Public procurement as policy instrument for innovation

Transcription

1 Public procurement as policy instrument for innovation --- INCOMPLETE DRAFT VERSION --- by Dirk Czarnitzki a,b,c,*, Paul Hünermund a,c, Nima Moshgbar a,b a KU Leuven, Dept. of Managerial Economics, Strategy and Innovation (MSI), Naamsestraat 69, 3000 Leuven, Belgium. b Centre for R&D Monitoring (ECOOM), KU Leuven, Naamsestraat 61, 3000 Leuven, Belgium. c Centre for European Economic Research (ZEW), L7,1, Mannheim, Germany. January 2017 Abstract During the last decade, policy makers discussed the potential of public procurement as instrument in the area of technology policy. As a consequence, the European Commission has recently passed a new legislation that explicitly allows to contract R&D and innovation components within public procurement contracts. Germany has implemented this new legislation from 2009 onwards. Consequently, we evaluate the potential of public procurement for innovation empirically. We estimate the average treatment effect on the treated (ATET) of innovation-directed public procurement on German firms share of sales of innovation and imitation using the 2013 wave of Germany s contribution to the Community Innovation Survey (CIS). Interestingly, we find that firms with such procurement contract indeed sell more innovative products than other firms. However, these new product sales refer to incremental innovation, i.e. the products are mainly new for the respective firms product portfolio, but those are not really market novelties. We do not find any positive effects on sales with market novelties. Thus we conclude that public procurement may be a powerful tool for accelerating the dissemination of new technologies rather than a trigger for original innovation. Keywords: Public Procurement, Innovation, Policy Evaluation JEL Classification: H57, O38

2 1 Introduction In order to set up the conditions for long-term economic growth, economic policy seeks to foster innovation. Typically, there are several instruments at the disposal of policy makers. The most widely implemented supply-side policy tools such as innovation and R&D subsidies have rather recently been complemented by demand-side policy tools explicitly aiming at firms innovation performance, such as innovation-directed public procurement. Direct subsidies have been extensively studied in the literature for their effect on innovative input and output performance. Subsidies show in most studies, both an increase in innovation inputs such as R&D investments as well as innovative outputs measured by innovative sales share or patenting output (see for an overview David et al., 2000; Klette et al., 2000; Cerulli, 2010 and Zúñiga-Vicente et al., 2014). While innovation subsidies might work through to additional innovative investments and in turn increase innovation output, this is less clear when it comes to public procurement. Innovation-directed public procurement may, for instance, lead to merely incremental innovation or rather imitation. We analyze the effect of innovation-directed public procurement for its effect on firms innovation and imitation share of sales for Germany. Since the policy legislation has changed in Germany in 2009, public procurers can explicitly demand innovation from the executing firm by contract. Innovation-directed public procurement thus becomes a formal policy tool for fostering innovation. We are interested in the effect of this formal instrument on firms product innovation performance. In order to estimate the average treatment effect on the treated (ATET), we apply several analysis techniques such as OLS, propensity score matching, nearest neighbor matching and IV-regression using generated instruments in the way suggested by Lewbel (2012). The average treatment effect of innovation-directed public procurement on the treated firm in Germany shows a significant increase in the share of sales of imitations and no effect 1

3 on the innovation share of sales. Innovation-directed public procurement induces an increase of about 8 percentage points in the share of imitation sales, whereas we find no effects on the share of sales of market novelties. This finding is robust to all applied estimation techniques taking into account the selection bias. There is in turn not much empirical evidence in the literature on the effects of innovation-directed public procurement. Aschhoff and Sofka (2009) show that innovative public procurement considerably increases the share of sales of market novelties using the 2003 wave of Germany s contribution to the Community Innovation Survey. Guerzoni and Raiteri (2014) analyze the effects of subsidies and innovative public procurement on innovation inputs implementing multiple treatment effect models using matching estimators. They find positive effects for innovative public procurement on innovative inputs, in turn much outweighing those of subsidies. These are to our knowledge the only two empirical studies on the input and output effects of public procurement as innovation policy scheme so far. None of the two studies however captures an explicit innovation-directed policy scheme in their empirical implementation. Their public procurement measures are rather limited to public procurement activities that yield innovation input or output on the side. The interesting question however is what effect a policy has that explicitly is directed at fostering innovation. 2 Review of the Empirical Literature Empirical literature on the treatment effect of innovation-directed public procurement on innovation performance is scarce. Only Aschhoff and Sofka (2009) have studied the effects of innovation related public procurement on the share of sales of market novelties. Unlike Aschhoff and Sofka (2009) we distinguish performance between innovation and imitation performance. We measure innovation performance as the share of sales of market novelties and imitation performance as share of sales of mere firm novelties. Aschhoff and Sofka 2

