WP-EMS

Working Papers Series in

Economics, Mathematics and Statistics

“THE SHAPING OF SKILLS:

WAGES, EDUCATION, INNOVATION”

• Valeria Cirillo (Department of Statistical Sciences, Sapienza University of Rome)

• Mario Pianta (Department of Economics, Society and Politics, University of Urbino)

• Leopoldo Nascia (Istituto Nazionale di Statistica)

WP-EMS # 2014/05

ISSN 1974-4110 (on line edition)ISSN 1594-7645 (print edition)

The Shaping of Skills:

Wages, Education, Innovation

Valeria Cirillo1*, Mario Pianta2, Leopoldo Nascia3

Abstract

This paper investigates the role of wages, education and innovation in shaping employment structures in manufacturing and services of five European countries (Germany, France, Spain, Italy and United Kingdom), with specific respect to skills in the long term (1999-2011). Using data on employment by skill level and several measures of industries’ technological efforts provided by four waves of Community Innovation Survey, we study the relationship between micro and macro factors and employment dynamics by skill. As micro factors, we consider the role of education and wages by employee; as macro elements we study the role of technologies and demand shaping job growth by skill group. Relying on a sectoral demand curve deriving from a translong cost function, we empirically estimate the relationship between wages, education, technologies, demand and employment. The results reveal that skills are differently affected by education, wages and technologies and a variety of employment patterns has to be detected. In 1999-2011, manufacturing shows a pattern of relative skill upgrading; conversely a smoothed polarizing trend is detected in services. While a process of relative skill upgrading is detected in manufacturing; conversely a smoothed polarizing trend is detected in services.

Sintesi

Il ruolo di salari, istruzione e innovazione nel determinare l’evoluzione della struttura dell’occupazione in base ai diversi livelli di qualifiche e’ al centro di questo lavoro. Vengono analizzate le determinanti della variazione dell’occupazione in 4 principali categorie professionali per alcuni maggiori paesi europei sulla abse di un’analisi settoriale che copra manifattura e servizi. L0analisi affronta sia l’evoluzione di lungo termine tra 1999 e 2011, sia variazioni di breve termine che tengono conto dei cicli economici. I risultati mostrano che ciascuna categoria professionale subisce influenze diverse da parte delle variabili considerate, e le dinamiche verso la polarizzazione delle qualifiche vengono messe in evidenza in contrasto con le aspettative di un generalizzato aumento delle qualifiche del lavoro.

JEL classification: J31; 030

Keywords: Skills; Innovation; Labor markets; Wages; Education

1 Post-doc Fellowship Researcher, Department of Statistical Sciences, Sapienza University of Rome. 2 Full Professor of Political Economy, Department of Economics, University of Urbino 3 Researcher, Istituto Nazionale di Statistica.

Introduction

Advanced economies are experiencing a major change in employment structure. An increasing trend toward job polarization has been detected both in US and Europe, even if with major differences. Job dynamics have been investigated under different approaches focusing on the absolute change of employment or on its the relative composition in terms of skills. Different streams of research have recognized the relationship between technology and employment change leading to the conclusion that innovation contributes to shape employment dynamics.

On the one hand, Skill Biased Technological Change (SBTC) has analyzed the relationship between innovation and employment focusing on the qualitative dimension of jobs. The complementarity nature of technologies and skills recognized by the SBTC leads to detect and predict an increasing share of skilled workers on unskilled ones (Berman, Bound and Griliches, 1994; Autor, Katz and Krueger, 1998; Chennels and Van Reenen, 1999; Acemoglu, 2002). More recently, the Routine Biased Technological Change (RBTC) approach provides a novel technology-based explanation of employment changes focusing on tasks in terms of routinization of jobs (Autor, Levy and Murnane, 2003; Autor and Dorn, 2010; Goos and Manning, 2007; Goos, Manning and Salomons, 2013). The analysis of skills and tasks allows to explain patterns of job polarization relying on consumption spillovers (Manning, 2004; Mazzolari and Ragusa, 2012; Leonardi, 2010), ageing of population (Capatina, 2014) and international trade (Grosmann and Rossi-Hansberg, 2008; Blinder and Krueger, 2009; Das, 2012; Mandelman, 2013).

On the other hand, neo-Schumpeterian, Evolutionary and Structural economists have focused on the disequilibrium nature of technological change stressing the specific content of technological innovations (Pianta, 2003). Firms’ business strategies matter in shaping technological patterns at sectoral level, in this sense at least two kind of trajectories have been recognized both in theoretical and empirical studies: cost and technological competitiveness strategies. Adopting the Schumpeterian definition of innovation in products, processes, organizations, markets and sources of supply (Schumpeter, 1934), technologies have a different impact on employment, both in terms of quality and quantity of it.

