Exports, R&D Activities, and Labour Productivity of SMEs. Martin Falk Austrian Institute of Economic Research (WIFO)

Transcription

1 Exports, R&D Activities, and Labour Productivity of SMEs Martin Falk Austrian Institute of Economic Research (WIFO) Abstract This paper investigates the link between the export behaviour and R&D activities of Austrian SMEs for the period using 18,000 firm observations. Its analysis focuses in particular on micro firms, young SMEs and those in services, and highly productive SMEs. The results of two-part models controlling for firm effect show that the export participation and export share of SMEs depends significantly and positively on their labour productivity level (relative to that of large firms in a given industry) and R&D-sales ratio. However, the marginal effect of the R&D-sales ratio is relatively small for micro enterprises, young SMEs, and service SMEs, while it is relatively large for SMEs in manufacturing. Furthermore, the interaction terms between the R&D-sales ratio and the relative labour productivity level of SMEs show that R&D activities are only significant when the labour productivity gap between SMEs and large firms in a given industry is not too large. Keywords: export activities, SMEs, micro firms, R&D activities, labour productivity, twopart models, panel data methods. JEL: F14. Acknowledgement: We would like to thank the Austrian R&D promotion agency for access to the data and funding. We gratefully acknowledge the helpful comments made by the participants in the Schumpeter conference 2012, IfW Workshop: Innovation: From Europe to China October 2011 and the Empirical Economics seminar at the University of Innsbruck We would like to thank Tyler Schaffner for careful proofreading of the text.

2 1 1 Introduction The export participation of small and medium-sized enterprises has increased rapidly over the last decade. Despite a general increase in exporting among SMEs, micro enterprises, young companies, and service SMEs face a significant disadvantage in exporting and exhibit lower export shares. SMEs also face a significant productivity gap relative to large firms. Meanwhile, firm-specific advantages such as high labour productivity, technological innovations, and investment in R&D are regarded as key to SMEs success in internationalising. The link between exporting and R&D activities, however, is likely to be different for different types of SMEs (including those with low and high productivity, as well as young SMEs and micro enterprises). This paper provides new estimates of the link between SMEs exporting and R&D activities. Specifically, we investigate whether the link between these activities differs between highand low-productivity SMEs and across specific firm characteristics, such as industry affiliation, age, and size. In order to study both export decisions (the extensive margin ) and export shares (the intensive margin ), we perform estimations using two-part models while including time means of the continuous explanatory variables to account for individual effects. The estimates are based on unbalanced panel data regarding firms engaged in R&D for the period ; this includes 18,000 firm observations. A unique feature of the data is that it covers micro enterprises; in fact, about 30 per cent of the SMEs in the database have less than 10 employees. Another unique feature of the data is that information on service firms is available and exports are not limited to those involving goods. In many other cases, existing firm-level data lacks information on service exports, while trade statistics often suffer from high minimum-reporting thresholds for exports. In addition, the database contains a significant large share of young enterprises. The relationship between exporting of SMEs or young enterprises and R&D activities is interesting for several reasons. Young R&D doing firms can suffer from the so called liability of newness. A special kind of young SMEs is the so called born globals. Such types of firms do not internationalise incrementally, but compete globally from inception. Global born firms can be often characterised by specific technologies, high R&D expenditures, knowledge or innovation (Bell, Crick and Young, 2004). In order to determine the extent to which the link between exporting and R&D differs between high- and low-productivity SMEs, interaction terms between the R&D-sales ratio

