OECD Employment Outlook 2025

Can We Get Through the Demographic Crunch?

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OECD Employment Outlook

9 July 2025

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5. Reviving growth in a time of workforce ageing: The role of job mobility

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Jonas Fluchtmann

Alexander Hijzen

Agnès Puymoyen

Abstract

Productivity growth has slowed across OECD economies, and its benefits have become less equally shared. This chapter focuses on the role of growth-enhancing job reallocation in aggregate wage and productivity growth in the context of population ageing. Using detailed linked employer-employee data from 17 OECD countries, it analyses how different types of worker movements contribute to the reallocation of workers towards firms paying higher wages and with higher productivity. The analysis distinguishes between job-to-job mobility, which often reflects voluntary career moves to better-paying firms, and employment flows in and out of work. Particular attention is put on how workforce ageing may affect these dynamics, given differences in mobility between younger and older workers. The chapter concludes with a discussion of policies that can support growth-enhancing job reallocation by providing sufficient flexibility to firms and promoting upward mobility for workers, especially mid-career and older ones.

In Brief

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Key findings

Productivity growth – gross domestic product (GDP) per hour worked – has slowed across OECD economies, and its benefits have become less equally shared. Average annual productivity growth has fallen from just above 2% in the late 1990s to less than 1% in the 2010s. Average annual wage growth has followed a similar evolution but has been weaker than productivity growth, especially among lower-wage workers, contributing to a declining labour share and increasing wage inequality, making growth less inclusive. Declining business dynamism, e.g. entry and exit of firms, job creation and destruction in continuing firms, may be one factor that has contributed to the slowdown and may partially be related to the difficulty of firms and workers in adapting to rapid and profound structural transformations in a context of population ageing. While productivity growth has been somewhat stronger since the COVID‑19 crisis, notably in the United States, it is too early to tell whether this reflects a short-term bouncing back from the COVID‑19 crisis or a change in the longer-term trend due to technological advances (digitalisation, AI) or changes in working practices (e.g. teleworking).

This chapter provides new insights into how growth-enhancing job reallocation contributes to aggregate wage and productivity growth in the context of population ageing. It responds to concerns about the declining dynamism of labour markets in OECD countries and its consequences for aggregate wage and productivity growth. It also considers the potential role of population ageing for wage and productivity growth through lower mobility of older workers. Special emphasis is placed on the job ladder, i.e. the importance of voluntary transitions to more productive firms paying higher wages (“job-to-job mobility”). The empirical analysis is based on linked employer-employee data from 17 OECD countries for the period 2000‑19: Austria, Belgium, Canada, Denmark, Estonia, Finland, France, Germany, Hungary, Italy, Lithuania, the Netherlands, New Zealand, Norway, Portugal, Spain and Sweden.

Job-to-job mobility plays a key role in growth-enhancing job reallocation and hence aggregate wage and productivity growth

Growth-enhancing job reallocation primarily occurs through job-to-job mobility and to a much lower extent through movements in and out of employment (i.e. “employment mobility”). Although both types of mobility contribute equally to overall labour market dynamics, job-to-job transitions play a greater role in reallocating workers to higher-paying and more productive firms. This suggests that job-to-job moves are more often voluntary and career-driven, while mobility in and out of employment is more likely to be influenced by personal factors, such as decisions about labour force participation, or be involuntary.
Job-to-job mobility is a key driver of overall growth, contributing 0.9 percentage points annually to wage and productivity growth on average between 2000 and 2019. However, a large part of this is offset by the negative effects of employment mobility, mainly due to life cycle dynamics as young workers start their careers in low wage, low productivity firms. As a result, overall job reallocation, the joint contribution of job-to-job and employment mobility, adds 0.3 percentage points to average wage and productivity growth respectively (16% and 22% of total growth).
Cross-country differences in job reallocation are important for understanding cross-country differences in aggregate growth rates. Growth-enhancing job reallocation accounts for 44% of the cross-country variation in wage growth and 41% of the variation in productivity growth. The remainder is explained by differences in growth rates among workers who stay in the same firm, which in part may reflect the role of learning and innovation in the workplace. In countries with high wage growth over the period considered (e.g. Estonia, Hungary, Lithuania), overall job reallocation plays a strong positive role, while in low-wage‑growth countries (e.g. Belgium, France, Italy, Spain), its role is small or negative as the structure of employment shifts to lower wage firms and industries.

Population ageing has tended to slow growth-enhancing job reallocation and contributed to weaker aggregate wage and productivity growth

Mid-career and older workers tend to be less mobile than younger workers and less likely to transition to higher-quality firms through the “job ladder”. As a result, workforce ageing may contribute to slow down aggregate wage and productivity growth.
Estimates suggest that, between the early 2000s and late 2010s, workforce ageing resulted in a slowdown of aggregate wage and productivity growth of, respectively, 0.13 and 0.10 percentage points on average across countries (compared with an average annual aggregate growth rate of, respectively, 0.6 and 1.0% among the set of countries considered). Going forward, given current policies, the impact of population ageing on the share of older workers in employment is expected to be modest.
From a policy perspective, a key question is why workforce ageing tends to slow aggregate growth. It may reflect weaker incentives for job mobility because older workers feel that they have little to gain from changing employer, either because they have already climbed the job ladder or because they have fewer working years remaining in their careers. Alternatively, it may reflect larger barriers to job mobility among older workers, which keeps them locked in low productivity firms paying low wages. This would raise important concerns about the adaptability of the workforce to structural change and provide a case for developing policies that support job mobility toward higher wage and more productive firms among mid-career and older workers.
There are important differences in the distribution of employment for young and older workers across firms of varying quality (in terms of productivity or wage‑setting practices). Younger workers tend to be concentrated in lower-quality firms than mid-career and older workers, and this pattern has become slightly more pronounced over time. This reflects the importance of the job ladder for integrating new cohorts of young workers in the labour market. There is no evidence that workforce ageing has influenced opportunities of younger workers for moving up the job ladder.

