Does ICT Enabled Innovation? (Preliminary version)

Transcription

1 Does ICT Enabled Innovation? (Preliminary version) June 15, 2009 Leila Ben Aoun 1 2 3, Anne Dubrocard Abstract The aim of this paper is to present a model estimating the probability to innovate depending on ICT uses and equipments. To reach it, after an introduction to define the background of this question, we define a model in which decision to innovate depends ICT uses and equipments; this model is estimated by an econometric dichotomous Probit model applied to a sample of 300 Luxembourg firms. The dataset comes from a unique merge of the Community Innovation survey 2006 and the Information and Communication Technologies Survey From this first step analysis the estimates show that different ICT and ICT equipment affect the probability to innovate. Keywords: Innovation, ICT, Probit models, Luxembourg. JEL Classification:O30,031,032,C25. 1 Authors are researchers at the CRP Henri Tudor and are involved into the Partnership with the Observatoire de la Compétitivité and the Statec which runs under the research convent signed by these institutions in leila.ben-aoun@statec.etat.lu,leila.benaoun@tudor.lu anne.dubrocard@statec.etat.lu,anne.dubrocard@tudor.lu 2 Centre de Recherche Publique - Henri Tudor 29, avenue John F. Kennedy L-1855 Luxembourg 3 STATEC - Service central de la statistique et des études économiques, P.B. 304, L-2013 Luxembourg 1

2 1 Introduction ICT is closely linked to the ability of firms to innovate, i.e.introduce new products and services, new business processes, and new applications. Firms that have already innovated achieve much better results from ICT than those that have never innovated. Moreover, ICT has helped facilitate the innovation process, for example in speeding up scientific discovery. ICT has also fostered networking, which has enabled greater outsourcing of R&D and enabled informal learning between firms, which is key to innovation in services firms. (OECD 2003) Information and Communication Technologies are a major driver of growth and innovation. Many studies on ICT and economic growth undertaken at macro level as well as at firm level show that the intensity of ICTs use combined with change in organization or increasing workers qualification contribute to improve the countries growth - throughout improving performance at firm level studies (OECD 2003 for some evidence from international comparison and Pilat 2004 for a literature review). Indeed, some evidence has been found demonstrating the impact of innovation on firm s performance and productivity. Nevertheless, how do the ICTs drive innovation is a relevant question that has not been so much developed notably throughout quantitative approaches. ICT enables innovation in science and technology and in organizations. Knowledge flows transfer and share as well as real-time network increase innovation in sciences and technology and, moreover, ICTs allow and enforce new practices and arrangements so called e-management, e-business, e-commerce and so on, that are organizational innovation themselves and improve performance of the firms. This kind of phenomenon requires analysis at firm s level to be catch. In this perspective, OECD set up a Microdata Project on Innovation in Existing working group has been invited to propose microdata-based work. One of the Working Party on Indicators for the Information Society (WPIIS) project aimed to look at the impact of ICT on Innovation. Moreover, project addressed following issue: How do 2

3 ICTs act as an enabler of innovation, with a special concern to organizational innovation? In this particular context, this paper aimed to identify and characterize the link between IT use and innovation at firm level for Luxembourg on behalf the work undertaken with the WPIIS. The main purpose is to measure in what extend firms whose have an extensive use of ICT are more often the one who innovates. ICT surveys provide information about type and number of connexions installed, type of internet technologies used and the way to use it with different as well as characteristic of labor skills. Nevertheless, there is little information regarding the firm characteristic and particularly their economic performance. So, in order to measure impact of ICT use and diffusion, other sources are needed. To carry out a measurement of impact on innovation, information can be drawn from the CIS. In order to achieve this goal, we use a matched database linking both innovation and ICT use variables throughout a unit firm level identifier. Since 2002, Luxembourg performs annual firms survey on ICT use. It covers a sample of around 2000 firms. Data coming from 2006 has been retained to match with the Community Innovation Survey 2006 survey, the later collecting information about innovation activities and results during this overall period. The Community Innovation Survey and ICT survey are coordinate via Eurostat and provides harmonized statistics on both business innovation and uses of the ICT in European Union countries. Underlined waves of both Community Innovation Survey and Survey on ICT usage and e-commerce in Enterprises sampled into the Luxembourger firms population had been matched providing a sample of 349 observations. Exploring and clarifying links between ICT use and dissemination that seem to be key factor to foster innovation in services firms is of particular interest in particular context of economy of Luxembourg mainly driven from services. Moreover, matching Luxembourger s data from ICT and Innovation survey had never been done before. This paper is organized as follow: section 2 presents the background, section 3 3