3 2 and labour productivity are introduced. Knowledge of the variation in the association of exporting and R&D is important for policy makers in order to maximise the benefits of R&D subsidies. The previous literature investigating the link between exporting and R&D activities for SMEs mainly uses data on manufacturing SMEs. For instance, using data on Spanish manufacturing SMEs for the period , Esteve-Perez and Rodriguez (2013) reveal a strong interdependence between exporting and R&D activities. In contrast, D Angelo (2012) finds that R&D expenditures have no impact on export intensity based on 689 high-tech Italian SMEs. However, the majority of studies do not provide separate estimates for the SMEs in question. In general, there is a positive relationship between R&D activities and exporting activities at the firm level (see Czarnitzki and Wastyn, 2010 for the Flemish region of Belgium; Egger et al., 2014 for France; Castellani and Zanfei, 2007; Fryges, Vogel, and Wagner, 2015 and Peters, Roberts, and Vuong, 2015 for Germany; Faustino and Matos, 2015 for Portugal; as well as Máñez, and Rochina Barrachina and Sanchis Llopis, 2015 for Spain). Some studies find that R&D activities are significantly related to decisions to export, but not to export intensity (see e.g. Girma, Görg, and Hanley, 2008). Roper and Love (2002) find that the presence of formal R&D departments and R&D activities (both measured as dummy variables) increase export probability and export intensity in the UK. Meanwhile, the impact of R&D on export intensity is only significant for Germany. Some studies that focus on the impact of R&D activities on export decisions rather than the quantity of exports also find that R&D activities raise the probability of exporting (see e.g. Arnold and Hussinger, 2005). However, Becchetti and Rossi (2000) determine that R&D intensity has no impact on export propensity. Barrios, Görg, and Strobl (2003) find that the impact of R&D on export probability disappears when accounting for random firm effects. Overall, the results of these studies are difficult to compare because of differences in how they measure R&D activities (R&D dummies or R&D intensity), define exports (export dummies or export intensity), cover SMEs and micro firms, and perform their estimations. 1 Furthermore, few studies address whether the impact of R&D on exporting differs by industry, firm size, firm age, or other firm characteristics. Tomiura (2007) finds that the 1 Some studies use product and process innovations as a measure of technology (Criscuolo, Haskel & Slaughter, 2010; Higón, & Driffield, 2011). However, information on technological innovations is not available in the data set.

4 3 relationship between R&D intensity and export decisions varies across firm size and industry affiliation, with zero or negative effects for large firms and the supplier-dominated sector. Yang, Chen, and Chuang (2004) find that the impact of R&D on export decisions is stronger for large firms. While there are many studies that investigate the link between R&D and export decisions (along with the corresponding amount of exports for the manufacturing sector), few are available for service firms. Some exceptions include Czarnitzki and Wastyn (2010); Eickelpasch and Vogel (2011); Gourlay, Seaton, and Suppakitjarak (2005); and Love and Mansury (2009), but to our knowledge, no comparison of the results between manufacturing and services has been conducted thus far. In summarising the literature, one can see that the majority of studies are limited to manufacturing firms, although the number of those that concentrate on service firms is growing. Another characteristic of the literature is that it pays little attention to small firms, particularly to micro enterprises. While the research community broadly agrees that larger firms as well as those with higher levels of labour productivity and R&D intensity are more likely to export and also exhibit a higher export share, relatively little is known about the impact of these factors on the export behaviour of micro enterprises. Some studies are based on small samples and/or focus on a specific industry. The lack of studies on micro enterprises is due to the difficulty of obtaining relevant information on exports and other firmspecific aspects (besides employment and age, which can be obtained from business registers). For instance, Eickelpasch and Vogel (2011) note that for German business service statistics, small enterprises with an annual turnover of 250,000 or less (corresponding to a firm with five employees, assuming an average of 50,000 in sales per employee) do not have to provide information about their export activities. It is important to include micro enterprises because they are a very dynamic group of firms characterised by a large share of young firms and high growth rates, but also high exit rates. Therefore, there are good reasons to believe that the firm-specific determinants of exporting differ between SMEs (defined as firms with employees) and micro enterprises (those with less than 10 employees). 2 Theoretical background and empirical model The literature indicates that the exporting behaviour of SMEs depends on firm-specific factors such as firm size, age, technological innovations, and R&D activities and productivity. According to Schumpeter, innovation activities are the main factors of success on the