Policies can support growth-enhancing job reallocation by providing sufficient flexibility to firms and promoting job mobility for workers

Policies should ensure that firms have sufficient flexibility to adapt to changing business conditions, while at the same time providing adequate security for workers. This includes maintaining balanced employment protection for open-ended contracts, as overly strict rules not only reduce dismissal risks but also discourage hiring, limiting job mobility. Overly strict employment protection for open-ended contracts may also incentivise the use of fixed-term contracts, which increases worker mobility without necessarily moving workers to better jobs. Job retention schemes offer firms flexibility to reduce working hours during downturns, strengthening labour market resilience. However, if used to respond to permanent structural shifts, they can impede efficiency-enhancing worker reallocation.
Policies should also remove unwarranted barriers to growth-enhancing job reallocation. This requires closer scrutiny of professional licensing regulations and the use of non-compete and other restraint clauses. While licensing helps to ensure service quality and non-compete clauses may protect legitimate business interests, their growing use may have negative impacts on competition and job reallocation. Voluntary certification, combined with consumer information systems, could serve as an alternative to strict licensing requirements. Limiting non-compete clauses to certain workers (e.g. high-wage employees) or imposing compensation and notification requirements may be warranted. Strict enforcement of such regulations is key.
A deeper understanding of how wage‑setting institutions affect efficiency-enhancing reallocation is needed. Wage floors may reduce job mobility by compressing wages, making it harder for productive or newly created firms to attract workers. However, they can also force unproductive firms to downsize or exit – potentially moving workers to more productive firms and boosting overall productivity – as well as ensure fair wages for vulnerable workers with a weak bargaining position. To support productivity growth through job reallocation, policies limiting wage dispersion between firms should be paired with measures that enhance voluntary job mobility and drive innovation in low-productivity firms.
Policies should support the mobility of mid-career and older workers. While many older workers have already moved up the job ladder, some face barriers that keep them in low-quality firms or hinder reemployment in good jobs after job loss. Targeted interventions, such as early support measures and wage insurance, can help displaced workers transition effectively. Job-search assistance and career guidance are crucial, especially for those with limited recent mobility. Public employment and career guidance should not just cater to unemployed workers but also to employed workers who are stuck in low-quality jobs. Addressing skill obsolescence changing skill needs through training is also key to ensuring continued career progression.

Introduction

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Productivity growth has slowed and become less inclusive across OECD economies. Since the early 2000s, average annual hourly labour productivity growth has been weak, falling from just above 2% in the late 1990s to 1.4% in the 2000s and less than 1% in the 2010s (see Annex Table 5.A.1. ). In several countries, the benefits of productivity growth have also become less evenly distributed, with wage growth, particularly for workers in the bottom half of the wage distribution, lagging behind even modest productivity gains. This has contributed to declining labour shares and increasing wage inequality in many countries. These developments partially reflect the difficulty of firms and workers in adapting to rapid and profound structural transformations (e.g. digital transformation, automation, net-zero transition) in a context of population ageing. Indeed, it is possible that workforce ageing due to its implications for learning and mobility has made it harder to adjust to structural change at a time when the need for adaptability is particularly high, with potentially important implications for future growth (Maestas, Mullen and Powell, 2023[1]) – see also Chapter 4. All in all, there are important questions about the ability of OECD economies to generate rising standards of living and well-being in a context of rapid structural changes and ageing populations. This has led to the development of far-ranging proposals to revive broadly shared productivity growth (e.g. Draghi (2024[2])).

Job mobility between firms can play a particularly important role in reviving broadly shared productivity gains in a context of an ageing workforce by promoting a more efficient allocation of resources across firms that differ in their productivity and wage‑setting practices. Labour markets that are able to allocate workers to their most productive uses are typically also better able to support structural transformation and less likely to be characterised by persistent labour shortages. However, the process of efficiency-enhancing reallocation may be significantly affected by workforce ageing if older workers are less likely to transition to more productive higher-paying firms than younger workers. Older workers face higher costs and fewer benefits when changing employer. They risk losing job security, while potential wage increases are less valuable to them since they have fewer working years remaining in their careers. Consequently, there is a risk that older workers get stuck in jobs with limited prospects for career advancement, reinforcing labour shortages and undermining the ability of high-performance firms to grow and flourish (OECD, 2024[3]) – see also Chapters 3 and 4.

As argued in the OECD Employment Outlook of 2022, job mobility between firms also has potentially important implications for the sharing of productivity gains with workers by containing the wage‑setting power of firms and reducing wage gaps between firms (OECD, 2022[4]). The extent to which job mobility can contribute to a broader sharing of productivity gains and lower wage inequality depends crucially on the extent to which opportunities for job mobility are equally shared between different groups of workers. When opportunities for upward mobility are skewed towards workers with higher skills, there is a risk that job mobility deepens wage inequalities. Opportunities for moving up the job ladder may also be affected unevenly by workforce ageing for different groups of workers. To the extent that ageing results in lower turnover in good firms, it may reduce job opportunities for younger workers to move to better firms. However, to the extent that older workers have skills that complement those of younger workers, it is also possible that ageing increases opportunities for young workers, particularly in firms where firm-specific human capital is important (Carta, D’Amuri and Wachter, 2021[5]).

This chapter aims to enhance our understanding of aggregate wage and productivity growth through growth-enhancing job reallocation and the consequences of workforce ageing. Specific emphasis is given to the nature of job mobility (see Box 5.1 for a glossary of the mobility concepts used) by distinguishing between job-to-job mobility (direct flows between jobs in different firms), which is more likely to be voluntary and often driven by career considerations, and employment mobility (flows in and out of employment), which is more likely to be involuntary or driven by personal considerations (e.g. labour force participation decisions). This distinction is important because voluntary job mobility requires policies that tackle barriers of workers to changing employer and involuntary mobility requires policies that provide flexibility to firms by allowing them to adjust employment levels in line with changing business conditions. The empirical analysis is based on linked employer-employee data from 17 OECD countries for the period 2000‑19: Austria, Belgium, Canada, Denmark, Estonia, Finland, France, Germany, Hungary, Italy, Lithuania, the Netherlands, New Zealand, Norway, Portugal, Spain and Sweden.1 Such data crucially allow documenting the role of job mobility in efficiency-enhancing reallocation across a diverse set of OECD economies, while assessing the extent to which this is shaped by workforce ageing.