4 (2009) base their analysis on a cross-section of 1149 manufacturing and business related service firms surveyed in the German contribution to the Community Innovation Survey (CIS) They constrain their estimation to a subsample of innovators only. However, unlike innovation subsidies, for which a firm applies with a specific innovation project, public procurement tenders are in principle open and therefore can be agreed upon by the government agency and innovating as well as non-innovating firms alike. Aschhoff and Sofka (2009) estimate tobit models due to the censoring of their dependent variable, sales from market novelties. As public procurement measure they use a question of the German CIS 2003 whether the responding firms have obtained product or process innovations because customers have demanded these. They assign these customer demands to the public sector by reported industry codes in an open question, for which therefore multiple responses were possible. Customer innovation demands are assigned to the public sector if they belong to public administration and defense (75.1), provision for services to the community as a whole (75.2) and compulsory social security activities (75.3) identified by the corresponding industry codes based on the Nace Rev. 1.1 classification, respectively. Measuring direct innovation subsidy grants from the same survey Aschhoff and Sofka (2009) cannot confirm any effect on the share of sales from market novelties associated with subsidies, whereas their measure for public procurement is associated with a significant increase of the share of sales from market novelties of about 9 percentage points. In turn, Guerzoni and Raiteri (2014) compare the treatment effects of innovation subsidy grants and innovative public procurement applying matching estimators introduced to the innovation policy evaluation literature by Almus and Czarnitzki (2003). The aim of their study is the treatment effect of alternative innovation policy instruments on the input side of innovation activities. They use data from the Innobarometer on Strategic Trends in Innovation , a survey conducted by the Gallup Organization on behalf of DG Enterprise and Industry. The dataset consist of 5238 firms from 27 member states of the 3

5 European Union, Norway and Switzerland. With the survey, the authors are however not able to directly measure an innovation-directed policy by means of public procurement as the survey question used to compute an indicator asks managers about any public procurement contract that allowed them selling innovation. In a setting of multiple policy instruments, Guerzoni and Raiter (2014) compute for each of the treatments separate matching estimators for inference about their impact on a subjective three point Likert scale whether the innovation inputs have decreased, increased or stayed the same in the year 2008 compared to Their subsidy measure is a subjective assessment of the responding manager on the change in the respective country s subsidy policy environment. The outcome variable they implement is a dummy variable that takes the value 1 if the respondents have reported an increase in innovation expenditures. They report a positive effect associated with both the subsidy as well as innovative public procurement treatments, whereas innovative public procurement obtains a higher effect. Their methodology takes also a further step and analyzes the different treatments in complete isolation from each other as well as their interactions. The results suggest that unlike innovative public procurement, subsidies in isolation have no effect, whereas their interaction shows a highly significant positive effect on innovative inputs. 3 Theoretical Considerations As we are interested in the treatment effect of innovation-directed public procurement on the treated firms innovation performance on the output side, we have to consider in what way and why public procurement might affect innovation output. Unlike direct subsidy grants for innovative activity, public procurement aiming at innovation does not (necessarily) require a firm to already be involved in innovative activity, possess an idea and file a costly application. Thus, plainly stated, innovation-directed public procurement does not require a firm to come up with its own new ideas. On the contrary, a public procurement tender, quite 4