In terms of absolute change of jobs, the relationship between innovation and employment has been deeply analyzed both at firm, sectoral and country level. In a cross-section framework, Entorf-Pohlmeier (1990), Brouwer et al. (1993) and Blanchflower and Burgess (1998) have detected a positive impact of product innovation on aggregate employment. Relying on panel databases, Machin and Wadhwani (1991), Van Reenen (1997), Doms et al. (1997), Smolny (1998), Greenan and Guellec (2000) and, more recently, Piva and Vivarelli (2005), Harrison et al. (2008), Hall et al. (2008), Bogliacino (2010), Lachenmaier and Rottmann (2011), Coad and Rao (2007) and Bogliacino, Piva and Vivarelli (2011) have identified a job creating potential for technological change, mostly in terms of product innovations.

The growing attention to the skill composition of employment, however, has not been adequately linked to the broader context of the quantity of jobs available; therefore, we analyze both relative and absolute change of employment.

We believe that a careful understanding of the relationship between technological change, employment and skills is needed considering the processes of structural change, the variety of

innovative patterns and the role of macro factors. From this point of view, the aim of this work is to combine neo-Schumpeterian and macro demand patterns on the determinants of employment dynamics, focusing on both quantitative and qualitative aspects of job creation and destruction – namely with the analysis of employment broken down by professional groups.

The relationship between technologies and employment requires attention to three major elements often disregarded by the current literature.

First, employment dynamics in terms of job creation and destruction have to take into account the skill dimension of jobs. The main drivers of employment change need to be studied considering the skill composition of the workforce. Most studies analyzing the qualitative composition of employment make reference to the task content of jobs in terms of physical or cognitive skills required (Capatina, 2014). We propose a different approach relying on the International Standard Classification of Occupations (ISCO) which allows to focus on professional groups and to overcome the dichotomy between high skill and low skill workers. ISCO classification has been previously adopted in the empirical literature to study employment dynamics by skill (among others Hollanders and Bas ter Weel, 2002; Felstead, Gallie, Green and Zhou, 2007; Oesch and Rodriguez Menés, 2010). As recognized by Hollanders and ter Weel (2002), the crude distinction between “unskilled” and “skilled” labor underestimates the variety of employment patterns related to skills. We propose to apply the ISCO classification relying on four main groups: Managers, Clerks, Craft and Manual workers which are able to reflect a rank both in terms of education attainments and wages.

Second, we consider the role of wages and education on employment dynamics by professional group. The negative relationship between wage and employment has to be differentiated according to the professional group analyzed. At sectoral level, changes in wages can differently affect skills; we expect low skill jobs to be more affected by wage changes than high skill jobs. Furthermore, education is expected to positively impact on high skill employment.

Third, a more careful understanding of technology is needed. From the neo-Schumpeterian approach we adopted the distinction between product and process innovation, and between firms’ strategies that search for technological competitiveness or for cost competitiveness (Pianta, 2001). The former are associated with the introduction of new products motivated by the search for high productivity and new markets. Cost competitiveness strategies focus on mechanization and restructuring of production processes and labor saving activities. While these strategies generally coexist in firms, at industry level it is generally possible to identify the dominant technological pattern and its impact on productivity and employment growth. In much of the current literature, “technology” (often associated with ICTs) is expected to have a uniform impact on all firms, industries and economies. The complexity of the process of technological change and the variety of innovative activities carried out in firms are often lost and the contrasting effects that various patterns of technological change may have in different contexts are disregarded. From an empirical point of view, technology is usually proxied by R&D, patents or the adoption of ICTs as indicators which leave most innovative activities carried out in firms out of the analysis. The complex impact of technologies on employment has been empirically detected by an increasing branch of literature (Bogliacino and Vivarelli, 2011; Simonetti, Taylor and Vivarelli, 2000; Vivarelli, Evangelista and Pianta, 1996; Pianta, 2001; Antonucci and Pianta, 2002; Evangelista and Savona, 2003).

Fourth, demand and supply factors shape employment dynamics; macro elements need to be included in the analysis stressing the importance of complement a micro level analysis of labor markets with macro factors.

The rest of the paper is organized as follows. Section 2 after introducing the dataset describes the evolution of skills in Europe focusing on professional groups. In section 3 we present the model and the econometric strategy applied for the empirical investigation. Section 4 presents the main results and section 5 concludes.

1. The evolution of skills in Europe: data and evidence

1.1 A sectoral approach: the data

The main analysis is conducted using data from the Urbino Sectoral Database (USD) developed at the University of Urbino and containing industry-level information on 21 manufacturing sectors, from 15 to 37 NACE REV.1, and 15 service sectors, from 50 to 74 NACE REV.14. The database prepared combines three main typology of information on employment, innovation and economy matching three different sources: Labour Force Survey (Eurostat), Community Innovation Survey (CIS) and OECD Structural Analysis (STAN) database. Due to the structural nature of the relationship studied and to different sources of data, countries have been selected in terms of the greatest available coverage of sectors and data reliability. Countries included in the analysis are Germany, France, Italy, Spain and United Kingdom (GER, FR, IT, SP, UK).

We aggregate data on occupations according to ISCO88COM nomenclature, creating 4 macro groups, as shown in figure 1.

We adopt the ISCED nomenclature (lower secondary education, upper secondary education, university and post university education) for variables on educational level. Non answers have been assigned to each professional group according to the share covered by each category on total employment at sectoral level.