The chapter is structured in three sections. Section 5.1 sets the scene by presenting key stylised facts related to the evolution of aggregate productivity, wages and wage inequality over the past two decades as well as that of labour market dynamism based on different measures of job mobility. Section 5.2 focuses on the importance of job-to-job and employment mobility for growth-enhancing job reallocation in general, as well as in the context of workforce ageing. It considers how moves from one employer to another, as well as moves in and out of employment, contribute to a changing structure of employment from less productive, or lower paying, firms to more productive, or higher paying, firms, and therefore contributes to wage and productivity growth. By looking at the worker-level trajectory across firms, it also sheds light on the implications of voluntary versus involuntary job mobility for aggregate wage and productivity growth. A key part of this section is dedicated to the differences in job mobility across age groups and the implications that workforce ageing may have for aggregate wage and productivity growth. Section 5.3 discusses how policies and institutions can revive broadly shared productivity growth through growth-enhancing reallocation by focusing on policies that determine the flexibility of firms and policies that support mobility to better firms for workers, including for mid-career and older workers.

Box 5.1. Mobility concepts

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This chapter includes several mobility concepts that are laid out below. Each of these concepts is measured using annual linked employer-employee data (see Annex Table 5.A.3 for details), expressed as a share of private‑sector non-agricultural dependent employment (average between previous and current year) and calculated in “excess” terms to abstract from changes in aggregate employment.1 While the source data tend to be very comprehensive, often covering the population of workers and firms, much of the analysis is based on large random samples (typically 20% of firms).

Gross worker mobility

Gross worker mobility or gross worker flows ( $G_{t}$ ) is defined as the sum of hires and separations in a country between two periods and includes gross job-to-job mobility and gross employment mobility. Gross job-to-job mobility refers to the sum of hires ( $H_{J_{t}}$ ) and separations ( $S_{J_{t}}$ ) through direct transitions between firms from one year to the next. Gross employment mobility refers to the sum of hires from outside private‑sector employment ( $H_{N_{t}}$ ) and separations out of private‑sector employment ( $S_{N_{t}}$ ). Workers not employed in the private sector may be employed in the public sector, self-employed, unemployed or inactive. Gross worker mobility consists of worker churn, i.e. the presence of simultaneous hires and separations within firms that are not associated with net employment changes in firms, and job flows, i.e. differences between hires and separations in firms that are associated with changes in net employment. This is one of the two concepts used in Section 5.2. Formally, it can be expressed as:

$G_{t} = \underset{G r o s s j o b - t o - j o b m o b i l i t y}{\underset{⏟}{H_{J_{t}} + S_{J_{t}}}} + \underset{G r o s s e m p l o y m e n t m o b i l i t y}{\underset{⏟}{H_{N_{t}} + S_{N_{t}}}}$

Gross job mobility or gross job reallocation

Gross job mobility ( $R_{t}$ ), also referred to as gross job reallocation, is defined as the sum of job creation and destruction across firms in the private sector during a specific period. Job creation refers to the sum of all employment gains at expanding firms between one year and the next ( $∆ {E_{i}}_{t} \geq 0$ ), while job destruction refers to the sum of employment losses in contracting firms ( $∆ {E_{i}}_{t} < 0$ ) (expressed in its absolute value). Job flows represent a subset of worker flows that result in changes in net employment at the firm level (denoted by $i$ ), but do not account for churn, i.e. simultaneous hires and separations in firms that do not change the level of employment in a firm.2 Gross job mobility is the second concept used in Section 5.2. Formally, it can be expressed as:

$R_{t} = \underset{J o b c r e a t i o n}{\underset{⏟}{\sum_{∆ {E_{i}}_{t} \geq 0} ∆ {E_{i}}_{t}}} + \underset{J o b d e s t r u c t i o n}{\underset{⏟}{\sum_{∆ {E_{i}}_{t} < 0} | ∆ {E_{i}}_{t} |}}$

Net job mobility

Net job mobility refers to changes in the structure of employment across firms or groups of firms that differ in their productivity or wage‑setting practices. For example, when grouping firms in productivity classes (e.g. quintiles of the productivity distribution), net job mobility ( $N_{J_{q, t}}$ ) refers to the difference in hires and separations in that productivity class $q$ in period $t$ . It therefore does not take account of mobility between firms with similar levels of productivity or flows between low and high productivity firms that cancel each other out. Net job mobility can be decomposed into net job-to-job mobility and net employment mobility, depending on whether hires and separations relate to direct movements between employers or movements in and out employment. This is the concept used in Section 5.2 (see Box 5.3. for further details). Formally, net job mobility in group $Q$ can be expressed as:

$N_{Q_{t}} = \underset{N e t j o b - t o - j o b m o b i l i t y}{\underset{⏟}{\sum_{i \in Q} H_{J_{i, t}} - S_{J_{i, t}}}} + \underset{N e t e m p l o y m e n t m o b i l i t y}{\underset{⏟}{\sum_{i \in Q} H_{N_{i, t}} - S_{N_{i, t}}}}$

1. The analysis excludes the public sector and self-employment for methodological consistency, as public sector employment is not recorded across all countries.

2. Gross job mobility also does not take account of the direction of flows along the distribution of wages and productivity. This is considered in the definition of net job mobility.

5.1. Setting the scene

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This section provides key stylised facts related to the evolution of aggregate wage and productivity growth, labour market dynamism, as measured by the process of job reallocation between firms, and the possible role of workforce ageing.

5.1.1. Growth has weakened and become less broadly shared

Labour productivity growth has been on a declining trend since the 1970s (André, Gal and Schief, 2024[6]; Goldin et al., 2024[7]) and has been particularly weak since the early 2000s (Figure 5.1, Panel A and Figure 5.2). While in the United States, productivity growth picked up briefly during the mid‑1990s thanks to investments in ICT, the boom did not last and growth has been weak since the mid‑2000s (Gordon and Sayed, 2020[8]). In other advanced economies, productivity growth has been on a secular downward trend from the early 1990s as the boom-and-bust cycle related to ICT was less pronounced or even absent. On average across countries, labour productivity growth declined from 2.1% during 1995‑2002, to 1.4% during 2002‑10 and 0.9% during 2010‑19. Productivity growth has been marginally stronger since the COVID‑19 crisis in about half of the OECD countries, and more notably so in the United States. However, it is too early to tell whether this reflects a short-term effect of the COVID‑19 crisis or a change in the longer-term trend due to technological developments (digitalisation, AI) and changes in working practices (e.g. teleworking).

