For Official Use DSTI/EAS/IND/WPIA(2016)1
Organisation de Coopération et de Développement Économiques Organisation for Economic Co-operation and Development 15-Feb-2016 ______English - Or. English DIRECTORATE FOR SCIENCE, TECHNOLOGY AND INNOVATION COMMITTEE ON INDUSTRY, INNOVATION AND ENTREPRENEURSHIP For Official Use DSTI/EAS/IND/WPIA(2016)1
Working Party on Industry Analysis
SKILLS AND GLOBAL VALUE CHAINS: A FIRST CHARACTERISATION
Paris, OECD Headquarters, 7-8 March 2016
This paper offers some preliminary results of the work jointly carried out by STI and the Directorate for Education and Skills (EDU) aimed to characterise the distribution of skills across countries, industries and occupations, and to investigate the relationships that exists between the skills composition of the workforce and the generation of value added from trade. The study has been prepared by Mariagrazia Squicciarini (STI/EAS), Stéphanie Jamet (EDU), Robert Grundke (STI/EAS) and Margarita Kalamova (EDU) as part of the “GVCs, jobs and skills” horizontal project. It is proposed under item 6 of the CIIE agenda, for information and discussion.
For further information, please contact : Mariagrazia SQUICCIARINI (STI/EAS); [email protected] Stéphanie JAMET (EDU); [email protected]
English English JT03390128
Complete document available on OLIS in its original format -
English Or. This document and any map included herein are without prejudice to the status of or sovereignty over any territory, to the delimitation of international frontiers and boundaries and to the name of any territory, city or area.
DSTI/EAS/IND/WPIA(2016)1
EXECUTIVE SUMMARY
This paper offers some preliminary results of the work jointly carried out by the Directorate for Science, Technology and Innovation (STI) and the Directorate for Education and Skills (EDU). This work aims to characterise the distribution of skills across countries, industries and occupations, and to investigate the relationships that exist between the skills composition of the workforce and the generation of value added from trade.
While existing studies agree on the importance of skills for trade patterns, they very seldom specify which skills matter for which type of GVC-related measures of participation or performance, or add an industry or occupation perspective to it. Uncovering these relationships, though, is needed for the design of effective policies in many areas, including industry, education and trade policies.
The present work contributes to address such shortcomings by identifying the skills that existing studies suggest being key for workers' performance on the job and for firm performance. Skills are selected based on an extensive survey of the literature, including economics, management, organisation science and psychology, and according to their prospect relevance in shaping the creation of value added in domestic markets as well as international production. The continuum of cognitive skills and personal traits (i.e. non- cognitive skills) identified as being relevant for economic performance is crystallised in a taxonomy grouping skills in a homogeneous fashion, with the aim to generate skill-related indicators that reflect the findings of the literature.
The proposed taxonomy is operationalised on data from the OECD Programme for the International Assessment of Adult Competencies (PIAAC) survey, as it provides a wealth of employee-level information about workers' skills, frequency of the performance of various tasks, as well as information about the industry where they work and does so for more than twenty OECD countries. For cognitive skills, indicators of both assessed skills (through tests) and frequency of the performance of cognitive tasks are considered. For skills that combine cognitive and personality traits aspects (e.g. management, interacting and communicating) and physical skills, indicators are built from information on the frequency of performance of related tasks. In the absence of directly relevant questions, some personality traits' indicators are constructed using replies to self-reported questions on related issues.
A number of stylised facts emerge from the skills indicators proposed and highlight the extent to which skills endowments and the frequency with which some tasks are performed differ across countries, industries and occupations.
• Large cross-country differences emerge in terms of shares of workers with high and low literacy and numeracy skills, with Finland and Japan having a large group of workers with high literacy and numeracy skills in both the services and manufacturing and Italy having a large group of workers with low skills along these two dimensions.
• Within countries, the distributions of skills in services and in manufacturing appears relatively similar, although in most countries there is a higher (lower) share of workers with relatively high (low) literacy skills in services (manufacturing).
2 DSTI/EAS/IND/WPIA(2016)1
• Large differences emerge in terms of skills endowment of the different occupational categories, with professionals and managers having the highest skills, on average. Large variations emerge across countries in terms of skills of workers in the same occupations, especially in manufacturing.
• In terms of problem solving skills, large heterogeneity emerges in the distribution of skills between countries and within countries, between manufacturing and services sectors, although no clear pattern arises across industries.
• In line with expectations, workers perform less complex ICT tasks more frequently than complex ones, in all industries. Data show large variations between countries in the use of ICT skills, especially for less complex skills, and for a group of industries a lower use of ICT skills at work emerges, as compared to the extent to which such skills are used at home. This may signal that the potential of employees is not fully exploited, although to different extents in different countries and industries.
• The frequency of the performance of tasks that require skills combining cognitive and personality traits components, such as interacting and communicating is low for a number of countries and sectors, despite these skills being found to be crucial for firm performance. Heterogeneity between countries is especially large for the frequency of the performance of managing tasks and to a lesser extent for self-organising tasks, suggesting that some countries have a large potential to better use and further develop the related skills.
• In terms of physical skills, a lot of heterogeneity emerges between sectors, with finance being the industry showing the lowest level of use of physical skills and agriculture the highest level. This large heterogeneity across industries can reflect differences in the production process but also in the types of goods that are produced and thereby, in the positioning in the GVC.
• A comparison of the use of skills between workers and non-employed (measured in the last job for the non-employed) shows a higher use by workers of all types of skills, with the exception of physical ones. In all countries, non-employed exhibit higher levels of skills use at home than workers in at least two out of the six skills considered. These statistics could point to the possible existence of untapped potential, which can come for instance from young people not yet in the labour force or unemployed having lost their jobs recently. However, the statistics might as well reflect that the non-working population can dedicate more time to those activities at home.
1. Skills indicators have been related to labour productivity and to selected indicators of Trade in Value Added (TiVA), and to shed some light on how different types of skills relate to economic performance and countries’ engagement in GVCs. These TiVA indicators account for countries and sectors participation and positioning in GVCs. They include a basic measure of value added, two indicators of the value added content of gross exports, one indicator of the direct industry value added and a measure of final demand.
The following features emerge from these exploratory correlations:
• The correlation of productivity with skills' median values, both cognitive skills and personality traits, is always significant, and almost always positively so, with the strongest correlations found with the frequency of the performance of complex numeracy tasks (0.42). In the case of the frequency of the performance of physical tasks the coefficients of the correlations between median skills and productivity turns out to be negative (-0.36) likely signalling a possible low productivity of tasks entailing physical work.
3 DSTI/EAS/IND/WPIA(2016)1
• The correlation of productivity with the 10th percentile of with the 25th percentile (i.e. the left hand part) of the skills distributions exhibits higher coefficients than that of the 75th or 90th percentile, and often also of those of mean values. While it is premature to draw any conclusion based on such a general descriptive exercise, data seem to support the intuition of the resource based view of the firm, whereby it is broad workforce skills that matter.
• Cognitive skills indicators, both assessed skills and the frequency of the performance of cognitive tasks, show generally significant positive correlations with the selected TiVA indicators. Correlations between skill indicators and the domestic value added embodied in foreign final demand are week, suggesting that the existence of intermediaries and the fact that production reaches final consumers through other countries matters for the extent to which skills relate to trade performance.
• The distribution of skills also seems to matter for participation in GVCs although more work will be needed to better understand how different parts of the skills distribution relate to TiVA indicators. In the case of assessed cognitive skills, the size of the correlation coefficients of the 10th and 90th percentiles is generally smaller than those with median values. In the case of skills as they emerge from the performance of tasks, correlations with the lower part of the skills’ distribution (25th percentile) are generally higher than those of median values. Conversely, the higher part of the skills’ distribution (75th percentiles) exhibit lower correlation with TiVA indicators than the ones emerging with median values.
• Different types of skills show different correlations with TiVA indicators. In the case of the three TiVA indicators for which positive values are observed, the strongest correlations are observed with respect to interacting and communicating (between 0.42 and 0.23); complex and less complex reading and writing (between 0.39 and 0.21, and between 0.38 and 0.18, respectively); less complex ICT skills (between 0.45 and 0.14); and managing (between 0.37 and 0.19).
• The frequency of the performance of physical tasks persistently shows a negative correlation with the TiVA indicators for which the other skills exhibit positive correlations and a positive although not strong correlation with the share of domestic value added embodied in foreign final demand.
• Differences emerge in and the extent to which skills' indicators relate to the economic performance and trade-related indicators uses, as some types of skills that are more strongly correlated with some TiVA indicators than with labour productivity. This suggests that participation in global value chains and competition in global markets may require different workforce skills than those needed to perform at the country level.
The analysis proposed in the present work has to be considered suggestive rather than conclusive. While the skill taxonomy based on the Survey of Adult Skills proposed builds upon a comprehensive meta- analysis synthesising the finding of studies in a wide array of fields - including economics, management, business, organisation and psychology - the stylised facts highlighted represent only the first steps in the analysis.
4 DSTI/EAS/IND/WPIA(2016)1
TABLE OF CONTENTS
EXECUTIVE SUMMARY ...... 2 SKILLS AND GLOBAL VALUE CHAINS: A FIRST CHARACTERISATION ...... 7 Background and motivation ...... 7 Introduction: skills and GVCs ...... 8 A review of the literature on the link between skills, jobs and economic performance ...... 9 Operationalising the link skills - performance in a GVC context ...... 22 Characterising skills endowments: a first cross-country perspective...... 30 Exploring the correlation between skills, performance and GVC indicators ...... 48 The work ahead: preliminary conclusions and next steps ...... 55 REFERENCES ...... 57 ANNEX ...... 62
Tables
Table 1. Performance-relevant skills that can be measured through the Survey of Adult Skills (PIAAC) ...... 26 Table 2. Differences in the skills of workers and non-employed ...... 47 Table 3. Correlations between skills indicators and labour productivity at a country industry level ...... 49 Table 4. Correlations between cognitive and problem solving skills and selected TIVA Indicators ...... 52 Table 5. Correlations between cognitive skills and personality traits and selected TIVA Indicators, across distribution of skills ...... 53
Figures
Figure 1. Skills rated as important by employer when recruiting higher education graduates (%) ...... 11 Figure 2. Performance-relevant skills: from the literature review to building indicators based on PIAAC ...... 25 Figure 3. Distribution of the literacy and numeracy skills of workers, services and manufacturing (2012) ...... 31 Figure 4. Cognitive skills by occupation, services and manufacturing sectors (2012) ...... 32 Figure 5. Distribution of problem solving skills in technology-rich environments of workers, by sector (2012) ...... 33 Figure 6. Problem solving skills in technology-rich environments by occupation, services and manufacturing sectors (2012 ...... 34 Figure 7. Numeracy proficiency and intensity of complex numeracy tasks performed on the job, by industry (2012 ...... 35 Figure 8. Frequency of complex and less complex ICT tasks performed on the job, by industry (2012) ...... 36 Figure 9. Frequency of ICT tasks performed at work and at home, by industry (2012) ...... 37 Figure 10. Frequency of the performance of interacting and communicating, managing and self- organising tasks, by industry ...... 38 Figure 11. Readiness to learn and the frequency of the performance of problem solving tasks, by industry ...... 39
5 DSTI/EAS/IND/WPIA(2016)1
Figure 12. Workers' conscientiousness, and trust in persons, by industry ...... 40 Figure 13. Frequency of the performance of physical tasks, by industry ...... 41 Figure 14. Frequency of performance of interacting and communicating, managing and self- organising tasks, by occupation and sector ...... 42 Figure 15. Readiness to learn and frequency of performance of problem solving tasks, by occupation and sector (2012)...... 43 Figure 16. Conscientiousness and trust in persons, by occupation and sector ...... 44 Figure 17. Frequency of the performance of physical tasks, by occupation and sector (2012) ...... 45 Figure 18. Distribution of literacy proficiency of workers and non-employed ...... 46 Figure 19. Correlation between complex ICT skills and per-capita labour productivity, by industry, 2011 ...... 50 Figure 20. Correlation between the frequency of interacting and communicating skills and direct domestic value added content as share of export, by sector, 2011-2012 ...... 54 Figure 21. Correlation between the frequency of physical tasks and direct domestic value added content as share of export, by sector, 2011-2012 ...... 55
6 DSTI/EAS/IND/WPIA(2016)1
SKILLS AND GLOBAL VALUE CHAINS: A FIRST CHARACTERISATION
Background and motivation
2. In 2014 the Working Party on Industry Analysis (WPIA) scoped for the Committee on Industry, Innovation and Entrepreneurship (CIIE) a horizontal project capitalising on existing work on Knowledge Based Capital (KBC), TiVA and Global Value Chains (GVCs). The aim of this project, detailed in DSTI/EAS/IND/WPIA(2014)2, was to broaden the scope of the GVCs and KBC analyses to encompass the skills and employment dimensions. The focus was on the complex interactions that exist between investment in KBC, workforce skills, employment, and participation and positioning in global value chains, both at the industry and economy levels.
3. The project consisted of three main lines of work, each articulated over several policy-relevant questions, some of which to be jointly carried out with the Directorate for Employment, Labour and Social Affairs (ELS), the Trade and Agriculture Directorate (TAD) and the Directorate for Education and Skills (EDU).
4. This paper offers some preliminary results of the work carried out with EDU aimed to characterise the distribution of skills across countries, industries and occupations, tasks performed on the job, and to investigate the relationship that exists between the skills composition of the workforce, tasks, and the generation of value added from trade. Identifying the skills that affect participation in GVCs is an important step in assessing how policies, such as those related to education, training and work organisation, as well as industrial policy aimed at enhancing productivity and growth can help countries capture value from trade, More precisely, this paper addresses research questions such as:
i) What are the skills that are more relevant for performance on the job and for firm performance? ii) How are these skills distributed across occupations, industries, economies? To what extent do economies, industries and occupations differ in the frequency of tasks performed on the job? iii) How does skill endowment and the frequency of tasks performed on the job relate to activities over GVCs?
5. In addition to contributing to the 2015-2016 programme of work and budget (PWB) of CIIE, the analysis presented in this paper will be used in the OECD Skills Outlook 2017, which will have a focus on "Skills and GVCs". The Skills Outlook provides an integrated approach to skills issues based on the multidisciplinary knowledge of the OECD and using the holistic lens of the OECD Skills Strategy. It provides governments and stakeholders with in-depth analysis, policy insights and concrete examples from country experience on specific skill-related issues. The 2017 edition aims at better understanding the role of skills and of skills-related policies in the relationships between GVCs and productivity, growth and inequalities. It also aims at characterising countries’ needs in terms of skills and skills-related policies to make the most of GVCs. Hence, the analysis presented in this paper will be at the core of the Skills Outlook 2017.
7 DSTI/EAS/IND/WPIA(2016)1
6. In a companion paper (DSTI/EAS/IND/WPIA(2016)2) attention is being devoted to the relationship between investment in KBC and trade in value added flows at the country and industry levels. Using measures of organisational capital and training that the WPIA Secretariat has been developing, as well as other KBC measures, the analysis will investigate the relationship between investment in KBC, industry performance and value added from trade.
7. As mentioned, the present work addressing the relationships between skills endowment, job performance, firm performance, participation and positioning in GVCs, is part of a bigger project seeing the participation of other OECD Directorates.
8. In particular, the work with the Trade and Agriculture Directorate (TAD) has led to two papers. A first paper (DSTI/EAS/IND/WPIA(2015)2), forthcoming as an STI working paper) proposes a novel measure of the routine content of occupations for 20 OECD countries. It builds on data from the Survey of Adult Skills (PIAAC) and exploits information on the extent to which workers can modify the sequence of their tasks and decide on the type of tasks to perform on the job. A number of indices synthesising these pieces of information are used to group occupations into four routine-intensity classes (high, medium, low, and non-routine, respectively). The paper also sheds light on the relationship that exists between the routine content of occupations and the skills of the workforce, intended as both the skills that workers are endowed with (i.e. independently of use) and those that they use on the job. In 2015 this work has been discussed at both WPIA and at the Working Party of the Trade Committee (WPTC).
9. A second paper, an STI Working paper (Marcolin et al., 2016), addresses the role of global value chains (GVCs), workforce skills, ICT, innovation and industry structure in explaining employment levels of routine and non-routine occupations. The analysis encompasses 28 OECD countries over the period 2000-2011 and relies on the new routine intensity measures proposed in DSTI/EAS/IND/WPIA(2015)2, as well as on new industry-level TiVA indicators of offshoring, domestic outsourcing, and the services content of manufacturing. Results suggest that, while generally positively associated with employment, the role played by high skills differs in manufacturing and services industries. Also technological innovation is found to relate positively to employment in all types of occupations: an increase in industry innovation leads to increased employment in each of the four occupational groups considered. Early versions of this paper (DSTI/EAS/IND/WPIA(2015)3; TAD/TC/WP(2015)15) were presented at the joint WPIA/CIIE workshop held in October 2015 and discussed at the December 2015 meeting of WPTC.
Introduction: skills and GVCs
10. The skill composition of the workforce, the tasks performed on the job and the interaction between skills and tasks are expected to contribute to the positioning of firms and economies in global value chains. Existing empirical evidence supports the predictions of theoretical models and suggests that skill endowments relate positively to the probability of being employed and to job quality, as captured by income levels and prospective career paths, and the ability of firms to innovate and perform.
11. How workers apply these skills to the tasks they perform or how they use their skills on the job also contribute to firm performance (Autor, 2013). High-skilled human capital is expected to carry out complex tasks, which should generally lead to better economic performance, e.g. in terms of productivity, either directly or through complementarity with technology (Acemoglu, 2002). While theory and empirical findings suggest the relations between skills and firm performance to hold at the country level, evidence on a cross-country or industry basis remains scarce. The literature on the relationship between skill endowments, tasks performed on the job, and participation and positioning in GVCs at the industry and economy-wide level is also fairly scant. The lack of empirical evidence in this respect hinders the design of policies aimed to improve participation in GVCs and ‘social upgrading’, intended as the up-skilling of the labour force and improvements in the returns to labour.
