The Human Side of Productivity - Key Highlights

The Human Side of Productivity Uncovering the role of skills and diversity for the productivity of firms KEY HIGHLIGHTS

The Human Side Key highlights of Productivity

1

Summary

Uncovering the role of skills and diversity for the productivity of firms

l Large productivity gaps between firms in the same industry and

country imply an enormous potential for aggregate productivity gains from promoting catch up of lower productivity firms to the best performers. l

Differences in human factors – i.e. workforce skills, managerial talent and diversity – account on average for about one third of the productivity gap between firms at the frontier and medium performers.

l

Adjusting the skill structure is an essential part of catching up to the frontier, and is associated with productivity gains corresponding to about 20% of the gap between medium and top performers. On average across countries, this would entail adjustments up to doubling the share of high skilled employees. Still, low and medium skilled employees, which make up more than half the workforce even at the frontier, remain essential.

l

Managers play a pivotal role in ensuring productivity gains, especially when working with a highly skilled workforce. Firms should therefore pay particular attention to upskilling managers and devoting sufficient resources to formal managerial roles.

l More gender and culturally diverse firms are more productive, and

diversity among managers is especially important: an inclusive work culture combined with a more diverse workforce – about one-half of women and up to 25% of employees with a foreign cultural background – is associated with productivity gains corresponding to up to 10% of the productivity gap between typical medium and top performers. l

Supporting catch up requires a comprehensive approach focusing on the SUM of three complementary policy goals: improving the supply of a skilled and diverse labour force, e.g. through education; upgrading of skills and firm organisation, e.g. through management training and lifelong learning; and mobilising hitherto unused potential to better match existing talent to labour needs, e.g. by tapping into a broader pool of talent thanks to telework and through well designed immigration policies.

1. Prepared by Chiara Criscuolo, Peter Gal, Timo Leidecker and Giuseppe Nicoletti. This document summarises the main findings from the project. The full report, including a detailed description of the methodology, data and a more extensive discussion of results will be available in the autumn of 2021. The support of the GFP network for this project, and in particular the access provided to linked employer employee data (see Box 1) is gratefully acknowledged. We also thank the Age Diversity and LinkEED projects of the OECD, and other colleagues for providing comments and advice in various stages of the project, including Francesca Borgonovi, Alain De Serres, Ricardo Espinoza, Patrick Lenain, Dirk Pilat, Glenda Quintini, Lea Samek and Mariagrazia Squicciarini.

2 | OECD – THE HUMAN SIDE OF PRODUCTIVITY: HIGHLIGHTS

Large disparities in productivity: why does it matter and what are the drivers?

Productivity growth is crucial in many ways: it is the ultimate source of rising living standards and it creates the necessary resources to fund public goods, including those that aim to address urgent challenges related to ageing and climate change. However, in most OECD countries, productivity has grown more slowly in recent decades. This slowdown at the macroeconomic level hides a large variation of productivity performance across businesses at the microeconomic level (OECD, 2015[1]; Andrews, Criscuolo and Gal, 2016[2]). It also coincides with a general trend towards a more polarised business environment: it seems that globalisation and

digitalisation provide new, promising opportunities for improved performance, but only few firms are well-placed to fully benefit from these opportunities (Brynjolfsson and McAfee, 2012[3]). While some firms thrive, others fail to keep up or even fall behind as they lack the capabilities – or the incentives – to implement the changes necessary to exploit these opportunities, i.e. to carry out complementary investments in business reorganisation, skills upgrading and other elements of “intangibles” (Haskel and Westlake, 2017[4]; Nicoletti, von Rueden and Andrews, 2020[5]).2 COVID-19, with its diverse impact on firms, has further added to these long-standing trends.

Figure 1. Large productivity gaps between firms are widespread 1

Domestic frontier normalised to 1 Medium performer

Productivity gap to frontier

1/2

Laggard

1/4

1/8

1/16 CRI

HUN

PRT

DEU

ITA

BEL

FRA

SWE

DNK

JPN*

Note: The figure shows the difference in value added based labour productivity between firms within the same country and industry at the top 10% of the productivity distribution (domestic frontier; normalised to 1) and at the median and at the bottom 10%.

Source: OECD GFP calculations based on cross-country micro-aggregated linked employer-employee data.

2. The project adds to a large body of previous and ongoing OECD work examining the role of firm differences for productivity growth (Berlingieri et al., 2020[20]; Corrado et al., 2020[21]; Nicoletti, von Rueden and Andrews, 2020[19]; Nicoletti et al., 2019[18]).

OECD – THE HUMAN SIDE OF PRODUCTIVITY: HIGHLIGHTS | 3

Large disparities in productivity: why does it matter and what are the drivers?

