J Knowl Econ DOI 10.1007/s13132-016-0441-1
ICT, Innovation and Productivity: Evidence Based on Eastern European Manufacturing Companies Aleksandra Skorupinska 1 & Joan Torrent-Sellens 2
Received: 4 June 2016 / Accepted: 15 December 2016 # Springer Science+Business Media New York 2017
Abstract The main motivation behind this study is to evaluate the relationships among ICT, management practices, innovation and human capital in a sample of manufacturing enterprises in Eastern European countries. Using data from the Management, Organisation and Innovation Survey 2009 for a representative sample of 444 companies in Bulgaria, Poland, Romania, Serbia and Ukraine and using structural equation modelling and ordinary least squares, we examined direct and indirect determinants of labour productivity. The principal finding that emerged from the study is that wage was the main direct determinant of labour productivity. Moreover, the relationship between ICT and its complementarities and productivity has been established indirectly, mainly by enterprise workers’ use of the ICT. The results of the investigation bridge the gap in insufficient academic research about Eastern European countries, extend existing research on the company-level labour productivity determinants and enable comparison of the results at the international level. Keywords Eastern European Countries . Company-level Productivity . Information and Communication Technologies (ICT) . Innovation . Manufacturing Industries JEL J24 . L60 . O33
* Aleksandra Skorupinska
[email protected] Joan Torrent-Sellens
[email protected]
1
Internet Interdisciplinary Institute (IN3), Open University of Catalonia (UOC), Avinguda Tibidabo, 39-43, 08035 Barcelona, Spain
2
Economics and Business Studies Department and Internet Interdisciplinary Institute (IN3), Open University of Catalonia (UOC), Avinguda Tibidabo, 39-43, 08035 Barcelona, Spain
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Introduction The widespread use of Information and Communication Technologies (ICT) is one of the main distinguishing features of today’s economic activity (Jovanovic and Rousseau 2005; Jorgenson and Vu 2007). The reason for this is twofold: first, their direct contribution to increased productivity and economic growth; second, their indirect contribution resulting from the generation of complementary innovations that improve the economy’s Total Factor Productivity (TFP) (Pilat 2006; Jorgenson et al. 2011; Ceccobelli et al. 2012). From the perspective of the impact analysis of ICT investment on productivity and economic growth, empirical evidence shows the following: 1) the rates of return on digital investment are relatively much higher than those on investment in other physical components; and 2) the reason is that the digital investment and use often go hand in hand with other endeavours, usually human capital improvement and organisational and institutional change (Bresnahan et al. 2002; Arvanitis 2005). The link between innovation and ICT has been identified in literature as a set of internal knowledge externalities to explain company productivity (Venturini 2015). The accelerating point of this literature is established with the consolidation of ICT as the general purpose technologies (Venturini et al. 2013). However, ICT does not give rise to generalised productivity improvements until companies and their workers have achieved the required technological, educational/training, strategic, organisational, labour and cultural competencies. In other words, the role of ICT as a generalpurpose technology needs organisational and business process changes to fully exploit its growth opportunities. In this context, the effects of ICT on company productivity are indirect. Complementary relationships (co-innovation) are established with other dimensions, in particular with human capital and workplace innovation. These spillovers are widely demonstrated in research using company data (for a review of this literature see Cardona et al. 2013; Díaz-Chao et al. 2015). Transition countries of Eastern Europe (EE) face considerable challenges in adapting their economies to effectively compete in regional and global markets. It is a key issue for them to find a way to increase their productivity and adapt their economies’ structure to global-knowledge competition, promote co-innovation and develop new goods and services that respond to the changing domestic and international demands. Thus, the impact of digital technological changes and their co-innovation processes on productivity is an important aspect in the region’s economic performance. The main motivation behind this study is to evaluate the relationship among ICT, management practices, innovation and human capital, and to evaluate the influence of these variables on productivity in a sample of manufacturing enterprises in Eastern European countries. The following are the main questions underpinning the research: 1) Does the existence of new co-innovative productivity sources (the usage of ICT, workplace organisation and human capital) affect the performance of manufacturing enterprises in Eastern European countries? 2) What are the interrelationships in these complementary factors? 3) What are the differences in the relationships of complementarity (co-innovation) as the sources of productivity in Eastern European manufacturing enterprises? The remainder of this paper is organised as follows: The literature review section presents a survey of empirical literature on the relationship among ICT, innovation and company productivity. The data section describes the Management, Organisation and
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Innovation (MOI) dataset and presents descriptive analysis of the sample. The empirical results section reports the results of the ordinary least squares (OLS) model, the structural equation modelling (SEM) model and the empirical findings. Finally, the conclusion provides a summary and policy implications based on those findings.