8 DSTI/EAS/IND/WPIA(2016)1
12. To address some of these shortcomings, this paper identifies the skills that existing studies suggest being key for workers' performance on the job and for firm performance. Skills are identified based on an extensive survey of the literature, including literature in economics, management, organisation science and psychology, and are selected according to their prospect relevance in shaping the creation of value added in domestic markets as well as international production. The continuum of cognitive skills and personal traits (i.e. non-cognitive skills) identified as being relevant for economic performance is then crystallised in a taxonomy grouping skills in a homogeneous fashion, with the aim to generate indicators that attempts to reflect findings in the literature. This taxonomy is then operationalised on data from the Survey of Adult Skills based on the OECD Programme for the International Assessment of Adult Competencies (PIAAC), as it provides a wealth of employee-level information about workers' skills and jobs, as well as information about the firms where they work (e.g. industry) and does so for more than twenty OECD countries.
13. The characterisation proposed in this study allows for the emergence of some first stylised facts, and to investigate the extent to which skills endowments, also as they emerge from the frequency of tasks performed on the job, differ across countries, industries and occupations. It further tries to uncover whether differences exist in the way in which workers use skills at home and on the job. Finally, this work proposes a very first attempt to relate skill endowments and tasks performed on the job to Trade in Value Added (TiVA) indicators, to be further pursed and improved in follow up work. This preliminary analysis sheds some light on how different types of skills relate to countries’ engagement in GVCs. It further provides information on the different roles of assessed cognitive skills, of skills as they emerge from the tasks performed on the job and of personality traits on this engagement. Finally, it provides some indications about the extent to which the different parts of the skills distribution can be related to TiVA indicators.
14. While the study generates interesting evidence, this characterisation is to be considered at best suggestive. Results will need to be verified in econometric analysis, as this analysis addresses a vast and unexplored territory: while existing studies agree on the importance of skills (generally proxied by education) for trade patterns, they very seldom specify which skill matters for which type of GVC participation or performance-related measure.
15. This paper is structured as follows. It first reviews the literature on the link between skills, jobs and economic performance. It then proposes a taxonomy of the relevant skills and develops indicators using data from the Survey of Adult Skills (PIAAC). A first characterisation of skill endowment and skills as they emerge from the tasks performed on the job is then offered, at the country, industry and occupation level. Correlations between skill-related indicators and productivity and TiVA indicators provide some first descriptive indications of the possible relationships that exist between skills endowment of the workforce and industry’s performance and participation in global value chains.
A review of the literature on the link between skills, jobs and economic performance
16. Understanding the links between workers' skills and capabilities - performance on the job – and economic performance requires a theoretical underpinning. Two frameworks appear particularly relevant in this respect: the Resource Based View (RBV) of the firm and the High-Performance Work Systems (HPWS) approach. In addition, the economics literature focusing on education and skills provides important insights on the role of individuals' abilities for labour market outcomes.
17. Thanks to the pioneering work of James Heckman in particular, individuals' skills have been recognised as fundamental determinants of economic and social success. In turn, formal academic institutions as well as families and firms have been recognised as sources of learning (Heckman, 2000). Cognitive skills involve conscious intellectual effort and include long- and short-term memory, auditory processing, visual processing, processing speed, and logic and reasoning. “Non-cognitive skills”, also
9 DSTI/EAS/IND/WPIA(2016)1 known as “soft skills” or personality traits or character qualities, involve the intellect in a more indirect and less conscious fashion than cognitive skills, and relate to individuals' personality, temperament, attitudes, integrity and interpersonal interaction. Examples of studies arguing for the importance of cognitive skills for job performance are Schmidt (2002), Schmidt and Hunter (2004) and Hanushek and Woessmann (2008). Heckman and Rubinstein (2001) emphasise the importance of noncognitive skills in particular. Finally, many analyses stress the importance of both cognitive and non-cognitive skills for occupational attainment and performance on the job (e.g. Heckman et al., 2006; Kautz et al., 2014).
18. The RBV emphasises the importance of internal firm resources, also and especially human resources (HR), as sources of competitive advantage, thus shifting the emphasis of the strategy literature away from external factors (e.g. industry position). In its original formulation, it posits that strategic resources are heterogeneously distributed across firms and suggests the use of four indicators, i.e. value, rareness, imitability, and substitutability, for the identification of firm resources able to generate sustained competitive advantage (Barney, 1991). The resource-based view of the firm has profoundly influenced the strategic human resource management literature and its exploration of HR’s role in supporting business strategy (see Wright et al., 2001, for a discussion).
19. The High-Performance Work Systems (HPWS) literature emphasises the need for a systematic and integrated approach to the management of human resources so that HR functions are aligned to the achievement of firms' strategies. As human capital becomes more and more central in the quest for sustainable competitive advantages, a skilled and motivated workforce, able to provide the speed and flexibility required by the new global market imperatives, becomes key for organisational performance. Workers are seen as a source of value rather than a cost to be minimised and high-performance work systems able to attract, develop, and retain key HC become significant elements in strategic decision making, especially as command and control organisational structures decline. HPWS are thus held to play a strategic role with respect to supporting the development of core competencies and to enabling an effective strategy implementation. Extremely interesting in this respect are Becker and Huselid's (1998) discussion of HPWS and firm performance; and Shin and Konrad's (2014) analysis of the directions of causality between high-performance work systems and organisational performance.
20. Surveys also indicate that skills are important from the employers’ perspective, with team working being ranked at the top for the very important skills in a Gallup survey (see Figure 1). Using a revealed preference approach based on employer surveys, Humburg et al. (2013) find that those skills that can enhance graduates’ chances to get hired for the job can be ranked as follows: professional expertise (19.5%), interpersonal skills (19.1%), commercial/entrepreneurial skills (17.6%), innovative/creative skills (16.0%), strategic/organisational skills (14.2%), and general academic skills (13.7%).
10 DSTI/EAS/IND/WPIA(2016)1
Figure 1. Skills rated as important by employer when recruiting higher education graduates (%)
European countries
Not important at all Rather unimportant Rather important Very important
100 90 80 70 60 50 40 30 20 10 0
Source: The Gallup Organisation (2010), Employers’ Perception of Graduate Employability, Flash Eurobarometer No. 304.
21. In what follows, attention is mostly confined to those parts of the cited literatures aimed at identifying key HR skills and abilities and at understanding the possible mechanisms through which they may lead to superior organisational and economic performance, and, consequently, contribute to firm and industry performance in a globalised world. Relevance for economic outcomes and for policy making, and the possibility to operationalise the analysis on a cross country basis motivate the succinct literature survey proposed. As such, this concise review does not do justice to the breadth and depth of existing contributions.
Cognitive skills
Reading, writing and numerical skills
22. Broad agreement exists in the fields of industrial organisation, psychology and labour about the fact that general cognitive ability - which is essentially the ability to learn - helps predict both occupational level attained and performance within one’s chosen occupation, as well as the ability to benefit from training (e.g. Schmidt, 2002, Schmidt and Hunter, 2004). A subset of cognitive abilities called specific aptitudes, or simply aptitudes, include verbal aptitude, spatial aptitude, and numerical aptitude.1
23. Many are the studies emphasising the importance of cognitive skills and, more recently, the joint importance of cognitive and non-cognitive skills (discussed later) for job and economic performance. For instance, Hanushek and Woessmann (2008) argue that there is strong evidence that the cognitive skills of the population, rather than school attainment, relate to individual earnings, the distribution of income and more generally economic growth. However, Heckman and Vytlacil (2001) caution that since cognitive
1. Numerical problem solving, which is different from problem solving on the job, is also considered a cognitive ability by the literature (see e.g. Schmidt, 2002).
11 DSTI/EAS/IND/WPIA(2016)1 ability and schooling are so strongly related it is basically impossible to independently vary these two variables and still be able to estimate their impact separately.
24. Hoyles et al. (2002) suggest that numeracy and mathematical skills are cognitive abilities that are conducive to business success. They underline that a growing number of jobs require not only numerical skills but also some mathematical literacy, i.e. the ability to apply a range of mathematical concepts integrated with a detailed understanding of the workplace context. In an increasingly competitive and technology-based global economy, mathematical literacy and the use of IT in the workplace get more and more linked. Hoyles et al. (2002) further argue that mathematical skills are always deployed towards certain goals, which they group on five interrelated goals, namely: improving efficiency, dealing with constant change and innovation, informing improvement, remaining competitive, and maintaining operations.
ICT skills
25. Existing studies in different fields point to the importance of Information and Communication Technologies (ICT) for firm performance, intended as investment in ICT hardware and software as well as ICT related skills.
26. Management and organisation-related analyses in an RBV context argue that ICT should be considered as an organisational capability and empirically investigate the relationship that exists between ICT capabilities and firm performance. For instance, Bharadwaj (2000) find that firms with high ICT capabilities - intended as ICT infrastructure, ICT-related human resources, and ICT-enabled intangible assets - tend to outperform comparable firms on a number of profit and cost-based performance measures. Santhanam and Hartono (2003) find that firms with higher ICT capabilities are characterised by higher performance, both contemporaneous and sustained over time, as compared to average industry performance. Tippins and Sohi (2003) further underline that the reason why ICT investment seemingly pays off for some companies but not others depends on organisation learning and on ICT competencies and show organisational learning to play an important role in mediating the effects of ICT competency on firm performance.
27. Among the economic studies in the field, Dickerson and Green (2004) propose a methodology for the measurement of job skills using survey data on detailed work activities, and use these measures to assess how the labour market value skills and whether the use of skills is growing. They find that in the United Kingdom the utilisation of computing skills, literacy, numeracy, technical know-how, communication skills, planning skills, and problem-solving skills has grown over time. They see that computer skills utilisation grew the fastest and the use of computers became more sophisticated. Also, they investigate whether computer use affected wages and find that both computer skills and high-level communication skills endowments carried positive wage premia. Wage premia and their determinants are also at the centre of MacCrory et al.’s (2015) analysis of skill-biased technical change (SBTC). In particular, they examine how the earnings related to different skills changed over the period 2006-2014 in the United States. They find that different skills indeed command different wage levels (although in a non- linear fashion), and that such wage effects depend on both the actual use of ICT in the occupation and the ICT intensity of the industry. Finally, Bloom et al.'s (2014) analysis based on a recent Census Bureau survey of management practices in the US finds that more structured management practices are highly associated with higher levels of ICT intensity. They find that structured management is strongly correlated with superior performance in terms of productivity and profitability as well as innovation and employment growth, and that management practices vary substantially across establishments.
12 DSTI/EAS/IND/WPIA(2016)1
Personality traits or “non-cognitive skills”
28. Much of research on personality traits (or non-cognitive skills), especially for North America, has been organised following the so-called "Big Five" factor model of personality (Goldberg, 1990), which suggested that most personality measures could be subsumed under an umbrella including five key factors: extraversion, agreeableness, conscientiousness (or dependability), emotional stability (vs. neuroticism), and openness to experience. One of these factors in particular, i.e. conscientiousness, has been found to be consistently predictive of job performance, occupational level and performance in job training programs (see e.g. Barrick and Mount, 1991). It was found that the level of validity of such prediction was higher when conscientiousness is assessed using ratings by others rather than self-report assessments (Schmidt and Hunter, 2004). Conscientiousness is associated to skills as grit, perseverance, delay of gratification, impulse control, achievement striving, ambition, and work ethic. Agreeableness translates into skills as empathy, perspective taking, cooperation, and competitiveness. Emotional stability relates to the locus of control, and to skills as self-evaluation and self-esteem, self-efficacy and optimism (see Kautz et al., 2014, for a discussion).
29. While psychology and personality studies appeared advanced at the beginning of the new millennium, still in 2001 Heckman and Rubinstein noted that too little was understood about how non- cognitive skills are generated or about their effects. Also they stated that while it was common knowledge outside academic journals that motivation, tenacity, trustworthiness, and perseverance were important for succeeding in life, research in the field was still is in its infancy.
30. Since then much progress has been made, not only to better understand the role of personality traits skills as such, but also and especially to assess whether and to what extent cognitive and personality traits jointly shape labour market outcomes and job performance.
31. Heckman et al. (2006) find that non-cognitive skills strongly influence schooling decisions and affect wages, given schooling decisions. They argue that schooling, employment, work experience, and choice of occupation are affected by latent non-cognitive and cognitive skills and that gender differences seem to exist in the effects of these skills. Also, they find that for a variety of dimensions of behaviour and for many labour market outcomes, changing non-cognitive skills from the lowest to the highest level has an effect on behaviours which is comparable to or even greater than corresponding changes in cognitive skills. Latent non-cognitive skills seemingly raise wages through their direct effects on productivity, as well as through their indirect effects on schooling and work experience. OECD (2015) reviews a large range of researches finding similar results.
32. Kautz et al. (2014) argue that, for many outcomes, the predictive power of non-cognitive skills rivals or exceeds that of cognitive skills. In addition, they state that IQ tests and achievement tests are not able to adequately capture non-cognitive skills or personality traits, nor those goals, character, motivations, and preferences that are typically valued not only at school but also in the labour market and in many other domains.
33. There is no perfect term to refer to these skills. The term "personality traits" suggests the idea that these skills are permanent and can to some extent be inherited; the wording "skills" suggests that these attributes can be to some extent learned. The term "non-cognitive" suggests that these skills do not have a cognitive component. However, many of these skills are a mix of traits that individuals are to some extent born with and of abilities that can to some extent be learnt and improved over the time.
34. OECD (2015) collects evidence on how policy makers, schools and families can facilitate the development of these skills. The report suggests that promoting strong relationships between educators (e.g. parents, teachers and mentors) and children, mobilising real-life examples and practical experience in
13 DSTI/EAS/IND/WPIA(2016)1 existing curricular activities, and emphasising hands-on learning in extracurricular activities represent effective approaches to enhance their sense of responsibility, capacity to work in a team and self- confidence.
Trust
35. Experts in psychology, sociology, management and economics agree on trust being a hallmark of effective relationships and to affect the extent to which motivation is converted into work group processes and performance. Most of the trust-related research initially positioned trust as a variable having a direct effect on work group processes and performance, arguing that increasing (decreasing) levels of trust should lead to superior (inferior) group processes (e.g. higher (lower) levels of cooperation) and higher (lower) performance. More recently, trust has also been looked at as a construct that indirectly influences group performance, through channelling group member’s energy toward reaching alternative goals (see e.g. Dirks, 1999).
36. Mayer and Gavin (2005) investigate how employees’ trust in plant managers and top management team relates to in-role performance. They find that trust in these two managerial profiles relates to the ability of employees to focus on value-producing activities, i.e. the work that needs to be done, and that this focus in turn relates to performance. Conversely, lack of trust diverts employees’ attention from activities contributing to the performance of their organisation. They further argue that different management levels affect employees’ ability to focus on the work to be carried out in different ways. Direct managers shape employees’ ability to focus their attention as their decisions affect employees' daily life: if employees feel that they need to monitor the managers’ actions or to worry about them, this will inevitably distract workers’ attention from their daily tasks. Conversely, as the top management and its decisions affects the culture and success of the organisation, including a company's financial position, strategies and policies, a lack of trust in top management likely makes employees spending time and mental energy speculating about the future and about possible layoffs. Meyer and Gavin (2005) further underline that while their empirical analysis provides evidence about the fact that trust in management can improve organisational performance, when trust cannot be used, performance can be enhanced through using e.g. training and performance incentives, and through a clear system of monitoring and controlling performance.
37. Colquitt et al (2007), in addition to reaffirming the importance of trust for having employees’ full attention devoted to job tasks (rather than, e.g. diverting energy to monitoring), underline that trust allows the development of more effective exchange relationships between workers, which in turn fosters better performance behaviours on the job. Using task performance variables as objective indices of the fulfilment of job duties, as well as supervisory assessments and self-ratings, they find that moderately strong relationships exist between trust and task performance. Trust turns to be vital for effective working relationships, as individuals who are willing to trust others tend to engage in better task performance and to commit fewer counterproductive behaviours.
Conscientiousness and job engagement
38. While the relationships between personality measures and job performance may to some extent vary depending on the which of the big five constructs is considered, both theoretical and empirical evidence agree on conscientiousness, and the consequent engagement into job tasks that it entails, as being positively related to work outcomes (see Schmidt, 2014, for a review).
39. Engagement can be described as an emotional concept, whereby employees devote their physical, cognitive, and emotional energies to perform on the job. Rich at al. (2010) identify in value congruence, perceived organisational support and core self-evaluations the antecedents of engagement, and argue that
14 DSTI/EAS/IND/WPIA(2016)1 engagement represents an investment into role performance which contributes to explain two aspects of job performance: task performance and organisational citizenship behaviour. Engaged individuals work with greater intensity on their tasks, also for longer periods of time; pay more attention to what they do; are more focused on their responsibilities; and are more emotionally connected to the tasks that constitute their role, and these behaviours directly enhance performance. In addition, engagement leads to less formal behaviours such as helpfulness, conscientiousness and civic virtue, which contribute indirectly to the functioning and performance of organisations, by means of fostering social and psychological environments conducive to work accomplishment.
40. Barrick at al. (2013) even propose a theory of purposeful work behaviour which builds, among others, on the big five factor model of personality with the aim to explain how personality traits and job characteristics interact and influence work outcomes. They argue that personality traits make people goal oriented and when such motivational forces act jointly with job characteristics, individuals end up in a state called "experienced meaningfulness". This triggers task-specific motivational processes, which in turn positively influence the attainment of work outcomes.