What are the right human capabilities – skills and management qualities – required for adopting the best practices and the latest technologies, and how do firms differ in these capabilities from their domestic peers?

This is a pressing policy concern since acquiring the right talent and building an effective organisation may present more of a challenge to most companies than simply purchasing the latest technologies (equipment or software). To provide a better understanding of these issues, the Human Side of Productivity project of the OECD Global Forum on Productivity (GFP) embarked on a novel, large scale data collection and analysis effort, conducted in close collaboration with partners from the GFP network. It draws on detailed information from millions of firms and their employees across all industries in the business sector for 10 OECD countries from 2000 onwards until the latest year available: Belgium, Costa Rica, Denmark, France, Germany, Hungary, Italy, Japan, Portugal and Sweden. Box 1 provides more details on the measurement framework and the data collection, listing and acknowledging our partner institutions and researchers involved.

Our focus is on the people – workers and managers – inside the firm, and how their characteristics (skills, demographic and cultural features) and interactions relate to the productivity of firms. As we show, this “Human Side” indeed plays an important role for firm performance: on average across countries, it accounts for nearly one third of the labour productivity gap between medium and top performing firms within the same detailed sector of economic activity (Figure 2). Differences in physical capital intensity also play a significant but more modest role. Put simply: investing in people – human capital –, in addition to investing in physical capital, is essential for closing the productivity gap.

The “Human Side”, even in combination with traditionally measured (mostly physical) capital, still leaves a significant part of productivity differences unexplained (Figure 2). This remainder may be related to differences in hard to measure intangible 4 | OECD – THE HUMAN SIDE OF PRODUCTIVITY: HIGHLIGHTS

Figure 2. Human factors account for nearly one third of the productivity gap between a medium performer and the frontier 100%

Productivity frontier

90%

80%

70%

60% Productivity gap

Productivity gaps between firms within the same country are indeed large – a top performing firm at the productivity “frontier” is more than twice as productive as a medium performer, and up to 8 times as productive as a low performer “laggard” firm within the same industry (Figure 1). Promoting catch-up to the domestic productivity frontier thus holds an enormous potential for raising overall productivity.

50%

40% Workforce composition (31.2%) 30%

20%

10%

0%

Capital (19.6%) Medium performer

Note: The figure shows the contribution of productivity enhancing adjustments of the workforce composition and capital intensity respectively to the catch up of a typical medium performer towards the productivity frontier in the same industry and country. Results are based on a firm-level regression controlling, among others, simultaneously for capital intensity and workforce composition. More details on the underlying analysis are provided in Box 1. All countries for which capital is measured have been included.

Source: OECD GFP calculations based on cross-country micro-aggregated linked employer-employee data.

capital, and to interactions between different types of capital – physical, intangible and human capital. For instance, new machinery often comes with new skill requirements; developing a brand, its design, or the culture of a company – which are also part of intangible capital – is due to the people – managers, engineers or marketing experts – inside the firm (Haskel and Westlake, 2017[4]). People and capital are thus closely intertwined; these interactions will be the focus of a follow-up project of the GFP.

Top performing firms have a more high-skilled workforce but utilise a range of skills

Top performing firms use high skilled employees intensively in all sectors; the role of low and medium skilled employees, however, varies considerably by economic activity (Figure 4). Low skilled employees play only a minor role in knowledge intensive services (ICT and professional services), but make up about a quarter of the workforce in manufacturing. More traditional, less knowledge intensive services (wholesale, retail, transport, hotels and restaurants) – the largest segment of the economy – show a distinct pattern: their best performing businesses rely more on medium skilled employees than their peers below the industry frontier.

These findings underline the fact that medium skills and even low skills – not only high skills – are crucial ingredients for high productivity.

Employees with low and medium skills are indeed an indispensable part of a productive economy, but ongoing structural changes across sectors will likely affect their

relative importance. Low skilled employees account for almost 30% of the workforce in manufacturing, but manufacturing itself accounts for an ever declining employment share of the business sector (typically less than 20%). In contrast, employment in services is rising, currently accounting for nearly two thirds of the business sector. Here the most productive firms rely on relatively few low skilled workers. Policies aimed at boosting productivity and preserving jobs should therefore focus on upskilling across the whole skill spectrum, including from low to medium skill levels.

Figure 3. Firms at the productivity frontier employ a more skill-intensive workforce 100% 15%

24%

80% Workforce skill composition

The skill structure of the workforce is a key determinant of firm productivity. According to our findings, a key distinguishing feature of top performing firms is that they rely more intensively on high-level skills, i.e. more productive firms generally employ larger shares of highskilled employees – about twice as much as the least productive firms. But low and medium skilled employees are indispensable: the most productive firms employ a substantial share from both of these skill groups as well, with medium skilled employees accounting for half the workforce even in the most productive firms.