In this article we consider innovation data from CIS 2 (1994-1996), CIS 3 (1998-2000), CIS 4 (2002-2004) and CIS6 (2006-2008). Two typology of information are considered: structural variables indicating share of firms at sectoral level performing some kind of innovation (only product, only process, both typologies) expressed in percentages and expenditure variables in thousands of euros per employee5. The list of variables used for the empirical investigation is shown in the Appendix.

Industry-level innovation and employment data are matched to economic performance indicators drawn from the OECD STAN database such as value added, labor compensation (including social contributions) and labor productivity, all in constant prices.

4 The detailed list of sectors is in the Appendix. Due to the change in the coding system of industries after 2007, we transform employment data expressed in the new classification (NACE REV.2) into the older one (NACE REV.1) applying the conversion matrix explained in the Appendix. It enables us to convert new coded data in the previous classification allowing a comparison for the entire period 1999-2011. 5 Expenditure variables have been deflated at 2000 current prices by the GDP deflators (Eurostat) and corrected in Purchasing Power Parity in order to assure comparability among countries and over time.

2.2 The evolution of skills in Europe

The employment structure in Europe has changed over time leading to pattern of upskilling and polarization of skills. A valid instrument to analyze employment dynamics in a temporal and geographical perspective is given by the International Standard Classification of Occupations (ISCO) which allows to focus on the skill dimension of jobs through a wide range of information related to the level of autonomy in the workplace, the average education required, the typology of work and the labor compensation provided. From this point of view, ISCO groups synthetize the multidimensional aspects behind jobs both in terms of tasks and wages allowing international comparisons between occupational structures. The ISCO88COM classification distinguishes ten typologies of occupations reported in the right side of table 1 which can be easily grouped in 4 main groups: managers, clerks, craft and manual workers reflecting a rank both in terms of wages and educational levels6.

Fig 1. Employment by professional groups and skill level

The skill composition of employment is changed over the long period 1999-2011 leading to upskilling in manufacturing with an increased share of managers on total employment and polarization of skills in services with an higher percentage of both managers and manual workers. As expected services register an higher share of high skilled workers (managers and clerks) compared to manufacturing where the share of low skill jobs is higher (craft and manual workers). Overall this structural difference between macro sectors in terms of skill composition of the workforce does not change over time, but it is partly weakened by an upskilling trend in manufacturing and a smooth polarizing pattern in services. The evolution of skills in Europe has to take into account the complex dimension of manufacturing and services which are endogenously characterized by different skill compositions of the workforce.

The employment dynamic over the entire period 1999-2011 is characterized by an upskilling trend with employment growth concentrated in managers and clerks and a sharp decrease of employees in craft and manual categories. This aggregate picture need to be detailed considering sector heterogeneity and country specificities.

6 Please make reference to the Appendix for a detailed picture of skills distribution according to the mean wage and the educational level by ISCO 1 digit (fig….).

Source: LFS, own elaboration.

In a dynamic perspective (1999-2011) we detect an overall negative trend for manufacturing which is only reverted by managers. In services, during 1999-2011 an overall increase in employment is registered (2.11), but still concentrated in more skilled groups (managers and clerks) with a strong reduction in middle-low skills. Craft workers are more penalized than manual workers compared to manufacturing; we confirm the employment expansion of ancillary jobs related to low qualified services (Eurofound, 2013). The secular process of structural change in terms of employment shifts from manufacturing to services is still evident favoring high skills (managers and clerks) compared to low skills (craft and manual workers).

In terms of country specificities, at European level we detect an overall pattern of polarization of skills except for Germany where upskilling as employment growth concentrated in managers and clerks is registered. In the long term (1999-2011), United Kingdom and Italy have been characterized by the highest level of polarization of skills compared to other countries.

Source: LFS, own elaboration.

The shaping of employment dynamics by skills requires attention towards both sectoral and country specificities allowing to stress the variety of skill patterns detected in aggregate terms.

1. The model and econometric strategy

The model we propose in order to study employment dynamics by skills is a combination of structural and micro factors. Much of the empirical literature which investigates changes in the employment structure relies on a translog model (Hollanders and ter Weel, 2002; Betts, 1997; Machin and Van Reenen, 1998; Adams, 1999; Goux and Maurin, 2000) which have been adapted following Christensen et al. (1973)’s approach to study employment share of skills as a function of different factors such that We modify this approach considering the absolute change of skills as a function of micro and macro elements.

On the one hand, micro elements such as education contribute to shape employment. On the other hand, technologies, demand and structural change impact on employment outcomes leading to expansions or contractions of skills. The relationship between employment change and structural factors has been widely studied among others by Bogliacino, Lucchese and Pianta (2011) and Bogliacino and Vivarelli (2011) underling the importance of a sectoral level analysis in order to estimate the impact of technologies on jobs; on the contrary, few studies have focused on the relationship between structural factors and employment by skill. From this point of view, we aim to explain the shaping of the occupational structure by skill in Europe combining both micro and macro factors. We consider wages, education, technologies and aggregate demand as major drivers of job changes at sectoral level.