Openness to experience
41. Openness to experience is one of the dimensions of the big five factors. Yet, openness to experience is difficult to capture and has thus been less covered by the literature than other major personality traits. Several articles have shown that openness to experience encourages workers to undertake training and enables them to better benefit from it (Barrick and Mount, 1991). Matzler et al. (2008) find significant correlations between openness to experience (as well as agreeableness and conscientiousness) and knowledge sharing using data of an internationally operating engineering company.
42. Evidence on how openness to experience affects work outcomes and firm performance is mixed. Openness to experience appears to be important under specific conditions. For instance, it can be an important skill when workers face unfamiliar environments and when they need to acquire new learning and experience to be successful in their jobs. To test this idea, Bing and Lounsbury (2000) analyse the impact of this skills for employees from the Appalachian region of the U.S. working in traditional Japanese manufacturing companies. They find a positive relationship between openness to experience, beyond both cognitive aptitude and the other four big five factors, and employees’ job performance along a number of dimensions including productivity, quality and attendance.
43. Openness to experience might also be important for relatively complex jobs that involve autonomy, require unconventional thinking and the adoption of new behaviours and ideas in order to achieve a high job performance level. Based on a study of Indian employees in a multinational medical transcription company, Mohan and Mulla (2013) find that openness relates positively to performance in high complexity jobs and negatively to performance in low complexity jobs.
44. Workers who have acquired their diplomas abroad or who have worked abroad or workers with an immigrant background may be more open to experience than other workers and, more generally, may have acquired some specific skills that are valued by firms, especially if operating in an international environment. Several theoretical articles have looked at the effect of the ethnic and cultural diversity on firm performance, but the link is unclear. On the one hand, ethnic-cultural diversity may affect firm performance negatively because it may hinder potential knowledge transfer among workers due to linguistic and cultural barriers (Lazear, 1999) or because workers distrust workers of other ethnic groups (Alesina and La Ferrara, 2002). On the other hand, cultural diversity brings about variety in skills, experiences, ways in which individuals interpret problems and use their cognitive skills to solve them, and cultures that may lead to innovation, creativity and productivity gains (Alesina and La Ferrara, 2005).
15 DSTI/EAS/IND/WPIA(2016)1
45. Alesina and La Ferrara, (2005) propose a theoretical framework in which the skills of individuals from different ethnic groups are complementary in the production process for a private good, implying that more diversity translates into increased productivity. However, for these productivity gains to be realised, the production process needs to be sufficiently diversified. A group of individuals with diverse but limited problem-solving skills can even outperform a more homogeneous group of high-skilled problem solvers (Hong and Page, 2004). According to Berliant and Fujita (2008), knowledge creation occurs at its fastest rate when common and differential knowledge is in balance. The process of cooperative knowledge creation reduces the heterogeneity of team members through the accumulation of knowledge in common. In this respect, a perpetual reallocation of members across different teams may be necessary to keep creativity alive.
46. Quantitative analyses generally find that the link between cultural diversity and economic performance depends on the context. Parrotta et al. (2014) analyse the effects of diversity within firms, measured in terms of the cultural, skill and demographic dimensions on total factor productivity, using a rich matched employer-employee dataset for Denmark. Cultural diversity is represented either by employees’ nationalities or by the languages they speak. Their estimates point towards a negative association between ethnic diversity and firm-level productivity. The negative coefficient of ethnic diversity among white-collar workers is lower than the coefficient associated with blue-collar occupations.
47. Several authors have conversely found a positive link. Trax et al. (2013) use German establishment data to estimate the effect of cultural diversity on total factor productivity. The authors find that productivity spillovers come from the diversification, not from the size of the group of foreign workers. A larger share of foreign employees – either inside the establishment or in the region – does not spur productivity gains while greater diversity of employees across different nationalities does lead to stronger total factor productivity. Using data for the United States, Ottaviano and Peri (2006) ask what is the economic value of the “diversity” that the foreign born bring to each city. Foreign born are different from US born in their skills and abilities and therefore could be valuable factors in the production of differentiated goods and services. The authors find that diversity has a positive impact of the wages of the natives. The result holds when the authors control for the average schooling of the foreign-born, which they interpret as a sign that foreign born have specific skills.
Cognitive skills/personality traits: skills that combine both aspects
48. An important point made by Kautz et al. (2014) is that while both cognitive skills and personality traits change and can be changed over the life cycle, this happens through different mechanisms and with different ease at different ages. Also, it is at times difficult to label (a set of) skills as pure cognitive or non- cognitive, as often skills are the results of purposeful learning processes as well as of personal traits and of the combination of different types of skills. Examples are management skills and creative problem solving skills. Also, it has been found that correlations between cognitive ability measures and personality measures are usually low, and correlations between measures of the big five construct are usually moderate. As both cognitive ability and personality are valuable predictors of job performance, and given their relative lack of correlation with each other, combinations of the two are likely to produce superior predictions of job performance (Schmitt, 2014).
49. In the present work, skills are considered as a continuum, with some skills having mostly a cognitive component, some skills being mostly linked to personality traits, and some skills coming from the interaction and combination of these two components. This latter category, while stemming from attitudes and propensities that some individuals have, can nevertheless be largely shaped and enhanced through (active) learning processes and experience.
16 DSTI/EAS/IND/WPIA(2016)1
Managerial and organisational competencies
50. More than half a century of psychology, human resources, organisation, management, finance, entrepreneurship, innovation and economics studies have underlined the relevance of different managerial profiles, tasks and abilities for the performance of firms.
51. The important contributions that the RBV of the firm has made in the field of human resource management (HRM) and its emphasis on people as strategically important to a firm’s success have led to a rapid growth of strategic human resource management studies, and to fostering interaction and convergence of strategy and HRM (see Jay et al., 2001, for a discussion). Among others, these highlight the importance of strategic management abilities for the generation of sustainable competitive advantages (e.g. Hill et al, 2014) and for enhancing competitiveness in global markets (e.g. Hitt et al. 2012). In turn, and given the centrality of human resources for the competitiveness of firms, emphasis is put on the need of suitable human resource management practices to effectively align organisational, group and individuals' objectives with organisations' strategies (see e.g. Lazear and Gibbs, 2014; Buller and McEvoy, 2012). Among the key tasks to be accomplished and the skills needed for an effective HRM and for strategic management more generally the literature suggests those related to: activities and workforce planning; advising and instructing staff; influencing decisions; negotiating; solving problems; and communicating (see Armstrong and Taylor, 2014, for an extensive discussion).
52. Recent cross-country studies further find that management practices differ substantially not only across firms, but also across countries and industries (e.g. Bloom and Van Reenen, 2010; Bloom et al., 2012), thus warning about generalising results related to specific industries and countries. Also, recent OECD efforts aimed to better define and measure organisational capital find that managers are not the sole to perform managerial tasks affecting the long-term functioning of firms such as developing objectives and strategies, organising, planning, supervising production and managing human resources (Squicciarini and Le Mouel, 2012; Le Mouel and Squicciarini, 2015). Such findings support the arguments put forward by Caroli and Van Reenen (2001) and von Krogh et al. (2012) about the fact that tasks traditionally carried out by managers have been progressively devolved upon a wide array of non-managerial occupational profiles. This decentralisation of authority and delayering of managerial functions in turn suggests that firm-specific managerial skills and abilities should be assessed and compared across a wide array of occupational profiles, and not only with respect to managers.
Creative problem solving
53. Common sense, theory and evidence all emphasise the importance of problem solving abilities, both individual-specific and group-related ones, as they are conducive to superior individual and organisational performance. The role of problem solving skills and abilities has been investigated in relation to a wide array of policy-relevant issues, including firm performance, creativity, innovation, leadership, management and entrepreneurship.
54. A part of this vast and diversified literature addresses the role played by problem solving in developing organisational capabilities and improving firm performance. Examples are Nickerson and Zenger (2004), who even propose a knowledge-based theory of the firm centred on problem solving and knowledge formation; and Katila and Ahuja (2002) who investigate the ways in which firms try to find solutions to problems to create new products and highlight the extent to which firms' differ in the depth and scope of their search efforts. Tippman et al. (2013) investigate how individuals, especially middle managers, solve non-routine problems and design new solutions, and how this translates into the development of organisational capabilities.
17 DSTI/EAS/IND/WPIA(2016)1
55. The literature addressing the problem solving and link is also very rich, both at the individual and the group level. Problem solving attitude and skills are found to be highly and positively correlated with both individual and "organizational creativity" (Woodman et al., 1993), intended as the creation of a valuable, useful new product, service, idea, procedure, or process by individuals working together in an organisation. Consensus also seems to exist about problem solving abilities being directly and indirectly - i.e. through the creativity conduit - related to innovation, and about problem solving ability and creativity being linked to entrepreneurial endeavours' creation and success. Finally, problem solving and creativity have been analysed in relationship to leadership's traits and needs, and found to be a key component of leadership, also managerial leadership.
56. A few examples of studies supporting these propositions are mentioned here, but they surely do not do justice to the extensive literature that exists in the different domains. Hargadon and Bechky (2006) investigate creative problem solving modes and emphasise that while some creative solutions are the product of individual insights, others are generated through momentary collective processes which are organisation-specific and reflect a qualitative shift in the nature of creative processes. Wang and Horng (2002) find that creative problem solving training has a positive effect on creativity and R&D performance. Reiter-Palmon and Illies (2004) investigate the role of leadership for creativity and argue that creative outcomes cannot be obtained without sufficient support from the organisations and organisational leaders. Hsieh et al. (2007) propose a theory of the entrepreneurial firm and argue that entrepreneurial opportunities are nothing more than valuable problem solution pairings, and that opportunity discovery stems from a deliberate search or recognition over the relevant solution space. Finally, Lee at al. (2004) contribute to that stream of literature looking at creativity and entrepreneurship from a regional viewpoint.
Self-organisational ability and flexibility on the job
57. Studies investigating the mechanisms through which high performance work systems lead to superior firm performance suggest adaptive capabilities and workers' flexibility to be key (see, e.g. Messersmith and Guthrie, 2010; Wei and Lau, 2010). As the current knowledge-based global economic paradigm calls for an ever greater ability to adapt to change and absorb shocks, systems and actors need to be able to adapt and remain flexible. This poses a number of management challenges and ultimately entails having a broader perspective on human resource management, especially in emergent organisations (Messersmith and Guthrie, 2010).
58. Bhattacharya et al. (2005) look at the extent to which the flexibility of employee skills, employee behaviours, and HR practices relate to HR flexibility and to superior firm performance. Results based on perceptual measures of HR flexibility and on accounting measures of firm performance suggest that skill, behaviour, and HR practice flexibility are significantly related to firm financial performance, and that skill flexibility contributes to cost efficiency.
59. An important stream of the economics literature investigates the factors that contribute to explain the movements of employment across countries along the production chain. Among them, the degree of codifiability and communicability of the tasks carried out by workers, and the way in which the routine- intensity of tasks, i.e. the extent to which tasks are carried out in a codifiable and repetitive way, affect such patterns. Evidence suggests that the routine intensity of occupations is partially decoupled from workers’ skills, as there are low-skill tasks that cannot be routinised (e.g. cleaning activities), whereas some high-skill tasks can (e.g. accounting). Also, the degree to which tasks can be routinised correlates positively with the degree of offshorability of tasks, e.g. in the form of shipments from affiliate to the headquarters (Oldenski, 2012); and negatively with employment levels at home, especially of those occupations which are intensive in such tasks (Becker et al., 2013; Autor and Dorn, 2013; Goos et al., 2014).
18 DSTI/EAS/IND/WPIA(2016)1
60. Recent OECD work (Marcolin et al, 2016) addressing the role of global value chains, workforce skills, ICT, innovation and industry structure in explaining employment levels of routine and non-routine occupations uncovers the existence of complex interactions between the routine content of occupations and skills, technology, industry structure and trade patents. These do not allow for a neat identification of “winners” and “losers” in a GVC context and point to the importance of decoupling the routine content of occupations from the skills of the workforce to better understand labour market outcomes in a GVC context.
Interacting and communicating
61. A vast strand of the literature has underlined the role of communication and interactions between workers and teamwork on the performance of firms. Cooperation and interaction between employees result in fewer missed signals between employees, and reduce the time wasted carrying out redundant communications, searching for missing information, and waiting to hear from co-workers (Gittell, Seidner, and Wimbush, 2010; Hamilton, Nickerson and Owan, 2003). Teamwork enables to develop complementarities in production among workers, it facilitate gains from specialisation by allowing each worker to accumulate task-specific human capital, and may encourage gains from knowledge transfer of idiosyncratic information that may be valuable to other team members. For these reasons, it is often argued that the knowledge and skills of a group of workers is higher than the sum of their individual knowledge.
62. For instance, Ichniowski, Shaw and Prennushi (1997) refer to the “connective capital” of firms as the capital emerging from creating teams of experts which can help to solve problems that cannot be solved by a worker’s knowledge. According to Collins and Smith (2006), cooperation, together with trust and shared codes and language contribute to the “social climate”, which determines knowledge exchange and combination and, through this channel, to firm performance.
63. Many empirical articles based on case studies find a link between cooperation within firms and their performance. For instance, using data on a garment plant in the US, Hamilton, Nickerson and Owan (2003) show that the introduction of team work was associated with an increase in productivity through two channels, a pure team work effect and the fact that the best workers have self-selected into the team. Hence, their result depart from other studies focussing on free-rider problems, which arise when actions taken by team members are not observable, and could make the best workers reject participation in the team. Based on data for 26 steel plants in the US, Ichniowski, Shaw and Prennushi (1997) find a positive link between “connective capital” and firms' efficiency and productivity.
64. Cooperation and interaction between workers mediate the impact of human resources practices on outcomes. Similarly, human resources practices can encourage team work and facilitate the exchange and combination of ideas and knowledge among employees. For instance, Collins and Smith (2006) examine how commitment-based HR practices facilitate the development of firm-wide social climate that encourages the exchange and combination of knowledge among knowledge workers. The authors find that a large part of the effects of HR practices on firms performance come from the effect of these practices on the development of exchange of knowledge in firms.
Physical skills
65. In contrast with many of the skills discussed above, physical skills tend to be relatively job- specific and to some extent less transferable to other occupations or sectors. Yet, they are crucial for completing several tasks and jobs and for firm performance in a number of sectors (e.g. construction, health and well-being, arts, etc.). Physical skills encompass skills relating to strength, coordination, manual or finger dexterity, and some of them take a long time to be acquired. Overall, physical skills are used in
19 DSTI/EAS/IND/WPIA(2016)1 both low-skilled and high-skilled jobs and in very different types of occupations (e.g. repairers, surgeons, sport teachers, musicians, etc.). In high-skilled jobs, they are often used in combination with other skills.
66. As some of the tasks involving physical skills can be (to some extent) automatised or outsourced to countries with lower labour costs, experts have questioned the role of these skills in the future, especially in developed economies. A relatively shared view is that technology has reduced demand for routine manual skills that can be accomplished following explicit rules (Autor et al., 2003; Goldin and Katz, 2007). When core tasks of these occupations follow precise, well-understood procedures, they can be increasingly codified in computer software and performed by machines. Trying to disentangle the effects of trade and technology on jobs and skills, Autor, Dorn and Hanson (2015) find that computerisation is associated with gains in the share of employment in low-education, manual-task-intensive occupations that demand physical flexibility and adaptability, relative to the employment in middle-skill, routine task- intensive jobs. By contrast, increases in trade exposure reduce overall employment in all categories of occupations and task considered by the authors, including in low-education manual-task-intensive occupations.
67. Some manual skills are complemented by a given technology or even made more necessary because of technology. Autor (2015) takes the example of a construction worker: the demand for manual skills has changed from being an expert with a shovel to driving an excavator. In addition, Autor (2015) argues that there are many tasks that people understand tacitly and accomplish effortlessly but for which neither computer programmers nor anyone else can enunciate the explicit “rules” or procedures. In terms of physical skills, those that include physical flexibility are difficult to replace by automation. Jobs that require visual and language recognition and in-person interactions are also challenging for automation and outsourcing. These skills are important in jobs such as food preparation and serving jobs, cleaning work, in-person health assistance, jobs in security and protective services. Finally, as the demand for some of the manual task-intensive activities appears to rise with aggregate incomes, demand may increase for these skills. Hence, new technologies and productivity growth in other areas may indirectly raise demand for manual task-intensive occupations, by increasing societal income.
Skills, tasks and GVCs
68. While skills are used to perform tasks, tasks and skills are not synonymous (Autor, 2013). Skills represent abilities that lead individuals to behave in certain way or to do some particular tasks. As personal traits and skills enable people and give them the capacity to function, the performance of any task depends on the skills of individuals, as well as on the effort put in the performance of such tasks (see Kautz et al., 2014). Evidence suggests that greater levels of skills foster social inclusion, promote economic and social mobility, create social well-being and enhance productivity (Kautz et al, 2014). The development of skills is a dynamic process and, while the early life of individuals is key for skills acquisition and development, both cognitive skills and character qualities continue to be shaped over time, especially in the working environment of individuals through the performance of tasks. Overall, it is the skills, tasks and interaction between skills and tasks that determine performance on the job and firm performance.
69. A recent literature in the field of labour economics assesses different dimensions of skills based on the observed tasks performed on the job (Ingram and Neumann 2006, Poletaev and Robinson 2008 as well as Gathman and Schoenberg 2010). Arguing that there is large unobserved skill heterogeneity within education classes and that educational attainment does not measure skills adequately, this literature uses survey information on the task contents of jobs to derive skills possessed by individuals. Ingram and Neumann (2006) and Poletaev and Robinson (2008) use U.S. data on 53 job characteristics from the Dictionary of Occupational Titles (DOT) in the year 1995 to extract four underlying skill dimensions. Ingram and Neumann (2006) estimate the labour market returns to this four skill dimensions and Politaev
20 DSTI/EAS/IND/WPIA(2016)1 and Robinson (2008) investigate to what extent the skills acquired in this four dimensions are specific and can be transferred across occupations. However, the DOT data is only collected from job specialists and not directly from workers and only includes binary information on whether the job includes the characteristic or not. These limitations of the DOT data are overcome in the study of Gathman and Schoenberg (2010) that uses German data from the German Qualification and Career Survey to investigate the transferability of skills accumulated in the labour market across occupations.