5 4 international market. Theoretical models predict a positive relationship between R&D or productivity and exports (Melitz, 2003), and only firms with superior performance become exporters (Dunning, 1970 and Melitz, 2003). For SMEs and micro firms in particular, sunk costs can be higher than for larger firms. In fact, a common finding of the empirical literature is that larger and more productive firms exhibit greater R&D spending, are more likely to export, and have higher export ratios (provided they are engaged in exporting) (Wagner, 2007, Fryges, Vogel and Wagner, 2015). However, the association between exporting and R&D is likely to be stronger for highly productive SMEs than for their less productive analogues. In order to test this hypothesis, an interaction term between the R&D-sales ratio and the productivity level of SMEs relative to large firms is introduced. Meanwhile, the relationship between age and exporting is not clear-cut. On the one hand, older SMEs have a higher probability of exporting and are also more successful in foreign markets because of learning effects. On the other hand, young firms are often more flexible and innovative than their larger counterparts. Given that the export sales ratio is bounded between zero and one, the parameters based on linear models such as OLS and fixed effects can provide a predicted value for the dependent variable from this bounded range. In this sample, 22 per cent of the firms do not export. A possible solution is to use the fractional probit estimator introduced by Papke and Wooldridge (2008). Here, unobserved time-invariant heterogeneity is controlled for by adding the time averages of all explanatory variables to the regression equation. This estimator does, however, require a balanced sample. Two-part models with time averages of all explanatory variables can be used as an alternative (see Lejárraga and Oberhofer, 2015 for a recent application). Specifically, a two-part model makes it possible to jointly investigate the extensive ( decision to export ) and intensive margins ( export share ). This paper s two-part model consists of a probit model indicating the probability of exporting and a linear export share equation for those companies that export. The probability that firm i exports can be modelled as follows: Pr( EX it l arg e 1 2 ln Lit 1 3youngit 4( R & Dit / Yit ) 5YLit / YLs l arg e 1 ) 6( R & Dit / Yit ) YLit / YLs bisdsecis. s1 birdregir cij yearij it r 1 j 1

6 5 Here, i denotes the firm and t the year. EX is a dummy variable that takes the value of 1 if a firm exports goods or services and 0 otherwise. L denotes the firm s size in terms of number of full-time employees. Here, R&D/Y is the ratio of R&D expenditures to total sales, and YL is labour productivity measured as sales per employee. The relative labour productivity level is measured as the productivity level of SMEs relative to the industry-level mean for large firms, l arg e it L s YL / Y. 6 is the cross product of the relative productivity level and the R&Dsales ratio. Young is a dummy variable equal to 1 if the firm was founded in the five years prior to the survey year. DSEC and DREG denote sets of dummy variables measured at the two-digit and federal-state level, respectively, and year stands for year dummy variables. represents the standard normal cumulative distribution function. Note that in a non-linear model the coefficient of an interaction term does not equal the marginal effect of the interaction term (Ai and Norton, 2003). Thus the interaction term cannot be interpreted with regard to the sign, significance and magnitude. Therefore, we use Stata margins postestimation command that correctly estimate the marginal effects in case of interaction terms. The main hypothesis is that the association between R&D and export status is stronger as the relative productivity level of SMEs rises. Given that SMEs are a heterogeneous group with respect to age and firm size, separate estimation results are reported for micro enterprises, young SMEs, and manufacturing and service SMEs. In addition to the probit equation, an export intensity equation is estimated. This equation is conditional on positive values for exports and is therefore only estimated for a subset of the data. Since the export share in the second part of the model is bounded between values close to zero and one, the generalised linear model (GLM) link is used: E(( EX it / Y it ) EX 0, X ) g 1 ( X ). To account for the accumulation of both very low and 100 per cent export shares, binomial distribution is used. Here, X includes the same variables as in the probit model. To take individual effects into account, we include the time means of the continuous variables across firms in both the probit model and the export share equation as suggested by Mundlak (1978). Given that the firm-specific determinants of SME exporting are likely to be different across industries, age, and size, the two-part model is estimated for several subsamples.