Literature Review - ICT, Innovation and Company Productivity Much effort has been put into research to understand the so-called Solow Paradox concerning the limited evidence of the positive productivity impact of ICT (Jorgenson and Stiroh 1999). The importance of ICT is a much debated question with extensive literature focused on explaining and understanding their role in economic growth, productivity and efficiency. Significant progress has been noted since 1990 in the analysis of ICT and productivity. Most empirical studies have been performed at the microeconomic level of company and industry, examining the relationship among ICT and economic growth and productivity. At the macroeconomic level, fewer studies have been conducted because of a shortage of datasets specifically related to the ICT investment and usage and other relevant national characteristics. Company-level analysis complements analysis conducted at the macro-level and enables researchers to better understand ICT diffusion effects and, especially, to more adequately reflect those quality changes brought about by ICT. Regarding the relationship between ICT and productivity at the company level, there are findings that ICTs alone are not enough to affect productivity (Bresnahan et al. 2002; Arvanitis 2005). Ignoring other complementarities may introduce bias to the analysis and overestimate or underestimate the effect of ICT on productivity. Significant changes within the company structure, such as a shift in the employment structure from low to high skills, the diffusion of ICT and the redesign of a company workplace organisation, can be observed over recent years and present new challenges for companies. Many authors have pointed out the importance of these interrelated changes as a shift towards a ‘new company paradigm’, which they have characterised using different labels: from a ‘mechanistic’ to an ‘organic’ company structure (Burns and Stalker 1994), from the ‘mass production model’ to the ‘flexible multiproduct company’ (Milgrom and Roberts 1990), or from a ‘tailoristic’ to a ‘holistic’ organisation of work (Lindbeck and Snower 2000). ICT potential will not be realised without business model changes and an increase in human capital and ICT skills (Bresnahan et al. 2002; Arvanitis 2005; Díaz-Chao et al. 2013). The main results that the international empirical evidence has suggested in relation to the new co-innovative sources of company productivity are presented in this section. The results are presented by country and date of publication, with special attention paid to the Eastern European countries. Most of the company-level studies have been focused on highly developed countries. The empirical study for the United States (Bresnahan et al. 2002) formulated and confirmed the new theory of skill-biased technical change (SBTC). The authors have shown evidence of a positive correlation between ICT use and investment, workplace organisation and skilled labour which have affected productivity. Moreover, they have concluded that with the growing spread and access to ICT, investment in complementarities is crucial, particularly in skilled labour. Brynjolfsson and Hitt (2003) identified a set of new organisational practices in companies (freedom of
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information and communication, decision-making rights, performance-related incentives, and investment in education and training) that, together with digital innovation, are determinants in the explanation of productivity growth. Furthermore, with regards to the United States, there are Black and Lynch’s (2001, 2004) studies of manufacturing establishments showing that productivity growth during the 1990s has its source in workplace organisation changes and innovations (employee involvement, team work, incentive pay and decision-making autonomy) along with the diffusion of computers. Furthermore, Autor et al. (2003) reinterpreted the role of technical progress in shaping labour markets and the argument of skill-biased technical change theory has been questioned. Routinization-biased technical-change (RBTC) states that ICT —and computers in particular— can replace labour in Broutine^ tasks (those tasks that involve step-by-step procedures and can therefore be codified) but not in Bnon-routine^ tasks. This leads to job polarization of the labour force, when the middle-class jobs disappear more than the jobs which require a few skills and expert jobs, which require many skills. The recent studies have confirmed the routinization-biased technical-change relation to productivity growth (Jung and Mercenier 2014; Cortes 2016). Investigations conducted in other countries followed the path of analysis initiated in the United States. In a sample of companies in Australia, Gretton et al. (2004) found interactions among the use of ICT and companies’ human capital, history of innovation, the use of advanced business practices and intensity of organisational restructuring (such as changes in the range of products and services, advertising, technical and onthe-job training, and business structure); they also observed a positive influence on productivity. Grimes et al. (2012) have confirmed that in New Zealand, adoption of broadband communications boosted company productivity by 7-10%. ICT effects appear consistent across the urban and rural locations, and across the high- and lowknowledge-intensive sectors. An analysis of panel data from British and French companies (Caroli and Van Reenen 2001) revealed that skilled workers adapt more easily to organisational changes. Keeping the above in mind, the authors presented empirical evidence of a relationship between workplace innovation and human capital, and its influence on productivity. Another comparative study of Swiss and Greek companies (Arvanitis and Loukis 2009) showed the positive effects