In a nutshell, firms at the productivity frontier make extensive use of high skills but ultimately rely on a range of different skill levels (Figure 3). This finding holds for a range of alternative skills measures, either relying on the task content of the occupations (our preferred measure) or on the educational attainment levels of the workforce. Box 2 provides more details on skills measurement.

60%

31%

56% 53% 50%

40%

20% 29% 0%

Laggard High skilled

24% Median Medium skilled

19% Frontier Low skilled

Note: The figure shows the skill composition of the firm’s workforce along the productivity distribution in terms of the share of low, medium and high skilled employees. Laggard, median and frontier refer to the least (bottom decile), medium (40-60th percentile) and most (top decile) productive firms in the same country and industry. Skill group shares are based on average cognitive skills by detailed occupation and country if available and education otherwise. More details on skill measurement are provided in Box 2. Productivity is based on value added per employee. Results shown are averaged across country / industry / year cells.

Source: OECD GFP calculations based on cross-country micro-aggregated linked employer-employee data.

OECD – THE HUMAN SIDE OF PRODUCTIVITY: HIGHLIGHTS | 5

Top performing firms have a more high-skilled workforce but utilise a range of skills

Figure 4. Low and medium skills are essential especially in manufacturing and less knowledge intensive services 100%

Workforce skill composition

16%

19%

23%

80% 52% 60%

56%

58% High skilled

50%

53%

52%

40%

Medium skilled Low skilled

43%

20% 27% 0%

29%

Median

37% 31%

25% Frontier

Manufacturing

19% 5%

4%

Median

Frontier

Knowledge intensive services

Median

Frontier

Less knowledge intensive services

Note: Knowledge intensive services include ICT and professional services; less knowledge intensive services include wholesale, retail, transport, hotels, restaurants and other private sector services. Also see notes below Figure 3.

Source: OECD GFP calculations based on cross-country micro-aggregated linked employer-employee data.

The more intensive use of high-skilled employees among top productivity firms is a general feature not only across sectors but also across countries (Figure 5 panel A). However, the degree to which each country’s top productivity firms specialise in using high-skills relative to less productive firms differs substantially.

German top firms rely on high skilled employees relatively less intensively than French top firms do – in fact they are at the opposite end of the crosscountry spectrum. Instead, German top performers rely relatively more intensively on medium skilled employees (Figure 5 panel B). Italy is a mix of these two cases: its top businesses rely significantly on medium skills, while also drawing considerably on high skills.

These patterns are observed when controlling for differences in countries’ sectoral specialisations, and they likely reflect differences in educational and adult training systems – e.g. vocational training – and thus showcase the potential for policies to mobilise different skill groups for top performance.

Important differences in the skill structure relate to general as well as specific skills: high productivity firms differ in their use of general skills – captured for instance by educational attainment –, but differences in specific skills – such as management and communication skills or ICT skills – are even more pronounced (Figure 6).

Put differently, top performance draws especially on skills that are tailored to specific tasks, and which are 6 | OECD – THE HUMAN SIDE OF PRODUCTIVITY: HIGHLIGHTS

often acquired through experience and learning-by-doing throughout the career.

The most productive firms differ systematically in their use of skills from less productive firms. The best practices and the latest technologies used at the productivity frontier generally require a more highly skilled workforce. Part of catching up to the frontier thus entails acquiring higher level skills, which is perhaps even more of a challenge than simply buying the latest technologies. Policies can support this adjustment. Better access especially to highskilled employees is crucial, but policies should aim to improve access to a range of skills, including the skills necessary to perform medium but also low skill tasks.

While the educational system plays an important role for “foundational”, basic skills, increasing the supply of specific skills, often acquired throughout the career, is key. More emphasis should be given to training and lifelong learning, where substantial scope exists for policies to improve training uptake and quality (OECD, 2019[6]). Firms themselves also play an important role for skill acquisition. Management can help employees to acquire better skills by supporting knowledge transfer and learning-by-doing, for instance.

Firms are also important as providers of training. Policies can play a role in supporting firms, e.g. through promoting partnerships with stakeholders and providing financial support in particular for smaller and less dynamically growing firms, who often find it more difficult to provide efficient training to their employees (OECD, 2021[7]).

Figure 5. High skills are important everywhere, but some countries also mobilise medium skills for the productivity frontier Panel A: Top performing firms are generally more high-skill intensive than medium performers

Panel B: But in some countries they also rely more on medium skills 0.14

DEU

Percentage point gap medium-to-top performer in share of high skilled

High-skill focus

JPN* DNK PRT SWE ITA* CRI BEL* HUN FRA

0.12

FRA

HUN BEL*

0.10

CRI 0.08

PRT

14

ITA*

0.06

DNK 0.04

JPN*

0.02

DEU Medium-skill focus

0.00 0 2 4 6 8 10 12 High-skill gap between medium and top performers (pp.)