The innovative activity of sectors has been investigated by considering the dominance of strategies based either on technological or cost competitiveness (Pianta, 2001). From the one hand, we expect a positive coefficient for technological strategies (TC) aiming to open up new markets, introducing new products and quality improvements. From the other hand, cost competitiveness (CC) is related to process innovations leading to job losses. This second strategy can also have a positive impact on jobs considering the reduction in prices stimulating new demand. Overall, technological strategies (TC) prevail in terms of opportunities of job creation.

Building on the existent literature (Piva and Vivarelli, 2005; Vivarelli, Evangelista and Pianta; 1996; Pianta, 2000; 2001; Antonucci and Pianta; 2002; Evangelista and Savona, 2002; 2003) we expect technological and cost competitiveness strategies having a contrasting effect on employment: job creation for product innovation oriented sectors and job contraction resulting from the introduction of new processes. The demand variable is expected to have a positive effect on employment; conversely, wages are expected to have an inverse relationship with employment creation, labor demand increases when labor costs decrease.

The model introduced in order to study the determinants of employment growth by professions derives from a translog model where as in Adams (1999) capital and technology stocks are assumed to be quasi-fixed. Wages, education, technologies and aggregate demand are considered as the main drivers of employment change. The model estimated is presented in formula 1:

(1)

where emp is the compounded annual rate of change of employment, w is the compound annual rate of change of labor compensation (changes in labor cost), is the share of workers with education, tc and cc are proxies for the technological and the cost competitiveness strategies, V.A. is value added, a proxy for demand, and ε is the error term, for industry i and time . The model is estimated at industry level for five European countries, the individual observation is a certain industry in a given country at a certain time. We introduce specific country effects in order to account for differences in country characteristics and sector specificities. From a theoretical point of view controlling for national patterns is important in terms of national system of industrial relations and welfare institutions, as well as economic and employment structures.

Econometric strategy

As a baseline equation, we estimate the following labor demand curve:

(2)

where is the employment variable, the vector of regressors, the individual/sectoral effect and the random disturbance, for industry and time . Equation 2 can be assumed to be the result of a

cost minimisation programme by a firm with a translog cost function. If variables are expressed in log scale, we can eliminate the individual effect by taking the first difference of Eq. (2).

(3)

In this way, the sectoral unobserved component potentially leading to biased estimates due to its correlation with the error component is differentiated out. This transformation permits to apply

Ordinary Least Squares Estimator (OLS).

As known, the difference in log approximates the rate of change, thus we express both dependent variable and regressors in rate of variation. Instead of considering long difference, we compute the compound annual growth rate. Innovation variables are not expressed in rates but as either shares of firms in the sector, shares of turnover or expenditure per employee. This can be justified considering innovative efforts as dynamic and capturing the change in the technological opportunity available to the industry. Furthermore, innovation variables make reference to a two years period considering the lagged impact of technologies.

Taking differences of variables, the model can be estimated consistently using OLS. Furthermore, the model is adjusted for heteroschedasticity (robust standard errors) and intra-group correlation at the industry level, checking for intra-sectoral heterogeneity. Dealing with different sized groups,

heteroschedasticity is quite common. We use a Weighted Least Squares procedure using employment as a weight.

We also control for the possibility of multicollinearity between regressors through a VIF (Variance Inflation Factors) test.

Finally, we allow for correlated error terms applying a seemingly unrelated equation model where the dependent variables are the changes in number of managers, clerks, craft and manual workers and the regressors are the main factors described above. The intuition behind the application of the seemingly unrelated equation model relies on the possibility of changes in employment by skill correlated one with the other. Instead of considering the change in the composition of jobs usually expressed in shares, we study the evolution of employment dynamics by skill allowing for correlated changes of skills. The final model estimated is expressed in equation 4:

(4)

2. Results

The baseline model

We firstly estimate a general regression with the baseline model for the entire period 1999-2011 using as a dependent variable the change in total employment. Coefficients are corrected for heteroscedasticity. We use two different proxy for innovation, a general one (share of firms introducing innovation at sectoral level) that can be roughly considered as a proxy for product oriented innovation and a second one specific for process innovation/cost competitiveness strategies (share of firms having suppliers as sources of innovation). On the basis of the descriptive analysis performed above (fig. 2), we decide to interact the two technology variables with a dummy for manufacturing sectors (2). In the long term, manufacturing and services have been registering different employment dynamics with a clear pattern of structural change pushing employees from manufacturing to services. In order to check our results, we estimate the same model using “Labor productivity” instead of “Value Added” as a proxy for demand at sectoral level (3). As a further control (4), we use two different proxies for innovation strategies such as the R&D expenditure per employee (product innovation) and the expenditure in machinery and equipment per employee (process innovation).