of physical capital, ICT, human capital and new organisational practices on labour productivity. However, Swiss companies are more efficient in combining and implementing those factors, while in Greek companies, physical capital still plays a crucial role in relation to labour productivity. Research on Catalan companies (Torrent-Sellens and Ficapal-Cusí 2010) confirmed the role of new co-innovative sources in technology and knowledgeintensive companies. Among the remaining 80% of companies, no evidence was found to show that those sources had any impact. In Japan, Miyazaki et al. (2012) classified ICT applications into four stages of sophistication (non-performing ICT assets, sectionwide system applications, company-wide system applications and inter-corporate system applications) and found that the impact of ICT on company productivity increases with a successive stage of ICT use sophistication. In Germany, a set of studies (Hempell 2005; Hempell and Zwick 2008) based on several time samples of its industry’s companies also confirmed the existence of relationships of the dependence among labour productivity, digital technology and organisational innovation processes. The authors found that ICT fosters product and process innovations by facilitating employee participation and communication, and ICT investment is most productive in companies
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with experience gained from earlier innovations. In recent work, Hall et al. (2013) examine the company-level relationships among product, process, organisational innovations, productivity, research and development (R&D) and ICT, using data from Italian manufacturing enterprises. They found that R&D and ICT are both strongly associated with innovation and productivity, with ICT investment being more important for productivity. ICT and R&D contribute to productivity both directly and indirectly through the innovation equation, but are neither complements nor substitutes. However, in individual terms, they both appear to have large impacts on productivity, suggesting some underinvestment in these activities by Italian companies. Although the transition of Eastern European economies from centrally planned economies to market-driven systems occurred more than two decades ago and those countries now actively participate in the global economic community, publications on ICT in transition economies are sparse (Roztocki and Weistroffer 2011). According to Roztocki and Weistroffer (2008, 2011) there are several explanations for the scarcity of published research: Firstly, there is a lack of funding for this type of research. Therefore, much of the published research dealing with ICT in transitional economies has been carried out by researchers employed at institutions in the developed countries. However, the transitional economies that joined the European Union can now access more funds and more research should be expected from these countries in the future. Secondly, in the communist period, research was directed to disciplines other than ICT, such as physics and chemistry, and was mostly theoretical in nature, with hardly any market implications. Moreover, the effect of many administrative structures and procedures that were implemented in the past still remain. Furthermore, reforms have been concentrated on economic changes rather than academics, with the existing structures at many universities still inhibiting research productivity. Economies that were closed blocked international linkages, which affected innovation, knowledge spillovers and technology adoption. The character of the command economic system resulted in low competitiveness and technological obsolescence (Winiecki 2004). Another crucial problem was connected with the different institutional milieu and cultural backgrounds within CEE countries at the beginning of the transition, in contrast to those within Western Europe, which had an influence on implemented changes on the micro and macroeconomic level (Theodoulides and Kormancova 2013). The first publications concerning Eastern European countries evaluated the impact of ICT on growth at the aggregate level. Van Ark and Piatkowski (2004) compare the productivity performance of 10 Central and Eastern European countries (CEE-10) and 15 European Union (EU-15) countries during the 1990s, examining productivity and income convergence hypothesis. Their investigation gives more support to the convergence hypothesis. Moreover, they show that ICT capital in the CEE countries has contributed as much to labour productivity growth as it has in the EU-15 countries and that ICT capital on its own has not been an important source of convergence. They emphasise the importance of consistent progress in the economic, institutional and regulatory environment, in the creation of modern institutions, the implementation of market-oriented policy reforms, and the increases in innovation and improvements in the quality of human capital. There are some pioneers of company-level analysis, such as Stare et al. (2006), who explored a link between ICT and the performance of service companies in Slovenia. They confirmed the positive impact of ICT use on
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productivity; however, due to the absence of data on complementary expenditures for training and organisational change, the results might overestimate the impact of ICT. Table 1 summarises the main results for a broad set of studies. Most of the international empirical evidence has confirmed the complementarities of new co-innovative company productivity sources: ICT investment and usage, human capital and new forms of work organisation. However, more empirical studies are still needed in this field. Eastern European countries are noticeable by their scarcity of studies on ICT, complementarities and productivity.