SWE

-0.08 -0.06 -0.04 -0.02 0.00 0.02 Percentage point gap medium-to-top performer in share of medium skilled

Note: Panel A shows the difference in the share of high skilled employees of firms at the frontier (top productivity decile) versus medium performers (40-60th percentile) in the same industry by country. Panel B shows as scatterplot the difference in the share of high-skilled and medium skilled employees of firms at the frontier versus medium performers for each country. Skills and productivity are measured in the same way as in Figure 3, see notes therein. *Skills measures are based on educational attainment.

Source: OECD GFP calculations based on cross-country micro-aggregated linked employer-employee data.

Figure 6. Firms at the productivity frontier differ even more in their use of specific skills Close to frontier Medium Low medium Laggards

Educational attainment

Productivity distribution

Close to frontier

Management and comm. skills

Medium Low medium Laggards Close to frontier Medium

ICT skills

Low medium Laggards 0

0.05

0.1

0.15 0.2 0.25 Skills distance from the frontier

0.3

0.35

0.4

Note: The figure shows deviations from the skill structure of firms at the productivity frontier (top decile) for different segments of the productivity distribution among firms in the same country and industry, for different skill measures respectively. More details on skill and skill deviation are provided in Box 2. Productivity is based on value added per employee. Results shown are averaged across country / industry / year cells.

Source: OECD GFP calculations based on cross-country micro-aggregated linked employer-employee data.

OECD – THE HUMAN SIDE OF PRODUCTIVITY: HIGHLIGHTS | 7

Managers are crucial for top performance, especially in combination with a highly skilled workforce A special focus on managers is warranted in view of their pivotal role: by “deciding what to do” and then “getting the organisation to do it” – including choosing, motivating and coordinating the workforce – managers have a disproportional impact on productivity (Bloom and Van Reenen, 2007[5]; Gibbons and Henderson, 2012[6]; Giorcelli, 2019[7]). Put simply: managers can be the enablers of – or the bottlenecks to – achieving high productivity.

Our analysis reveals systematic differences in the role of management between top performing and less productive firms: top firms employ more managers relative to their workforce, especially in services (Figure 7 panel A).

When isolating and quantifying the role of managers for productivity country-by-country (see methodology in Box 1), we find that devoting more human resources to dealing with formal managerial tasks is linked to productivity gains in most countries – by up to 8% in

Figure 7. Firms at the productivity frontier devote more employees to managing the firm Panel A: Top productivity firms devote more human resources to management

Panel B: But the productivity gains from a higher share of managers differ across countries 8%

18%

7.5

6%

12%

Change in productivity

Share of managers

14%

10% 8% 6%

5.0

4%

2.5 2%

4%

Percentage point difference in manager-share

16%

2% 0%

0% Manufacturing

Less knowledge intensive services

Laggard

Medium

Knowledge intensive services Frontier

SWE

PRT

DNK

CRI

HUN

FRA

0.0

Gain from adjusting share of managers (left axis) Gap in share of managers to frontier (right axis)

Note: Panel A shows the share of managers along the productivity distribution of firms in the same industry and country by sector. Panel B shows the productivity gains for medium performers associated with adjusting the share of managers to the level observed at frontier firms. Managers are identified using detailed occupations. Laggard, median and frontier refer to the least (bottom decile), medium (40-60th percentile) and most (top decile) productive firms. The firm-level regression analysis underlying panel B is described in Box 1. Productivity is based on value added per employee. Results shown in Panel A are averaged across country / industry / year cells.

Source: OECD GFP calculations based on cross-country micro-aggregated linked employer-employee data.

8 | OECD – THE HUMAN SIDE OF PRODUCTIVITY: HIGHLIGHTS

Managers are crucial for top performance

France, for instance (Figure 7 panel B). In countries with arguably less hierarchical organisational modes – such as Denmark and Sweden – the link between the share of management and productivity is found to be weaker.

Beyond the share of managers, their skills also play a pivotal role. Productivity gains associated with upskilling an equal number of employees are about 3 to 4 times larger for managers than for workers (Figure 8). Upskilling managers can therefore lead to disproportional productivity gains.

However, the crucial role of managers for firm productivity does not imply neglecting worker skills; after all, the bulk of the firm’s workforce consists of non-managerial workers. Indeed, manager skills are found to have the largest impact on productivity when combined with a highly skilled workforce (Figure 9). Manager and worker skills thus complement each other and reveal their full potential for firm performance only in combination.