6 exactly defines the nature and properties of the good the procurer eventually wants to purchase. If such a tender in turn is at least partly used as means of innovation policy, the nature of the innovation must be rather clearly definable to qualify as subject matter of a contract. Why imitation and not innovation? The fulfilment of the contract between the firm and the public procurer involves the delivery of a clearly defined product. Original innovation however might not be as clearly definable ex ante. It is costly and a successful delivery is at higher risk than an incremental innovation or mere imitation. In turn, a contractual compliance is virtually equal to an immediate sale. Therefore, (eligible) firms have a rather high incentive to compete for an innovation-directed public procurement tender involving incremental innovation or imitation. On the other hand, a tender that otherwise would require a considerable degree of research and development to meet contractual agreements might however be too risky and thus deterring rather than attracting firms to compete for being granted with the procurement. Therefore, public procurement as innovation policy tool is arguably suitable for fostering incremental innovation or mere imitation. 4 Empirical Analysis In order to estimate the ATET of innovation-directed public procurement, we apply OLS, propensity score matching, nearest neighbor matching and IV regression using generated instruments as introduced by Lewbel (2012). Each method is applied to the full sample of 2844 manufacturing and business-related service firms, representative for the German economy as well as to a subsample of 541 firms with public procurement contracts. Ideally we would like to compare the outcome for a treated firm in the hypothetical case it had not received the treatment, that we can never observe. As treatment is however possibly not assigned randomly we apply analyses additional to OLS. Matching estimators and Lewbel s 5

7 IV confirm our OLS findings of a policy induced ATET of about 8 percentage points increased imitation sales and no effect on original innovation performance. As the matching estimators draw a nearest neighbor to each treated firm with replacement, standard errors have to be adjusted according to Abadie and Imbens (2006) and Abadie and Imbens (2012). For nearest neighbor matching we apply bias adjustment for large samples introduced by Abadie and Imbens (2011) rendering nearest neighbor matching root-n consistent. 4.1 Data We use the German contribution to the Community Innovation Survey (CIS) In this wave we observe firms with a public procurement contract explicitly demanding innovation by contract. Thus, we measure the treatment of innovation-directed public procurement. Unfortunately, this measure is constraint to one wave only. Therefore, we cannot apply panel estimators for inference. As the survey period is 3 years, information on the treatment is measured for the period ranging from 2010 to Table 1 summarizes the variables used in the analysis. The outcome variables of interest are product innovation performance measured as share of sales of market novelties (innovation), share of sales of firm novelties (imitation) and the sum of the two as overall share of innovation sales. We measure these variables in For control variables to be predetermined, we lag them whenever possible. Thus, the control variables are all measured before 2012, so between t-1 and t-3. However, treatment and control variables are mostly measured in the same period due to the measurement period of the survey. The full sample contains 2844 manufacturing and business-related service firms. Firms on average obtain overall innovative sales of 6.4%, market novelties and firm novelties amount to 1.5% and 4.9% on average, respectively. Firms innovative sales are an immediate candidate as an outcome to be studied concerning innovation-directed public procurement because the public procurer aims at purchasing a product in the first place. 6

8 Table1: Descriptive statistics. Full sample. N = 2844 VARIABLES Mean Std. Dev. Min. Max. OUTCOME VARIABLES % Innovation Sales % Sales Market Novelties % Sales Firm Novelties TREATMENT VARIABLE Innovation-Directed PP COVARIATES Public procurement (PP) Inno-Intensity Group Foreign East log(emp)_t (Ratio Educated Emp.)_t (Export Share)_t (Share Successful Projects)_t (Costs/Emp)_t The treatment variable is a dummy indicating innovation-directed PP (public procurement) in the survey period. So, 3% of the firms in the sample have obtained a public procurement contract explicitly demanding innovation as terms of condition. In order to account for the non-randomness of treatment assignment, we specify our models according to possible selection criteria modelling the selection mechanism. We choose as main treatment selection criteria the education level of employees, firms success rate in innovation projects and costs per employee. Firms with a higher share of highly educated employees might be more likely to be assigned with the treatment by the public procuring agency. In addition to that, these firms are more likely to file a better application on average. Public procurement tenders constitute a competitive bidding mechanism, in which for two otherwise equal bids the successful applicant only differs by the costs the project is assigned with. In addition, we control for the share of successful innovation projects in the pre-treatment period due to a picking-the-winner rationale of the public procuring agency. Additionally, we control for innovation investment intensity, size, group membership, foreign ownership and for eastern Germany, which is a German specificity due to sustaining differences in productivity and innovative performance despite the unification. 7