Data Our empirical descriptive and econometric analysis is based on data from the Management, Organisation and Innovation (MOI) Survey 2009, a joint initiative of the European Bank for Reconstruction and Development (EBRD) and the World Bank Group (the World Bank). The MOI survey was undertaken for the first time in 2008-2009, covering 1870 manufacturing establishments with between 50 and 5000 employees from 10 Eastern European countries: Belarus, Bulgaria, Kazakhstan, Lithuania, Poland, Romania, Russia, Serbia, the Ukraine, Uzbekistan, with Germany as a developed country benchmark and India as a developing country benchmark. MOI interviews were conducted face-to-face with interviewers recruited by local survey companies and took place between October 2008 and April 2010. The interviews were conducted with managers and lasted on average 50 minutes. The response rate was 44%. The questionnaire comprised seven sections organised by topic. Initially, questions were posed about the characteristics of the company, such as legal status, ownership and number of years in operation. This was followed by sections on company management practices, organisation, innovation and R&D, and the degree of competition and labour. The MOI questionnaire was developed and tested in two pilot surveys prior to its implementation in the field. The two main objectives of the sample are to measure and compare management practices across the countries and to conduct a company performance analysis focusing on determining how management practices affect productivity and job creation in manufacturing. The MOI survey was used to determine if the quality of management practices is positively associated with various measures of company performance in Eastern European countries (Bloom et al. 2012; Schweiger and Friebel 2013). The survey uses a standardised survey instrument and a uniform sampling methodology to minimise measurement error and generate a sample representative of the manufacturing sectors in each country. Data are comparable across the countries and the sample size is large enough to conduct statistically robust analysis with levels of precision at a minimum of 7.5% for 90% confidence intervals (EBRD and World Bank 2008). Data from the MOI survey was complemented by company performance data (balance sheets and income and loss statements) from the Bureau van Dijk’s Orbis database. Given that the output variables from the Bureau van Dijk’s Orbis database are not available for all countries or even for all companies in a particular country, we run the risk that results are driven by the specific country. Performance data: the operating revenue and cost of employees are winsorised at 1% to limit the impact of outliers on the result (this means that all the data below the 0.5th percentile are set to the 0.5th percentile and all the data above the 99.5th percentile are set to the 99.5th percentile as in Bloom et al. (2012)).
J Knowl Econ Table 1 Literature Review Summary Authors
Region
Time period
Key results
1989-2002
Support for the convergence hypothesis in the comparison of CEE countries to EU-15 countries. Emphasis on complementarities to ICT investment.
Macroeconomic literature Van Ark and Piatkowski (2004)
CEE-10 & EU-15
Microeconomic (company-level) literature Caroli and Van Reenen (2001)
United Kingdom & France
1984,1990, 1992, 1996
Skilled workers more easily adapt to changes in organisation. Evidence of relationship among workplace innovation and human capital and their influence on productivity.
Autor et al. (2003)
United States
1960-1998
Computerization is associated with reduced labour input of routine manual and routine cognitive tasks and increased labour input of non-routine cognitive tasks (RBTC theory).
Bresnahan et al. (2002)
United States
1987-1994
Positive correlation of ICT, workplace organisation and skilled labour which has affected productivity.
Brynjolfsson and Hitt (2003)
United States
1987-1994
The set of new organisational practices in companies, together with digital innovation, are determinants in the explanation of productivity growth.
Black and Lynch (2001, 2004)
United States
1987-1993, 1997
ICT together with workplace organisation have a significant and positive impact on productivity.
Gretton et al. (2004)
Australia
1994-1998
Interactions among ICT use and human capital, innovation, business practices and organisational restructuring.
Hempell (2005)
Germany
1994-1999
ICT investment is linked to innovation and is most productive in companies with experience from earlier innovations.
