Impact of introducing ecoinnovation measures on productivity in transport sector companies

IMPACTOFINTRODUCINGECO‐INNOVATIONMEASURES ONPRODUCTIVITYINTRANSPORTSECTORCOMPANIES Dr.AlejandroGarcía‐Pozo1,Dr.JoséLuisSánchez‐Ollero2 1,2DepartmentofAppliedEconomy,UniversityofMalaga(SPAIN) [email protected] ;jlsanche[email protected] 1. PURPOSE 2. DATA & METHODOLOGY 4. CONCLUSIONS The transport and storage sector, like other sectors of the Spanish economy, is experiencing the effects of the current economic crisis. The Annual Services Survey prepared by the Spanish National Institute of Statistics (INE) shows that between 2008 and 2013 there was an 11.9% reduction in turnover and an 18.3% reduction in paid workers in companies in sector. However, in the same period, there was a 7.8% increase in labour productivity. The main aim of this study was to determine the impact of factors related to innovation and the environment, among others, on labour productivity in the Spanish transport and storage sector. Few studies are available on this topic, which is most likely due to the scant statistical information available for the sector analysed. The structural analysis model used in this study was an extended version of the well‐ known Cobb‐Douglas production function applied to data obtained from the Technological Innovation Panel (PITEC). PITEC is a panel type database (for 2003‐2013), which was developed jointly by the Spanish National Institute of Statistics (INE) and the Spanish Foundation for Science and Technology. We expanded the classic Cobb‐Douglas production function by incorporating the following variables: firstly, three variables that indicate whether the company has implemented innovaon in products (I1), processes (I2), or the organization (I3)inthe2years preceding the reference year; and, secondly, a variable that indicates whether these innovations are relevant to the company’s aim of reducing the environmental impact ‐EI‐ of its activity (i.e., the company's aim is eco‐innovation). We also incorporated other control variables related to other characteristics of the company and to the year the observations were conducted. The proposed production function is: ೙ ೙ for =1,2,3(1) where represents production estimated by the company’s number of sales, the stock of deflated physical capital, the number of employees, the elasticity of production in terms of physical capital, the elasticity of production in terms of labour, the semi‐ elasticity of production according to each type of innovation, and the semi‐elasticity of production taking into account the relevance of eco‐innovation to the company. The equation to estimate was obtained by taking logarithms in (1) and subtracting in both members the work factor : (2) 3.RESULTS&FINDINGS 1. Firstly, the values of elasticities of productivity in terms of capital stock (α=0.2094) and of the number of workers (β=0.7747) suggest the existence of constant returns to scale of the production factors, which was confirmed by the Wald test. 2. The introduction of innovation measures in this sector is positively associated with productivity, as shown in the estimated semielasticities. Product innovation (22.71%) and organizational innovation (21.47%) have a greater impact than process innovations (9.04%). 3. Companies in this sector that value the reduction of the environmental impact of its activities by implementing innovations obtain improvements in labour productivity (3.41%) compared to companies that do not value this aim. 4. Other factors, such as belonging to a business group or obtaining aid from the Spanish government to implement innovation activities, also have a positive impact on labour productivity in companies in this sector. 5. Finally, the region in which the headquarters of the transport and storage company is located has a marked impact on productivity. Compared to other Spanish reference regions, the impact on productivity is positive in Madrid and Catalonia (9.92% and 14.37%, respectively), but negative in Andalusia (‐20.34%). As indicated, the data used in this study was obtained from the PITEC. The sample comprised 1920 observations of 225 transport and storage sector companies (group H of the National Classification of Economic Activities, 2009) for the period 2005 to 2013. Equation (2) was estimated by Ordinary Least Squares, with labour productivity as the dependent variable. Table 1 shows the results. Table1.Labour productivityequation. Notes: Significantat:*1%,**5,***10%. White‐correctedstandarderrors. Thecoefficientsofthevariablescorrespondingtotheyearof observationhavebeenestimated. Source: PITEC Variables Coefficient SD Capitalstock(log)10.2094* 0.0413 Numberofworkers(log)1‐ 0.2253* 0.0171 Inn.Products 0.2271* 0.0555 Inn.processes 0.0904*** 0.0475 Inn.organitation 0.2147* 0.0610 Eco‐innovationaim 0.0341** 0.0141 Belongstoabusinessgroup 0.1665* 0.0421 Governmentfunding 0.2349** 0.0944 CompanybasedinMadrid 0.0992*** 0.0516 CompanybasedinCatalonia 0.1437** 0.0598 CompanybasedinAndalusia ‐ 0.2034** 0.0801 Cons. 10.8188* 0.1321 Obs.1920(225firms) AdjustedR2:0.2247 Ftest:F(19,1950),31.03* RSS:1471.23 1WaldtestH0:α+β=1:0.86(0.3541)