9 The average firm in our sample invests 3.3% of their turnover in 2012 for innovation activities including internal and external R&D, R&D equipment, purchase of external knowledge and employees training for innovative activities. This variable captures the systematic innovation activities aimed at product and process innovation success. Size is controlled for with the natural logarithm of lagged average employees, log(emp)_t-1. (Ratio Educated Emp.)_t-1 is the lag of the ratio of employees with higher education (higher than secondary school) and university to overall employees. On average, about 20% of the staff had obtained higher education and university degrees. Costs are controlled for with lagged operating costs in Mio. per employee. Table2: Descriptive statistics. Subset of firms with public procurement contracts. Untreated firms N = 469 Treated firms N = 72 t-test on mean VARIABLES Mean Std. Mean Std. difference OUTCOME VARIABLES % Innovation Sales *** % Sales Market Novelties *** % Sales Firm Novelties *** COVARIATES (Inno-Intensity)_t *** Group Foreign East log(emp)_t ** (Ratio Educated Emp.)_t *** (Export Share)_t *** (Share Successful Projects)_t *** (Costs/Emp)_t Note: *** (**, *) indicate a significance level of 1% (5%, 10%). Table 2 shows summary statistics for the subsample of firms with public procurement contracts. 72 firms in our sample obtained innovation-directed public procurement contracts. The t-tests on the mean difference of the outcome and control variables show that firms with innovation-directed public procurement are on average much more performing on the overall innovative sales, as well as on sales with market novelties and firm novelties. The treated firms invest significantly more in innovation and are bigger on average. They have a higher 8

10 share of higher educated employees, a higher share of successful innovation projects and a higher export share. Overall, treated firms perform better than non-treated firms in the bivariate t-tests. Note, that also on the share of sales of market novelties, so the original innovations, treated firms show significant higher values on average. Table 3 shows the industries we obtain in our sample, together with Nace Rev. 2 codes and absolute as well as relative frequencies. In all regressions we control for a set of 12 industry dummies in order to control for industry fixed effects. Table 3: Industries. Nace Rev. 2 Description Frequency Percent Wholesale and retail trade , 31 Wood, cork, paper, printing & furniture Textiles, apparel & leather , Refinery, rubber, plastic & non-metallic mineral products , 64-70, Other services Manufacture of food products and beverages , 33 Metal industry, repair & installation of machinery & equipment 1-9, 12, 32, Other industries , Production companies, telecommunications, computer programming, architectural & engineering services, technical testing & analysis Machines & transport equipment Manufacture of computer, electronic and optical products & electrical equipment Chemical and pharmeceutical industry Econometric Results Using Cross-sectional Data Analyzing the German contribution to the Community Innovation Survey (CIS) from 2013 we find a policy induced ATET of about 8 percentage points on treated firms imitation sales. We cannot confirm any effect of innovation-directed public procurement on the share of sales from market novelties however. Public procurement as means of innovation policy seems rather to stimulate the share of sales with firm novelties, thus imitations. The results are robust to adjusting for possible selection bias by means of propensity score matching, nearest neighbor matching and IV regression using generated instruments as introduced by Lewbel 9

11 (2012). Unlike Aschhoff and Sofka (2009), we do not find any effect of innovation-directed public procurement on the share of sales due to market novelties. The executing firm in a contractual relation with a public procurer will only reap the benefits from this contract if the agreements of the contract are met. In other words, only if the good or service contractually specified can be delivered to the public procurer --- in this case including the defined innovation. Firms might therefore only bid for a given tender in case the delivery adheres the implementation of at most incremental innovation or mere imitation. The innovative aspect involved in the innovation-directed public procurement contract therefore is likely to be of incremental nature or rather an imitation for the firm granted with the procurement. Table 4: OLS results on the full sample. (1) (2) (3) VARIABLES % Innovation Sales % Sales Market Novelties % Sales Firm Novelties Innovation-Directed PP 9.062*** *** (2.901) (1.458) (2.536) Public Procurement (PP) (0.656) (0.343) (0.586) (Inno-Intensity)_t 0.488*** 0.206*** 0.282*** (0.0737) (0.0535) (0.0584) (COSTS/EMP)_t (0.996) (0.398) (0.906) (RATIO SUCCESSFUL PROJECTS)_t *** *** *** (0.0134) ( ) (0.0118) (RATIO EDUCATED EMP)_t *** ** *** (0.0159) ( ) (0.0141) log(emp)_t (0.209) (0.0800) (0.185) (EXPORT SHARE)_t *** ** *** (0.0181) (0.0105) (0.0167) GROUP MEMBER (0.947) (0.413) (0.849) FOREIGN OWNED (1.644) (0.706) (1.490) EAST GERMANY * (1.510) (0.600) (1.374) Constant 5.094*** *** (1.757) (0.694) (1.626) Observations 2,844 2,844 2,844 Adjusted R-squared F-stat Prob > F Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 10