70. As Autor (2013) underlines, recent evidence suggests that, especially in industrialised countries, changes in the allocation of job tasks between capital and labour and between domestic and foreign workers has altered the structure of labour demand and triggered the polarisation of employment, whereby employment increases mainly or solely in the highest and lowest paid occupations. The task approach, he argues, has the potential to help shed light on the endogenous interactions occurring among skill supplies, technological capabilities, and trade and offshoring opportunities. In particular, it can contribute to explain how relative skill supplies affect relative wages and thus aggregate demand for skills, the assignment of skills to tasks and the participation of countries in GVCs. In his essay, which proposes a model of the assignment of skills to tasks based on comparative advantages, in order to avoid confusion, he cautions for the need to have precise and consistent task definitions when bringing task-based approaches to the data.
71. Theoretical predictions underline the importance of skills in contributing to explain international trade patterns. Recent models of international trade incorporate heterogeneous labour to investigate the way in which trade affects wages and wage distribution, the relationship between skills distribution and comparative advantage, and how trade shapes productivity and efficiency via its impact on the labour market (see Grossman, 2013, for a survey; Autor, 2013). Theoretical studies are supported by empirical evidence, which often rely on educational attainment as a proxy for skills.
72. This is the case, for instance, of Zhu and Trefler (2005) who use the ratio of completed secondary education to non-completed secondary education to account for the relative supply of skills; of Breinlich and Criscuolo (2011) who measure skills as the share of university graduates in all employees; and of Becker et al. (2013) who distinguish between highly educated workers with college qualifications and workers with less schooling. Moreover, in recent analysis based on matched employer–employee data information on skill-related variables (mainly education) is accompanied by information related to the demographics of the workforce, the wage and job history of workers, as well as firm-related information (Frias et al. 2009, Schank et al. 2007, Amiti and Davis 2012). An exception to this approach is Bombardini et al. (2012) who use cognitive skills data (IALS, a predecessor survey of the Survey of Adult Skills) and trade data to look at the effect of the skills distribution on trade. The authors show that the effect of skill dispersion on trade flows is quantitatively similar to that of the aggregate endowment of skills.
73. The international trade literature argues that it is only the high performing and most productive firms that participate in international trade and GVCs (Melitz 2003, Helpman et al. 2004, Bernard et al. 2007, Irrazabarra et al. 2013). Thus, the skills of the workforce that lead to a higher firm productivity should also increase exports and participation in GVCs at the industry level. It further offers insights on the links that exist between GVCs participation and firm and industry productivity (Amiti and Konigs 2007, Amiti and Khandelwal 2013, Goldberg et al. 2010). Imports of intermediate inputs may act as a driver for technological change and increase firm and industry productivity as well as the skills of the workforce exposed to the technological frontier.
21 DSTI/EAS/IND/WPIA(2016)1
Operationalising the link skills - performance in a GVC context
Skills and GVCs
74. The present exploratory analysis aims to contribute to this literature by identifying the types of skills and tasks that are more likely to shape firm and industry performance and trade patterns, and by characterising the distribution of these skills across countries, industries and occupational categories. This characterisation, to be used in econometric analysis relating the types and distribution of skills to trade in value added patterns, is made possible by recent OECD efforts aimed on the one hand to assess the skills of the adult population (through the Survey of Adult Skills, a product of the OECD Program for the International Assessment of Adult Competencies, PIAAC) and to measure the value added from trade and to analyse global value chains patterns (through the TiVA indicators), on the other hand.
75. While having an enormous potential in terms of the policy-relevant information that they may provide, caution needs to be applied when looking at the descriptive statistics and correlations based on these data sources and reflected in this paper, for a number of reasons. Among them, the fact that this work is still preliminary and the patterns and relationship suggested by this first characterisation will need to be verified in econometric analysis. Also and importantly, this work entails opening two black boxes at the same time, namely workforce skills' endowment and distribution and trade in value added patterns, and looking at the relationships between them. While, as mentioned, the literature stresses the importance of skills for trade patterns, it very seldom specifies which types of skills (besides education) matter for which types of trade flows, and as such the results presented in this paper are exploratory in nature.
Building indicators of skills based on the Survey of Adult Skills (PIAAC)
76. There is always a gap between conceptualising skills, as they emerge from a literature review as the one above, and efforts to measure them for empirical purposes. When subjected to measurement, personality traits tend to include a component of cognitive aspects and assessed cognitive skills also depend on individuals’ personality traits. Overall, the boundaries between different types of skills appear to be less distinct when efforts are made to proxy them with metrics. In addition, the Survey of Adult Skills does not encompasses all the skills mentioned above, which necessarily reduces the ability to proxy some of the aspects identified in the literature review. Figure 2 explains the shift from the skills identified in the literature review to the indicators built using the Survey of Adult Skills.
77. The source of data for the skills-related analysis is the Survey of Adult Skills (PIAAC). This survey contains information about cognitive skills (numeracy, literacy and problem solving in technology-rich environment), which are assessed using administered tests and other skills which are measured through self-assessment ratings. The Survey of Adult Skills further contains information about a variety of tasks that workers perform on the job (e.g. numeracy, literacy, ICT, problem solving, etc.), as well as data on the workers themselves (e.g. gender, age, occupational category, etc.) and on their workplace (including the industry in which they work and the public or private nature of the company). This information on job tasks is used to construct indicators proxying a wide array of workforce skills.
78. Maximal international comparability in terms of educational attainment, field of economic activity, and occupation is ensured through the use of international classifications: educational attainment is measured according to the 1997 version of International Standard Classification of Education (ISCED1997), whereas industries are classified following the International Standard Industrial Classification of All Economic Activities (ISIC) rev. 4 classification and occupations are defined according to the International Standard Classification of Occupations (ISCO) 2008.
22 DSTI/EAS/IND/WPIA(2016)1
A typology of performance-related workforce skills
79. Table 1 proposes a typology of the different skills that existing studies have been found to matter for performance on the job and for firm performance, and which can be measured through the Survey of Adult Skills. This table does not aim to provide a conceptual framework of skills, but simply to group together skills in a coherent fashion, based on their cognitive and content and on their pertinence for key personality traits. While skills are grouped into four categories, overlaps exist among the different categories and the skills mentioned should be considered as parts of a continuum:
• Cognitive skills: the Survey of Adult Skills first provides an assessment of the proficiency of adults in literacy, numeracy and problem solving in technology-rich environments (similar to those assessed in PISA). In addition, the Survey gives information on cognitive tasks and practices undertaken on the job and at home. These skills can be grouped into complex and less complex reading, writing, numerical and ICT skills. • Cognitive skills/Personality traits: these skills combine both aspects. While the Survey of Adult Skills does not measure these skills directly, it contains a number of questions providing information on how frequently tasks that combine cognitive skills and personality traits are performed on the job. It should be acknowledged that some workers may have such skills, but for which there is no such a requirement in their jobs. The Survey has questions on how frequently workers perform problem solving, self-organisational, interacting and communicating tasks. In addition, it is possible to infer management skills through a group of questions reflecting the managing tasks. • Personality traits. As these skills cannot be directly observed, they have to be measured indirectly through self or observers' reporting or through task performance. While the Survey of Adult Skills can help to approximate some of these skills, it is important to acknowledge that the Survey has not been designed for this purpose. Hence, the proposed indicators have to be considered experimental and may be subject to criticism. The Survey helps to approximate the readiness to learn, creative thinking, and the level of trust through self-reporting questions. Some aspects of conscientiousness can be tentatively approximated through a self-reporting question combined with a question on education choices, following a task performance approach. • Physical skills. The Survey of Adult Skills measures the extent to which workers perform physical tasks through a broad question and a narrower one approximating finger dexterity.
Data used: Survey of Adult Skills and TiVA
The Survey of Adult Skills (PIAAC)
80. The full Survey of Adult Skills database contains information for 165.599 individuals in 22 OECD countries and Russia.2 While this study exploits information on all individuals, both employed and self-employed - whether in employment or not -, most statistics are shown with respect to individuals who are working (i.e. employed or self-employed) at the time of the survey. This sample includes 111.728 working individuals.3 In the full Survey of Adult Skills dataset, only 68% of the individuals are working,
2. The OECD countries are: Australia, Austria, Belgium, Canada, the Czech Republic, Germany, Denmark, Spain, Estonia, Finland, France, the UK, Ireland, Italy, Japan, Korea, the Netherlands, Norway, Poland, the Slovak Republic, Sweden and the U.S.A. For Belgium, data refer to Flanders only; for the UK data refer to England and Northern Ireland only; the Russian data excludes the Moscow metropolitan area 3. We exclude 50 individuals that report working in an extraterritorial organization (ISIC rev 4 code 99).
23 DSTI/EAS/IND/WPIA(2016)1 whereas 32% (42.565) report being unemployed or out of the labour force. To offer a complete picture, a number of separate statistics are also shown with respect to the non-working population.4
81. In terms of working population, the Survey of Adult Skills dataset includes 50.6% men and 49.4% women. 13% of working individuals are younger than 25 years of age, 22% are between 25 and 34 years old, 24% are aged 35-44 years, 24% are in between 45 and 54 year, and 17% are in the group 55-65 years. Regarding the non-working population, the share of women is 60% and thus larger than that of the working population. The shares of the very young individuals (less than 25) as well as the older ones (55- 65) are 31%, respectively, which is more than 50% higher than the respective shares in the working population. . The shares of the middle aged categories (25-34, 35-44 and 45-54) are significantly lower (13%, 12% and 13%, respectively).
82. To assess the distribution of skills within and across countries covered by the Survey of Adult Skills this analysis relies on assessed literacy, numeracy and problem solving skills as well as on the newly created skill indicators detailed in Table 1 that are based on the skills use-related questions contained in the background questionnaire. For the construction of the different indicators, single items had to be rescaled in the form of a 0-1 interval, with 1 being the highest skill score and 0 the lowest one.
83. In particular, all questions in section F, G and H are frequency questions scaled between 1 ("Never") and 5 ("Every day"). The only exception is question F_Q01b, which is also scaled between 1 and 5 but for which categories are 1 ("None of the time"), 2 ("Up to a quarter of the time"), 3 ("Up to half of the time"), 4 ("More than half of the time"), 5 ("All the time"). The questions used from Section D and I are also scaled between 1 and 5, but these capture the respondent’s extent of agreement with a statement, and answers range between 1 ("Not at all") and 5 ("To a very high extent"). In the case of the trust-related questions, the scaling is still a 1 to 5 one, but has other labels, with 5 remaining the highest trust-related score and 1 the lowest5. Question D-Q08b asks the number of people supervised, with replies scaled between 1 and 5 as follows: 1 ("1-5 people"), 2 ("6-10 people"), 3 ("11-24 people"), 4 ("25-99 people") and 5 ("100 and more people"). The only categorical variable used in the study is B_Q03a (1=Yes, 2=No).
4. The statistics related to the non-working population exclude 10.963 non-working individuals, i.e. those aged 60-65 (of which 10.635 are out of the labour force), as these individuals are equally dispersed across countries and are assumed to have opted for early retirement schemes or to have no incentives to join the workforce. 5. The question is formulated in a negative fashion and answers are: 1-strongly agree, 2-Agree, 3- Neither agree or disagree, 4-Disagree, 5strongly disagree.
24 DSTI/EAS/IND/WPIA(2016)1
Figure 2. Performance-relevant skills: from the literature review to building indicators based on PIAAC
Source: Authors' own compilation.
25 DSTI/EAS/IND/WPIA(2016)1
Table 1. Performance-relevant skills that can be measured through the Survey of Adult Skills (PIAAC)
PIAAC questions Degree of Type of skill Skill name complexity On the job At home
Literacy Assessed through administered tests
COGNITIVE Numeracy Assessed through administered tests SKILLS
Problem-solving in technology-rich Assessed through administered tests environment G_Q01d Frequency of Reading scientific articles H_Q01d Frequency of Reading scientific articles Reading and G_Q01f Frequency of Reading manuals H_Q01f Frequency of Reading manuals writing G_Q02c Frequency of Writing reports H_Q02c Frequency of Writing reports G_Q03h Frequency of Use complex algebra and statistics H_Q03h Frequency of Use complex algebra and statistics G_Q01g Frequency of Reading financial invoices, bills etc. H_Q01g Frequency of Reading financial invoices, bills etc. Complex Numerical skills G_Q01h Frequency of Reading diagrams, maps and schematics H_Q01h Frequency of Reading diagrams, maps and schematics G_Q03f Frequency of preparing charts and tables H_Q03f Frequency of preparing charts and tables G_Q03g Frequency of Use simple algebra and formulas H_Q03g Frequency of Use simple algebra and formulas COGNITIVE G_Q05e Frequency of excel use H_Q05e Frequency of excel use SKILLS G_Q05g Frequency of programming language use H_Q05g Frequency of programming language use ICT skills (USING G_Q05d Frequency of transactions through internet (banking, H_Q05d Frequency of transactions through internet (banking, INFORMATION selling/buying) selling/buying) ON TASKS PERFORMED) G_Q01a Frequency of Reading directions and instructions H_Q01a Frequency of Reading directions and instructions G_Q01b Frequency of Reading letters, emails, memos H_Q01b Frequency of Reading letters, emails, memos Reading and G_Q01c Frequency of Reading newspapers H_Q01c Frequency of Reading newspapers writing G_Q01e Frequency of Reading books H_Q01e Frequency of Reading books Less G_Q02a Frequency of Writing letters, emails, memos H_Q02a Frequency of Writing letters, emails, memos complex G_Q02d Frequency of fill in forms H_Q02d Frequency of fill in forms G_Q03b Frequency of Calculate prices, costs, budget H_Q03b Frequency of Calculate prices, costs, budget Numerical skills G_Q03c Frequency of Calculate fractions, decimals, percentages H_Q03c Frequency of Calculate fractions, decimals, percentages G_Q03d Frequency of using calculator H_Q03d Frequency of using calculator
26 DSTI/EAS/IND/WPIA(2016)1 G_Q05a Frequency of email use H_Q05a Frequency of email use ICT skills G_Q05c Frequency of simple internet use H_Q05c Frequency of simple internet use G_Q05f Frequency of word use H_Q05f Frequency of word use F_Q05a Frequency of problem solving 5 minutes Problem-solving F_Q05b Frequency of problem solving 30 minutes D_Q11a extent of own planning of the task sequences D_Q11b extent of own planning of style of work Self- D_Q11c extent of own planning of speed of work organisational KILLS D_Q11d extent of own planning of working hours (duration of work) S skills COMBINING F_Q03a Frequency of planning own activities in the job COGNITIVE F_Q03c Frequency of planning the use of own time SKILLS AND F_Q01b Time collaborating or cooperating with co-workers PERSONALITY F_Q02a Frequency of information sharing with co-workers TRAITS Interacting and F_Q02c Frequency of giving speeches and presentations (USING communicating F_Q02d Frequency of client interaction selling a product or a service INFORMATION ON F_Q04b Frequency of negotiations within the firm or with other TASKS outside actors PERFORMED) G_Q05h Frequency of communication through internet D-Q08a-b Do you supervise people, how many? F_Q03b Frequency of planning activities of others Managing F_Q02b Frequency of instructing and teaching people F_Q02e Frequency of advising people F_Q04a Frequency of persuading or influencing others Conscientiousnes B_Q03a Did you ever start studying for any formal qualification, but leave before completing it? s and job I_Q04j I like to get to the bottom of difficult things engagement I_Q04m If I don't understand something, I look for additional information to make it clearer PERSONALITY Readiness to I_Q04h When I come across something new, I try to relate it to what I already know TRAITS learn and creative I_Q04b When I hear or read about new ideas, I try to relate them to real life situations to which they might apply thinking I_Q04d I like learning new things I_Q04l I like to figure out how different ideas fit together I_Q07a Trust only in few people Trust in persons I_Q07b Fear of being exploited by others PHYSICAL SKILLS (USING F_Q06b Frequency of working physically over long periods INFORMATION ON Physical skills F_Q06c Frequency of working accurately with fingers TASKS PERFORMED) Source: Authors' own compilation based on the Survey of Adult Skills (PIAAC) (2012).
27 DSTI/EAS/IND/WPIA(2016)1
84. When rescaling some of the questions, e.g. on ICT use or managing people, frequency questions have been combined with additional binary information on whether the person has ever carried out the activity (e.g. has used a computer or has ever managed people). This has been done to reduce the number of missing observations in the respective frequency questions as otherwise individuals answering no in the binary item would have missing observations in the frequency question. In the rescaled item, a zero value was attributed to individuals responding that they had never done the respective activity and answers then rescaled using steps of 0.2 in a 0-1 interval.
85. Before showing the indicators proposed and the related descriptive statistics it is important to highlight three main features of this exploratory work. The first one is that, in the operationalisation phase, a different number of questions from the Survey of Adults Skills have been used to construct the different indicators. While the Survey of Adult Skills offers a wealth of information, it was not conceived in view of analyses like the present one. This entails the impossibility of relying on balanced sets of information and on answers provided always over the same scale. A second feature of this first characterisation exercise is that all questions contributing to the construction of one indicator carry equal weights. This implicitly implies assuming that, e.g. employees' answers related to complex ICT skills (i.e. G_Q05e Frequency of excel use; G_Q05g Frequency of programming language use; and G_Q05d Frequency of transactions through internet) all matter to the same extent. However, it may well be argued that, in the complex ICT skills case, it is especially the frequency of programming that makes the difference. Ongoing work not shown here is addressing this important issue, and the most suited way to attribute weights to the different components of the indicators proposed. Results will be shared in due course.