The long-term decline in productivity growth is typically attributed to a slowdown in multifactor productivity (MFP) growth, i.e. the slower pace of advancements in efficiency with which capital and labour are used in the production process. This may either reflect the pace of learning and innovation in the workplace, including through the adoption of more advanced production technologies and management practices, or the speed with which capital and labour are reallocated from less to more efficient firms. Baqaee and Farhi (2020[9]) show for the United States that each account for about half of productivity growth and its slowdown. In principle, both may be affected by population ageing if the productivity of older workers grows more slowly than that of younger workers or if older workers are less likely to move to more efficient firms. Ageing may also shift the structure of consumption towards less efficient, lower productivity sectors such as healthcare and leisure (André, Gal and Schief, 2024[6]). There is some indication that the process of efficiency-enhancing job reallocation between sectors has slowed as consumer services have gained in importance (see Annex Figure 5.A.1). Since the global financial crisis, weak MFP growth has been compounded by a reduced pace of capital deepening due to a prolonged decline in investment.

Labour productivity growth has also become less broadly shared (Figure 5.1, Panel B, and Figure 5.2). Declining productivity growth has not only led to weakening average wage growth but in addition average wage growth has increasingly fallen short of already weak productivity growth, resulting in a declining share of labour in national income.2 Average wage growth for the OECD as a whole has declined from 1.6% during 1995‑2002, to 1.0% during 2002‑10 and 0.8% during 2010‑19 and been consistently weaker than growth in labour productivity. Various factors may have contributed to the decline in the labour share, including capital-augmenting technological change, the rise in product market concentration, the decline in the bargaining position of workers or changes in the composition of firms due to reallocation (Karabarbounis, 2024[10]). Growth-enhancing reallocation has been shown to reduce the labour share because more productive firms also tend to be more capital intensive, see e.g. Autor et al. (2020[11]), Schwellnus et al. (2018[12]), and Cho et al. (2025[13]). The decline in the labour share has coincided with rising wage inequality, as median wage growth has failed to keep up with average wage growth (Figure 5.1 Panel C, and Figure 5.2). Median wage growth for the OECD as a whole amounted to just 0.5% during 2002‑10 and 0.4% during 2010‑19. In some countries, including Japan and the United Kingdom, median wage growth even has turned slightly negative.

Figure 5.1. Aggregate productivity growth has weakened and become less broadly shared
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Real average annual growth rates in labour productivity, average wages and median wages by country and period

Figure 5.2. Real wages have decoupled from labour productivity
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Real average annual growth rates in labour productivity, average wages and median wages, index 2002 = 100

5.1.2. Slower growth may in part reflect a decline in the pace of growth-enhancing job reallocation between firms

Weaker aggregate wage and productivity growth can stem from declining growth within firms, for example due to changes in the pace of innovation, technology adoption and skill development, or from changes in how labour reallocates between firms that differ in their wage‑setting practices or productivity (see definition in Box 5.1). While the main focus of this chapter is on the latter, Box 5.2 provides some stylised facts on the evolution of within-firm productivity growth.

In many OECD countries, there has been a decline over time in economic dynamism, as measured by the rate of gross job reallocation between firms, with potentially important implications for aggregate wage and productivity growth (Figure 5.3). Job reallocation has significantly declined from 29% in 1990 to 23% in 2022, from 23% in 2002 to 17% in 2019 in Canada, from 18% in 2001 to 16% in 2019 in New Zealand and from 25% in 2003 to 21% in 2019 on average across selected Euro area countries. These stylised facts are broadly in line with previous evidence for the United States (Decker et al., 2020[14]) and a subset of OECD countries (Cho, Manaresi and Reinhard, 2025[13]; Causa, Luu and Abendschein, 2021[15]). To the extent that high-wage and high-productivity firms are typically more likely to expand and low-wage and low-productivity firms more likely to contract, the decline in gross job reallocation may also have slowed aggregate wage and productivity growth.

Given its potential implications for aggregate wage and productivity growth, the decline in job reallocation raises important policy questions. A first question is whether this reflects reduced returns to job reallocation, as differences in productivity and wages between firms have tended to narrow, for example due to declining volatility in product markets, or alternatively, increased costs to job reallocation, weakening the responsiveness of firm employment to changes in productivity. While developments vary across countries, the evidence suggests that disparities in productivity and wages among firms have, if anything, increased (Andrews, Criscuolo and Gal, 2016[16]; Berlingieri, Blanchenay and Criscuolo, 2017[17]; OECD, 2022[4]). If so, a decline in the pace of job reallocation is more likely to reflect a change in responsiveness. A second question is to what extent a reduced responsiveness of firm employment reflects greater costs on the side of firms associated with adjusting employment levels to changing business conditions or greater barriers on the side of workers to changing employers. The latter may reflect, among others, the role of skills or geographical mismatches and employers’ market power but also the role of workforce ageing for labour shortages.

Figure 5.3. The pace of job reallocation has tended to slow
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Gross job reallocation rates over time, selected countries, percentage of employment

Box 5.2. The slowdown in productivity growth within firms in selected OECD countries

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This box complements the analysis of between-firm productivity growth through job reallocation with descriptive statistics on within-firm productivity growth based on data from five OECD countries. More specifically, it compares within firm-productivity growth in the first year available before the global financial crisis and the last year before the COVID‑19 crisis on average across countries and firms or specific groups of firms.

There is some indication that within-firm productivity growth has slightly declined in the period considered for the selected countries. On average across countries, within-firm productivity growth amounted to 2.7% in the early 2000s and 2.3% in the late 2010s (Figure 5.4).