4 specify the model, section 4 describes variables construction and descriptive statistics and in section 5 results obtained. Section 5 concludes summarizing findings presenting ideas for the future analysis. 2 Background For years, studies failed in the aim of identifying and measuring impact of ICT investment on national growth and productivity. Large ICT investment undertaken in order to decrease cost of communication and coordination within and outside the firms, changing work and life of every day seems to have no impact on productivity growth. To overtake issues related to measurement and identification of the impact of ICT investment on growth at macro level, studies has been undertaken at micro level providing new evidence from different countries. Regarding ICT impacts and measurement, Ciccone Di Maria (2004) using data from ICT survey 2003 found a link between degree of use of internet and e-commerce and performance. Performance, measured throughout an index of perceived competitiveness earnings, increase with ICT investment supporting e-commerce development. Micro-level data allowed to highlight intangible organizational investment and products and service innovation associated with computer (Brynjolfsson and Hitt (2000)). It Seems to be clear now that, outside delay effect and difficulties to catch intangibles throughout national statistics, ICT investment impact and efficiency are closely related to other factors. OECD (2003) pointed complementarity to skills, organizational change and innovation at the firm level. While Brynjolfsson and Hitt (2000) investigate the complementarity investment needed in business process and work practices to match organizational structure to technology capabilities, some authors emphasize the skill composition impact on firm performance (see Pilat D. 4

5 (2004) for a short review). In order to achieve effective results from ICT investment, the part of innovation is not the less complex to analyze. Factors that drive or enable innovation have been of interest for Luxembourg National Statistics office which launch a study to determine how factors like firm and market characteristics influence innovative performance and at the end firm performance. A-L. Asikainen (2008) using data from the CIS 2004, found that firm size, sectors and market structure, as well as research and development intensity, trade share and foreign ownership matter a lot and are all linked to innovative activities. Those results are in accordance with numerous studies conducted in other countries. More precisely, probability to innovate increases with size but investment per employee and results obtained decrease. Belonging to a holding increases significantly probability as well as propensity to innovate. Competition and demand pressures are important factors to decide to innovate. Issues in identification and measurement of ICT and innovation relationship come from the double nature of information technologies. First, those are major technological innovation (cluster of innovations) which in turn, throughout new application and process allows faster development in innovation process itself. So, ICT are enabling innovation and so far productivity of all input. But in turn, innovation process and producers accelerate and modify the ICT deployment in a co-inventory process whose make ICT useful. At the European level some studies has been performed regarding impact of being ICT-based innovators on firm performance. P. Koellinger (2008) using a sample of 7,302 European enterprises, shows that all studied types of innovation, including Internet-enabled and non-internet-enabled product or process innovations, are positively associated with turnover and employment growth. Firms that rely on Internet-enabled innovations are at least as likely to grow as that rely on non- Internet-enabled innovations. Impact of being innovative on ICT adoption have been explored by Hempell 5

6 (2002). Using German data, he shows that impact of ICT on productivity is much higher for firms that have deployed process innovation in the past years. At the end, efficiency of ICT investment depends on complementarity of ICT use. Hempel (2004) found that ICT investment has to be coupled with product or process innovation or with change in organization or with permanent non-technological innovations, to increase impact measured on productivity. From German data used, he obtained the maximum impact on Total Factors Productivity for services enterprises with permanent innovation s activities. Impact of ICT on innovation capabilities has been explored by Van Leeuven and van der Wiel (2003). Using a balanced panel data of market services firms in The Netherlands, they show that the average productivity of innovators increase for those identified as high intensity in ICT use. It means that in order to obtain complete gains from their innovation efforts, firms has to achieve beforehand a high level of ICT adoption. Abello and Prichard (2008) from the Australian bureau of Statistics exploring business use of IT and innovation using linked firm-level data, found important correlation between being innovator and different measurement of ICT use such as IT skills, connecting Internet throughout broadband,web presence, ordering via Internet and linking this process to other business systems. Fine tunning results by estimating logistic regressions, they found that different IT and non-it factors significantly explain each type of innovation. (For example, while employing an IT specialists remains a parameter enforcing propensity to innovate in a broad sense, it fails explaining each type of innovation separately except for organizational/managerial innovation.) From a technical point of view, it means that the sense of causality has to be carefully controlled when stating the model. None of the previous studies reviewed above and using Luxembourger s data included ICT use as innovation enabler.element presented above can be sum up in the following conceptual framework: 6