Table 3. Determinants of employment growth in 1999-2011 in European industries. Pool of manufacturing and service industries. Dependent variable: Change in Employment 1999-2011 (1) (2) (3) (4)

Labor compensation per employee (rate of growth) -0.5036 (0.0632)***

-0.3114 (0.0645)***

-0.0895 (0.0552)

-0.5288 (0.0660)***

Share of University Education 0.0522 (0.0185)**

0.0534 (0.0275)*

0.0719 (0.0252)**

0.0642 (0.0192)***

Share of firms performing innovation

0.0642 (0.0220)**

0.0510 (0.0297)*

Share of firms performing innovation # Manufacturing 0.0662 (0.0229)**

Share of firms performing innovation # Services 0.0620 (0.0373)*

Share of firms having suppliers as sources of innovation -0.0663 (0.0193)***

-0.0322 (0.0185)*

Share of firms having suppliers as sources of innovation # Manufacturing

-0.0664 (0.0223)**

Share of firms having suppliers as sources of innovation # Services

-0.0646 (0.0310)**

Expenditure in R&D per employee 0.0794 (0.0403)*

Expenditure in Machinery per employee

-0.2440 (0.1016)**

Value Added (rate of growth) 0.4635 (0.0785)***

0.4592 (0.0735)***

0.4247 (0.0830)***

Labour Productivity (rate of growth)

-0.3086 (0.0756)***

Pavitt dummies (SB-SS-SI-SD) Yes Yes Yes

Country dummies Yes Yes Yes Yes

Sector dummies Yes

R2 0.7860 0.7861 0.8943 0.7546

N observations 181 181 181 170 Weighted Least Squares regression with robust standard errors. Standard Errors in parentheses: * significant at 10% ** significant at 5% *** significant at 1% level

The results broadly confirm the expected relationships.

Wages are negatively related to employment demand as stated by the general Neoclassical labor market theory, an increment in labor compensation per employee strongly reduces the overall employment demand. The elasticity of such movement is between 0.5 and 0.3. An increase by 1 percentage point in the share of employees at sectoral level with University education is positively associated to employment by

7 Make reference to the Appendix for a detailed list of sectors by Pavitt group.

on average 0.06 percentage points. We detect the same elasticities in both models with value added and labor productivity.

As in Bogliacino, Lucchese and Pianta (2011), the effects of technological and cost competitiveness strategies are comparable in magnitude but with opposite signs. Overall, the introduction of innovations in the sector proxied by the share of firms performing some kind of innovation has a positive impact on our dependent variable, variation of total employment in the long term. Considering the lagged effect of innovation activity on employment, our CIS data are built as the average of four CIS waves, being the last one referred to 2006-2008 in order to take into account the following impact on 2008-2010 employment. An increase by one percent in the share of firms performing some kind of innovations would add on average and ceteris paribus 0.06 percentage points to the average growth of employment.

Job losses of the same amount would happen if the share of firms having suppliers as sources of innovation (proxy for cost competitiveness strategy) increase by one percentage point. As expected from Innovation studies, process innovations are negatively associated with employment with an elasticity between -0.06 and -0.03. Due to differences in employment patterns among sectors, we interact the “innovation” variables with a manufacturing dummy in order to capture the changing slope of the coefficient. Surprisingly, when we interact the innovation variable with the dummy for manufacturing, it does not show variability between macro sectors. In manufacturing and services, the coefficient for innovation variables (product and process innovations) register the same elasticity (|0.06|).

As expected, at sectoral level value added growth positively impact on employment change confirming the leading role of demand in creating employment. The magnitude of the coefficient is almost equal in all models (0.4) showing a pattern of stability across all specifications.

Furthermore, we introduce a set of dummy variables in order to control for sector and country specificities. Due to our knowledge of sectors as characterized by specific innovative patterns, we check for multicollinearity with the innovation variables. The Variance Inflation Factor (VIF) test allows to reject the possibility of multicollinearity. As a further control, we estimate the same model including sector dummy for each single sector instead of dummies for Pavitt groups (Science Based, Specialized Suppliers, Scale Intensive and Suppliers Dominated)7. Signs and magnitude of coefficients seem to be stable confirming the positive variation of employment in sectors with an higher share of firms introducing innovations and a negative percentage change of employment for sectors characterized by suppliers dominated firms.

Finally, we estimate the same model including two other different innovation variables as proxies for technological and cost competitiveness strategies. The first variable is the expenditure per employee in R&D; the second one is the expenditure for machinery per employee. Also in this case, we found a positive relation with the share of innovative firms at sectoral level. On the contrary, expenditure in machinery has, as expected, a negative impact on job creation. The alternation of coefficients for technological variables confirms the hypotheses stated above: a positive effect of product technologies on employment and a negative impact of process technologies aiming to substitute workers with machinery.

The model for professional groups

The relationships estimated above point out the role of different typologies of technologies in shaping

employment dynamics. In this section we aim to evaluate the impact of micro and macro drivers of employment by professional groups. First, we apply the econometric specification of formula (1) using the same innovation variables introduced in the aggregate model as proxies for different innovation processes: share of firms introducing innovations and share of firms having suppliers as sources of innovations, both interacted with sectoral dummies. This procedure allows the coefficient of the variable to vary according to manufacturing and services.