86. Third, this study is the first one that uses the Survey of Adult Skills to present descriptive statistics on skill distributions at the country-industry and the country-sector-occupation level. Because the sampling design of the survey as well as its weighting did not consider industry or occupation variables (OECD, 2013), descriptive statistics at the industry and occupational level should be taken with caution. For the computation of the descriptive statistics, we use the final weights of the survey that account for the country-specific sampling designs, for survey non-response as well as disproportionate sampling of sub- groups along certain variables, e.g. age, gender, region, education and economic status (OECD, 2013, chapter 15). To reduce possible bias at the industry or sector-occupation level due to disproportionate sampling within industries or sector-occupations, we chose a higher aggregation level for industries (TiVA 18 industries) and occupations (one digit ISCO 2008) with sectors only divided into manufacturing and services.6 In addition, we are currently working on the computation of new weights that try to correct for disproportionate sampling within industries and occupations by adjusting the Survey of Adult Skills weights using country specific population totals within industries and occupations.
The Trade in Value Added indicators
87. Conventional published trade statistics are based on gross trade flows and thus, may offer a distorted picture of the importance of trade to economic growth and income as the value of intermediate products that cross borders several times for further processing are counted multiple times. While gross trade flows can help shed light on global production networks and the interconnectedness of economies, they may contribute to misleading analyses when related to indicators of domestic value added and
6. In addition, all country-industry and country-occupation-sector cells with less than 25 observations are excluded. Among the 414 country-industry cells, only 10% have less than 24 individual observations, whereas 50% have at least 90 observations. Out of all country-sector-occupation cells, 10 % have less than 18 individual observations and 50% include at least 128 observations. Occupation 6 (Skilled agricultural, forestry and fishery workers) is excluded from the occupation level analysis. Detailed descriptive statistics on the number of observations per country-industry and country-sector-occupation can be found in the Appendix.
28 DSTI/EAS/IND/WPIA(2016)1 national income, or to components such as profits or wages, and by extension, employment. For example, exported goods may require significant intermediate inputs from both domestic and foreign manufacturers who, in turn, require significant intermediate imports. In such a case, much of the revenue, or value added, from selling the exported good may accrue abroad, reflecting the purchases of intermediate imports used in production, leaving only marginal benefits in the exporting economy.
88. Trade in Value Added (TiVA) measures address the issue of double counting implicit in reported gross trade flows by capturing the flows related to the value that is added, by countries and industries, in the production of any good or service exported i.e. measuring the origin of value added embodied in exports. Similarly, the TiVA framework can reveal the global origins of the cumulative value added present in final goods and services consumed by households, government and businesses i.e. the origins of value added in final demand. In both cases new insights into global interconnectedness, and bilateral relationships, are revealed. For example, when bilateral trade balances are measured in gross terms, a deficit with final goods producers (or the surplus of exporters of final products) may be exaggerated because it incorporates the value of foreign inputs. The underlying imbalance is in fact with the countries who supplied inputs to the final producer. Accounting for trade in value added (especially trade in intermediate parts and components) redistributes surpluses and deficits across partner countries while leaving the overall trade balances of countries with the rest of the world unchanged.
89. The infrastructure underpinning TiVA can also provide insights into the impact of globalisation on jobs, tasks and skills: i.e. the extent to which a nation’s employment profile is driven by production to meet foreign final demand and thus how the benefits from international trade are distributed. When comparative advantages relate to specialisation in certain tasks and the availability of certain skills, rather than to the final products sold, then understanding international flows of value added can shed light on the skill composition of the labour force required to meet global demand and, on the development level of countries participating in global value chains (GVCs). Industrialised countries tend to specialise in high skill tasks, which are better paid, and tend to capture a larger share of the total value added embodied in final goods and services.
90. Indicators related to GVCs patterns and dynamics are based on the October 2015 update of the OECD ICIO and covering 61 countries, 34 industries and 7 years (1995, 2000, 2005 and all years between 2008 and 2011). The data used in the statistics shown in this paper relate to the year 2011. Constructing a global input-output table presents many challenges, and entails making a number of assumptions, as well as reconciling and balancing data. Due to lack of information on services trade, the data are generally weaker for services industries. However, the underlying input-output structure comes from national accounts and trade data are aligned with this framework thus providing more consistent measures of offshoring.
91. Several GVC-related measures are considered in this exploratory exercise aimed at a first broad characterisation of the relationship existing between trade in value added flows and the skills distribution of the workforce. These are a basic measure of value added, two indicators of the value added content of gross exports, one indicator of the direct industry value added and a measure of final demand:
1) Value added as a percentage of production (PROD_VASH) represents country c industry i's value added share of its gross output. These value added shares are a major determinant of a country's shares of value added embodied in trade and final demand (see metadata in oe.cd/tiva, October 2015 release). 2) Domestic value added share of gross exports (EXGR_DVASH), which is defined as domestic value added in gross exports (EXGR_DVA) by industry i divided by total gross exports of industry i, in %. It is a domestic value added and reflects how much value-added is generated by an industry per unit of its total gross exports (see metadata in oe.cd/tiva, October 2015 release).
29 DSTI/EAS/IND/WPIA(2016)1
3) Foreign value added share of gross exports (EXGR_FVASH), corresponding to foreign value added in gross exports (EXGR_FVA) divided by total gross exports, in %. It is a foreign value added intensity measure often referred to as 'import content of exports' and considered as a reliable measure of 'backward linkages' in analyses of GVCs (see metadata in oe.cd/tiva, October 2015 release).
4) The Direct domestic value added content (EXGR_DDC), which measures the direct contribution made by industry i in country c to the production of goods and services for export (see metadata in oe.cd/tiva, October 2015 release). For the analysis, this measure is standardised by gross exports of industry i in country c. 5) The Domestic value added embodied in foreign final demand (FFD_DVA). This indicator captures the value added that industries export both directly, through exports of final goods or services and indirectly via exports of intermediates that reach foreign final consumers (households, government, and as investment) through other countries. The measure reflects how domestic industries (upstream in a value-chain) are connected to consumers in other countries, even where no direct trade relationship exists. The indicator thus illustrates the full upstream impact of final demand in foreign markets to domestic output and can be interpreted as exports of value added (see metadata in oe.cd/tiva, October 2015 release). For the analysis, this measure is standardised by total value added of industry i in country c. 92. The domestic value added of gross exports comes from three components: the direct industry value added, the value added coming from the use of domestic intermediates and the re-imported domestic value added. The statistic presented in this present paper relate only to the domestic value added share of gross export, as the foreign value added share of gross export is used to double check the correctness of the results, since it is corresponds to one minus EXGR_DVASH.
93. While workforce skills are generally held to positively relate to firm and industry performance and hence to higher value added of export, and evidence suggest that exports and export destinations affect the utilisation of skilled labour (Brambilla et al, 2012), it is not ex ante clear which type(s) of skills might be most relevant for which type of participation in global value chains, as the literature addressing these relationship is in its infancy.
Characterising skills endowments: a first cross-country perspective
Workers' cognitive skills
94. Large cross-country differences emerge in terms of shares of workers with high and low literacy and numeracy skills, with Finland and Japan having a large group of workers with high literacy and numeracy skills in both the services and manufacturing and Italy having a large group of workers with low skills along these two dimensions (Figure 3). Within countries, the distributions of skills in services and in manufacturing appears relatively similar, although in most countries there is a higher share of workers with relatively high literacy skills (levels 4 and 5) in services than in manufacturing. Likewise, there is a smaller share of workers with relatively low literacy skills (level 1 and below) in services than in manufacturing. Exceptions to this result include Japan, which features both a lower share of workers with high literacy skills and a higher share of workers with low literacy skills in services, and a group of countries with a relatively high share of workers with low literacy skills in services (France, Sweden, and the United States). The results are more nuanced in terms of numeracy skills. In Austria, Belgium, Canada, Denmark, Finland, France, Germany, Ireland, Italy, Japan, Norway, Sweden, the United Kingdom, and the United States, manufacturing has a higher share of workers with high numeracy skills than services. In a majority of countries, there is also a higher share of workers with low numeracy skills in manufacturing.
30 DSTI/EAS/IND/WPIA(2016)1
Figure 3. Distribution of the literacy and numeracy skills of workers, services and manufacturing (2012)
Source: OECD calculations based on the Survey of Adult Skills (PIAAC) (2012) (database).
95. Large differences emerge in terms of skills endowment of the different occupational categories, with professionals and managers having the highest skills, on average. As can be seen from Figure 4, showing the country, sector and occupation specific values of literacy and numeracy proficiency, large variations emerge across countries in terms of skills of workers in the same occupations, especially in manufacturing. It is also interesting to note that while Finland and Japan often appear as top performers, the picture is less clear for the low performers, with workers in e.g. elementary occupations having particularly low cognitive skills in Canada and France and, and managers having low numeracy skills in services in Italy. For most occupations, cross-country heterogeneity is larger in manufacturing than in services.
31 DSTI/EAS/IND/WPIA(2016)1
Figure 4. Cognitive skills by occupation, services and manufacturing sectors (2012)
Literacy proficiency
Numeracy proficiency
Note: Figures are based on country and sector specific averages, by occupational category. Each bar shows the country exhibiting the minimum and maximum country value. Source: OECD calculations based on the Survey of Adult Skills (PIAAC) (2012) (database). 96. In terms of problem solving in technology-rich environments skills, large heterogeneity emerges in the distribution of skills between countries and within countries, between manufacturing and services sectors (Figure 5), although no clear pattern is evident between sectors. Some countries have more high
32 DSTI/EAS/IND/WPIA(2016)1 skilled workers and less low skilled workers in manufacturing. This is the case for Austria, Denmark, Germany, Japan, Korea, the Slovak Republic, the UK and the US. In some countries, the differences in the distribution of skills between services and manufacturing are marked. This is for instance the case of Austria and Japan, with larger shares of skilled workers in manufacturing. Problem solving skills by occupation also show large variations between countries, in manufacturing, as can be seen in Figure 6.
Figure 5. Distribution of problem solving skills in technology-rich environments of workers, by sector (2012)
Source: OECD calculations based on the Survey of Adult Skills (PIAAC) (2012) (database).
33 DSTI/EAS/IND/WPIA(2016)1
Figure 6. Problem solving skills in technology-rich environments by occupation, services and manufacturing sectors (2012)
Source: OECD calculations based on the Survey of Adult Skills (PIAAC) (2012) (database).
97. Cross country heterogeneity in the frequency of the performance of numeracy tasks is even bigger than the cross country heterogeneity in the assessed numeracy skills, as shown in Figure 7. Hence, not only countries vary in the endowment of cognitive skills across industries and occupations, but they vary even more in terms of the way these skills are used on the job through tasks. These results confirm the need to look at both assessed skills and tasks performed on the job to better understand the links between skills and performance within GVCs.
34 DSTI/EAS/IND/WPIA(2016)1
Figure 7. Numeracy proficiency and intensity of complex numeracy tasks performed on the job, by industry (2012)
Source: OECD calculations based on the Survey of Adult Skills (PIAAC) (2012) (database).
98. Another dimension that may influence how countries engage in GVCs is the ability of workers to perform complex tasks (Ottaviano et al., 2013). Among them, ICT seem of paramount importance in, among others, enabling communication across different locations and ensuring coordinated production processes. On average, in the countries covered by the Survey of Adult Skills, workers perform more frequently less complex ICT tasks than complex ones, in all sectors (Figure 8). The data show again large variations between countries in the frequency of ICT tasks, especially for less complex ones.
35 DSTI/EAS/IND/WPIA(2016)1
Figure 8. Frequency of complex and less complex ICT tasks performed on the job, by industry (2012)
Source: OECD calculations based on the Survey of Adult Skills (PIAAC) (2012) (database).
99. The ways skills are used on the job contribute to firm performance. By making the best use of skills, firms can make the most of the allocation of skills between industries, which is to some extent inherited from industrial historical patterns and geography. To assess the extent to which skills are effectively used at the workplace, one can compare the frequency of the performance of tasks at work and at home. For ICT skills, it appears that for a group of industries, on average, ICT skills are more intensively used at home than at work although differences are not large (Figure 9). This may signal some untapped skills potential, although this varies across countries.
36 DSTI/EAS/IND/WPIA(2016)1
Figure 9. Frequency of ICT tasks performed at work and at home, by industry (2012)
Source: OECD calculations based on the Survey of Adult Skills (PIAAC) (2012) (database).
Workers' personality traits and skills emerging from the performance of tasks
Industry-specific patterns
100. Indicators on the frequency of the performance of tasks that require combining cognitive skills and personality traits show low values for a number of countries and industries, especially for interacting and communicating, and managing tasks (see Figure 10). This seemingly points to room for improvement, and for countries and industries to further develop and use these combined skills which the literature suggests to be fundamental for economic performance.
101. In particular, as can be seen from the dispersion across countries (the height of the bars), heterogeneity between countries is large for the frequency of performing managing and self-organising tasks. While interacting and communicating skills have been identified as being crucial for firm performance both by existing studies and in employers’ surveys, the frequency of interacting and communicating tasks show the smallest degree of heterogeneity between countries and across industries. Perhaps workers' interacting and communicating skills are as valued as scarce.
37 DSTI/EAS/IND/WPIA(2016)1
Figure 10. Frequency of the performance of interacting and communicating, managing and self-organising tasks, by industry
Source: OECD calculations based on the Survey of Adult Skills (PIAAC) (2012) (database).
38 DSTI/EAS/IND/WPIA(2016)1
Figure 11. Readiness to learn and the frequency of the performance of problem solving tasks, by industry
Source: OECD calculations based on the Survey of Adult Skills (PIAAC) (2012) (database).
102. It is interesting to see that the frequency of the performance of problem solving tasks and the readiness to learn, which are approximated through a consistent set of self-assessment questions in the Survey of Adult Skills, (Figure 11) display similar patterns in the extent to which industry specific values vary across countries, with Korea and Japan appearing at the bottom of the bars in several industries. Moreover, with the exception of mining and quarrying, where variation across countries is more marked, industries do not seem to vary much in terms of the frequency of performance of these tasks, especially in the case of creative thinking.
39 DSTI/EAS/IND/WPIA(2016)1
Figure 12. Workers' conscientiousness, and trust in persons, by industry
Source: OECD calculations based on the Survey of Adult Skills (PIAAC) (2012) (database).
40 DSTI/EAS/IND/WPIA(2016)1
103. Other indicators proxying the personality traits reveal that there is little heterogeneity between countries and sectors in terms of conscientiousness and more heterogeneity for trust in persons (Figure 12). It is also interesting to note the large heterogeneity between industries and countries in terms of the reported trust in persons of workers. These two patterns could perhaps be explained by differences in work practices between sectors and may reflect country specificities (with, for instance, Denmark often appearing at the top of other similar indicators). On average, the indicators show a high level of conscientiousness for all countries and industries. This may to some extent reflect the limitation of the data. Conscientiousness is measured both indirectly and directly but through a relatively general and subjective question that can only imperfectly capture this personality trait.
104. In terms of physical tasks performed on the job, a lot of heterogeneity emerges between sectors (Figure 13). Unsurprisingly, the finance sector shows the lowest level of use of physical skills and agriculture the highest level. What is more surprising is to see the high level of heterogeneity in the frequency of the performance of physical tasks between countries for the same industry. This can reflect differences in the production process but also in the types of goods that are produced and thereby, in the positioning in the GVC.
Figure 13. Frequency of the performance of physical tasks, by industry
Source: OECD calculations based on the Survey of Adult Skills (PIAAC) (2012) (database).
Occupation-specific patterns
105. Looking at the performance of interacting and communicating, managing and self-organising tasks by occupational categories differences emerge in their frequency between manufacturing and services, with manufacturing generally exhibiting greater dispersion by country (Figure 14).
41 DSTI/EAS/IND/WPIA(2016)1
Figure 14. Frequency of performance of interacting and communicating, managing and self-organising tasks, by occupation and sector
Source: OECD calculations based on the Survey of Adult Skills (PIAAC) (2012) (database).
42 DSTI/EAS/IND/WPIA(2016)1
106. Moreover the frequency of the performance of these tasks seem to be increasing in the occupational category considered, i.e. the higher the occupational category, the generally higher median value of the frequency. This suggests that the higher variance observed by industry might be determined by the extent to which different occupational profiles populate different industries.
Figure 15. Readiness to learn and frequency of performance of problem solving tasks, by occupation and sector (2012)
Source: OECD calculations based on the Survey of Adult Skills (PIAAC) (2012) (database).
107. While the occupation specific patterns exhibited by the frequency with which problem solving tasks are performed are very much in line with those observed in the case of communicating and managing shown in Figure 14, the bottom panel of Figure 15 highlights a substantial homogeneity both across occupations and sectors in creative thinking. The sole exception is represented by professionals, which exhibit much lower median values.
43 DSTI/EAS/IND/WPIA(2016)1
Figure 16. Conscientiousness and trust in persons, by occupation and sector
Source: OECD calculations based on the Survey of Adult Skills (PIAAC) (2012) (database).
44 DSTI/EAS/IND/WPIA(2016)1
108. As Figure 16 shows, both conscientiousness and trust in persons are generally (albeit slightly) increasing in occupational categories: the higher the occupational group, the higher the value of the personality trait considered.
109. Finally, looking at the frequency of tasks involving physical skills by occupational categories (Figure 17), as could be expected, the frequency of these tasks is generally decreasing in the occupational level: managers display lower median levels than craftsmen, machine operators and elementary occupations. With the exception of machine operators, median values are generally very similar in manufacturing and services.