7 Furthermore and closing the description of this framework, more and more literature investigates the firm and market environment as its seems that impact of ICT use depends on its combination with other factors. Because information technology and Internet have to do with network they have also specific economic property - constant fixed costs and zero marginal costs its generalization has huge impact on market structure. Varian (2000) analysis the relationship between technology and market structure and point that the challenge facing us now is to re-engineer the flow of information through the enterprise, and not only within the enterprise the entire value chain is up for grabs. It seems that the diffusion of ICT and especially network technologies is changing shares of the gains along the value chain and, moreover, has an impact redesigning the firm s frontiers and market competition. Brynjolfsson and Hitt (2000) highlight those transformation with examples of vertical integration or renewed definition of business for existing firms directly induced by ICT technical and organizational capabilities. ICT modifying organizational practice changes as well the pace of change in producing new technologies and as a result, market conditions. Askenasy, Thesmar and Tuening (200) explore the role of time based competition allowed by new technologies of information. It seems that widespread ICT diffusion boost value of innovation and R&D investment among firms, increasing in the same time the competitive pressure on the product market which in turn enforce more reactive organizational forms renewing the 7

8 original trade-off between creative-reactive-decentralized and efficient-hierarchicalbureaucratic models of organization. This kind of approach highlight both ICT and organization impact on ability to innovate in process, product and organization. 3 Model We analyze the impact of different level of ICT use of the firm on their probability and propensity to innovate testing different definitions aiming to address the special concern of measuring impact of organizational innovation. In this perspective, we consider, in one hand, two types of technological innovators : the one which introduced new goods or new services, and whose have been deploying new in process, in other hand the non technological innovators that introduced major organizational change or marketing innovation. We develop a model in which a firms innovative behavior depends on several factors and moreover we focus our attention on the ICT use variables. We suggest in our approach that different kind of ICT do not have the same impact in the different kind of innovation. For this varieties of innovation ; we only observe a dichotomous variable with value 1 if the the firm during the last 2 years has introduce an innovation ( an innovating firm ) and 0 otherwise ( a non-innovating firm ). We do not have information on whether this innovation had a positive/ null or negative impact on the firm except in the case of product innovation where they are asked give the percentage of your total turnover in 2006 from goods and service innovations introduced during that where only new to your market or only new to your firm. Due to this lack of information on the spillover for the other innovations (such as reduction of cost for the organizational innovation) we have chosen to not measure the impact of innovation on performance in this first step study. 8

9 The probability to innovate is modelized as follow : P r(y = 1) = β X φ(t)dt = Φ(βX) (1) where Φ is normal distribution function and we suppose (as usual) an error term with normal distribution with mean 0 and variance 1. Moreover, when the ICT variable is endogenous we correct it in a first step and add to the residuals from this stage to the explaining variables. This variable will be correlated with the ICT variable but not with the innovative dummy[see Wooldridge,2002 (p )]. Following the OECD s Oslo Manual, innovation is defined as the implantation of a new or significantly improved product or process, a new marketing approach, or a new organizational method in business practices, the workplace or in external relations. This covers both technological and non technological forms of innovation, and both the creation of new products and their diffusion. Following this approach, the Community Innovation Survey 2006 provides categorized information for each type of innovation performed by firms. Innovators can be defined considering the firms declaring they have introduced: new products during the last past two years new services during the last past two years new process during the last past two years organizational change marketing innovation or some mixed of the above. 9