Table 4. Determinants of employment growth in 1999-2011 by professional group (European industries). Pool of manufacturing and services industries (I). Dependent variable: Compound Annual Rate of Growth

Managers

Clerks

Craft Workers

Manual Workers

Labor compensation per employee (rate of growth) -0.4898 (0.1134)***

-0.5057 (0.1151)***

-0.4869 (0.2362)**

-0.6295 (0.1495)***

Share of University Education

0.0406 (0.0202)**

Share of Secondary Education

-0.0046 (0.0282)

-0.0544 (0.0747)

0.1215 (0.0754)

Share of innovative firms in the sector # Manufacturing 0.0645 (0.0389)*

0.0523 (0.0392)

0.1288 (0.0594)**

0.1594 (0.0656)**

Share of innovative firms in the sector # Services 0.0878 (0.0581)

0.0917 (0.0624)

0.1237 (0.1448)

0.0640 (0.0947)

Share of firms having suppliers as sources of innovation # Manufacturing Share of firms having suppliers as sources of innovation # Services

-0.1787 (0.0484)*** -0.1258 (0.0666)*

-0.0725 (0.0502) -0.0833 (0.0557)

0.1129 (0.0962) 0.0013 (0.1387)

-0.1517 (0.0504)*** -0.0287 (0.0733)

Value Added (rate of growth) 0.4724 (0.1631)**

0.3742 (0.1558)**

0.9647 (0.3698)**

0.4641 (0.1582)**

Pavitt dummies Yes* Yes** Yes* Yes***

Country dummies Yes Yes Yes Yes N obs

181

179

166

173

R2 0.7250 0.5757 0.4243 0.3922 Weighted Least Squares regression with robust standard errors. Standard Errors in parentheses: * significant at 10% ** significant at 5% *** significant at 1% level

In terms of labor compensation, as expected, wages are more relevant for manual workers compared to managers showing an higher negative elasticity of 0.62. The relationship is negative for all professional groups as stated by Neoclassical Labor market theory, an increase in wages by 1 percentage point decreases employment growth by around 0.5. The steepness of the curve detecting this relationship has different elasticities according to the professional group considered. Manual workers are the most affected by an increase in labor compensation compared to managers, registering the lowest elasticity. This result can be explained by the high skill level required for apical professions which is often correlated with higher wages used to push labor productivity (efficiency wage theory).

In terms of technological strategies, for all professional groups we can distinguish between a positive relation for product innovations proxied by share of innovative firms in the sector and cost competitiveness strategies proxied by share of firms having suppliers as sources of innovation, which are negatively related to job creation. In manufacturing we can detect the highest impact of

technologies, innovation variables interacted with the sectoral dummy do not show significance for services. In manufacturing, technological patterns of sectors are more evident both for product and process innovations; managers and manuals are more affected than clerks and craft workers by the introduction of product or process technologies.

Overall, value added is a key determinant for job creation for all professional groups, mostly for low skill workers.

Summing up, the effect of technologies on professional groups is more evident in manufacturing than services and at the extremes of the job distribution impacting mostly on managers and manual workers.

We use different proxies for technological strategies by professional group better fitting the impact of technologies on each skill. We expect expenditure variables to better capture changes in employment for middle skills; on the contrary, innovation variables proxied by firm strategies such as share of firms innovating to open new markets or reduce labor costs can have a more evident impact for manual workers. High skills (Managers) can be most affected by technologies influencing the relationship with clients and suppliers (share of firms who indicate clients as source of innovation versus share of firms having suppliers as source of innovation).

Table 5. Determinants of employment growth in 1999-2011 by professional group (European industries). Pool of manufacturing and services industries (III). Dependent variable: Compound Annual Rate of Growth

Managers

Clerks

Craft Workers

Manual Workers

Labor compensation per employee (rate of growth) -0.4781 (0.1177)***

-0.4929 (0.1214)***

-0.4839 (0.2188)**

-0.6506 (0.1622)***

Share of University Education

0.0274 (0.0202)

-0.0186 (0.0277)

Share of Secondary Education

-0.0598 (0.0792)

0.1220 (0.0757)

Share of firms who indicate clients as source of innovation 0.0823 (0.0365)**

Share of firms having suppliers as sources of innovation

-0.1336 (0.0550)***

Expenditure in R&D per employee -0.0233 (0.1202)

0.2560 (0.2247)

Expenditure in Machinery per employee -0.2325 (0.2396)

-1.4136 (0.5192)**

Share of firms innovating to open up new markets 0.0968 (0.0818)

Share of firms who innovate to reduce labour costs -0.2307 (0.1076)**

Value Added (rate of growth) 0.5170 (0.1461)**

0.3524 (0.1881)*

1.0878 (0.4195)**

0.5447 (0.1626)***

Pavitt dummies Yes Yes Yes Yes

Country dummies Yes Yes Yes Yes N obs

181

175

162

174

R2 0.7175 0.5669 0.4247 0.3904 Weighted Least Squares regression with robust standard errors. Standard Errors in parentheses: * significant at 10% ** significant at 5% *** significant at 1% level

In the long run, product innovation affects more high skill jobs (managers) than low skills, on the contrary process innovation impacts more on low skills. The job creating potential of demand is verified for all categories while labor cost changes have a stronger effect on low skilled workers.