The decline in within-firm productivity growth is primarily driven by large frontier firms in manufacturing. Productivity growth in frontier firms, defined as those in the top 10% of the employment-weighted firm distribution, fell sharply from just above 4.6% to almost ‑1.3%, while it also slowed among large firms and firms in manufacturing. At the same time, lagging firms (the other 90%), small firms and firms in the services sector saw slight increases in within-firm productivity growth. While these figures hide substantial heterogeneity across countries, the decline in dynamism among frontier firms is present in all countries considered.

These stylised facts suggest that to understand the decline in broadly shared productivity gains, it is not sufficient to focus on growth-enhancing reallocation, but attention should also be given to factors that determine within-firm productivity growth such as learning, innovation and technology adoption.

Figure 5.4. Within-firm wage and productivity growth has tended to decline
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Average annual within-firm productivity growth on average and by group of firms, first and last sample period, percentage

5.1.3. Workforce ageing tends to slow worker mobility

Demographic developments in the form of workforce ageing and declining youth cohorts have tended to weigh down on worker mobility and are expected to slow mobility further in the coming decades (Figure 5.5). On average across countries, more than one in two workers changed employer or employment status from one year to the next (see Box 5.1 for details). However, older workers are much less likely to change job status than younger workers. Only 37% of older workers aged 55‑74 change employer or employment status from one year to the next, compared with 54% for workers aged 15‑54. Consequently, both workforce ageing and declining youth cohorts drag down mobility (see also André, Gal and Schief (2024[6]) and Engbom (2019[18])). On average across countries, and assuming no change in age‑specific mobility (admittedly, a strong assumption),3 changes in the age of structure of employment reduced worker mobility by 1.6 percentage points between 2000 and 2022 and is expected to further lower mobility between 2022 and 2060 by 0.4 percentage points based on population and employment projections. The overall decline in worker mobility due to workforce ageing between 2000 and 2060 is expected to amount to 2 percentage points or almost 4%. The decline is entirely driven by direct job-to-job mobility between firms, i.e. transitions without intermediate spells out of work. Employment mobility i.e. transitions in and out employment (including those related to decisions to participate in the labour force) slightly increased but is expected to remain broadly constant from now on. These overall patterns reflect the fact that older workers are much less likely than younger workers to change employer voluntarily. However, as will be shown in the next section, this is precisely the type of mobility that is most relevant for growth-enhancing job reallocation.

Figure 5.5. Workforce ageing is weighing down on worker mobility
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Implied evolution of gross worker mobility and its components between 2000 and 2060 due to actual and projected changes in the age structure of the workforce (projected since 2022)

5.2. Growth, reallocation and ageing

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This section documents the importance of job mobility for growth-enhancing job reallocation and its implications for aggregate wage and productivity growth in the context of an ageing workforce. It starts by looking at the role of job mobility in growth-enhancing job reallocation and aggregate wage and productivity growth using annual linked employer-employee data for 17 countries for the period 2000‑19.4 It then proceeds by drawing out the implications of workforce ageing for the evolution of wage and productivity growth between 2001 and 2019.

5.2.1. Job mobility, job reallocation and growth

Efficiency-enhancing job reallocation is defined as the reallocation of labour towards more productive firms. Since this increases the weight of more productive firms in the economy, it raises aggregate productivity.5 Since more productive firms tend to pay higher wages, changes in the structure of employment towards more productive firms may also be wage‑enhancing. However, the link between wages and productivity across firms is far from perfect (OECD, 2022[4]).6 Job reallocation between firms that differ in their pay practices may therefore look somewhat different from that between firms that differ in their productivity and yield important insights about the extent to which job mobility contributes to the career progression of workers, aggregate wage growth and improvements in the living standards of workers. This section therefore considers both wage and productivity-enhancing job reallocation. It refers to the general process of reallocation towards more productive firms or higher-paying firms as growth-enhancing reallocation.

Growth-enhancing job reallocation mainly takes place through job-to-job mobility

Job reallocation tends to shift employment towards higher paying and higher productivity firms (Figure 5.6). The difference in average annual employment growth in the bottom 20% of incumbent firms7 in terms of wages and productivity and aggregate average annual employment growth is consistently negative, whereas it is consistently positive in the top 20% of incumbent firms. In other words, employment is continuously being reallocated from low-wage, low-productivity firms to high-wage, high-productivity firms, contributing to higher aggregate wage and productivity growth. A detailed description of the methodology used here can be found in Box 5.3.

Box 5.3. Methodology I: Growth-enhancing job reallocation

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Growth-enhancing job reallocation is defined as the change in the structure of employment from lower quality to higher quality firms, either in terms of wages or productivity. In practice, it is measured by the coefficient from an employment-weighted firm-level regression of employment growth ( $∆ E_{j t}$ ) on lagged firm quality ( $π_{j t - 1}$ ), lagged employment ( $E_{j t - 1}$ ) and a set of fixed effects (𝛿) (Decker et al., 2020[14]).1 Since the model can only be estimated for incumbent firms, firm entry is ignored. Firm quality may be measured in terms of log labour productivity (value added per worker or, if not available, sales per worker) or average log wages.1 To reduce the role of measurement error, firm quality is averaged between $t - 1$ and $t - 3 .$ Firm quality may be measured as a continuous variable or dummies for quintiles of the distribution.2 Formally, this yields:

$∆ E_{j t} = β_{0} + β_{1} π_{j t - 1} + β_{2} E_{j t - 1} + δ_{s t} + ε_{j t}$

where employment growth in firm j is defined as the change in employment divided by the average level of employment between the current and previous year $.$ Employment growth can be replaced by either the contribution to employment growth of job-to-job mobility or the contribution to employment growth of employment mobility.

Equation (1) is estimated separately with industry-by-year fixed effects and just year fixed effects. The estimated coefficient on firm quality in the regression with industry fixed effects, $β_{1},$ provides an indication of the speed of growth-enhancing job reallocation within industries, while the coefficient in the regression without industry fixed effects provides an indication of the speed of the overall process across firms no matter their industry. The role of growth-enhancing job reallocation between industries can be obtained by taking the difference between these two coefficients.