Stare et al. (2006)
Slovenia
1996-2002
Positive impact of ICT use on productivity.
Hempell and Zwick (2008)
Germany
2002-2004
ICT fosters product and process innovations by facilitating employee participation and communication.
Arvanitis and Loukis (2009)
Switzerland & Greece
2005
Positive effects of physical capital, ICT, human capital and new organisational practices on labour productivity.
Torrent-Sellens and Ficapal-Cusí (2010)
Spain (Catalonia)
2003
No relevant impact of ICT use in 80% of companies. Significant delay in the implementation of co-innovative productivity sources in Catalonia.
J Knowl Econ Table 1 (continued) Authors
Region
Time period
Key results
Grimes et al. (2012)
New Zealand
2005-2006
Broadband adoption boosts company productivity.
Miyazaki et al. (2012)
Japan
2006
Impact of ICT on company productivity increases with a successive stage of sophistication in ICT use.
Hall et al. (2013)
Italy
1995-2006
R&D and ICT are associated with innovation and productivity. They contribute to productivity both directly and indirectly through the innovation equation.
Díaz-Chao et al. (2015)
Spain (Catalonia)
2009
Wage is the main determinant of labour productivity. The study establishes an indirect relationship between co-innovation and productivity in companies that initiate international expansion.
Cortes (2016)
United States
1976-2007.
Workers staying in routine jobs perform significantly worse in terms of wage growth than workers staying in any other type of occupation. Results of company predictions of RBTC theory.
Source: The authors’ own elaboration.
For the present study, we have included 444 complete cases from five Eastern European countries: 95 from Bulgaria, 41 from Poland, 84 from Romania, 110 from Serbia and 114 companies from Ukraine. Other countries were excluded from the analysis due to lack of financial data available in 2008. The companies operate mainly in low technology industries (52% of companies), where 37% produce textiles, textile products, leather and foot wear, and 33% produce food products, beverages and tobacco. 22% of companies belong to medium-low technology industries, where 53% produce basic metals and fabricated metal products. Furthermore, 21% of the sample are medium-high technology companies, where 40% produce machinery and equipment. Lastly, only 5% of companies operate in a hightechnology industry, where 64% produce medical, precision and optical instruments. The companies in the sample are medium-sized (below 250 employees) manufacturing companies (78% in total) and have on average 207 employees. Table 2 gives summary statistics of variables of interest for the sample of 444 companies included in econometric analysis as well as additional data to conceptualise. Most countries of Eastern Europe, during the transition from a command-driven to market-driven economy, have restructured and reallocated resources to foster great efficiency in the use of resources. Despite a significant growth in the service sectors after the transition, those countries’ manufacturing sectors still contribute significantly to their GDP. The manufacturing value added as a share of GDP ranged from 15% for Bulgaria to 21% for Romania. There is a significant gap between Poland and other EE
J Knowl Econ Table 2 Summary statistic Total
Bulgaria Poland Romania Serbia Ukraine
Average company productivity in thousands USD
47.26
37.66
136.48 39.84
54.51
21.66
Average wage in thousands USD
6.61
4.12
14.89
9.41
3.37
6.12
Companies with internet access (% of total)
90.09
95.79
100.00 96.43
80.00
86.84
ICT usage (% of employees using a computer)
23.33
20.53
32.61
20.11
31.92
14.52
Companies with patents (% of total)
31.08
33.68
29.27
20.24
20.91
47.37
Companies with product innovation (% of total)
63.96
63.16
80.49
60.71
60.00
64.91
Education (% of employees with a university degree)
19.78
18.15
15.97
14.92
12.53
33.08
Quality of management*
0.00
0.14
-0.04
-0.04
-0.13
0.05
Average number of employees
207.65 152.89
222.12 188.88
188.99 279.92
GDP per capita. PPP (current International $)
-
13.62
18.80
14.64
11.09
6.31
Manufacturing value added (% of GDP)
-
15.40
18.49
21.55
16.32
17.81
Country population (million)
-
7.59
38.15
21.48
7.32
46.05
Number of companies in sample
444
95
41
84
110
114
*Construction of the quality of management indicator is explained in the section’s OLS estimation. Source: The authors’ own elaboration. Orbis database, MOI survey and World Bank World Development Indicators (2009).