Finally, due to the possible correlation between changes in employment by skill, we decide to estimate the same relationships through a seemingly unrelated equation estimator (Zellner, 1962) which improves the standard OLS allowing for correlated error terms. In this case, the OLS estimator will be not only consistent, but also efficient.

Table 6. Determinants of employment growth in 1999-2011 by professional group (European industries). Pool of manufacturing and services industries (IV). Seemingly unrelated equations estimator. Dependent variable: Compound Annual Rate of Growth

Managers

Clerks

Craft Workers

Manual Workers

Labor compensation per employee (rate of growth) -0.5653 (0.0721)***

-0.7752 (0.1211)***

-0.6774 (0.1683)***

-0.5029 (0.1184)***

Share of University Education

0.0062 (0.0197)

Share of Secondary Education

0.0012 (0.0294)

-0.0750 (0.0334)**

0.0166 (0.0260)

Share of innovative firms in the sector # Manufacturing 0.0323 (0.0329)

-0.0941 (0.0549)*

0.1033 (0.0761)

0.1147 (0.0536)**

Share of innovative firms in the sector # Services 0.1288 (0.0379)***

-0.0930 (0.0613)

0.1111 (0.0858)

-0.0737 (0.0613)

Share of firms having suppliers as sources of innovation # Manufacturing Share of firms having suppliers as sources of innovation # Services

-0.1722 (0.0380)*** -0.1654 (0.0399)***

-0.0725 (0.0502) -0.0001 (0.0660)

0.0474 (0.0887) -0.0764 (0.0924)

-0.1506 (0.0632)** -0.0505 (0.0656)

Value Added (rate of growth) 0.4991 (0.0856)***

0.4713 (0.1448)***

0.5630 (0.1996)***

0.5653 (0.1408)***

Pavitt dummies Yes Yes Yes Yes

Country dummies Yes Yes Yes Yes N obs

161

161

161

161

R2 0.6227 0.5064 0.4634 0.5520 Seemingly Unrelated with robust standard errors. Standard Errors in parentheses: * significant at 10% ** significant at 5% *** significant at 1% level

In order to test the efficiency of the seemingly unrelated regression estimator, we test for the equality of coefficients across the four models. We can reject at 1% of confidence level the equality of coefficients among professional groups. Furthermore, the Breush-Pagan test of independence applied on the correlation matrix of residuals allows to reject the hypothesis of independence between residuals for the four different professional groups ( ). The seemingly unrelated regression model is consistent and efficient. In terms of results, the dynamics detected in table 5 are confirmed. The impact of technological strategies on manual workers is more evident in manufacturing than services, as expected. While the interaction between innovation and managerial jobs defines clearer patterns in services than in manufacturing. Overall product innovations lead to job expansion; conversely process innovations contract employment. This employment dynamic is particularly clear at the extremes of the jobs distribution.

Robustness checks

Testing for Endogeneity

In this section we aim to control for endogeneity issues, testing if our proxy for demand in the model is effectively exogenous in determining the change in employment by skill, or on the contrary it is possible to consider a reverse causality meaning a causal relationship between change in employment by skill and value added at sectoral level. Even if this kind of problem concerns more firm level studies, there is a rich stream of research focused on the complex relationship between skills and economic performance (Grillinches, 1969; Borghans, Green and Mayhew, 2001; Guellec and Van Pottelsberghe de la Potterie, 2004; Ulku, 2007). From a macro perspective new growth theories are interested in the role of human capital on economic growth and on skills as a determinant of economic performance. Starting from these considerations we want to exclude the possibility of reverse causality between value added and employment meaning an impact of employment on value added. The presence of endogeneity can produce biased estimates because OLS is not only inefficient, but even inconsistent violating the zero mean conditional assumption [E(x│u)=0] required by Ordinary Least Squares. In order to check for endogeneity, we decide to apply the Hausman procedure consisting in saving the residuals from the structural model and including them in the final model. We firstly estimate the same baseline model of table 3 using the change in Value Added for the period 1999-2011 as dependent variable and omitting the change in employment as regressor. We save the residuals of this first stage equation (5) and we perform a second estimation as the one in equation (1) adding also the residuals saved in the previous model (5).

(5)

The null hypothesis of Hausman’s exogeneity test is absence of endogeneity defined as correlation between independent variables and error term due to omitted variables or reverse causality or measurement errors: versus . With an F1,164=1.79 we cannot reject the null of exogeneity of the change in Value Added in the employment regression estimation. From this point of view, we can rely on model 1. As a further control for exogeneity of Value Added in the model, we consider the change in Value Added as an endogenous variable and then we instrument it through innovation (share of innovative firms in the sector). At sectoral level, it is reasonable to consider an impact of the introduction of innovations on Value Added; those sectors with an higher proportion of firms performing innovation are also characterized by higher value added growth. Generally, a good instrument should have two properties: firstly it should be highly correlated with the endogenous variable and secondly, it should not be correlated with the dependent variable. In this test we are going to consider value added impacting on employment change through the introduction of innovations. Due to the inefficiency of the conventional Instrumental Variable regression in the presence of heteroskedasticity, we apply as estimator the generalized method of moments (GMM), introduced by Hansen (1982).