The coefficients on firm quality are normalised by the standard deviation of firm quality in the corresponding year to enhance their comparability across countries or age groups. This takes account of the fact that the speed of growth-enhancing job reallocation depends not only on the responsiveness of employment growth to firm quality but also the dispersion of firm quality between firms. The model is estimated for the full sample as well as for sample splits by year, broad sector (manufacturing, services), age group and gender.

1. Note that measuring firm quality in terms of wages and labour productivity ignores several other aspects of firm quality that can be important for job mobility such as job security, the quality of the working environment, and geographical proximity (Cazes, Hijzen and Saint-Martin, 2015[21]; Sorkin, 2018[22]; Le Barbanchon, Rathelot and Roulet, 2021[23]).

2. Results based on dummies for quintiles of the firm-distribution of wages and productivity are expressed in deviation from the aggregate employment growth rate, ensuring that the sum of the coefficients on the quintile dummies equals zero.

The shift in the structure of employment towards higher wage and higher productivity firms mainly takes place through job-to-job mobility, while the contribution of employment mobility is relatively minor. This insight is based on the decomposition of wage and productivity-enhancing job reallocation in its contributions related to job-to-job mobility, which is likely to be voluntary, and those related to employment mobility, i.e. movements in and out of private sector-dependent employment. The latter reflects a range of factors, such as the decision to participate in the labour force, the risk of unemployment and the choice of working in the public sector or being self-employed. The bulk of growth-enhancing job reallocation is driven by job-to-job mobility rather than employment mobility. This is despite job-to-job mobility and employment mobility being approximately equally important in overall worker mobility.8 It suggests that job-to-job mobility is more likely to be voluntary and driven by wage considerations, whereas employment mobility is more likely to be involuntary or driven by personal considerations, including the decision to participate in the labour force. The positive role of job-to-job mobility in growth-enhancing job reallocation is sometimes interpreted as a job ladder at work, i.e. the process through which workers climb the rungs of the ladder as they advance in their careers.

The importance of the job ladder is a highly robust stylised fact. First, it is present in all countries considered irrespective of ranking of firms on wages or productivity. Second, it is robust to different modelling choices. Whereas the present results use the country-wide distribution to rank firms, consistent with the aggregate growth decompositions discussed below, results are quantitatively similar when focusing on the average pattern within detailed industries, indicating that firm quality plays only a limited a role for between-industry reallocation. This suggests that job reallocation between industries is not predominantly driven by efficiency differences, but rather by changes in the structure of product demand (e.g. the growing demand for consumer services due to population ageing). The results are also similar when focusing on the correlation between net employment growth and firm quality as in Decker et al. (2020[14]) instead of quintiles of the firm-quality distribution. Third, the results presented here are consistent with previous evidence for the United States (Haltiwanger et al., 2018[24]), France and Italy (Berson, de Philippis and Viviano, 2020[25]), Denmark (Bertheau and Vejlin, 2024[26]) and Norway (Hijzen, Lillehagen and Zwysen, 2024[27]).9

Figure 5.6. Growth-enhancing job reallocation is primarily driven by job-to-job mobility
Copy link to Figure 5.6. Growth-enhancing job reallocation is primarily driven by job-to-job mobility

Average annual net employment growth among incumbent firms and its components due to net job-to-job and employment mobility by quintile of the firm-distribution of wages and productivity, relative to average employment growth

Job-to-job mobility accounts for a sizeable part of aggregate wage and productivity growth, while that of overall job reallocation is more modest

Aggregate wage and productivity growth reflects a combination of within-firm growth – increases in wages and productivity driven by workers who remain in the same firm from one year to the next (i.e. “stayers”) – and between-firm growth – increases in wages and productivity driven by the reallocation of workers across firms through job-to-job and employment mobility (i.e. “job reallocation)”. We now analyse how much of aggregate wage and productivity growth is accounted for by job reallocation between firms, including its components related to job-to-job mobility and employment mobility, and how much by within-firm growth. A detailed description of the methodology used here can be found in Box 5.4.

By shifting the structure of employment to high wage and high productivity firms, job-to-job mobility plays a significant role in aggregate wage and productivity growth (Figure 5.7). On average across countries, job-to-job mobility contributes 0.9 percentage points to average annual wage and productivity growth (which, in contrast to Figure 5.6, here also includes wages and productivity in entering firms). While the contribution of job-to-job mobility is considerable, it is largely offset by the negative contribution of employment mobility. This mainly reflects life cycle effects that arise as young workers start their careers in low wage and low productivity firms. Consequently, the large contribution of job-to-job mobility primarily reflects its role in integrating new entrants into the labour market. On net, overall job reallocation, which is the sum of job-to-job and employment mobility, contributes 0.3 percentage points to aggregate wage and productivity growth (respectively, 16% and 22% of total growth). The remaining part reflects wage and productivity growth within continuing worker-firm matches (“stayers”). The importance of job-to-job mobility for aggregate wage growth is consistent with previous findings by Hahn, Hyatt and Janicki (2021[28]) for the United States and Hijzen et al. (2024[27]) for Norway.

Box 5.4. Methodology II: Decomposing aggregate growth

Copy link to Box 5.4. Methodology II: Decomposing aggregate growth

The contribution of the different types of labour market mobility to aggregate wage and productivity growth ( $∆ y_{t}$ ) between t and t‑1 is documented using a worker-level decomposition following Hahn, Hyatt and Janicki (2021[28]). In each period t, and for each worker i, dummy variables capture the mobility state for job stayers $(r_{i t})$ , job-to-job movers ( $j_{i t}$ ), hires from non-employment ( ${h_{N}}_{i t}$ ) and separations to non-employment ( ${s_{N}}_{i t}$ ). The total number of each worker type is further defined as $R_{t}$ , $J_{t}$ , ${H_{N}}_{t},$ and ${S_{N}}_{t}$ , respectively. The number of workers employed in period $t - 1$ is then ${{E_{t - 1} = R}_{t} + J_{t} + S}_{N}_{t}$ and ${{E_{t} = R}_{t} + J_{t} + H}_{N}_{t}$ in period $t$ .¹ With this, $∆ y_{t}$ can be decomposed into the contributions of different labour market mobility types:

(2) $∆ y_{t} = \underset{S t a y e r s}{\underset{⏟}{\frac{(\frac{R_{t}}{E_{t}} + \frac{R_{t}}{E_{t - 1}}) \sum_{i} r_{i t} ∆ y_{i t}}{2 R_{t}}}} + \underset{J o b - t o - j o b m o v e r s}{\underset{⏟}{\frac{(\frac{J_{t}}{E_{t}} + \frac{J_{t}}{E_{t - 1}}) \sum_{i} j_{i t} ∆ y_{i t}}{2 J_{t}}}} + \underset{E n t r a n t s f r o m n o n - e m p l o y m e n t}{\underset{⏟}{\frac{\frac{{H_{N}}_{t}}{E_{t}} \sum_{i} {h_{N}}_{i t} (y_{i, t} - \tilde{y_{t}})}{{H_{N}}_{t}}}} - \underset{E x i t s t o n o n - e m p l o y m e n t}{\underset{⏟}{\frac{\frac{{S_{N}}_{t}}{E_{t - 1}} \sum_{i} {s_{N}}_{i t} (y_{i, t - 1} - \tilde{y_{t}})}{{S_{N}}_{t}}}}$

where $y_{i t}$ denotes the firm quality (average wage or productivity) for individual i and year t and $\tilde{y_{t}}$ a reference level for firm quality for non-employed workers based on a weighted average for stayers and job-to-job movers:

${\tilde{y}}_{t} = \frac{R_{t}}{R_{t} + J_{t}} (\frac{\sum_{i} r_{i t} ({y_{i, t} + y}_{i, t - 1})}{{2 R}_{t}}) + \frac{J_{t}}{R_{t} + J_{t}} (\frac{\sum_{i} j_{i t} ({y_{i, t} + y}_{i, t - 1})}{2 J_{t}})$

In contrast to the firm-level analysis of growth-enhancing job reallocation, the worker-level decomposition of aggregate wage growth is not restricted to workers in incumbent firms and therefore also includes workers in entering firms. In addition, it measures firm wages and productivity in period t, rather than as an average between $t - 1$ and $t - 3$ and is performed on the universe of workers (where available).

1. Note that job stayers ( $R_{t}$ ) and job-to-job movers ( $J_{t}$ ) do not change total employment between period t‑1 and t. Any changes in total employment represent hires from non-employment ( $H_{N_{t}}$ ) and separations to non-employment ( $S_{N_{t}}$ ).

The contribution of job-to-job mobility to aggregate wage and productivity growth is particularly strong in good times, in line with the literature (Haltiwanger et al., 2018[24]). Its contribution to aggregate wage growth is 1.2 percentage points in periods of below-average cyclical unemployment compared with 0.6 percentage points in periods of above‑average cyclical unemployment. Similarly, its contribution to aggregate productivity growth is 1.0 percentage point in periods of below-average cyclical unemployment compared with 0.7 percentage points in periods of above‑average cyclical unemployment. This shows that the job ladder slows during periods of high unemployment. This is sometimes referred to as the “sullying effect” of recessions as this slows the speed of job reallocation (Haltiwanger et al., 2025[29]). In the literature, there is also some indication that recessions have a “cleansing effect” that increases the speed of job reallocation by destroying jobs in low-quality firms (Haltiwanger et al., 2025[29]). Wage growth among job stayers also slows during periods of high unemployment. This may in part reflect reduced worker bargaining power as workers may be at greater risk of losing their job and face fewer opportunities of moving to a better job. In contrast, labour shortages during the initial years following the COVID‑19 pandemic were associated with stronger wage growth through job-to-job mobility as the competition for workers increased, while high inflation depressed wage growth among all workers (see Box 5.5).

It is difficult to draw firm conclusions about the long-term evolution of the contribution of net job-to-job mobility to aggregate wage and productivity growth due to the relatively short period considered here. Decker et al. (2020[14]) show for the United States that firm-level employment in the 2000s was considerably less responsive to productivity differences across firms than in the 1980s and that this reduced aggregate productivity growth by a non-trivial amount of about 2 to 3%. They argue that this reflects increases in the cost of adjusting employment for firms rather than changes in the need for job reallocation in response to changes in business opportunities. Similarly, Baksy, Caratelli and Engbom (2024[30]) show using CPS data for the United States that wage‑enhancing job reallocation through job-to-job mobility decreased during the period 1979‑2023 and that this reduced average annual wage growth by 1 percentage point, with most of the decline taking place before the 2000s. They argue that this is not because workers are better matched in recent years but rather that increased labour market concentration reduced the ability of workers to transition to higher paying firms.10

Figure 5.7. Job-to-job mobility accounts for a large part of aggregate wage and productivity growth
Copy link to Figure 5.7. Job-to-job mobility accounts for a large part of aggregate wage and productivity growth

Decomposition of aggregate wage and productivity growth, all years, good/bad years, first/last year, percentage

Box 5.5. Labour shortages and job reallocation in the aftermath of the COVID‑19 crisis in Austria

Copy link to Box 5.5. Labour shortages and job reallocation in the aftermath of the COVID‑19 crisis in Austria

While labour shortages were already present prior to the COVID‑19 pandemic, especially in high-skill sectors, the recovery from the pandemic saw a rapid intensification (OECD, 2024[31]; 2024[32]; Causa et al., 2025[33]). This gap in labour demand and supply may have had significant implications for job mobility and wage dynamics. To analyse these issues, this box provides a decomposition of aggregate real wage growth in each detailed industry in the aftermath of the pandemic into components related to on-the job wage growth, and those related to job-to-job mobility and employment mobility using quarterly data between Q1 2022 and Q4 2023 for Austria. Note that average wage growth tended to be negative during this period due to the impact of energy price shocks on inflation.

Industry differences in average quarterly real wage growth during the post-COVID‑19 period in Austria are to a large part explained by differences in the contribution of job-to-job mobility (Figure 5.8). These are typically manufacturing industries characterised by significant labour shortages. Labour shortages increase competition between firms for employed workers, with high productivity firms poaching workers from low productivity firms, generating a strong job ladder. There is also some indication that wage growth among stayers was stronger in high-wage industries. In part, this is mechanical since stayers comprise the majority of workers in an industry. However, it may also partially reflect the role of labour shortages on the bargaining position of stayers.