countries in labour productivity (measured as a company’s operating revenue in 2008 divided by the number of full-time employees). Considering this indicator, on average the companies in Poland have more than two and half times higher productivity than those in Serbia and more than three times higher productivity than those in the EU countries of Bulgaria and Romania. Regarding ICT indicators, an average 80% of manufacturing enterprises in Serbia have a high-speed internet connection, whereas in Poland all companies are connected. Moreover, in Polish and Ukrainian manufacturing companies, an average of more than 32% of employees regularly use personal computers on the job, while less than 15% do so in Romania and less than 20% in Bulgaria and Serbia. Polish companies are the most innovative in comparison with the countries in the sample. 80% of Polish companies, compared to 60% of companies in other countries introduced a new product or service in the last three years. Furthermore, a strong patent protection, which protects new innovations, is associated with higher levels of total factor productivity. Patents can also be used as a measure of the output of innovation. Patents are registered in almost half of the Ukrainian companies, in 34% of Bulgarian companies and in 29% of Polish. Companies in Ukraine have the highest share of employees with university degrees, as 33% of the total number of workers possess a university degree. In the rest of the countries, the share of employees with higher education varies from 12% to 20%. The final variable included in our analysis is the quality of management. On the average, the management practices are better in the Bulgarian companies included in the sample, followed by the Ukrainian companies.1 1 Quality of management scores differ from results of Bloom et al. (2012), due to different sample size included into analysis.
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Empirical Results The OLS Estimation Our methodology for the estimation of co-innovative sources of company productivity is an extension of the well-established traditional growth and productivity-accounting approach, based on the Solow growth model (Solow 1957) and its subsequent elaboration by Jorgenson and Griliches (1967). The co-innovative productivity sources are incorporated in the efficiency component (Total Factor Productivity, TFP). This is an important contribution in the analysis of the determinants of company productivity because this methodology allows us to incorporate several variables simultaneously (e.g. ICT use, work organisation or human capital). This allows the use of explanatory elements that go beyond pure investment, which contemplate the management and effective transformation of business activity. For example, in the case of ICT, investment in such technologies is not an automatic determinant of efficiency improvements. For improvements to be made, ICT goods and services must be used effectively by a company’s value elements, which entails the need to capture indicators of use. Our study is a one of the first empirical studies using this methodology for Eastern European manufacturing enterprises. In order to examine labour productivity sources in Eastern European manufacturing enterprises, we used the following as dependent variables: wage, ICT usage and infrastructure, product innovation, patents, human capital and organisation. The company production function of the Cobb-Douglas type takes the form: Yi ¼ Ai Kαi Lβi Iγi where, for any given company i, Y is the level of operating revenue; A is the production efficiency (Total Factor Productivity); K is the input of physical capital; L is the input of labour; and I is the input of intermediate production costs. The coefficients α, β and γ represent the elasticities of physical capital, labour and intermediate production costs over the level of company operating revenue. In line with the literature (Bresnahan et al. 2002; Arvanitis 2005; Timmer et al. 2010), innovative sources of productivity are incorporated into the production efficiency indicator. This element shows the effects of company innovation that are not associated directly with factors of production. Thus, the indicator of efficiency Ai takes the following functional form: Ai ¼ expðδ0 þ δ1 ICTUi þ δ2 ICTUIi þ δ3 INNOVi þ δ4 PATENTi þ δ5 EDUi þ δ6 ORGi Þ
After the logarithmic transformation of the first equation and after incorporating the production efficiency indicator model, innovative sources of productivity take the form: lnYi −lnLi ¼ β0 þ β1 ðlnKi −lnLi Þ þ β2 ICTUi þ β3 ICTIi þ β4 INNOVi þ β5 PATENTi þ β6 EDUi þ β7 ORGi þ εi
where, β0 (constant) incorporates the logarithmic difference of intermediate costs per worker, βi for i=1…7 represents the elasticities (coefficients) of the explanatory components of company productivity and εi is the estimation error.