Figure 8. Managers’ skills have a disproportionate impact on productivity

Figure 9. Exploiting the full potential of managers requires building on strong synergies with skilled workforce 1.00 0.90

4% Share of skill-related productivity gains

Productivity gains from upskilling same number of employees

5%

Managers are thus key to productivity. Policies should pay particular attention to enhancing the supply of managerial skills. In addition, to catch up to the top performers, firms may need to consider adjustments to their organisational structure by devoting more resources to formal managerial roles and management tasks. Policy support may be required to incentivise and help facilitate such adjustments (Giorcelli, 2019[10]). Such more targeted policies are also a key complement to wider upskilling efforts, including improvements in skills of nonmanagerial workers. Policies may also need to go beyond improving the supply of able managers; in addition, they should address the incentives of people selecting these managers, that is, the firm’s owners. For instance, country-specific work on Italian firms conducted in the broader context of this project confirms that family ownership often leads to weak performance, presumably because managers are chosen from a more narrow pool and thus are less likely to be well suited for management (Andretta, Brunetti and Rosso, 2021[11]).

3%

2%

1%

0.80 0.70 0.60 0.50 0.40 0.30 0.20 0.10

0%

Knowledge intensive services

Manufacturing Managers

Less knowledge intensive services

Workers

Note: The figure shows the productivity gains for medium performers (40-60th percentile) associated with replacing medium skilled managers (workers) with high skilled managers (workers) corresponding to 1% of the firm’s workforce, for firms in the same industry and country, separately by sector. Skill measures are based on average wages by detailed occupation and country. More details on skill measurement are provided in Box 2. The underlying firm-level regression analysis is described in Box 1. Productivity is based on value added per employee. Results shown are averaged across countries.

Source: OECD GFP calculations based on cross-country micro-aggregated linked employer-employee data.

0.00

Less knowledge intensive services Manager skills

Knowledge intensive services Complementarities

Manufacturing Worker skills

Note: The figure shows, separately by sector, the share of productivity gains, attributable to managers and workers, for medium performers (40-60th percentile) associated with adjusting their skill structure to match frontier firms (top decile) in the same industry and country. Complementarities are based on interaction terms between the share of high skilled managers and high skilled workers. The underlying firm-level regression analysis is described in Box 1. Skill measures are based on average wages by detailed occupation and country. More details on skill measurement are provided in Box 2. Productivity is based on value added per employee. Results shown are averaged across countries.

Source: OECD GFP calculations based on cross-country micro-aggregated linked employer-employee data.

OECD – THE HUMAN SIDE OF PRODUCTIVITY: HIGHLIGHTS | 9

Gender and cultural diversity, especially among managers, goes hand in hand with a better productivity performance The diversity of the workforce has several dimensions: gender, age and cultural background (nationality or country of origin). Combining young and older employees can raise firm productivity, most likely through better knowledge sharing between different cohorts of employees, as was shown by a closely related OECD project on Age Diversity (OECD, 2020[12]). The results below will therefore focus on the role of gender and cultural diversity.

We find that firms that employ a more gender- or culturally diverse workforce are more productive. Figure 10 illustrates this visually by showing an inverted U-shape relationship with firm productivity for the share of women and the share of employees coming from a different culture (have foreign nationality or have been born abroad). For gender diversity, we find that firms

with more equal shares of men and women are more productive. For cultural diversity, productivity is highest when firms employ a substantial share of up to 25% of managers with foreign cultural background. Higher shares of employees with foreign cultural background may be particularly prevalent among multinational enterprises, which operate in several countries.

These findings demonstrate the potential benefits of employing a more diverse workforce based on past and recent levels of diversity. Arguably, however, current levels do not exploit the full potential of diversity. For instance, many women experience career breaks after pregnancy and gain less experience. In the future, diversity may be even more beneficial if hitherto underrepresented groups become better integrated into

9%

9%

6%

6% Productivity premium

Productivity premium

Figure 10. Firms benefit from diversity especially among managers

3%

0%

-3%

0%

50%

100%

0%

0%

-3%

Share of women Managers

3%

Workers

5%

25%

50%

Share of foreign cultural background Managers

Workers

Note: Panel A shows the productivity premium associated with employing a more gender diverse workforce separately among workers and managers. Panel B shows the productivity premium associated with employing a more culturally diverse workforce separately among workers and managers. Both figures show smoothed results of dummy estimates for share categories. Baseline category for gender diversity is women share 0-20% and for cultural diversity share of foreign background employees of 0-5%. More information on the underlying firm-level regression analysis is provided in Box 1. Cultural diversity is based on country-of-origin or nationality. Results for very high shares of cultural diversty are not shown as few observations were available. Productivity is based on value added per employee. Results shown are averaged across countries, replacing insignificant estimates with 0.

Source: OECD GFP calculations based on cross-country micro-aggregated linked employer-employee data.

10 | OECD – THE HUMAN SIDE OF PRODUCTIVITY: HIGHLIGHTS

Gender and cultural diversity

the labour market and become more productive, e.g. by gaining more experience in managerial positions (Ciminelli, Schwellnus and Stadler, 2021[13]).

