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Cost-Benefit Approach to Public Support of Private R&D Activity. Bettina Peters Centre for European Economic Research (ZEW) DIMETIC Doctoral European Summer School Pecs, July 14, 2010. Part II: Econometrics of Evaluation of Public Funding Programmes. Motivation.

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    1. Cost-Benefit Approach to Public Support of Private R&D Activity Bettina Peters Centre for European Economic Research (ZEW) DIMETIC Doctoral European Summer School Pecs, July 14, 2010

    2. Part II:Econometrics of Evaluation of Public Funding Programmes

    3. Motivation • Public support of private R&D 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 R&D 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 R&D support: • What is the effect of an R&D subsidy on the subsidized firm‘s R&D expenses (input)? or • Or the impact on other variables like patent applications, firm growth, employment etc. (output)

    5. Different Effects of R&D 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 R&D 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(YT|S = 1) can be estimated by the sample mean of Y in the group of treated (subsidized) firms • Problem: The counterfactual situation E(YC|S = 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 R&D 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 R&D 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 R&D expertise, • i.e. firms with high high R&D in the past, professional R&D management, good success with their other R&D 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 R&D 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 R&D expenses in t and t-1. • ATT could be estimated based on the average difference of R&D of treated firms in t (Yit) and the R&D 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. The DiD estimator is based on a „before-and-after“ comparison of subsidized firms and a non-subsidized control group. Difference-in-Difference Estimator 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!) Disadvantage: - strategic behavior of firms to enter programs would lead to biased estimates („Ashenfelter‘s 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 R&D 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 + b2*ability (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 R&D 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 Heckman‘s work, 1974, 1976, 1979): • estimate the propensity to get an R&D 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 R&D expenditure of publicly funded firms, which they would have conducted if they had not received public R&D funding, from the expected R&D 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 => 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. wij w12 w11 xi - xj Matching • Treatment effect for firm i: • Two common matching estimators: • Nearest Neighbor: wij=1 for the most similar firm, zero otherwise => 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 Silverman‘s 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) • Ω 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 London

    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-Difference • 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 R&D 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, CIS3+4, 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 R&D subsidies on R&D Expenditure Using Matching Estimators Schmidt and Aerts (2008), Two for the price of one? Additionality effects of R&D subsidies: A comparison between Flanders and Germany, Research Policy37 (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 R&D expenditure? • Nearly all empirical studies reject the hypothesis of a total crowding out (i.e. no change in total private R&D 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 R&D 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 R&D expenditure? • Crowding in effects: Public R&D subsidies stimulates net R&D expenditure (total R&D 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 R&D employees but not the number of R&D 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 R&D 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 R&D and publicly funded R&D increase patenting output. Subsidized R&D is a little less productive, though.

    45. Effects on Innovation Behaviour(Behavioural Additionality) • Example: Current practice in Europe is to support research consortia (firms+firms / 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. R&D collaborations in Germany and Finland • R&D collaboration achieves R&D 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. 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 R&D 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)

    47. More Literature • NBER Summer Course in Econometrics by Guido Imbens and Jeff Wooldridge • • Includes videos of lectures and extensive lecture notes • Survey by Imbens and Wooldridge (2009) • • published in Journal of Economic Literature

    48. Part III:Simple Cost-Benefit Approach to Public Support of Private R&D Activity

    49. State Aid for R&D&I Community Regulation Dec. 30, 2006 • „State aid for R&D&I shall be compatible if the aid can be expected to lead to additional R&D&I 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 R&D&I“ • “Negative effects of the aid to R&D&I must be limited so that the overall balance is positive”.