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Finally, the labour productivity function of the Eastern European manufacturing enterprises, which would be estimated by the OLS method, takes the following form: LPi ¼ β0 þ β1 WAGEi þ β2 ICTUi þ β3 ICTIi þ β4 INNOVi þ β5 PATENTi þ β6 EDUi þ β7 ORGi þ εi
Regarding the specific indicators and variables used in the estimation, the following comments need to be made: The dependent variable, company labour productivity (LP), has been approximated by the logarithm of operating revenue in 2008 in thousands of USD divided by the number of full-time employees. This definition of LP as the dependent variable is similar to Hall et al.’s (2009) definition. Considering the independent variables, the procedure described below has been followed. Firstly, it should be noted that the logarithmic difference between the intermediate production costs and full-time employees, which is required for the conversion of the operating revenue indicator into added value, has been incorporated into the constant of the model to be estimated. Secondly, the effect of physical productive capital on company productivity has been captured by the logarithmic difference between the cost of employees in 2008 and the number of full-time employees (WAGE). The existing empirical literature on causal relationship between labour productivity and wages of workers presents opposite directions with respect to the flow of causality. The link between the variables is established through two routes – the marginal productivity theory and efficiency wage theory. In our analysis, we follow the efficiency wage theory developed by Alfred Marshall and supported by Katz (1986), Shapiro and Stiglitz (1984). Moreover, five additional arguments of the direction from the wage growth to labour productivity are analysed in Nayak and Patra (2013). In line with the recent empirical evidence (Lallemand et al. 2009; Faggio et al. 2010; Mahy et al. 2011) the WAGE variable allows us to determine the relationship from the wage structure to company productivity. Regarding the use of wage as an indicator of productive physical capital in companies, it is important to make two points. First, we should note that in the context of the companies analysed, the wage cost is configured as a very important component of productive physical capital. Within these contexts of analysis, wage is an indicator that partially illustrates human capital investment, in addition to the components attached to individuals in the process of company activity. Second, note that when dealing with companies, financial information on total productive investment is not always available. Given this limitation, it was considered appropriate to introduce wage as an indicator of productive physical capital. Firstly, and as noted by economic theory, this introduction was done because of the evident relationship between wage and productivity and, secondly, because wage is an indicator of the capitalisation available with data sets. Thirdly, the set of independent variables represents ICT and its complementarities. Regarding the ICT, we included two variables: ICTU and ICTI. The first variable, ICTU, represents ICT usage in the company and is the percentage of company employees that regularly use personal computers in their jobs. The second variable ICTI is an indicator of ICT infrastructure and takes two values: 1 when the company has a high-speed internet connection on its premises and 0 if otherwise. We have two hypotheses related to the direct effect of ICT on productivity.
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To show a company’s innovatory dynamics, we used two dummy variables: product innovation (INNOV) which takes value 1 when the company has introduced new products or services in the last three years and 0 if otherwise; and the variable PATENT which is 1 when the company has any patents registered abroad or at home and 0 if otherwise. Moreover, we have one indicator of human capital, the variable EDU which is a percentage of the company’s full-time employees (including production and nonproduction workers) with a university degree. Lastly, the variable ORG is an indicator of management quality. To compose this indicator, we followed the procedure used by Bloom et al. (2012). Management practices were grouped into four areas: operations, monitoring, targets and incentives. The operations question focused on how the establishment handled a process problem, such as machinery breakdown. The monitoring questions covered collection, monitoring, revision and use of the production performance indicators. The target question focused on the time-scale of production targets and the incentives questions covered promotion criteria, practices for addressing poor employee performance and rewarding production target achievement. As the scaling varied across management practices, the scores were converted to z-scores by normalising each practice (i.e. each question) to the mean of zero and the standard deviation of one. To avoid putting the most emphasis on the monitoring aspect of management practices (which had the most underlying questions), an unweighted average was first calculated across z-scores for a particular area of the four management practices and was then normalised. Furthermore, an unweighted average was taken across the scores for the four practices and was then normalised. This means that the average management practices across all the companies in all the countries in the sample are equal to zero, and the actual management practices of the companies deviate from zero either to the left (‘bad’ practices) or to the right (‘good’ practices). Results of the OLS Estimation The results of the OLS estimation of the productivity of manufacturing establishments in Eastern European countries are presented in Table 3. We estimated the model for all the countries and sample divisions for every country: Bulgaria, Poland, Romania, Serbia and Ukraine. The models include a set of two-digit industry-fixed effects; also, the model for all countries includes country-fixed effects as the additional controls that will affect productivity. All the models estimated are significant (p-value