10 4 Sample and Variables For this analysis, we use microdata on the firms included in the ICT survey 4 in Luxembourg in ICT survey in the next sections - and the Community Innovation Survey survey - CIS 2006 in the next sections- not existing before our process of matching. The first collecting information about ICT equipment 4 and use in firm in Luxembourg in The latter collecting information about innovation activities and results for The Community Innovation Survey and ICT survey are coordinate via Eurostat and provides harmonized statistics on both business innovation and uses of the ICT in European Union countries. The ICT survey is annual while the CIS is every two years since In both surveys, the strata are based on industry classification (NACE codes) and firm size. The design of the ICT survey is to show how good the access to ICT is and how these technologies are used in the firms. Its questionnaire covers a broad range of activities such as access to and use of IT (PCs, workstations and Internet), e-commerce (use of online purchasing and sales), use of computer networks and network systems for the operation of orders and purchases, etc. Nevertheless, in the ICT survey, firms from the financial sector do not answer to the e-commerce part. Underlined waves of both Community Innovation Survey and Survey on ICT usage and e-commerce in Enterprises sampled into the Luxembourger firms population had been matched providing a sample of 349 observations. The sample does not contain the financial sector (37 firms) due to the fact that they are not considered in the e-commerce part. As shown in table [2], almost 7 firms among 10 are innovative in Luxembourg in 2006, but there are not innovating in the same way. Only 3 firms over 10 have introduced a new product between 2004 and 2006 when half of the sample recognize an organizational change during the same period. From the table [3] we have an 5 Complete reference Metadata at science_technology_innovation/data 10

11 overlook of the sample depending on the size and the NACE 6, but also the main market scope. The table [4 ] offers a cross view of innovation and what we consider as control variables. First of all, small enterprise (between 10 and 19 employees) have the lower percentage of innovators in all category considered,and reversely, firm with more than 100 employees innovates much more. But considered the size with the intermediate category there is not a clear linear relationship between the size of the firm and the decision to innovate. Considering the statistics by NACE it is obvious to admit that firm in different NACE, meaning in different economic activities, are not innovating in the same field. Manufactural firm are mainly concentrated in process and product innovation whereas firms from transport and communications are more focused in organizational change. Housing and rental sector 7 is as well targetting organizational changes and services innovations. In this paper we are interested in how ICT enabled innovation, thanks to the ICT survey we get many information about their equipment and their use. From the table [5] and [6] we note that almost 9 firm among 10 are connected with fast internet and around 70% of the sample own a web page. Some variables used in our model would come from composite variables and some as scale of use. Less than one firm over 10 is doing only e-commerce 8 but more than 40% are doing e-commerce and owning a website. From the ICT survey we have constructed 2 scales, the fist one scale kind ICT is the score (checked with Cronbach s alpha) of the following variables : use of extranet, electronic forum, ERP, intranet and visio/videoconference. This scale indicates how the firm is from low ICT user to high ICT user. In our sample, one third are considered as low user (they use none of this ICT) and 10% are considered as well equiped. 6 NACE refers to the NACE Revision 1.1 in Luxembourg. 7 Housing and rental and business services sector are defined as NACE 72 to 74 from NACE Luxembourg Revision e-commerce is defined as selling and/or buying over internet. 11

12 The second scale is scale ICT internet which is the score of the following variables : use internet for banking and financial services, training or education, market monitoring, receiving digital products, providing after sales support and using open source. This scale inform us about the motivation to use internet, and it easy to see that the firms in Luxembourg use internet for more than one reason and around 60% exploits internet for at least 3 reasons and the main one is banking & financial services but also market monitoring. To conclude our first look at the behavior of the firm, around 40% of employees in this sample are connected to internet and 35% of the firm have at least 50% of employees connected to internet but only a quarter has a higher degree. 5 Results The table 7 reports the results of this analysis through the marginal effects and the standard deviation. The first column reports results of all type of innovation, the second one report the results of product and/or service innovation. The third one reports a special case where only product innovation. The last 3 columns are the result for the process, organizational and marketing innovation. All this regressions are based on the same sample (N=289). In unreported results, we take into account of possible endogeneity effects of ICT variables. The regression results show, as we mentioned in the previous section that the link between different ICT use or equipment and innovating is not clearly defined from the estimates. To examine the effect of the ICT on product innovation, it appears that having 3 endowments over the 5th defined in scale kind ict 9 increase the probability to be innovator. However, a firm which has internet and do some e-commerce and does not have a website has a lower probability to be innovator in product &/or services. This result can be explained by the fact that this combination is not optimal or is not enough 9 let remind to the reader that scale kind ict is a score of 5 ICT: extranet,intranet, forum, ERP, and visio or videoconference. 12