Figure 17. Frequency of the performance of physical tasks, by occupation and sector (2012)
Source: OECD calculations based on the Survey of Adult Skills (PIAAC) (2012) (database).
Unutilised potential? Skills of the non-employed
110. If the skills of the workforce are key for economic performance and for participation and positioning in GVCs, comparing the skills and frequency of the performance of tasks between workers and the non-employed should give some indication of the extent to which firms manage to tap into the available repository of skills, and whether there might be some unutilised potential coming from non- employed persons. 7 Evidence suggests (see Figure 18) that, with the exception of Japan and Korea, the share of individuals exhibiting levels of literacy proficiency of 2 and above, i.e. those with sufficient to very good literacy proficiency skills, is higher in the case of workers than in the case of non-employed individuals. Also, it can be observed that, in general, the proportion of individuals scoring 4 and 5, i.e. the most proficient individuals, is (in some cases remarkably) higher than the corresponding proportion of individuals who are non-employed.
7. The non-working population includes 42.565 individuals who report being unemployed or out of the labour force.
45 DSTI/EAS/IND/WPIA(2016)1
Figure 18. Distribution of literacy proficiency of workers and non-employed
Source: OECD calculations based on the Survey of Adult Skills (PIAAC) (2012) (database)
.
46 DSTI/EAS/IND/WPIA(2016)1
Table 2. Differences in the skills of workers and non-employed
Country AUS AUT BEL CAN CZE DEU DNK ESP EST FIN FRA GBR IRL ITA JPN KOR NLD NOR POL RUS SVK SWE USA Assessed Skills Literacy 18.01 10.47 12.31 18.77 4.31 13.09 17.69 15.26 8.43 14.45 6.92 23.47 16.83 4.43 -2.49 -6.48 20.18 21.11 8.01 6.89 14.24 25.27 16.20 Numeracy 25.42 13.81 17.85 24.99 10.68 23.90 25.69 20.95 16.01 19.73 16.21 30.15 20.71 14.01 6.94 -3.64 26.37 30.02 15.81 5.25 23.78 31.07 26.89 Problem Solving 6.19 -0.65 -4.31 5.06 -5.25 0.03 0.83 -4.60 -3.78 12.46 5.84 1.13 -10.19 11.98 9.07 -2.24 3.27 2.19 0.92 5.54 Skills used on the job and others Complex ICT 0.09 0.11 0.18 0.13 0.12 0.13 0.15 0.12 0.17 0.15 0.11 0.12 0.09 0.10 0.09 0.13 0.11 0.15 0.11 0.02 0.12 0.16 0.11 Complex Numeracy 0.09 0.08 0.12 0.11 0.12 0.12 0.12 0.10 0.12 0.12 0.07 0.11 0.07 0.09 0.11 0.10 0.10 0.11 0.10 0.03 0.12 0.13 0.08 Complex Reading Writing 0.10 0.13 0.19 0.13 0.14 0.14 0.14 0.15 0.12 0.17 0.09 0.13 0.14 0.11 0.12 0.12 0.11 0.16 0.11 0.01 0.16 0.20 0.11 Less Complex ICT 0.17 0.21 0.32 0.23 0.20 0.24 0.25 0.22 0.27 0.27 0.20 0.22 0.16 0.16 0.18 0.18 0.18 0.28 0.19 0.04 0.22 0.34 0.20 Less Complex Numeracy 0.14 0.12 0.19 0.15 0.17 0.16 0.18 0.15 0.15 0.16 0.12 0.11 0.10 0.11 0.14 0.15 0.11 0.11 0.12 0.05 0.16 0.20 0.11 Less Complex Reading Writing 0.15 0.17 0.22 0.16 0.14 0.18 0.17 0.17 0.18 0.18 0.13 0.17 0.16 0.12 0.16 0.18 0.14 0.17 0.14 0.00 0.15 0.23 0.14 Interacting Communicating 0.07 0.05 0.07 0.05 0.04 0.09 0.08 0.05 0.06 0.07 0.05 0.04 0.05 0.03 0.03 0.08 0.02 0.06 0.06 -0.01 0.04 0.08 0.01 Managing 0.50 0.37 0.37 0.43 0.35 0.34 0.43 0.33 0.40 0.46 0.34 0.47 0.44 0.32 0.33 0.35 0.40 0.47 0.36 0.35 0.27 0.46 0.48 Problem Solving on the Job 0.07 0.13 0.17 0.11 0.13 0.13 0.12 0.14 0.11 0.09 0.09 0.10 0.09 0.13 0.12 0.09 0.08 0.13 0.09 0.04 0.12 0.13 0.05 Self Organizing 0.62 0.67 0.67 0.62 0.67 0.62 0.71 0.60 0.66 0.71 0.55 0.62 0.56 0.56 0.64 0.57 0.62 0.66 0.63 0.52 0.54 0.71 0.61 Conscientiousness 0.04 0.05 0.03 0.03 0.06 0.07 0.11 0.01 0.06 0.04 0.05 0.05 0.02 0.02 0.03 0.04 0.03 0.10 0.04 0.07 0.08 0.10 0.04 Societal Engagement 0.02 0.05 -0.01 0.00 0.01 0.04 0.06 0.02 0.01 0.05 0.04 0.00 0.03 0.01 0.02 -0.02 0.01 0.04 0.02 0.04 0.01 0.07 0.04 Relational Abiliy and Creative Thinking 0.04 0.05 0.05 0.03 0.01 0.02 0.03 0.05 0.04 0.02 0.03 0.05 0.04 0.04 0.02 0.01 0.05 0.03 0.06 0.06 0.07 0.02 0.03 Trust 0.04 0.03 0.03 0.04 0.01 0.01 0.12 0.03 0.03 0.06 0.02 0.05 0.04 0.03 -0.02 0.00 0.06 0.11 0.00 0.00 0.01 0.11 0.03 Physical ability -0.03 -0.11 -0.14 -0.05 -0.01 -0.06 -0.10 -0.08 -0.10 -0.04 -0.10 -0.03 -0.01 0.00 0.00 0.10 -0.01 -0.07 -0.05 -0.03 -0.10 -0.08 -0.05 Skills used at home Complex ICT 0.06 0.04 0.03 0.03 0.01 0.03 0.04 0.04 0.05 0.04 -0.01 0.05 0.04 0.01 0.00 -0.04 0.06 0.03 0.04 0.01 0.05 0.02 0.05 Complex Numeracy -0.01 -0.04 -0.05 -0.03 -0.04 -0.04 -0.03 -0.02 -0.04 -0.04 -0.09 0.00 -0.04 -0.04 -0.05 -0.08 -0.02 -0.01 -0.02 -0.03 0.00 -0.06 -0.02 Complex Reading Writing -0.01 -0.01 -0.01 -0.06 -0.05 -0.01 0.00 0.02 0.00 0.01 -0.06 0.00 -0.03 -0.01 0.01 -0.05 0.02 -0.01 -0.01 0.00 0.03 -0.07 -0.02 Less Complex ICT 0.05 0.02 -0.02 0.00 -0.04 0.01 0.01 0.03 0.04 0.01 -0.03 0.04 0.03 0.02 -0.02 -0.05 0.06 0.01 0.06 0.01 0.06 -0.04 0.03 Less Complex Numeracy -0.05 -0.07 -0.11 -0.05 -0.10 -0.08 -0.03 -0.04 -0.04 -0.07 -0.11 -0.06 -0.09 -0.09 -0.10 -0.10 -0.06 -0.08 -0.03 -0.02 -0.01 -0.09 -0.05 Less Complex Reading Writing -0.02 -0.02 -0.01 -0.03 -0.04 -0.01 0.02 0.03 0.01 0.00 -0.02 -0.01 0.00 0.00 -0.04 -0.05 0.00 0.00 0.00 -0.04 0.03 0.00 -0.04
Note: For the non-employed, indicators on the frequency of the tasks performed on the job are measured in the past job. Numbers show the difference in the indicator value for workers and non-employed. Assessed skills are measured on a 0-500 scale, whereas skills measured on the basis of performance of tasks are defined between 0 and 1. Source: OECD calculations based on the Survey of Adult Skills (PIAAC) (2012) (database).
47 DSTI/EAS/IND/WPIA(2016)1
111. Table 2 shows differences that exist in mean values of the skills of employed and non-employed. Green cells indicate that the mean values for the skills of the employed are higher than those of the non- employed. Conversely, red cells indicate that the non-employed have higher skills or perform more frequently tasks than employed workers. The darker the shade of red (green), the higher the negative (positive) difference for workers as compared to non-employed. Actual differences are displayed in light grey in the cells. Different scales for the colour coding are used in the case of assessed skills on the one hand and the frequency of tasks performed on the job and at home on the other hand, as these respective indicators are measured on different scales.
112. In the case of assessed literacy and numerical skills, in all countries but Korea and to a lesser extent Japan, workers are generally more skilled than the unemployed. However, in the case of assessed problem solving in technology-rich environment skills, non-employed seemingly outperform employed workers in a number of countries, including Belgium, the Czech Republic, Estonia, Finland, Korea and Poland.
113. In the case of the frequency of tasks performed on the job (measured in the last job for the non- employed), with the exception of those related to physical skills, workers' mean indicators generally outperform those of the non-employed. Finally, the picture is very much heterogeneous in the case of the frequency of tasks (ICT, numeracy, reading and writing) performed at home. In the Czech Republic, France and Korea non-employed perform these tasks more frequently than workers. In all countries, in at least two out of the six tasks considered, unemployed exhibit higher frequency than employed workers. These statistics points to the possible existence of untapped potential, which can come for instance from young people not yet in the labour force or unemployed having lost their jobs recently. The statistics might as well reflect that the non-working population can dedicate more time to those activities at home. Better understanding the composition of the untapped potential would deserve further investigation in future studies.
Exploring the correlation between skills, performance and GVC indicators
114. The stylised facts highlighted so far show that the skills endowment of the workforce differ substantially across countries, industries and occupations, across all types of skills considered, and that the frequency with which tasks are performed often differs on the job and at home.
115. In what follows, some first exploratory correlations are shown. These on the one hand see the country and industry-specific skills endowment of the workforce and frequency of tasks performance and on the other hand labour productivity and selected trade in value added indicators, all considered at the same level of aggregation. The aim is to start investigating the extent to which different types of skills and tasks matter for GVC-related performance. The tables and figures shown below are to be considered as very first results of work in progress, to be continued in future months.
Skills and labour productivity
116. Table 3 shows the correlations that emerge between several points of the skills’ distributions and per capita labour productivity, at the industry level. As can be seen, the correlation of productivity median skills values is always significant, and almost always positively so, with values that range between 0.17 in the case of trust in other persons and 0.42 in the case of complex numeracy. Only in the case of conscientiousness and physical ability the coefficients of the correlations between median skills and productivity turns out to be negative, and especially so in the case of physical abilities (-0.36). While in the case of conscientiousness this may partially reflect issues in terms of the way in which such a personality trait is operationalised in the data, the negative correlation between physical skills and productivity (which
48 DSTI/EAS/IND/WPIA(2016)1 remains negative and significant for all the points of the distribution considered) may reflect the low productivity of tasks entailing physical work.
Table 3. Correlations between skills indicators and labour productivity at a country industry level
Median Perc10 Perc90
Assessed Skills Literacy 0.31*** 0.34*** 0.13 Numeracy 0.26*** 0.28*** 0.13 Problem Solving in technology-rich 0.27*** 0.36*** 0.05 environments
Skills from the information on tasks Median Perc25 Perc75 performed Complex ICT 0.33*** 0.37*** 0.26*** Complex Numeracy 0.42*** 0.27*** 0.37*** Complex Reading Writing 0.38*** 0.33*** 0.26*** Less Complex ICT 0.31*** 0.38*** 0.23*** Less Complex Numeracy 0.24*** 0.15* 0.07 Less Complex Reading Writing 0.30*** 0.38*** 0.22*** Interacting and Communicating 0.34*** 0.32*** 0.20** Managing 0.35*** 0.34*** 0.27*** Problem Solving on the Job 0.38*** 0.26*** 0.17* Self-Organising 0.18** 0.23*** 0.07
Personality traits Median Perc25 Perc75 Conscientiousness -0.16* -0.07 -0.39*** Readiness to learn and Creative Thinking 0.29*** 0.22*** 0.19** Trust in persons 0.17* 0.28*** 0.29*** Physical ability -0.36*** -0.31*** -0.30***
Note: Countries included are: Australia, Canada, Czech Republic, Germany, Denmark, Finland, France, Ireland, Italy, Netherlands, Norway, Poland, the Slovak republic, Sweden and the US. The median, the 10(25) percentile and the 90(75) percentile of the skill indicator within country-industry groups are considered. *, **, and *** indicate that coefficients are significant at the 5%, 1% and 0.1% levels. Source: OECD calculations based on the Survey of Adult Skills (PIAAC) (2012) and STAN database for structural analysis.
117. Another feature that emerges is that, in most of the cases, the correlation of productivity with the 10th percentile of with the 25th percentile (i.e. the left hand part) of the skills distributions exhibits higher coefficients than that of the 75th or 90th percentile, and often also of those of mean values. While it is premature to draw any conclusion based on such a general descriptive exercise, data seem to support the intuition of the resource based view of the firm, whereby it is broad workforce skills that matter: the skill endowment of the workers at the bottom of the distribution (i.e. those exhibiting values below the median) is the one that is more strongly correlated with productivity.
118. Finally, it can be observed that in the case of assessed cognitive skills the values of the 90th percentiles do not significantly correlate with productivity. Also, ICT-related skills are the only cognitive skills assessed through the performance of relevant tasks for which correlation with productivity is almost identical in the case of complex and less complex ICT skills.
49 DSTI/EAS/IND/WPIA(2016)1
119. Figure 19 depicts the relationship that exists between complex ICT skills and productivity, at the industry level. The scatter plot and the fitted lines synthesising the relationship that exist between productivity and the 25th percentile (light blue), median (median blue) and the 75 percentile values (dark blue) of the distribution of complex ICT skills clearly show that such a relationship is strongest in the case of the 25th percentile, and its strength decreases the more one moves up in the skill distribution.
Figure 19. Correlation between complex ICT skills and per-capita labour productivity, by industry, 2011
Note: Countries included: Australia, Canada, Czech Republic, Germany, Denmark, Finland, France, Ireland, Italy, Netherlands, Norway, Poland, the Slovak republic, Sweden and the US. Source: OECD calculations based on the Survey of Adult Skills (PIAAC) (2012) and STAN database for structural analysis.
Skills and trade in value added
120. As can be seen from Table 4, displaying the correlations between selected trade in value added indicators and cognitive skills, both assessed skills and frequency of tasks, generally positive and significant relationships emerge between the two sets of variables considered. Literacy, numeracy, problem solving and ICT skills all appear to positively relate to the share of direct domestic value added contained in gross exports, to the domestic value added share of gross export and to value added as percentage of production. The only exception is represented by the negative, albeit not always significant and not too strong correlation that emerges between these skills and the share of domestic value added embodied in foreign final demand. A possible explanation may be that when industries export intermediates that reach foreign final consumers through other countries it is relative cost advantages rather than the skill endowment of the workforce that matters. Moreover, if skills command wage premia, and hence higher costs of production, this might negatively affect cost-driven trade patterns.
50 DSTI/EAS/IND/WPIA(2016)1
121. It is also interesting to notice that the size of the correlation coefficients between assessed literacy and numeracy skills and the frequency with which tasks related to these skills are performed at work on the one hand and TiVA indicators on the other hand is always very similar. The measures of frequency of tasks performed at work further tend to exhibit slightly higher coefficients, confirming the intuition that both the skills supply and its interaction with tasks performed on the job play a role in GVCs. Finally, results seem to suggest that both simple and complex cognitive skills as they emerge from the performance of relevant tasks relate to a very similar extent to trade in value added indicators of performance. Only in the case of ICT-related skills, the difference between the performance of simple or complex tasks seems to matter for the size of the correlation coefficients, which turns to be generally higher in the case of less complex ICT tasks. This might signal that widespread basic ICT skills may be important for participation and positioning in GVCs, a conjecture which might deserve further investigation.
122. More generally, to try and understand whether the way in which skills are distributed matters for trade in value added patterns, Table 4 displays the correlations between TiVA indicators and the median, 10th and 90th percentile of assessed skills-related scores and the median, 25th and 75th percentile values of the frequency of tasks and other skills. Different points of the distribution are considered in the case of assessed skills, the frequency of tasks and other skills as these tasks and skills are measured over a much narrower interval than externally assessed skills.8
123. A clear pattern emerges, whereby the value of the correlations between the TiVA indicators and skill and task-related variables varies depending on the part of the distribution considered for the skill variable.
124. In the case of assessed skills, with the exception of the share of domestic VA embodied in foreign final demand, the correlation between skills and TiVA indicators is always significant and positive, with coefficients that range between 0.12 and 0.31. Also, generally the size of the correlation coefficients of the 10th and 90th percentiles is generally smaller than those with median values.
125. In the case of the frequency of tasks performed on the job, correlation coefficients related to the 25th percentile of the indicators’ distribution are generally higher than those of median values. The reverse is true in the case of the 75th percentiles, which exhibit lower correlation values than those of the medians. In relation to the three TiVA indicators for which positive values are observed, the strongest correlations are observed with respect to interacting and communicating (between 0.42 and 0.23); complex and less complex reading and writing (between 0.39 and 0.21, and between 0.38 and 0.18, respectively); less complex ICT tasks (between 0.45 and 0.14); and managing (between 0.37 and 0.19).