7 6 3 Data and descriptive results We use an unbalanced panel of Austrian firms in the manufacturing and service sectors for the period The data is based on a survey of all firms that invest in R&D and have applied for R&D funding from the Austrian R&D promotion fund. Information is available for all firms without minimum employment size. 2 These firms are requested to submit information on their (i) total sales (in thousands of euros), (ii) share of exports in overall sales, (iii) number of employees (full-time equivalents), (iv) number of R&D employees (full-time equivalents), (v) expenses for research and development (in thousands of euros), and (vi) cash flow (in thousands of euros). In addition, the survey includes information on each firm s legal form and geographic location. Firms are required to provide the data surveyed for the three years prior to their application for R&D project funding. Note that all firms even those that have previously succeeded in obtaining R&D funding from the Austrian agency are required to provide complete and correct information on their R&D expenditures and related employment. Among most of the studies conducted thus far, the resulting database is one of the most detailed in terms of coverage and data quality. One special feature of the data is that it covers very small firms; in fact; approximately 30 per cent of the firms in the estimation sample have less than 10 employees. While the data includes service firms, it has limitations, as well, such as a lack of information on physical investment. Table 1 lists the medians of the key variables for the total sample and various subsamples. The share of exporting SMEs is 78 per cent, and the median export to sales ratio is 37 per cent. Average (median) employment is 19. The productivity level of an SME relative to that of a large firm in a given industry is 78 per cent on average. 2 Note that the official R&D survey for Austria conducted by Statistics Austria includes micro enterprises. However, there is no information on export activities. Note also that export of goods is conducted by the Statistics Austria, while exports of services is conducted by the Austrian National Bank which complicates the empirical analysis based on official statistical data.

8 7 Table 1: Descriptive statistics total SMEs microenterprises young SMEs manufact uring SMEs service SMEs # obs export status % of firms export to sales ratio in % mean young (0-5 yrs) % of firms R&D to sales ratio in % median mean employment in 1000 median sales per employment in 1000 euros median relative productivity level mean Source: Austrian R&D promotion fund. Own calculations. 4 Empirical results Table 2 shows the estimates obtained from the two-part model of the factors that determine Austrian SMEs exporting activities for the total sample in the period Separate estimation results are reported for manufacturing and non-manufacturing SMEs (Table 4), as well as for micro enterprises and young SMEs (Table 3). The regressions contain a full set of year dummy variables, dummy variables for industry affiliation (at the NACE rev. two-digit level), and regional fixed effects (at the federal-state level). Unreported results show that industry, regional, and year fixed effects are each jointly significant at the five per cent level. As suggested by Mundlak (1978), all of the regressions include unobserved firm effects measured as time means. We report the results of the pooled model and the model accounting for individual effects, measured as time means of the (continuous) explanatory variables. The results indicate that R&D activities (measured as the R&D to sales ratio) are significantly and positively related to both the probability of exporting and the export share conditional on exporting. As expected, the estimates of the R&D-sales ratio decrease in value and significance when individual firm effects are taken into account. Based on the two-part model with firm effects, the marginal effect is 0.10, indicating that an increase in the R&D-sales ratio by 10 percentage points will cause the export share to rise by one percentage point. Based on the two-part model that does not account for individual firm effects, the marginal effect is This clearly indicates that not accounting for individual effects leads to overestimation of the link between R&D activities and exporting. Note that the individual effects (measured as time means) cannot be interpreted. Overall, the magnitude of the link between R&D activities and exporting is quite low.

9 8 Table 2: Two-part model estimates of the determinants of exporting of micro enterprises and young SMEs (1-249) TPM with firm effects Pooled TPM Probit estimates coeff z coeff z R&D sales ratio *** ** R&D sales ratio X relative labour productivity 0.87 *** *** 4.57 ln employment 0.40 *** *** ln relative labour productivity 0.22 *** *** 8.36 young (0-5 yrs) mean of R&D sales ratio mean of ln employment *** mean of rel labour productivity constant *** Log pseudo likelihood # of observations GLM estimates coeff z coeff z R&D sales ratio *** R&D sales ratio X relative labour productivity 0.45 *** *** 4.28 ln employment 0.22 *** *** ln relative labour productivity 0.37 *** *** young (0-5 yrs) 0.14 *** *** 5.37 mean of R&D sales ratio 0.92 *** mean of ln employment mean of rel labour productivity constant 0.71 ** ** 2.24 Log pseudo likelihood # of observations combined marginal effect R&D sales ratio 0.10 *** *** combined marginal effect labour productivity 0.12 *** *** Notes: The table reports coefficients of the logit model of the probability of exporting of SMEs and the GLM estimates for the determinants of the export share of exporting firms. z-values are based on robust standard errors. ***, ** and * denotes significance at the 1, 5 and 10 per cent significance levels. Industry dummy variables at the two digit industry level, time dummy variables and regional dummy variables are included but not reported. The marginal effects are computed using STATA's margins command. Source: Austrian R&D promotion fund. Furthermore, larger and more productive SMEs are more likely to export and exhibit a higher exports-to-sales ratio. The interaction term between the R&D-sales ratio and relative labour productivity of SMEs is positive and significant at the one per cent level. This holds true for both the export decision and the export share equation. This means that the positive relationship between exporting and R&D activities increases with the ratio of SMEs labour productivity to that of large firms. The marginal effect of relative labour productivity is 0.22, indicating that an increase in relative labour productivity of one percentage point will raise the corresponding export share by 0.22 percentage points. A comparison of the magnitude of the marginal effects of R&D activities and labour productivity reveals that the association between exporting and labour productivity is stronger than the link between exporting and the