For workers, the potential difficulties related to cultural diversity in everyday interactions may be relatively more important, thus leading to smaller gains.

Employing a more diverse workforce may become more important for firm performance over time due to the general trend towards a more knowledge-based and global economy. Firms that are able to mobilise the specific human capabilities of a diverse workforce seem to be particularly well-placed to succeed in such an environment: a broader perspective – drawing on the diverse backgrounds of its employees – allows for better decision making; considering a broader pool of candidates, including from hitherto underrepresented groups, improves job matching; finally, cultural diversity may be crucial to exploit the opportunities of a globalised economy (Lazear, 1999[14]; Hsieh et al., 2019[15]).

Many firms in our sample employ a workforce with very low levels of diversity, so policies promoting diversity could lead to substantial productivity gains – for instance, our findings indicate that more than half of the firms may be able to raise productivity by employing a more diverse managerial workforce. But increasing diversity should be done with caution, and build on a comprehensive approach: more than simply hiring more people with different backgrounds, firms also need to adopt and instil an inclusive work culture to fully benefit from diversity. Indeed, forced diversity, in the absence of such a culture, could end up being counterproductive (Calder-Wang, Gompers and Huang, 2021[16]). Policies aimed more broadly at mobilising the productive capacity of underrepresented groups may raise the gains from diversity. For instance, more flexible working conditions would improve the job prospects especially for women, and so raise the human capital they acquire on-the-job (Ciminelli, Schwellnus and Stadler, 2021[13]).

We find that diversity among managers is more tightly related to firm performance than diversity among workers.

This is due to the role of managers – taking decisions, identifying and exploiting business opportunities – which is more likely to benefit from the advantages of diversity.

Policies acting on the Human Side have the potential to close the productivity gap by more than one third To illustrate the potential magnitude from the gains of upgrading skills and improving diversity, Figure 11 depicts by how much each component of the “Human Side” can contribute to closing the productivity gap (Box 1 describes the methodology in more detail). The calculations assume that a typical medium performer firm is closing the skills gap vis-à-vis firms at the

frontier, adopts the same managerial structure as firms at the frontier and improves its diversity. Of course, in practice, changes in the workforce composition and upgrading human capital usually go hand-in-hand with additional complementary investments such as adopting more advanced technologies or reorganising business processes.

OECD – THE HUMAN SIDE OF PRODUCTIVITY: HIGHLIGHTS | 11

Policies acting on the Human Side have the potential to close the productivity gap by more than one third

How can policies promote catch up via these different components? On the one hand, these results indicate the need for targeted policies: for instance, promoting the supply of managerial talent and devoting more resources to formal managerial roles (e.g. through tax incentives and subsidies); promoting incentives for selecting good managers (e.g. by encouraging competition); or providing high quality training for specific skills (e.g. by raising awareness and encouraging the supply and take up of training). On the other hand, our results reveal important complementarities between different policy areas: managerial talent is complemented by workforce skills; management plays a key role in providing and incentivising training e.g. through performance based pay or the use of high performance work practices (OECD, 2021[7]); skill acquisition through training or learning-by-doing in turn builds on foundational skills acquired during education; gains from diversity depend on company culture and policies improving the productivity especially of underrepresented groups. To support productivity growth through these factors, a comprehensive policy approach is required.

This approach should aim to promote the different components of the “Human Side” by increasing the supply of managers, skills, and by activating people from underrepresented groups. It should promote upgrading

the firm’s organisational structure in line with best practices, facilitating adjustments where necessary.

Finally, it should mobilise hitherto unused potential and allow for a better matching of existing talent to the firm’s labour needs. The best results may be achieved with a combination – the SUM – of these policies. Table 1 provides more details on which policy areas should form part of such a comprehensive policy mix.

Figure 11. The Human Side uncovered: the skills and diversity of managers and workers can contribute to closing the productivity gap 35% Contribution to closing productivity gap in percent

On average across countries, the largest gains are associated with adjusting the skill structure to the one observed in frontier firms, drawing on the complementarities between managers and workers (1/5th of the gap). Large gains are also associated with increasing gender and cultural diversity to their optimal level, especially among managers (more than 1/10th of the gap). Finally, adjusting the firm’s organisational structure by devoting more labour resources to management to match the manager share of top performing firms points to an additional, though more moderate, productivity boost (around 2% of the gap).

