits

• Returns are discounted with discount factor i,
  consisting of
• the time preference rate r (reflecting e.g. the
  interest rate of risk-free investment) and
• the risk premium p (additional return a firm
  requires to invest in risky RD projects)
• Present value of the benefits of subsidizing
  private RD having a finite time-horizon of T
• having an infinite time-horizon

55
Benefits

• Alternative assumptions about multiplier effects
  m based on econometric evaluation studies
• 0 (total crowding out)
• 0.6 (strong crowding out)
• 0.9 (weak crowding out)
• 1.0
• 1.15 (weak crowding in, preferred conservative
  estimate)
• 1.3 (medium crowding in average estimated
  effect reported in the survey
• by Parson Philips 2007)
• 2 (strong crowding in)
• Social rate of return s based on spillover
  literature
• Assumption additionally publicly funded RD
  yields the same average social return
• Preferred assumption s0.5 (alternatives 0.15 /
  0.3 / 0.7 and 1.0)

56
Benefits

• Wage elasticity of labour ?
• Goolsbee (1998) based on data of 17,700 US
  scientists and engineers from the years 1968 to
  1994 he estimates that an increase of public RD
  funding by 11 increase wages on average by 3.3
  (wage elasticity varies between 2 and 6
  depending on educational background).
• Given that 2/3 of RD expenditure is for labour,
  they estimated the actual increase in research to
  be roughly 23 lower.
• Marey and Borghans (2000) 20-30 of increase in
  RD expenditures due to introduction of tax
  credits is related to higher wages
• Effect will depend on labour market specifities
• Preferred assumption 10 alternative
  assumptions 5, 20 and 30

57
Benefits

• Depreciation rate d
• d15 (alternatively 10, 20)
• Time preference rate r
• r3.5 (alternatively 5)
• Risk premium p
• p 3 (alternatively 5)
• Time horizon
• T15 (alternatively 5, 10, 20 years and infinite
  horizon)

58
Costs

• Different types of costs
• Direct programme costs (P) in period t0 (size of
  subsidy or forgone taxes)
• Administrative costs of government
• Administrative costs of firms
• Tax funding of subsidies induce a distortion of
  resource allocation ?welfare loss (marginal
  excess burden)
• Forgone returns of an alternative investment
• Present value of costs
• cs public administrative costs
• cu administrative costs of the firm
• tx macroeconomic costs of tax financing
• ß return to the alternative investment

59
Costs

• Administrative costs
• Till now scarcely evaluated Gunz et. al. (1997)
  and Parsons and Phillips (2007) for Canada
• Administrative costs of firms
• Administrative costs of the firms varies with the
  policy measure
• They are expected to be much lower with RD tax
  credits than with RD project funding (require
  less paperwork and entail fewer layers of
  bureaucracy)
• Proportion of the administrative costs decreases
  with absolute project size resp. absolute
  altitude of the tax abatement
• For project based funding 3-25 of support
  received (average 8)
• For fiscal funding 15 (10, 5) of the tax
  abatement if tax abatement (lt100,000 ,
  100,000-500,000 , gt500,000 )
• Basic specification cU8 (alternative
  assumptions 5, 10, 20)

60
Costs

• Administrative costs to government
• 1.7 related to the whole taxes foregone in case
  of tax credits
• 3-8 in case of project based funding
• Basic specification cS3 (alternative
  assumptions 2, 5, 10)
• Macroeconomic costs of tax financing
• Lattimore (1997) estimated welfare losses due to
  distortive effects of tax financing of public
  funding 15-50 of direct program costs
• Distortive effect depends on type of taxes raised
• Parsons and Phillips (2007) estimated a
  distortive effect of 27.
• Basic specification tx30 (alternative
  assumptions 15, 50)
• Return to the alternative investment
• Basic specification ß5

61
A Simple Cost-Benefit Approach to Public Support
of Private RD Activity
Benefit-to-Cost-ratio
Using the preferred parameter estimates, benefits
of public RD subsidies would exceed costs by
roughly 1.66
62
Multiplier, Social Benefits and the
Cost-Benefit-Relationship of Public RD Funding
area of probable combinations of multiplier
effects and social benefit rates
welfare gains
macroeconomic costs macroeconomic benefits
multiplier
welfare losses
social rate of return
63
Effect of Time Preference Rate, Risk Premium and
Depreciation Rate
Additional parameter assumptions m1.15, s0,5,
P1,cU0.08, cS0.03, tx0.3, ?0.1, ß0.05 and
T15. Source Peters et al. (2009)
64
Effect of Tax Distortion and Administrative Costs
Additional parameter assumptions m1.15, s0,5,
P1,r0.035, p0.03, d0.15, tx0.3, ?0.1,
ß0.05 and T15. Source Peters et al. (2009)
65
Effect of Wage Elasticity and Time Horizon
Additional parameter assumptions m1.15, s0,5,
P1,cU0.08, cS0.03, r0.035, p0.03,
d0.15,ß0.05 and tx0.3. Source Peters et al.
(2009)
66
Summary

• Using the preferred parameter estimates, benefits
  of public RD subsidies would exceed costs by
  roughly 1.66.
• Positive effects for a broad range of parameter
  values.
• Overall effect of public RD funding crucially
  depend on the amount of social returns to RD and
  multiplier effects.
• Even in case of low social returns to RD public
  funding might be beneficial, the likelihood
  increases with increasing multiplier effects.
• Crowding out effects do not necessarily imply a
  welfare loss (v.v.)
• E.g. strong crowding out effects (m0.6) could be
  compensated by an average social rate of return
  of 0.5.
• Other parameters are less important.
• Only a modest impact of time preference rate,
  risk premium and administrative costs.
• Moderate impact of depreciation rate, tax
  distortion and time horizon

67
Limitations

• Ideally, policy makers would like to know which
  program design is presumably the most efficient.
• Program-specific cost-benefit analysis would
  require program-specific estimates of model
  parameters (multipliers, social rates of return,
  )
• not yet available
• It is argued that there is presumably a
  trade-off
• Social benefits are expected to be higher for tax
  credits whereas multiplier effects are expected
  to be higher for public subsidies.

68
Back-up slide
69
Aid Intensities within EU State Aid Rules
Share of Public Funding in Total Project Costs