10 9 R&D-sales ratio. The control variables are significant at the one per cent level. The likelihood of a firm making the decision to export, as well as its export share, increase with its size. Table 3 lists the results of the two-part model for micro enterprises and young SMEs. We again find that the interaction term between the R&D-sales ratio and relative labour productivity is positive and significant in most of the cases. This indicates that the magnitude of the relationship between exporting and R&D increases with the productivity level of SMEs relative to that of large firms (within a given industry). Table 3: Two-part model estimates of the determinants of exporting of micro enterprises and young SMEs (1-249) Micro enterprises Young SMEs Probit estimates coeff. z coeff. z R&D sales ratio ** R&D sales ratio X relative Labour productivity 0.62 *** *** 2.89 ln employment 0.42 *** *** 8.18 ln relative labour productivity 0.24 *** *** 6.64 young (0-5 yrs) mean of R&D sales ratio mean of ln employment * mean of rel labour productivity *** constant *** Log pseudolikelihood # of observations GLM estimates coeff. z coeff. z R&D sales ratio R&D sales ratio X relative labour productivity * 1.77 ln employment ln relative labour productivity 0.20 *** *** 2.62 young (0-5 yrs) 0.17 *** mean of R&D sales ratio 0.62 *** *** 5.92 mean of ln employment *** 3.63 mean of rel labour productivity constant 1.64 *** *** 3.22 Log pseudo likelihood # of observations combined marginal effect of R&D sales ratio 0.06 ** ** 2.36 combined marginal effect of labour productivity 0.10 ** ** 6.41 Notes: The table reports coefficients of the logit model of the probability of exporting of SMEs and the GLM estimates for the determinants of the export share of exporting firms. z-values are based on robust standard errors. ***, ** and * denotes significance at the 1, 5 and 10 per cent significance levels. Industry dummy variables at the two digit industry level, time dummy variables and regional dummy variables are included but not reported. Source: Austrian R&D promotion fund. This holds true for the sample of both micro enterprises and young SMEs. However, the magnitude of the impact of R&D intensity on the combined effect of the decision to export and a firm s export share is relatively small: The marginal effect is 0.06 indicating a 0.6

11 10 percentage point increase in the ratio of exports to sales following an increase in R&D intensity by 10 percentage points. Graph 1 shows that micro enterprises with a productivity level equal to 50 per cent of that of large firms exhibit a marginal effect of 0.03 in their R&Dsales ratio. The corresponding marginal effect of SMEs with productivity levels similar to those of large firms is The results are similar for young SMEs. Here the marginal effects are 0.04 and 0.10 respectively. The positive relationship between R&D intensity and the probability of exporting is found not only for the sample of micro enterprises and young SMEs, but also for SMEs in nonmanufacturing industries. Again, however, the association is relatively weak. In particular, an increase in the R&D-sales ratio by 10 percentage points will cause decisions to export and export shares to rise by 0.7 percentage points for service SMEs (combined effect). In contrast, the link between exporting and R&D is higher for manufacturing SMEs. Here, the average marginal effect is about 0.4 (see Table 4). However, the marginal effects vary greatly across the productivity distribution of manufacturing SMEs, ranging between 0.1 for firms with a relative productivity level of 30 per cent to 0.5 for manufacturing SMEs with a productivity level similar to that of large firms (read: 100 per cent). In summary, we find that the link between exporting and R&D is lower for micro enterprises, young SMEs, and service SMEs, but significantly higher for high-productivity SMEs and those in manufacturing. The results for services are consistent with Fryges et al. (2015) who find a positive and significant association between R&D activities and exporting in business services. However, the magnitude of the association is rather low.