12 Table 4 summarizes the results from the OLS analysis on the full sample. The three dependent variables are the overall innovation sales share, the sales share from market novelties and the sales share from firm novelties, respectively. The treatment, namely innovation-directed public procurement, is associated with 9%-points higher overall innovation sales and 7.8%-points imitative sales, whereas no effect on the share of market novelties is found. The control variables show the theoretically predicted direction. Innovation intensity, the ratio of successful innovation projects and employees education level are positively related to product innovation success. Other types of public procurement than the of the innovation policy scheme show no effect on product innovation performance. The comparison between innovation-directed public procurement and other types of public procurement is shown in table 5. Here OLS results on the subsample of firms with public procurement contracts is shown. The results are very similar to the regressions on the overall sample. The policy seems to affect only imitative sales, rather than innovative sales. Table 5: OLS results on the subsample of firms with public procurement contracts (1) (2) (3) VARIABLES % Innovation Sales % Sales Market Novelties % Sales Firm Novelties Innovation-Directed PP 8.362*** *** (2.862) (1.825) (2.548) (Inno-Intensity)_t 0.628*** 0.238* 0.390*** (0.123) (0.124) (0.135) (COSTS/EMP)_t (2.782) (1.190) (2.225) (RATIO SUCCESSFUL PROJECTS)_t *** ** (0.0219) (0.0113) (0.0204) (RATIO EDUCATED EMP)_t (0.0296) (0.0158) (0.0277) log(emp)_t (0.562) (0.222) (0.483) (EXPORT SHARE)_t ** ** (0.0465) (0.0360) (0.0436) GROUP MEMBER (2.583) (1.019) (2.339) FOREIGN OWNED ** ** (4.171) (2.213) (3.468) EAST GERMANY * (3.801) (1.317) (3.424) Constant * 11

13 (5.850) (4.397) (2.880) Observations Adjusted R-squared F-stat Prob > F Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 As one of the robustness analyses, we explicitly model the selection mechanism as put forward above with a propensity matching. In the first stage of the propensity score matching, we obtain the estimated propensity to participate in the policy scheme. As covariates we use the same control variables as in the OLS except for the investment in innovation, as this is regarded as a potential outcome variable of the innovation policy. In the first stage of the two stage approach we fit a probit model. The central selection criteria are the employees education level, the ratio of successful innovation in the past and operating costs per employee. Propensity score matching should increase comparability of treatment and control group. For this aim, we draw from the potential control group exactly one nearest neighbor to each treated firm. This nearest neighbor is evaluated at its propensity to participate in the policy scheme. In order to ensure comparability, we ensure common support by deleting treated firms, that either obtain a propensity score lower than the minimum of the potential control group or higher than the maximum of that distribution. The post matching sample, selected as control group shows no significant differences on the selection criteria anymore. The Probit results from the first stage of the propensity score matching are shown in Table 6. Firms with lower costs per employees are associated with a significantly higher likelihood to be selected into the public innovation procurement policy scheme. This result is consistent with economical project selection by the procuring agency. The procuring agency probably also follows a picking-the-winner strategy. We test this hypothesis with the ratio of successfully finished innovation projects in the past. Firms with a higher share of successful innovation projects on average are associated with a higher likelihood to be participants of the public innovation procurement policy scheme. Firms with a higher share of educated 12