13 if we think in term of production cycle. This observation persist if we only focus on product innovation only. Using ICT or being well equiped on ICT do not have an impact on product or services innovation. Whereas the equipment on ICT has an impact on the probability to recognize a process innovation and the reason to use internet an impact on the organizational innovation. If we consider the skill of the labour force, it appears that the higher the percentage of high degree employees is in the firm and the higher the probability to innovate. In parallel, if the main market of the firm is the national market, it diminishes the propensity to innovate in product and/or services or in change of organization. Concentrating our attention on the scale of ICT use the estimates show that the lower the firm is in the scale (only 1 reason to use internet) and the lower the probability to innovate is. At the opposite, if the firm is high user of internet -use internet for all possible reasonsthe lower the probability to observe a change in the organization. We can assume that this phenomena is linked to the maturity of the company, the idea behind is that high user of internet are completely mature in their evolution. Maurity of the firm can explain as well the fact that when the company is only equiped bu internet and is doing e-business without having a website it decreases the propensity to have a product innovation. Finally, marketing innovation is not explained by ICT use or equipment. To conclude, from this first analysis we have shown that innovation can not be considered globally but must be study with all its subtility. We cannot consider marketing innovation and organizational innovation in the same way. 6 Conclusion Conclusion about ICT use and equipment linked with innovation is not clearly defined. The maturity of the firm is an important component of the firm and we have to focus on other point of view to emphasize the impact of ICT on innovation. An interesting starting point to this analysis would be to construct a pseudo panel 13

14 using different ICT surveys and evaluating the maturity in ICT of the firm. In the further analysis two main point would be treated. The first one is to link this ICT and innovation to the performance of the firm as a Crepon Duguet Mairesse setting. The second point will be to estimates the production function of those firms taking into account of the ICT and the non ICT capital. 14

15 References OECD (2003) ICT and Economic Growth - Evidence from OECD Countries, Industries and Firms in The Economic impact of ICT : Measurement, Evidence and Implications Ciccone, J., Di Maria,C.H., Des technologies de l information et de la communication aux affaires électroniques Le cas des entreprises luxembourgeoises (2006) Economie et Statistiques N 4/2006, Statec, Luxembourg. OECD (2005) The measurement of Scientific and Technological Activities - Oslo manual - Guidelines for collecting and interpreting innovation Data 3dr Edition OECD (2005) Economic and social impact of broadband OECD (2008) Innovation Strategy: Harnessing the power of Innovation SG/INNOV(2008) Abello, R., Prichard, G., Exploring Business use of IT and innovation Using linked firm-level data WP Australian Bureau of Statistics (juin,2008) Crépon, B. Duguet, E. Mairesse, J Research and Development, Innovation and Productivity: An Econometric Analysis at the Firm Level. Economics of Innovation and New Technology, vol. 7, p Pilat, D. (2006) Le paradoxe de la productivité: l apport des micro-données, revue économique de l OCDE, n 38. Asikainen, A-L,Innovation and Productivity in Luxembourg STATEC Economie et statistiques n23, avril

16 Koellinger P., The Relationship between Technology, Innovation, and Firm Performance: Empirical Evidence on E-Business in Europe ERIM Report Series Research in Management ERS ORG, mai

17 7 Annexes Table 1: Definition of innovation Variable definition new good in the firm has introduced new or significantly improved goods new service in the firm has introduced new or significantly improved services in the firm has introduced new or significantly improved goods new product & or services or services in the firm has introduced new or significantly improved methods of manufacturing or producing goods or services OR logistics, delivery or distribution methods OR supporting activities for your pro- new process cesses in introduce New business practices OR New knowledge management systems OR New methods of workplace organization OR organizational change New methods of organizing external relations in introduce Significant changes to the design or packaging OR marketing innovation New or significantly changed sales or distribution methods Table 2: Summary statistics about innovation Variable Mean Std. Dev. All kind Product or service Product only Process Organizational Marketing N 312 source : CIS to read this table : 66.7% of the sample declare being innovator.

18 Table 3: Summary statistics about innovation Variable Mean Std. Dev. N Firm from 10 to 19 employees Firm from 20 to 49 employees Firm from 50 to 99 employees Firm with more than 100 employees Manufacture Industry Automobile and retails Transports and communications Housing and rental National market Grande Region market European market Other market source : CIS 2006 ICT survey 2007 from Luxembourg. To read this table : 27.6% of firm of the sample have 10 to 19 employees.