30% 25%

Worker cultural diversity Manager cultural diversity Worker gender diversity Manager gender diversity

20%

Manager-worker skill complementarities

15% 10% 5% 0%

Worker skills

Manager skills Manager share

Note: The figure shows the contribution to catch up, expressed as percentage of the productivity gap between medium performers (40-60th percentile) and firms at the productivity frontier (top decile) in the same industry and country, from adjusting the various components of the “Human Side”. Contributions for manager share and skill structure are based on matching what is observed for frontier firms; contributions for gender and cultural diversity are based on moving from low to optimal diversity (gender: 20-40% women-share; cultural background: 5-10% foreign share). The underlying firmlevel regression analysis is described in Box 1. Skill measures are based on average wages by detailed occupation and country. More details on skill measurement are provided in Box 2. Productivity is based on value added per employee. Results shown are averaged across countries. To cover more countries, capital has not been included as control. The total contribution therefore deviates slightly from Figure 2.

Source: OECD GFP calculations based on cross-country micro-aggregated linked employer-employee data.

Table 1. Policies to support catching up through various aspects of the “Human Side” of firms Manager share

Manager & worker skills

Manager & worker gender diversity

Manager & worker cultural diversity

Supply

Management training

Education & training

Social benefit and tax system

Immigration policies

Upgrade

Information campaigns

Facilitate reallocation

Management quotas

Mobilise

Increase effective labour supply through mobility and flexible working arrangements, e.g. telework, transport, housing

12 | OECD – THE HUMAN SIDE OF PRODUCTIVITY: HIGHLIGHTS

1

BOX 1. MEASUREMENT AND ANALYSIS OF THE HUMAN SIDE OF PRODUCTIVITY The Human Side of Productivity is based on the analysis of a novel dataset containing information on the productivity and workforce characteristics of the universe of firms across a broad range of countries. This box briefly describes how this information was collected and analysed to derive the main results.

Collecting comparable information across countries Collecting the necessary information for the analysis of the Human Side – i.e. detailed information on firms and their employees across countries – required addressing two challenges: (1) data access is usually restricted by country-specific confidentiality requirements; (2) data need to be harmonised across countries. To meet these challenges, the GFP applied – in close collaboration with country partners – a distributed microdata approach, building on OECD expertise from e.g. the OECD MultiProd and DynEmp projects and exploiting synergies with current OECD projects LinkEED and Age Diversity (OECD, 2020[4]; Criscuolo et al., 2020[5]): For data harmonisation, the GFP liaised with country partners on data preparation and shared a common code producing micro-aggregated results satisfying confidentiality requirements; data access was provided by country partners, who prepared data, implemented the code and shared results with the GFP Team for further cross-country analysis.

This data collection effort resulted in a novel dataset including information from 10 countries on firm productivity and detailed workforce characteristics – e.g. on skills, age, gender and cultural background for managerial and non-managerial employees –, usually covering the universe of firms with at least 10 employees, and spanning a period from 2000 until 2019.

Providing access by the partners and institutions listed in the table below is gratefully acknowledged.

Belgium

Central Bank

Emmanuel Dhyne, Gert Bijnens

Costa Rica

Central Bank

Alonso Alfaro Urena, Catalina Sandoval Alvarado, Evelyn Munoz

Denmark

Ministry of Industry, Business and Financial Affairs

Søren Gaard, Katrine Bagge Thorball, Magnus Skafte-Larsen, Louis Konstantyner

France

CASD

Germany

IAB Nuremberg / HU Berlin

Alfred Garloff

Hungary

Ministry of Finance

Peter Toth, Istvan Szabo, Andras Svraka, Katalin Horvath, Balint Van, Tibor Keresztely

Italy

ISTAT; INAPP and University of Milan

Caterina Viviano, Patrizia Cella, Maria Carla Congia, Fabio Bacchini; Giorgio Barba Navaretti, Anna Rosso, Camilla Andretta

Japan

Institute of Economic Research, Hitotsubashi University

Ryo Kambayashi

Portugal

Ministry of Economy, Statistics Portugal

Ricardo Alves

Sweden

Growth analysis

Pontus Mattsson

Age Diversity and LinkEED projects of the OECD

Alexander Hijzen, Oliver Denk, Cyrille Schwellnus, Wouter Zwysen

Further support has been provided by OECD colleagues, including Glenda Quintini, Lea Samek, Mariagrazia Squicciarini, Flavio Calvino, Nathalie Scholl, and Rudy Verlhac.

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BOX 1. Measurement and analysis of the Human Side of Productivity

Quantifying the gains from upskilling and increasing diversity The project leverages this dataset to examine how top performing firms differ in terms of their Human Side – i.e. their workforce composition – , and how much medium performers can potentially gain from upskilling. To quantify the gains, the project relied on country-specific regression analysis of the following baseline model to disentangle the contributions in each period t of different components of the Human Side to productivity of each firm i: Productivityit = F (skills; demographics; organisation; controls)it + Errorit The model estimates the link between productivity – measured as the log of value added per employee – and measures of the workforce composition – e.g. the skill structure and demographic composition in terms of age, gender and cultural background for managerial and non-managerial employees –, and other firm organisation measures such as the manager share and task diversity within the firm. The model includes several controls, e.g. gender-specific shares of part-time employees, and industry-year as well as firm-size group fixed effects – implying that estimates are identified comparing firms within the same detailed industry, year and firm-size group and with otherwise similar characteristics.