12 11 Table 4: Two-part model estimates of the determinants of exporting of for manufacturing and non-manufacturing SMEs (1-249) Manufacturing SMEs Service SMEs probit estimates coeff z coeff z R&D sales ratio ** ** R&D sales ratio x relative labour productivity 1.95 *** *** 3.99 ln employment 0.46 *** *** ln relative labour productivity 0.25 *** *** 7.34 young (0-5 yrs) ** mean of R&D sales ratio * 1.84 mean of ln employment *** *** mean of rel labour productivity constant *** Log pseudolikelihood # of observations GLM estimates coeff z coeff z R&D sales ratio *** R&D sales ratio X relative labour productivity 2.54 *** * 1.88 ln employment 0.21 *** *** 5.28 ln relative labour productivity 0.35 *** *** 9.98 young (0-5 yrs) *** 5.51 mean of R&D sales ratio 1.18 *** *** 7.98 mean of ln employment ** mean of relative labour productivity 0.03 * constant *** Log pseudolikelihood # of observations combined marginal effect R&D sales ratio 0.40 *** *** 3.02 combined marginal effect labour productivity 0.16 *** *** Notes: The table reports coefficients of the logit model of the probability of exporting of SMEs and the GLM estimates for the determinants of the export share of exporting firms. z-values are based on robust standard errors. ***, ** and * denotes significance at the 1, 5 and 10 per cent significance levels. Industry dummy variables at the two digit industry level, time dummy variables and regional dummy variables are included but not reported. Source: Austrian R&D promotion fund.

13 12 Graph 1: Marginal effect of the R&D sales ratio depending on relative labour productivity marg. eff. R&D/Y for micro enterprises depending on rel. labour productivity marg. eff. R&D/Y for young SMEs depending on rel. labour productivity combined marg. eff. of the R&D sales ratio combined marg. eff. of the R&D sales ratio rel. labour productivity of SMEs rel. labour productivity of SMEs marg. eff. R&D/Y for manufacturing SMEs depending on rel. labour productivity marg. eff. R&D/Y for non-manufacturing SMEs depending on rel. labour productivity combined marg. eff. of the R&D sales ratio combined marg. eff. of the R&D sales ratio rel. labour productivity of SMEs rel. labour productivity of SMEs Note: Calculations are based on two-part model reported in tables 2 and 3. The dots represent the 95 % confidence level Source: Austrian R&D promotion fund.

14 13 5 Conclusions This paper investigates the link between the export behaviour and R&D activities of Austrian SMEs for the period Its estimation of two-part models facilitate joint modelling of the determinants of exporting firms decisions to export and exports-to-sales ratios. To account for individual firm effects, time means of the explanatory variables across firms are included. Our analysis focuses in particular on micro firms and young SMEs. The results show that the export participation and export shares of SMEs depend significantly and positively on their labour productivity level (relative to that of large firms in a given industry) and R&D-sales ratio. This holds true for both the export decisions and export shares of exporting firms. Accounting for individual firm effects reduces the marginal effect of the R&D-sales ratio by more than a half. Overall, firm-specific advantages such as high R&D spending and high productivity are primary drivers of both the export decisions and export shares of SMEs, with smaller effects for the subgroup of micro enterprises, young SMEs, and service SMEs. The magnitude of the link between exporting and exporting is highest for manufacturing SMEs. One key result of the study is that R&D activities are only significant when the labour productivity gap of SMEs to that of large firms is not too large. For micro enterprises and young enterprises, R&D is only significant at a labour productivity level (relative to that of large firms) of 60 per cent or more. For manufacturing SMEs in general (as well as service SMEs), a relative productivity level of 30 per cent is the threshold. Furthermore, we find that larger and more productive SMEs are more likely to export and exhibit higher export shares. The link between exporting and R&D activities is once again the least pronounced for micro enterprises. Several policy conclusions can be drawn from this empirical analysis. First, policies aiming to increase productivity and R&D activities indirectly increase the export activities of SMEs. Second, the insignificant relationship between R&D intensity and exporting for lowproductivity SMEs demonstrates that stimulating R&D intensity is not an effective means of encouraging the internationalisation of SMEs. Finally, the magnitude of the link between exporting and R&D activities although highly significant is rather low. This indicates that other measures, such as export promotion programmes, are needed to support the SME internationalisation process.

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