14 employees show a significantly higher likelihood to be selected into the policy scheme. The firms thus are probably preferred over other firms due to supposedly higher expected performance. The indicator for firms based in eastern Germany is slightly significant in the Probit regression on the full sample which is a weak indication that eastern German firms might be preferred supposedly as target for structural development policy. Size, measured by the average number of employees in logarithms, seems to be an important determinant for contractor selection in the policy scheme as well. Public procurers therefore are likely to prefer larger firms as contractors in the policy scheme. The post matching samples, selected as control group show no significant differences on the selection criteria anymore. Table 6: Probit estimation. Public innovation procurement as dependent variable. Full sample Subsample: firms with PP contracts Variables Coef. (Std. err.) Coef. (Std. err.) (COSTS/EMP) t *** ** (0.525) (0.735) (RATIO SUCCESSFUL PROJECTS) t ** 0.004* (0.002) (0.002) (RATIO EDUCATED EMP) t *** 0.008*** (0.002) (0.003) log(emp) t *** 0.119* (0.046) (0.067) (EXPORT SHARE) t * 0.008** (0.003) (0.004) GROUP MEMBER (0.191) (0.269) FOREIGN OWNED (0.289) (0.447) EAST GERMANY 0.499* (0.296) (0.423) Constant *** *** (0.433) (0.647) Observations 2, Industry dummies YES *** YES LR chi Prob > chi Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 Table 7 shows the outcomes of the propensity score matching procedure for the full sample as well as the subsample. The number of observations of the two samples differ by one and two observations, respectively. This is due to the fact that we delete observations not 13

15 meeting the common support assumption. Overall innovative sales are higher for the group of treated firms compared to its selected comparison group of about 8%-points. Market novelties show no ATET, whereas the effect seems to be entirely driven by firm novelties. The ATET for firm novelties ranges between 7.6%-points to 8.5%-points confirming the findings from the OLS analysis. Table 7: Propensity score matching. ATET of innovation-directed public procurement. Full sample: AI Robust N = 2843 ATET Std. Err. z P > z % Innovation Sales % Sales Market Novelties % Sales Firm Novelties Subsample of public procurers: N = 539 % Innovation Sales % Sales Market Novelties % Sales Firm Novelties Note: In order to meet the common support assumption one and two observations are dropped for case of the full sample and the subsample, respectively. As second robustness analysis we apply nearest neighbor matching with the same covariates as above. Again we choose a single nearest neighbor to each treated firm in order to select a suitable control group. In order to find a twin we calculate the Mahalanobis distance given the covariates and choose the lowest distance while adjusting for large sample bias as suggested in Abadie and Imbens (2011). As we draw a nearest neighbor again with replacement from the potential control group we use Abadie/Imbens standard errors. Table 8: Nearest neighbor matching. ATET of innovation-directed public procurement. Full sample: AI Robust N = 2844 ATET Std. Err. z P > z % Innovation Sales % Sales Market Novelties % Sales Firm Novelties Subsample of firms with PP contracts: N = 541 % Innovation Sales % Sales Market Novelties % Sales Firm Novelties In table 8 we obtain the results from the nearest neighbor matching. For the full sample there is no significant effect to be found, albeit the p-values for firm novelties is close 14

16 to 10%. For the subsample of public procuring companies, we confirm the findings from OLS and propensity score matching, as public procurement as innovation policy induces higher sales ranging from 7.4%-points for firm novelties to 8.6%-points for overall innovative sales. As a third component of our robustness analysis, we apply IV regressions using generated instruments as introduced by Lewbel (2012). In case of selection bias an instrument meeting the requirements of validity and relevance should enable identification. In case instruments are absent, the suggested method allows IV regression with generated instruments, that are obtained by products of regressors and heteroscedastic error terms uncorrelated with the regressors. Identification is thus based on higher moments. This situation is put forward by Lewbel (2012) to occur often in case of an unobserved common factor. The convenience is not only the generation of instruments out of the same set of information available, but also that usual evaluation criteria apply to this estimator, such that we can test the relevance and validity of the generated instruments with the usual methods. Tables 9 and 10 summarize the results from the application of Lewbel s IV estimation using generated instruments for the full sample controlling for general public procurement as well as for the subsample of all firms with public procurement contracts, respectively. The results from these regressions confirm the OLS and matching findings from above. The ATET from the innovation policy induces higher sales of firm novelties rather than market novelties, and thus affects only incremental innovation or mere imitation rather than original innovation new to the market. Hansen s J-test is not rejected in either of the regressions from tables 8 and 9 indicating that none of the generated instruments are invalid. The strength of the instruments in case of the full sample fulfills the requirements of at most 5% bias evaluated at the critical values reported by Stock and Yogo (2005) critical values. In case of the subsample of public procurement executing firms shown in table 9, maximal bias is between 5% and 10%. The critical values for maximal 5% and 10% bias lie at an F-statistic of about 21 and 11, respectively. Thus the Cragg-Donald and Kleinbergen-Paap F-statistics of about 20 and 18 15