19 Table 4: Innovation and control variables Control variables all kind Product product Process Organizational Marketing &/or services yes no yes no yes no yes no yes no yes no size Firm from 10 to 19 employees Firm from 20 to 49 employees Firm from 50 to 99 employees Firm with more than 100 employees NACE Manufacture Industry Automobile and retails Transports and communications Housing and rental MARKET National market Grande Region market European market Other market source : CIS 2006 & ICT survey 2007 from Luxembourg. To read this table : 57% of firm with 10 to 19 employees are innovative. 17% of firm with 10 to 19 employees are innovative in product only.

20 Table 5: Summary statistics about ICT Variable Mean Std. Dev. N No internet connexion Modem ISDN xdsl Number of Internet uses Number of automatic IT links Dummy own web page Number of web page facilities Dummy Purchases over the Internet Dummy sales over the Internet No internet access or internet access without website & e-commerce Internet access + Website Internet access + e-commerce Internet access + Website + e-commerce scale kind ICT scale kind ICT = scale kind ICT = scale kind ICT = scale kind ICT = scale kind ICT = Scale kind ICT = Extranet Electronic Forum Enterprise Ressource Planning (ERP) Intranet visio or videoconference Open source or third party free source : CIS 2006 Scale kind ICT is the score of 5 ICT equipments : extranet,intranet, forum, ERP, and visio or videoconference.

21 Table 6: Summary statistics about ICT (continue) Variable Mean Std. Dev. N reason to use internet Banking & financial service Training or education Market monitoring (e.g prices) Receiving digital products Providing after sales support Scale ICT internet Scale ICT internet= Scale ICT internet= Scale ICT internet= Scale ICT internet= Scale ICT internet= Scale ICT internet= Scale ICT internet= % of all employees connected to the Internet At least 50% of all employees connected to the Internet % of employees with degree Main market is National market Scale ICT internet is the score of the following variables : use internet for banking and financial services, training or education, market monitoring, receiving digital products, providing after sales support and using open source.

22 % of all employees connected to the Internet Table 7: Results from probit estimation all type product service or product only process organizational marketing (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) Scale kind ICT=1 (d) (0.076) (0.09) (0.072) (0.082) (0.088) (0.06) Scale kind ICT=2 (d) (0.079) (0.106) (0.094) (0.100) (0.100) (0.086) Scale kind ICT=3 (d) * * (0.103) (0.123) (0.121) (0.125) (0.120) (0.107) Scale kind ICT=4 (d) * (0.138) (0.159) (0.158) (0.158) (0.160) (0.117) Scale kind ICT=5 (d) (0.234) (0.223) (0.168) (0.210) (0.194) (0.185) Scale ICT internet = 1 (d) ** * (0.16) (0.169) (0.129) (0.133) (0.136) (0.145) Scale ICT internet = 2 (d) (0.164) (0.167) (0.131) (0.138) (0.140) (0.159) Scale ICT internet = 3 (d) (0.169) (0.171) (0.147) (0.156) (0.147) (0.159) Scale ICT internet = 4 (d) (0.178) (0.176) (0.138) (0.124) (0.144) (0.164) Scale ICT internet = 5 (d) (0.194) (0.181) (0.194) (0.176) (0.165) (0.164) Scale ICT internet = 6 (d) *** (0.247) (0.218) (0.136) (0.162) (0.104) (0.062) Internet access + e- commerce (d) Internet access + website + e-commerce (d) % of employees with a higher degree Main market is National market (d) *** (0.114) (0.079) (0.082) (0.091) (0.119) (0.09) (0.072) (0.075) (0.065) (0.071) (0.077) (0.049) 0.512** 0.424** (0.163) (0.164) (0.132) (0.146) (0.165) (0.101) ** ** ** (0.065) (0.072) (0.063) (0.066) (0.071) (0.044) Number of employees ** (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) Squared number of employees Automobile reparation and retails (d) * (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) *** (0.096) (0.093) (0.06) (0.053) (0.098) (0.065) Transports and communications(d) ** *** *** * *** (0.087) (0.067) (0.066) (0.087) (0.033) Housing and rental (d) * *** ** *** (0.137) (0.098) (0.074) (0.119) (0.034) coefficient are marginal effects ( dy/dx is for discrete change of dummy variable from 0 to 1) standard deviation are in brackets (d) : dummy variables 0-No ;1-Yes significance level : * p < 0.05, ** p < 0.01, *** p < 0.001