After estimating the model with instrumented value added through GMM estimator, we perform the Hayashi (2000) C statistic, also known as the difference-in-Sargan statistic and we do not reject the null of exogenous regressors [χ2

(1)=0.1622]. Then, we can conclude that OLS estimations are correct due to the exogeneity of the change in Value Added in the specification model. Testing for Specification Errors As further control, we test for omitted variables in the estimated model (1) applying the Ramsey Reset test using powers of the fitted values of regression (1) and inserting them in a new regression. Under the null of absence of omitted variables, we cannot reject H0 with F3,131=0.55. In this case, the model is correctly specified.

5. Conclusions

In this work we investigate the role of micro and macro factors in shaping the skill structure of employment in Europe. A general pattern of skill polarization is detected in services while manufacturing follows to expand employment for upskilled groups and contracting low skill jobs. The variety of employment dynamics need to be recognized at country level shedding light on country specificities. The sectoral level of analysis allows to combine both micro and macro factors as main drivers of job creation and destruction by skills. The expected negative relationship between wages and employment is confirmed for all professional groups. Low skill jobs are more reactive to wage growth compared to high skill jobs. At aggregate level, we detect a positive impact of education on employment dynamics which is partly lost when we study employment dynamics by skill. In terms of technologies, the results on the determinants of employment confirm the findings of recent literature (Bogliacino and Pianta, 2010; Bogliacino and Vivarelli, 2011). At the industry level the analysis of long term relationships between employment and technologies shows a general positive impact of technological competitiveness on employment and a negative effect of cost competitiveness efforts. The job creation potential of demand is confirmed; those sectors characterized by higher value added growth are also experiencing employment expansion. The relevance of sectoral and country patterns is also verified. A key finding of our work is the importance of a breakdown by skills in the analysis of employment dynamics which is able to combine both qualitative and quantitative analysis. The diversities in the relationships across skill groups are substantial. In the long run, Managers are less affected by changes in wages than manual workers; the job creation potential of demand is more evident for manuals than for managers. In terms of innovations, technological strategies related to the introduction of new products positively impact on high skills, while cost competitiveness innovations are more relevant for low skill jobs. Product innovation creates employment for all professional categories and particularly for “managers” and “manuals”. Process innovation leads to job destruction mainly for “manual workers” being substituted by machines. These relationships are more evident in manufacturing than services. When we analyze the impact of technologies on jobs without distinguishing between manufacturing and services, the job creation potential of product innovations is detected only for “managers”. Conversely, the job destruction effect of process technologies concerns craft and manual workers.

Overall, we show the relevance of different relationships between wages, education and technology from one side and job dynamics by skill. At the sectoral level, technological patterns influence employment in different ways having an expansionary or contractionary effect according to the skill considered. Summing up, our results show the importance of overcoming the simplistic dichotomy between high skill and low skill workers; therefore, we focused on skills in terms of four professional categories. Instead of relying on tasks which are often ranked on the basis of routine/non-routine competencies required by workers, we considered professional groups provided by the International Standard Classification of Occupations (ISCO). The results found for the determinants of employment change show that each professional group is characterized by a specific set of effects coming from technological, demand and market structure variables. A lack of consideration for such structural distinctions conceals important aspects of the issues we address. Second, the conceptualization of technology as a complex phenomenon allows a distinction between the dominance of product-oriented efforts to improve technological competitiveness, and a strategy – typical of more traditional sectors – relying on labor-saving technologies. At aggregate level, we confirmed the empirical evidence of a differentiated impact of technological strategies on employment. The relevance of the differentiation between technological strategies is even more evident by professional groups. From the one hand, high skills (managers) are favored by the introduction of product innovations; from the other hand, process innovations mostly affect low skills. The results found for the determinants of job growth by professional group underlined the importance of distinguishing between technological patterns. Third, instead of analyzing the relative composition of employment as for the skill-biased technological change, we have studied the determinants of job change for each skill group. We therefore considered the changes in both the quantity and the quality of jobs. Moreover, we relaxed the assumption of the determinants of change are identical across professional groups. Investigating change in employment is crucial to understand the overall economic performance which is characterized by persistence of unemployment and major cycles effects.

Appendix Table 2. Key variables and periods

Variables Source Long Run

Percentage change of number of employees LFS 1999-2011 Percentage change of number of Managers LFS 1999-2011 Percentage change of number of Clerks LFS 1999-2011 Percentage change of number of Craft W. LFS 1999-2011 Percentage change of number of Manuals W. LFS 1999-2011 Share of firms introducing innovation CIS 2,3,4,6 Innovative machinery expenditure per employee CIS 2,3,4,6 Share of firms having internal sources of innovation CIS 2,3,4,6 Share of firms identifying suppliers as innovation sources CIS 2,3,4,6 Expenditure in R&D per employee CIS 2,3,4,6 Expenditure in Machinery per employee CIS 2,3,4,6 Average Firm size – Employees per firm CIS 2,3,4,6 Percentage change of Value Added STAN 1999-2011 Percentage change of Labour Compensation per employee STAN 1999-2011 Percentage change of Labour Productivity STAN 1999-2011