17 respectively, lead to the conclusion that a slightly higher bias than at maximum 5% might be present in the estimation results. Table 9: IV regression using generated instruments as in Lewbel (2012). Full sample. (1) (2) (3) % Sales Firm VARIABLES % Innovation Sales % Sales Market Novelties Novelties Innovation-Directed PP 8.872*** *** (2.891) (1.549) (2.533) Public Procurement (PP) (0.672) (0.373) (0.598) (Inno-Intensity)_t 0.489*** 0.207*** 0.282*** (0.0734) (0.0535) (0.0581) (COSTS/EMP)_t * (0.992) (0.396) (0.903) (RATIO SUCCESSFUL PROJECTS)_t *** *** *** (0.0134) ( ) (0.0117) (RATIO EDUCATED EMP)_t *** ** *** (0.0159) ( ) (0.0141) log(emp)_t (0.208) (0.0798) (0.185) (EXPORT SHARE)_t *** ** *** (0.0181) (0.0105) (0.0166) GROUP MEMBER (0.943) (0.412) (0.845) FOREIGN OWNED (1.637) (0.703) (1.484) EAST GERMANY * (1.503) (0.596) (1.368) Constant * (0.859) (0.333) (0.774) Observations 2,844 2,844 2,844 Adjusted R-squared Cragg-Donald F-stat Kleinbergen-Paap rk F-stat Hansen's J-stat Jtest P-val Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 16

18 Table 10: IV regression using generated instruments as in Lewbel (2012). Subsample public procurers. (1) (2) (3) VARIABLES % Innovation Sales % Sales Market Novelties % Sales Firm Novelties Innovation-Directed PP ** (4.867) (4.567) (4.588) (Inno-Intensity)_t 0.637*** 0.257* 0.380*** (0.122) (0.135) (0.135) (COSTS/EMP)_t (2.724) (1.223) (2.217) (RATIO SUCCESSFUL PROJECTS)_t *** ** (0.0214) (0.0108) (0.0200) (RATIO EDUCATED EMP)_t (0.0295) (0.0143) (0.0274) log(emp)_t * (0.551) (0.223) (0.475) (EXPORT SHARE)_t ** ** (0.0457) (0.0371) (0.0439) GROUP MEMBER (2.516) (1.009) (2.306) FOREIGN OWNED ** ** (3.997) (2.197) (3.504) EAST GERMANY (3.716) (1.423) (3.442) Constant (2.383) (0.827) (2.166) Observations Adjusted R-squared Cragg-Donald F-stat Kleinbergen-Paap rk F-stat Hansen's J-stat Jtest P-val Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 6 Econometric Results Using Panel Data Note to the IIOC reviewers: this is the incomplete part of the paper. These econometric analyses are currently in progress. They will be completed by the end of February For further robustness check, we have linked our data discussed above to three other sources: 1. the panel data of the Community Innovation Survey which delivers information (for a subsample of firms) on past values of the dependent variables; 17