126. The frequency of physical tasks persistently shows a negative correlation with the TiVA indicators for which the other skills exhibit positive correlations and a positive although not strong correlation with the share of domestic value added embodied in foreign final demand
8. 10th and 90th percentile values often correspond to the maximum and minimum values in the case of the frequency of tasks and other skills and do not help shedding light on distributional features.
51 DSTI/EAS/IND/WPIA(2016)1
Table 4. Correlations between cognitive and problem solving skills and selected TIVA Indicators Assessed literacy, numeracy and problem-solving skills and use of these skills (median value) EXGR_DDC_SH (Share of direct domestic value added content of gross exports) Skills (using information on Assessed Skills tasks performed) Literacy 0.31 * Complex Reading Writing 0.35 * Less Complex Reading writing 0.33 *
Numeracy 0.20 * Complex Numeracy 0.22 * Less Complex Numeracy 0.25 *
Problem solving in technology- 0.26 * Problem solving on the job 0.23 * rich environment Complex ICT 0.31 * Less Complex ICT 0.37 *
EXGR_DVASH (Domestic value added share of gross exports),
Skills (using information on Assessed Skills tasks performed) Literacy 0.25 * Complex Reading Writing 0.23 * Less Complex Reading writing 0.23 *
Numeracy 0.12 * Complex Numeracy 0.09 * Less Complex Numeracy 0.21 *
Problem solving in technology- 0.24 * Problem solving on the job 0.12 * rich environment Complex ICT 0.23 * Less Complex ICT 0.27 *
FFD_DVA_SH (Share of domestic value added embodied in foreign final demand) Skills (using information on Assessed Skills tasks performed) Literacy -0.07 Complex Reading Writing -0.14 *
Less Complex Reading writing -0.12 *
Numeracy 0.05 Complex Numeracy 0.01
Less Complex Numeracy -0.09
Problem solving in technology- -0.10 Problem solving on the job 0.06 rich environment Complex ICT -0.02 Less Complex ICT -0.09
PROD_VASH (Value added as a percentage of production)
Skills (using information on Assessed Skills tasks performed) Literacy 0.28 * Complex Reading Writing 0.34 * Less Complex Reading writing 0.32 *
Numeracy 0.17 * Complex Numeracy 0.18 * Less Complex Numeracy 0.20 *
Problem solving in technology- 0.22 * Problem solving on the job 0.23 * rich environment Complex ICT 0.27 * Less Complex ICT 0.35 *
Source: OECD calculations based on the Survey of Adult Skills (PIAAC) (2012) and the TiVA (October 2015 release) databases. * indicates that coefficients are significant at the 5% level.
.
52 DSTI/EAS/IND/WPIA(2016)1
Table 5. Correlations between cognitive skills and personality traits and selected TIVA Indicators, across distribution of skills
EXGR_DDC_SH EXGR_DVASH FFD_DVA_SH PROD_VASH Share of direct domestic VA Domestic VA share of gross Share of domestic VA embodied in VA as a percentage of production content of gross exports exports foreign final demand
Assessed Skills Median Perc10 Perc90 Median Perc10 Perc90 Median Perc10 Perc90 Median Perc10 Perc90
Literacy 0.31 * 0.26 * 0.25 * 0.25 * 0.14 * 0.26 * -0.07 -0.06 -0.10 * 0.28 * 0.23 * 0.22 * Numeracy 0.20 * 0.16 * 0.16 * 0.12 * 0.06 0.13 * 0.05 0.03 0.02 0.17 * 0.13 * 0.13 * Problem Solving 0.26 * 0.23 * 0.18 * 0.24 * 0.13 * 0.25 * -0.10 0.01 -0.11 * 0.22 * 0.19 * 0.16 * Skills (from the information on Median Perc25 Perc75 Median Perc25 Perc75 Median Perc25 Perc75 Median Perc25 Perc75 tasks performed) Complex ICT 0.31 * 0.38 * 0.18 * 0.23 * 0.27 * 0.12 * -0.02 -0.03 0.01 0.27 * 0.34 * 0.15 * Complex Numeracy 0.22 * 0.20 * 0.15 * 0.09 * 0.08 0.06 0.01 0.04 0.06 0.18 * 0.16 * 0.11 * Complex Reading Writing 0.35 * 0.39 * 0.29 * 0.23 * 0.27 * 0.21 * -0.14 * -0.13 * -0.13 * 0.34 * 0.37 * 0.28 * Less Complex ICT 0.37 * 0.45 * 0.18 * 0.27 * 0.32 * 0.14 * -0.09 -0.10 * -0.04 0.35 * 0.41 * 0.17 * Less Complex Numeracy 0.25 * 0.25 * 0.16 * 0.21 * 0.20 * 0.13 * -0.09 -0.08 -0.04 0.20 * 0.20 * 0.13 * Less Complex Reading Writing 0.33 * 0.38 * 0.26 * 0.23 * 0.25 * 0.18 * -0.12 * -0.11 * -0.13 * 0.32 * 0.36 * 0.25 * Interacting Communicating 0.42 * 0.29 * 0.38 * 0.32 * 0.23 * 0.29 * -0.11 * -0.09 -0.12 * 0.40 * 0.27 * 0.36 * Managing 0.32 * 0.36 * 0.24 * 0.29 * 0.29 * 0.19 * -0.17 * -0.15 * -0.14 * 0.34 * 0.37 * 0.25 * Problem Solving on the Job 0.23 * 0.28 * 0.18 * 0.12 * 0.18 * 0.08 0.06 -0.01 0.07 0.23 * 0.27 * 0.18 * Self-Organising 0.24 * 0.36 * 0.19 * 0.21 * 0.29 * 0.20 * -0.12 * -0.15 * -0.13 * 0.23 * 0.35 * 0.16 * Physical skills -0.41 * -0.28 * -0.40 * -0.34 * -0.27 * -0.31 * 0.14 * 0.09 0.11 * -0.38 * -0.25 * -0.36 * Personal traits Conscientiousness 0.00 -0.04 0.00 -0.12 * -0.19 * -0.08 0.13 * 0.13 * 0.09 0.04 -0.02 0.02 Readiness to learn and Creative 0.22 * 0.16 * 0.20 * 0.12 * 0.09 0.13 * 0.02 0.04 -0.02 0.25 * 0.19 * 0.23 * Thinking Trust in persons 0.08 0.10 * 0.19 * 0.12 * 0.10 * 0.24 * -0.06 -0.02 -0.14 * 0.11 * 0.10 * 0.20 *
Source: OECD calculations based on the Survey of Adult Skills (PIAAC) (2012) and the TiVA (October 2015 release) databases. * indicates that coefficients are significant at the 5% level.
53 DSTI/EAS/IND/WPIA(2016)1
Figure 20. Correlation between the frequency of interacting and communicating skills and direct domestic value added content as share of export, by sector, 2011-2012
Source: OECD calculations based on the Survey of Adult Skills (PIAAC) (2012) and the TiVA (October 2015 release) databases.
127. To better appreciate the meaning of the correlations shown in Table 5, in what follows two correlation figures are proposed, as a teaser of what can be shown. Figure 20 plots the country-industry median values of the indicator accounting for the frequency with which interacting and communicating tasks are performed in relation with the direct domestic value added content as share export. Blue dots indicate manufacturing industries and black dots picture services ones. The remaining dots relate to agriculture and mining. The graph suggests that, while the overall relationship is positive (as, in any case, already seen from Table 4) different sectors exhibit different patterns, with services and agriculture and mining that seem to benefit the most from higher frequency of communication and interaction tasks.
128. Finally, Figure 21 displays the correlation emerging between the frequency of physical tasks and direct domestic value added content as share of export. As shown in Table 4, the correlation is generally negative, and more markedly so for manufacturing, although this time the relationship seems to be the very same one for many services and manufacturing industries, as can be appreciated from the overlapping of blue and black dots.
54 DSTI/EAS/IND/WPIA(2016)1
Figure 21. Correlation between the frequency of physical tasks and direct domestic value added content as share of export, by sector, 2011-2012
Source: OECD calculations based on the Survey of Adult Skills (PIAAC) (2012) and the TiVA (October 2015 release) databases.
The work ahead: preliminary conclusions and next steps
129. While existing studies agree on the importance of skills for trade patterns, they very seldom specify which skills matter for which type of GVC-related measures of participation or performance, or add an industry or occupation perspective to it. Uncovering these relationships, though, is needed for the design of effective policies in many areas, including industry, education and trade policies.
130. This paper offers some preliminary results of work aimed to characterise the distribution of skills across countries and industries and to investigate the determinants of flows of trade in value added. To this end, it identifies workforce skills that existing studies suggest being relevant for performance of the job and for firm performance. It then looks at the endowment of these skills and frequency of the performance of tasks at the industry, country and occupation level, thus shedding light on a number of stylised facts related to skill endowment and distribution. Finally it proposes a few correlations between the workforce skills and trade in value added indicators, which represent the very first steps of a wider and deeper analysis, to be carried out in future months.
131. These initial results have to be considered suggestive rather than conclusive. While the skill taxonomy based on the Survey of Adult Skills proposed here builds upon a comprehensive view of studies in a wide array of fields - including economics, management, business, organisation and psychology - the stylised facts highlighted represent only the first step in the analysis. For instance, the analysis would need to investigate whether and to what extent the skills endowment of the workforce differs by age groups and age groups are distributed across industries, and then relate these indicators to performance.
132. In particular, ongoing work (which is too preliminary to be shared at present) relies on explorative factor analysis (especially Bayesian factor analysis à la Conti et al., 2014) to extract a narrower
55 DSTI/EAS/IND/WPIA(2016)1 set of skills-related indicators from the data. Furthermore, the allocation of items to the indicators that are proposed in the skill typology on this paper will be statistically tested and principal component analysis (PCA) will be used to assign refined weights to the single items within one indicator. Once completed this phase, attention will be devoted to identifying key skill-related indicators for the use in econometric analysis addressing the links between skill endowment of the workforce and participation and positioning in global value chains.
133. Delegates are invited to:
• Comment on the work carried out, including the taxonomy, the indicators created and the characterisation proposed, and advice on ways to improve it; • Provide advice on next steps and on the focus of the analysis; • Identify relationships of interest, to be further investigated in econometric analysis.
56 DSTI/EAS/IND/WPIA(2016)1
REFERENCES
Alesina, Alberto and Eliana La Ferrara (2002), “Who trusts others?”, Journal of Public Economics 85: 207-234. Alesina, Alberto and Eliana LaFerrara (2005), “Ethnic diversity and economic performance”, Journal of Economic Literature 43: 762–800. Amiti, M. and J. Konings (2007), “Trade liberalization, intermediate inputs, and productivity”, American Economic Review 97, 1611–1638. Amiti, M. and D.R. Davis (2012), “Trade, firms, and wages: theory and evidence”, Review of Economic Studies 79, 1–36. Amiti, M. and A. K. Khandelwal (2013), “Import Competition and Quality Upgrading”, Review of Economics and Statistics 95, no. 2: 476-490. Armstrong, Michael, and Stephen Taylor (2014). Armstrong's handbook of human resource management practice. Kogan Page Publishers. Autor, D., D. H. Dorn, and G. H. Hanson, G. (2015), “Untangling trade and technology: evidence from local labour markets”, The Economic Journal 125: 621–646. Autor, D., F. Levy, and R. Murnane (2003), “The skill content of recent technological change: an empirical exploration”, Quarterly Journal of Economics 118, no. 4. Autor, D. (2013), “The ‘task approach’ to labor markets: an overview”, Journal of Labour Market Research 46: 185-199. Autor, David H. (2015), “Why are there still so many jobs? The history and future of workplace automation”, Journal of Economic Perspectives 29, No. 3: 3–30. Autor, David H., and D. Dorn, (2013). “The Growth of Low-Skill Service Jobs and the Polarization of the US Labor Market”. American Economic Review 103, no. 5: 1553–1597. DOI: http://dx.doi.org/10.1257/aer.103.5.1553. Barney, Jay (1991). “Firm resources and sustained competitive advantage.” Journal of Management 17, no. 1: 99-120. Barney, Jay, Mike Wright, and David J. Ketchen (2001). “The resource-based view of the firm: Ten years after 1991.” Journal of Management 27, no. 6: 625-641. Barrick, Murray R. and Michael K. Mount (1991). “The Big Five Personality Dimensions and Job Performance: A Meta-Analysis.” Personnel Psychology 44, no 1: 1-26. Barrick, Murray R., Michael K. Mount, and Ning Li (2013). “The theory of purposeful work behavior: The role of personality, higher-order goals, and job characteristics.” Academy of Management Review 38, no. 1: 132-153. Becker, Brian E., and Mark A. Huselid (1998). “High performance work systems and firm performance: A synthesis of research and managerial implications.” Research in Personnel and Human Resource Management 16: 53-101. Becker, Sacha.O., K. Ekholm, and M.A. Muendler (2013). “Offshoring and the onshore composition of tasks and skills”, Journal of International Economics 90, no. 1: 91-106. DOI: 10.1016/j.jinteco.2012.10.005. Berliant Marcus and Masahisa Fujita (2004), “Knowledge creation as a square dance on the Hilbert Cube”, International Economic Review 49, no. 4. Bernard A. B., Jensen J. B., Redding S. J. and P. K. Schott (2007), “Firms in international trade”, Journal of Economic Perspectives 21, no. 3:105–130.
57 DSTI/EAS/IND/WPIA(2016)1
Bharadwaj, Anandhi S. (2000). “A Resource-based perspective on Information Technology capability and firm performance: An empirical investigation”. MIS Quarterly 24, no. 1. Management Information Systems Research Center, University of Minnesota: 169–96. doi:10.2307/3250983. Bhattacharya, Mousumi, Donald E. Gibson, and D. Harold Doty (2005). “The effects of flexibility in employee skills, employee behaviors, and human resource practices on firm performance.” Journal of Management 31, no. 4: 622-640. Bloom, Nicholas, and John Van Reenen (2010). “Why do management practices differ across firms and countries?.” The Journal of Economic Perspectives: 203-224. Bloom, Nicholas, Brynjolfsson, Erik, Foster, Lucia, Jarmin, Ron S., Patnaik, Megha, Saporta Eksten, Itay and John Van Reenen (2014), “IT and management in America”. CEPR Discussion Paper No. DP9886. Available at SSRN: http://ssrn.com/abstract=2444907 Bloom, Nicholas, Christos Genakos, Raffaella Sadun, and John Van Reenen (2012). “Management practices across firms and countries.” The Academy of Management Perspectives 26, no. 1: 12-33. Bombardini, M., Gallipoli, G. and G. Pupato, (2012), “Skill Dispersion and Trade Flows”, American Economic Review, 102(5), 2327-48. Brambilla, Irene, Daniel Lederman, and Guido Porto (2012). "Exports, Export Destinations, and Skills." The American Economic Review 102, no. 7: 3406-3438 http://dx.doi.Org/10.1257 . Breinlich, Holger, and Chiara Criscuolo (2011). "International trade in services: A portrait of importers and exporters." Journal of International Economics 84, no. 2: 188-206. Buller, Paul F., and Glenn M. McEvoy (2012). “Strategy, human resource management and performance: Sharpening line of sight.” Human Resource Management Review 22, no. 1: 43-56. Caroli, E, and J. Van Reenen, (2001). “Skill-biased organisational change? Evidence from a panel of British and French establishments”, Quarterly Journal of Economics 116, no. 4: 1449-1492. Collins, Christopher J., and Ken G. Smith (2006), "Knowledge exchange and combination: The role of human resource practices in the performance of high-technology firms." Academy of Management Journal 49, no. 3: 544-560. Colquitt, Jason A., Brent A. Scott, and Jeffery A. Le Pine (2007). “Trust, trustworthiness, and trust propensity: a meta-analytic test of their unique relationships with risk taking and job performance.” Journal of Applied Psychology 92, no. 4: 909–927. Dickerson, Andy, and Francis Green (2004). “The growth and valuation of computing and other generic skills.” Oxford Economic Papers 56, no. 3: 371-406. Dirks, Kurt T. (1999) “The effects of interpersonal trust on work group performance.” Journal of Applied Psychology 84, no. 3: 445-455. DOI : http://dx.doi.org/10.1787/5jm5dcz2d26j-en. Feenstra, Robert C. and Gordon H. Hanson (1996), “Globalization, Outsourcing, and Wage Inequality”, American Economic Review 86, no. 2: 240-245. Francois, J., and Hoekman, B. (2010). “Services trade and policy”. Journal of Economic Literature 48, no. 3: 642-692. http://www.jstor.org/stable/20778764. Frank L. Schmidt (2002) “The Role of General Cognitive Ability and Job Performance: Why There Cannot Be a Debate”, Human Performance 15, 1-2: 187-210, DOI: 10.1080/08959285.2002.9668091 Frank L. Schmidt (2002). “The role of general cognitive ability and job performance: Why there cannot be a debate.” Human Performance 15, no. 1-2: 187-210. DOI:10.1080/08959285.2002.9668091. Frias J., Kaplan D., and E. Verhoogen (2009). “Exports and wage premia: evidence from Mexican employer employee data”, American Economic Review Papers & Proceedings 102, no. 3: 435-440. Gathmann, C., and Uta Schoenberg (2010), “How general is human capital? A tasked-based approach”, Journal of Labor Economics 28, no. 1: 1-49.