These estimates allow computing how much higher productivity would be for a typical medium performer if that firm adjusted a specific component of the Human Side to match a typical firm at the productivity frontier – and thus how much adjusting the Human Side could contribute to closing the productivity gap.

These estimated gains may deviate from actual gains to some degree. One potential source of bias lies in the fact that firms may differ in characteristics not observed in the above model – but which affect productivity and co-vary with components of the Human Side. For instance, one important factor not included in the baseline model pertains to capital, and firms e.g. employing a more highly-skilled workforce may also be more capital intensive; productivity gains associated with adjusting the skill structure may thus partly reflect adjustments in capital. To check for this possibility, for countries with information on capital stocks the model was re-estimated including measures for capital intensity – reassuringly, productivity gains associated with the Human Side in the smaller sample remained largely unchanged.

Note that the total contribution to closing the productivity gap shown in Figure 12 amounts to 34.9%. The contribution of the Human Side shown in Figure 2 is slightly smaller, with 31.2%. This is because the model for Figure 2 was estimated including measures for capital intensity, which is not available in many countries. Overall, the Human Side robustly contributes to about one third of closing the productivity gap to the frontier.

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BOX 2. MEASURING THE FIRM’S SKILL STRUCTURE ACROSS COUNTRIES

Skills – i.e. the capability to perform various tasks (Acemoglu and Autor, 2011[12]) – form an essential component of the Human Side, but skill measurement is fraught with challenges, especially in a cross-country context. This box describes how skills are measured in this project.

What are the challenges of measuring skills across countries?

Skills comprise many aspects – which makes skills difficult to capture comprehensively in a single measure: skills can be general or specific, i.e. some capabilities are transferable across tasks, such as cognitive skills, while others are more specific to tasks, e.g. ICT skills, industries or even firms; and skills can reflect largely immutable features or can be acquired, i.e. how capable a person is depends on their innate ability as well as their human capital accumulated through education, training or experience.

Capturing these aspects with good quality and widely available measures poses a challenge in practice – and even more so in a cross-country context, where comparability remains important. For instance, education and occupations are not observed in sufficient detail in all countries, or not in fully comparable ways, even after applying conversion tables (i.e. ISCO or ISCED for occupations and education respectively). There is thus a trade-off between cross-country coverage and measurement quality.

Which skill measures are used in the Human Side?

Our analysis uses a range of measures to balance this trade-off and present a robust picture on the role of skills for productivity: 1. Education-based: Educational attainment reflects mostly general skills, both innate and acquired; it captures skills acquired through training or on-the-job experience less well.

2. Cognitive test score-based: Average PIAAC (OECD Survey of Adult Skills) scores from tests measuring cognitive abilities by detailed occupation and country capture mostly general skills. The measure assumes that employees are matched well to job skill requirements on average; recent work supports this assumption (Brun-Schammé and Rey, 2021[16]).

3. Task-based: Similarly, people employed in occupations that are intensive in particular tasks, e.g. ICT tasks, are likely to be highly skilled in these tasks; information on task content from PIAAC by detailed occupation and country thus allows measuring several specific skills.

4. Wage-based: People employed in occupations commanding high average wages are likely to be high skilled, assuming that tasks requiring more capabilities are more highly priced in the labour market.

For all measures we define three skills levels – low, medium and high – to capture the idiosyncratic roles of different skill groups (e.g. Acemoglu and Autor, 2011[12]). In addition, for each measure and using all skill groups we compute the Euclidean distance from the skill shares (that is, the sum of squared differences for all skill levels) observed at the most productive firms as a compact measure for deviations from the optimal skill structure.

How do different skill measures affect the link with productivity?

In practice, these measures likely under-state the role of skills for firm productivity: first, they abstract from differences in e.g. innate ability, experience or educational quality within skill groups, and more productive firms may be systematically better at attracting the best people (e.g. in terms of quality of education and experience) within each group. Second, to the extent our skill measures are also noisy, regression estimates additionally suffer from attenuation bias.

Both issues, however, work against finding a significant positive link between higher skill levels and productivity. While the exact magnitude of the estimated link between skills and productivity should thus be taken with caution for any single measure, taken in combination these measures establish the robustness of the importance of various types of skills for productivity. This holds true also when controlling for more firm characteristics, e.g. tangible and intangible capital.

OECD – THE HUMAN SIDE OF PRODUCTIVITY: HIGHLIGHTS | 15

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