19 2. the Tender Electronics Daily (TED) database of the European Commission which includes panel information on public procurement contract awards in European countries including Germany since the PATSTAT database which includes information on global patenting activity. The combination of these data allows conducting two major robustness checks. First, we extend the analysis presented above to a (conditional) difference-in-difference framework where we observe firms innovation performance before and after the policy change. With the TED data we can also control for (the volume of) public procurement contracts the firms had before and after the policy change. Note, however, that the TED data have no detailed information on whether a contract entailed innovation components. For the latter, we still have to rely on the indicator variable used in the cross-sectional study above. Second, we utilize information on patent activity of the firms to conduct a further differencein-difference analysis where we use patents as an alternative outcome variable instead of new product sales. Given the results discussed above, we would expect that public procurement for innovation does not lead to increased patenting as this would correspond to original, inventive activity. As we argued earlier, however, innovation procurement lead mainly to faster adoption, modification and thus diffusion of new technologies and thus not primarily to new patentable inventive activity. 7 Conclusions With this paper we evaluate a policy change by the European Commission and its ratification in Germany that allows public procurement tenders and contracts to explicitly demand innovation terms of condition. We argue that such a tender and eventual contract might merely lead to incremental innovation or mere imitation as firms potentially bidding in the course of such a tender will on average not seek to acquire a project that requires considerable investment in R&D for a potentially unknown outcome. The reason is that 18

20 meeting the contractual terms of condition requires the delivery of the innovative component. Otherwise the agreement will not be fulfilled and a (full) payment will not take place. As such a contract reflects immediate selling of goods and services to the public agency in case its terms of condition are met, the policy is evaluated at the product innovation performance of the executing firms. We analyze the effect of the innovation-directed public procurement by means of multiple methods including OLS, propensity score matching, nearest neighbor matching and IV regression using generated instruments as proposed by Lewbel (2012). The results seem robust to the application of these methods. We obtain an average treatment effect on the treated (ATET) of about 8%-points increase in sales of firm novelties. We find however no effect for the share of sales of market novelties, which confirms our theoretical priors. The policy is thus not inducing original innovation sales in the treated firms on average. Evaluated at the effect on the treated firm, the policy can however be regarded as successful because the increase in sales due to imitation means the diffusion of knowledge to this firm that otherwise would not have necessarily occurred. In addition, firms might have started imitating that were otherwise not involved in the innovation process at all. Due to a lack of panel data we however cannot evaluate the latter effect. In addition to the effects of the policy on the treated firm, the public sector has started assigning innovative requirements to a demand it anyways would have exercised. Public procurement in Germany amounts to about 10% of the German GDP. This constitutes a huge potential that only with the law change in 2009 can be utilized for innovation policy. Due to the design of the policy however, the incentives are such that rather imitations are fostered than innovation. 19

21 References Abadie, A., and G. W. Imbens (2006): Large sample properties of matching estimators for average treatment effects, econometrica, 74(1), (2011): Bias-Corrected Matching Estimators for Average Treatment Effects, Journal of Business & Economic Statistics, 29(1) (2012): Matching on the estimated propensity score, Discussion paper, National Bureau of Economic Research. Aschhoff, B., and W. Sofka (2009): Innovation on demandcan public procurement drive market success of innovations?, Research policy, 38(8), Cerulli, G. (2010): Modelling and Measuring the Effect of Public Subsidies on Business R&D: A Critical Review of the Econometric Literature*, Economic Record, 86(274), David, P. A., B. H. Hall, and A. A. Toole (2000): Is public R&D a complement or substitute for private R&D? A review of the econometric evidence, Research Policy, 29(4), Guerzoni, M., and E. Raiteri (2015): Demand-side vs. supply-side technology policies: Hidden treatment and new empirical evidence on the policy mix, Research Policy, 44(3), Klette, T. J., J. Møen, and Z. Griliches (2000): Do subsidies to commercial R&D reduce market failures? Microeconometric evaluation studies, Research Policy, 29(4), Lewbel, A. (2012): Using heteroscedasticity to identify and estimate mismeasured and endogenous regressor models, Journal of Business & Economic Statistics, 30(1), Stock, J. H., and M. Yogo (2005): Testing for weak instruments in linear IV regression, Identification and inference for econometric models: Essays in honor of Thomas Rothenberg. 20

22 Zúñiga-Vicente, J. Á., C. Alonso-Borrego, F. J. Forcadell, and J. I. Galán (2014): Assessing the effect of public subsidies on firm R&D investment: a survey, Journal of Economic Surveys, 28(1),