58 DSTI/EAS/IND/WPIA(2016)1
Gittell, Jody Hoffer, Rob Seidner, and Julian Wimbush (2010), "A relational model of how high- performance work systems work." Organization Science 21, no. 2: 490-506. Goldberg, Lewis R. (1990) “An alternative" description of personality": the big-five factor structure.” Journal of Personality and Social Psychology 59, no. 6: 1216. Goldberg, P. K., Khandelwal A. K., Pavcnik N. and P. Topalova (2010), “Imported intermediate inputs and domestic product growth: evidence from India”, Quarterly Journal of Economics 125, 1727–1767. Goldin, C. and L. F. Katz (2007), “The Race between Education and Technology: The Evolution of U.S. Educational Wage Differentials, 1890 to 2005”, NBER Working Papers, No. 12984. Goos, M., A. Manning, and A. Salomons (2014). “Explaining job polarization: Routine-biased technological change and offshoring”, American Economic Review 104, no. 8: 2509-26. DOI: 10.1257/aer.104.8.2509. Grossman, Gene M. (2013) "Heterogeneous workers and international trade." Review of World Economics 149, no. 2: 211-245. Hamilton, Barton H., Jack A. Nickerson and Hideo Owan (2003), “Team Incentives and Worker Heterogeneity: An Empirical Analysis of the Impact of Teams on Productivity and Participation”, Journal of Political Economy 111, no. 3: 465-497. Hanushek, Eric A., and Ludger Woessmann (2008). “The role of cognitive skills in economic development.” Journal of Economic Literature: 607-668. Hargadon, Andrew B., and Beth A. Bechky (2006). “When collections of creatives become creative collectives: A field study of problem solving at work.” Organization Science 17, no. 4: 484-500. Heckman, James J. (2000) “Policies to foster human capital.” Research in Economics 54, no. 1: 3-56. Heckman, James J., and Yona Rubinstein (2001). “The importance of noncognitive skills: Lessons from the GED testing program.” American Economic Review: 145-149. Heckman, James J., Jora Stixrud, and Sergio Urzua (2006). “The effects of cognitive and noncognitive abilities on labor market outcomes and social behavior.” Journal of Labor Economics 24, no. 3: 411- 482. Heckman, James, and Edward Vytlacil (2001). “Identifying the role of cognitive ability in explaining the level of and change in the return to schooling.” Review of Economics and Statistics 83, no. 1: 1-12. Helpman, E., Melitz, M. and Yeaple, S. (2004), “Export versus FDI”, American Economic Review 94, 300–316. Hill, Charles, Gareth Jones, and Melissa Schilling (2014). Strategic management: theory: an integrated approach. Cengage Learning. Hitt, Michael, R. Duane Ireland, and Robert Hoskisson (2012). Strategic management cases: competitiveness and globalization. Cengage Learning. Hong, Lu, and Scott E. Page (2004), “Groups of diverse problem solvers can outperform groups of high- ability problem solvers”, Proceedings of the National Academy of Sciences of the USA, 101: 123– 139. Hoyles, Celia, Alison Wolf, Susan Molyneux-Hodgson, and Phillip Kent (2002). “Mathematical skills in the workplace: final report to the Science Technology and Mathematics Council.”. Hsieh, Chihmao, Jack A. Nickerson, and Todd R. Zenger (2007). “Opportunity discovery, problem solving and a theory of the entrepreneurial firm.” Journal of Management Studies 44, no. 7: 1255-1277. Humburg, M., R. van der Velden and A. Verhagen (2013), “The employability of higher education graduates: the employers’ perspective”, European Commission. Ichniowski, Casey and Kathryn L. Shaw (2009). “Connective capital as social capital: the value of problem-solving networks for team players in firms”, National Bureau of Economic Research Working Paper No. 15619.
59 DSTI/EAS/IND/WPIA(2016)1
Ichniowski, Casey, Kathryn Shaw, and Giovanna Prennushi (1997). “The Effects of Human Resource Management Practices on Productivity: A Study of Steel Finishing Lines”, American Economic Review 87: 291-313. Ingram, B. F. and G. R. Neumann (2006), “The returns to skill”, Labour Economics 13, 35-59. Irarrazabal, A., A. Moxnes, and L. D. Opromolla (2013), "The Margins of Multinational Production and the Role of Intrafirm Trade", Journal of Political Economy 121, no. 1: 74-126. Katila, R. and Ahuja, G. (2002). “Something old, something new: A longitudinal study of search behavior and new product introduction”. Academy of Management Journal 45, no. 6: 1183-1194. Kautz, Tim, James J. Heckman, Ron Diris, Bas Ter Weel, and Lex Borghans (2014). “Fostering and measuring skills: Improving cognitive and non-cognitive skills to promote lifetime success”. No. w20749. National Bureau of Economic Research. Lazear, E. P. (1999), “Globalisation and the market for team-mates”, Economic Journal, 109: 15–40. Lazear, Edward P., and Mike Gibbs (2014). Personnel economics in practice. John Wiley & Sons. Le Mouel, Marie and Mariagrazia Squicciarini (2015). Cross-country estimates of employment and investment in organisational capital: A task-based methodology using PIAAC data. OECD Science, Technology and Industry Working Papers, No. 2015/8. OECD Publishing, Paris. Lee, Sam Youl, Richard Florida, and Zoltan Acs (2004). “Creativity and entrepreneurship: a regional analysis of new firm formation.” Regional Studies 38, no. 8: 879-891. Lodefalk, M. (2014). “The Role of Services for Manufacturing Firm Exports”. Review of World Economics 150, no. 1: 59-82. DOI: 10.1007/s10290-013-0171-4 . MacCrory, Frank, George Westerman, and Erik Brynjolfsson. “Identifying the Multiple Skills in Skill- Biased Technical Change.” Mimeo. Marcolin, Luca, Sébastien Miroudot and Mariagrazia Squicciarini (2016). Routine jobs, employment and technological innovation in global value chains. OECD Science, Technology and Industry Working Papers, No. 2016/01, OECD Publishing, Paris. Matzler, Kurt, Birgit Renzl, Julia Muller, Stephan Herting and Todd A. Mooradian (2008), “Personality traits and knowledge sharing”, Journal of Economic Psychology 29, pp. 301–313. Mayer, Roger C., and Mark B. Gavin (2005). “Trust in management and performance: who minds the shop while the employees watch the boss?.” Academy of Management Journal 48, no. 5: 874-888. Messersmith, Jake G., and James P. Guthrie (2010). “High performance work systems in emergent organizations: Implications for firm performance.” Human Resource Management 49, no. 2: 241- 264. Mohan, Gouri and Zubin R. Mulla (2013), “Openness to experience and work outcomes: exploring the moderating effects of conscientiousness and job complexity”. Great Lakes Herald 7, no.2: 18-36. Melitz, M. J. (2003), “The impact of trade on intra-industry reallocations and aggregate industry productivity”, Econometrica 71, 1695–1725. Nickerson, Jack A., and Todd R. Zenger (2004). “A knowledge-based theory of the firm—The problem- solving perspective.” Organization Science 15, no. 6: 617-632. OECD (2013). Technical Report of the Survey of Adult Skills (PIAAC). Download: http://www.oecd.org/site/piaac/_Technical%20Report_17OCT13.pdf. OECD (2015), Skills for social progress: the power of social and emotional skills, OECD Skills Studies, OECD Publishing, http://dx.doi.org/10.1787/9789264226159-en. Oldenski, L., (2012). “The task composition of offshoring by U.S. multinationals” Economie Internationale, 131: 5-21. Ottaviano, Gianmarco and Giovanni Peri (2006), “The economic value of cultural diversity: evidence from US cities”, Journal of Economic Geography 6, no.1: 9–44.
60 DSTI/EAS/IND/WPIA(2016)1
Ottaviano, Gianmarco, Giovanni Peri and Greg C. Wright (2013), “Immigration, Offshoring, and American Jobs”, American Economic Review 103, no.5: 1925–1959. Parrotta, Pierpaolo, Pozzoli, Dario and Mariola Pytlikova (2014) “Does labor diversity affect firm productivity?”, European Economic Review 66: 144–179. Politaev, M. and C. Robinson (2008), “Human Capital Specificity: Evidence from the Dictionary of Occupational Titles and Displaced Worker Surveys, 1984-2000, Journal of Labor Economics 26, no. 3: 387-420. Reiter-Palmon, Roni, and Jody J. Illies (2004). “Leadership and creativity: Understanding leadership from a creative problem-solving perspective.” The Leadership Quarterly 15, no. 1: 55-77. Rich, Bruce Louis, Jeffrey A. Lepine, and Eean R. Crawford (2010). “Job engagement: Antecedents and effects on job performance.” Academy of Management Journal 53, no. 3: 617-635. Santhanam, Radhika, and Edward Hartono (2003). “Issues in linking Information Technology capability to firm performance”. MIS Quarterly 27, no. 1. Management Information Systems Research Center, University of Minnesota: 125–53. http://www.jstor.org/stable/30036521. Schank T., Schanbel C., and J. Wagner (2007), “Do exporters really pay higher wages? First evidence from German linked employer-employee data”, Journal of International Economics 72, 52–74. Schmidt, Frank L., and John Hunter. (2004) “General mental ability in the world of work: occupational attainment and job performance.” Journal of Personality and Social Psychology 86, no. 1: 162. Schmitt, Neal (2014) “Personality and cognitive ability as predictors of effective performance at work”, Annual Review of Organizational Psychology and Organizational Behavior, 1, no. 1: 45-65. doi: 10.1146/annurev-orgpsych-031413-091255 Shin, Duckjung, and Alison M. Konrad (2014). “Causality between high-performance work systems and organizational performance.” Journal of Management: 0149206314544746. Squicciarini, Mariagrazia, and Marie Le Mouel (2012). Defining and measuring investment in organisational capital: Using US microdata to develop a task-based approach. No. 2012/5. OECD Publishing. Tippins, Michael J., and Ravipreet S. Sohi (2003). “IT competency and firm performance: is organizational learning a missing link?.” Strategic Management Journal 24, no. 8: 745-761. Tippmann, Esther, Vincent Mangematin, and Pamela Sharkey Scott (2013). “The two faces of knowledge search: New solutions and capability development.” Organization Studies 34, no. 12: 1869-1901. Trax, Michaela, Stephan Brunow and Jens Suedekum (2015), "Cultural diversity and plant-level productivity," Regional Science and Urban Economics 53(C): 85-96. von Krogh, G., Nonaka, I. and Rechsteiner, L. (2012). “Leadership in organizational knowledge creation: A review and framework.” Journal of Management Studies, 49: 240–277. Wang, Ching–Wen, and Ruey–Yun Horng (2004). “The effects of creative problem solving training on creativity, cognitive type and R&D performance.” R&D Management 32, no. 1: 35-45. Wei, Li-Qun, and Chung-Ming Lau (2010). “High performance work systems and performance: The role of adaptive capability.” Human Relations, May: 1-25 DOI: 10.1177/0018726709359720 . Woodman, Richard W., John E. Sawyer, and Ricky W. Griffin (1993). “Toward a theory of organizational creativity.” Academy of Management Review 18, no. 2: 293-321. Wright, Patrick M., Benjamin B. Dunford, and Scott A. Snell (2001). “Human resources and the resource based view of the firm.” Journal of Management 27, no. 6: 701-721. Zhu, Susan Chun, and Daniel Trefler (2005). "Trade and inequality in developing countries: a general equilibrium analysis." Journal of international Economics 65, no. 1: 21-48.
61 DSTI/EAS/IND/WPIA(2016)1
ANNEX
62 DSTI/EAS/IND/WPIA(2016)1
Annex Table 1. Detailed names of sectors and occupations
Agriculture Agriculture, hunting, forestry and fishing Mining Mining and quarrying Food products Food products, beverages and tobacco
Textiles and apparel Textiles, textile products, leather and footwear
Wood and paper Wood, paper, paper products, printing and publishing Chemicals Chemicals and non-metallic mineral products Basic metals Basic metals and fabricated metal products Machinery and equipment Machinery and equipment n.e.c Electrical equipment Electrical and Optical Equipment Transport equipment Transport equipment Manufacturing Manufacturing n.e.c; recycling Electricity Electricity, gas and water supply Construction Construction Wholesale and retail trade Wholesale and retail trade; Hotels and restaurants Transport and storage Transport and storage, post and telecommunication Finance and insurance Finance and insurance Real estate Real estate, renting and business activities Personal services Community, social and personal services
Managers Managers Professionals Professionals Technicians Technicians and associate professionals Clerical support workers Clerical support workers Service and sales workers Service and sales workers Craft workers Craft and related trades workers Plant and machine operators Plant and machine operators, and assemblers Elementary occupations Elementary occupations
63 DSTI/EAS/IND/WPIA(2016)1
Annex Table 2. Number of observations available by industry and country
Industry AUS AUT BEL CAN CZE DEU DNK ESP EST FIN FRA GBR IRL ITA JPN KOR NLD NOR POL RUS SVK SWE USA Agriculture 135 164 48 445 74 69 130 159 253 145 138 74 172 122 87 176 36 73 330 46 113 81 39 Mining 120 6 7 327 27 13 13 13 42 13 5 17 9 3 1 4 2 67 71 11 15 6 17 Food products 117 65 93 280 78 95 125 82 124 50 129 97 84 71 117 83 72 63 179 50 69 36 34 Textiles and apparel 23 17 26 52 42 22 13 21 130 17 28 22 10 55 31 79 51 6 52 23 59 5 29 Wood and paper 58 67 72 323 82 89 73 41 224 126 72 74 44 53 66 80 66 66 139 46 66 88 59 Chemicals 48 98 141 254 141 107 140 55 100 69 108 96 95 76 139 130 74 29 162 37 89 57 68 Basic metals 72 90 98 161 97 103 79 64 121 95 83 48 22 78 115 81 60 30 121 13 90 67 37 Machinery and equipment 28 70 40 142 111 108 148 14 30 74 37 45 15 42 49 64 52 31 64 26 48 40 45 Electrical equipment 28 87 24 151 78 92 73 16 113 79 43 52 45 45 137 199 38 18 75 22 75 44 48 Transport equipment 42 56 65 201 169 129 10 56 49 22 119 65 17 38 112 128 28 41 95 17 162 54 45 Manufacturing 66 68 33 232 72 71 88 43 138 59 43 83 77 58 34 47 103 30 132 31 131 37 41 Electricity 70 35 24 170 60 46 48 20 72 35 44 35 30 34 28 20 20 25 46 29 51 38 17 Construction 466 261 228 1309 237 234 308 253 463 286 343 318 185 209 254 333 218 283 536 123 289 200 229 Wholesale and retail trade 1103 733 503 4066 724 657 833 739 947 600 733 1025 708 528 808 1097 709 679 1147 478 588 467 675 Transport and storage 344 210 224 1108 249 175 273 215 392 260 281 300 194 160 214 249 193 201 286 196 220 196 180 Finance and insurance 190 148 130 741 117 157 152 80 99 74 127 201 215 104 96 154 126 64 130 60 76 69 157 Real estate 653 343 353 2052 378 475 653 366 520 496 473 700 392 319 379 374 507 435 441 309 330 540 487 Personal services 1951 1138 1238 7016 907 1275 2144 1105 1502 1385 1693 2374 1358 858 1191 1093 1572 1575 1102 658 820 1304 1333 Source: OECD calculations based on the Survey of Adult Skills (PIAAC) (2012) and the TiVA (October 2015 release) databases.
64 DSTI/EAS/IND/WPIA(2016)1
Annex Table 3. Number of observations available by occupation and country
Occupation (isco08) Sector AUS AUT BEL CAN CZE DEU DNK ESP EST FIN FRA GBR IRL ITA JPN KOR NLD NOR POL RUS SVK SWE USA Managers Manufacturing 64 54 72 216 56 44 53 29 88 37 53 100 32 18 70 31 76 28 58 33 45 26 52 Managers Services 559 187 258 1752 197 111 262 169 468 128 311 527 251 80 181 103 442 236 238 231 261 183 309 Professionals Manufacturing 45 75 61 250 63 122 142 23 110 124 80 70 69 42 67 110 67 53 83 63 63 68 68 Professionals Services 1167 600 685 3848 504 605 1492 575 1006 724 629 932 822 449 504 552 822 752 701 586 442 831 713 Technicians Manufacturing 49 147 110 390 148 94 150 45 136 114 202 76 57 107 135 47 100 73 113 35 132 119 74 Technicians Services 693 627 390 3114 489 619 572 284 591 626 739 614 355 446 448 451 565 518 435 216 406 481 512 Clerical support workers Manufacturing 65 54 69 111 81 109 57 78 37 39 32 59 37 56 97 179 65 20 78 11 37 25 26 Clerical support workers Services 491 321 320 1341 321 351 340 366 240 251 349 753 376 241 451 473 381 180 389 107 176 143 242 Service and sales workers Manufacturing 14 22 18 57 36 36 22 13 20 11 18 7 21 19 26 48 21 13 37 8 18 7 14 Service and sales workers Services 788 538 425 3231 566 660 824 602 688 708 687 1177 681 483 894 951 625 820 910 405 492 664 723 Craft workers Manufacturing 166 179 154 443 250 232 178 124 300 169 136 117 116 150 213 188 100 57 390 89 253 100 92 Craft workers Services 361 234 213 1111 198 253 283 252 403 271 288 269 211 190 184 220 165 247 472 123 192 193 192 Plant and machine operators Manufacturing 98 75 92 307 232 170 111 59 318 117 151 129 73 134 162 201 46 40 208 43 227 106 71 Plant and machine operators Services 217 121 102 732 151 94 133 126 219 170 200 184 105 91 106 212 74 99 207 86 163 128 117 Elementary occupations Manufacturing 44 44 25 181 61 48 70 38 89 12 27 56 34 24 56 106 62 8 95 10 63 10 24 Elementary occupations Services 364 182 248 1081 165 211 418 356 300 206 412 408 221 189 162 328 231 127 274 68 180 111 226 Note: We exclude occupation 6 (Skilled agricultural, forestry and fishery workers) from the occupation level analysis, because we will only look at manufacturing and services sectors. Source: OECD calculations based on the Survey of Adult Skills (PIAAC) (2012) and the TiVA (October 2015 release) databases.
65