Figure 5.8. High wage growth was driven by net job-to-job mobility
Copy link to Figure 5.8. High wage growth was driven by net job-to-job mobility

Decomposition of aggregate real wage growth by STAN A38 industry, Q1 2022 – Q4 2023, Austria

Cross-country growth differences reflect a combination of within and between-firm growth

A key question is to what extent differences in growth-enhancing job reallocation can account for the large variation in aggregate growth rates across countries. To look at this important issue, we assess to what extent cross-country differences in aggregate wage and productivity growth reflect differences in within-firm growth (“stayers”) or between-firm growth (“job reallocation”), and the extent to which differences in between-firm growth rates are driven by differences in the contributions of job-to-job and employment mobility (Figure 5.9) – see Box 5.6.

In countries with high wage growth (Panel A), such as Estonia, Hungary, Norway and Lithuania, the positive contribution of job reallocation is particularly important, whereas in countries with low wage growth such as Belgium, France, Italy and Spain, the contribution of job reallocation tends to be negative.11 Among the countries with information on labour productivity (Panel B), job reallocation contributes negatively in Finland, Italy and Portugal. Negative contributions of job reallocation are likely to reflect the shift in employment towards lower wage or productivity industries rather than that of reallocation towards lower wage or productivity firms within industries. Italy stands out as a country with negative aggregate productivity growth due to negative on-the‑job productivity growth.

To quantify the part of the cross-country variation that can be explained by the different components presented in Figure 5.9, supplemental “beta values” are calculated (Annex Figure 5.A.2), following Fujita and Ramey (2009[34]).12 This suggests that about 56% of the cross-country variation in aggregate wage growth is accounted for by on-the‑job wage growth and 44% by job reallocation of which almost half (20%) is accounted for by job-to-job mobility.13 Similarly, about 59% of the cross-country variation in aggregate productivity growth is accounted for by within-firm productivity growth and 41% by between firm-productivity growth due to job reallocation, of which about two‑thirds (27%) reflects the role of job-to-jo mobility.

Cross-country differences in the importance of job reallocation and job-to-job mobility in aggregate wage and productivity growth may reflect different factors (Decker et al., 2020[14]). First, differences across countries may reflect differences in frictions that determine the responsiveness of firm employment to differences in average wages or and productivity. This may reflect frictions on the side of firms related to the costs of adjusting employment in line with business conditions or on the side of workers related to the costs of changing jobs. Second, differences across countries may reflect changes in the volatility of business conditions (i.e. idiosyncratic productivity shocks). To the extent that volatility has declined, consistent with narratives before the global financial crisis of the “great moderation” (Clark, 2009[35]), this may have slowed the speed of job reallocation. Similarly, transition and emerging economies undergoing particularly fast structural change may see a higher speed of reallocation without necessarily having a higher responsiveness of firm-level employment to business conditions. Third, changes in the structure of employment towards high-wage or high productivity firms are more “valuable” in terms of aggregate growth in countries where differences in productivity and wages between firms are more important. The degree of dispersion between firms in a country depends to an important extent on the volatility of productivity shocks and the responsiveness of firms to changes in business conditions.

To provide an indication of the importance of responsiveness in the cross-country variation in wage and productivity growth between firms, a counterfactual analysis is conducted that isolates the contribution of responsiveness from any other factors that contribute to between firm productivity and wage growth, following a similar exercise is conducted in Decker et al. (2020[14]) (see Box 5.6 for details). This exercise shows that responsiveness accounts for 51% of the cross-country variation in between-firm wage growth and 49% of the cross-country variation in between-firm productivity growth (Annex Figure 5.A.4). This indicates that frictions related to firms or workers account for a sizable component of cross-country differences in aggregate growth between firms.

Figure 5.9. Within and between firm growth both contribute to cross-country differences in aggregate growth rates
Copy link to Figure 5.9. Within and between firm growth both contribute to cross-country differences in aggregate growth rates

Decomposition of aggregate wage and productivity growth, all years, by country, percentage

Box 5.6. Methodology III: Comparing aggregate growth rates across countries

Copy link to Box 5.6. Methodology III: Comparing aggregate growth rates across countries

The pace of growth-enhancing job reallocation depends both on initial conditions, i.e. the current allocation of jobs across firms, and the responsiveness of employment to firm quality, i.e. employment adjustment costs for firms and barriers to job mobility for workers. A convenient feature of the regression-based job reallocation model (Equation 1) is that it can be used to conduct counterfactual analyses of aggregate growth, keeping constant the estimated coefficient on firm quality (“responsiveness”) or the distribution of firm quality (“dispersion”). More specifically, the difference in aggregate growth (Δy) due to job reallocation between two countries 1 and 0 can be decomposed in a component that reflects differences in responsiveness (β) for a given distribution of firm quality and a component that reflects differences in the dispersion in firm quality ( $X$ ) for a given level of responsiveness, as follows:

(3) $E [∆ y_{1} X_{1}] - E [∆ y_{0} X_{0}] = β_{1} | X_{1} - β_{0} | X_{0} = \underset{R e s p o n s i v e n e s s}{\underset{⏟}{{[β}_{1} | X_{0} - β_{0} | X_{0}]}} + \underset{D i s p e r s i o n}{\underset{⏟}{{[β}_{1} | X_{1} - β_{1} | X_{0}]}}$

In practice, this decomposition is implemented in three steps, alternatively fixing the responsiveness or the level of dispersion. First, partial predictions of net employment growth, net job-to-job mobility and net employment mobility are retrieved for each quintile of the employment-weighted firm-quality distribution. Second, the corresponding employment shares for each segment of the employment-weighted firm quality distribution are recalculated. Third, the contribution to aggregate growth is calculated by taking the employment-weighted average of firm quality across segments.1

1. The decomposition presented in Equation 3 can alternatively be expressed using $β_{0} | X_{1}$ as the counterfactual (i.e. using the responsiveness of country 0 and the level of dispersion in country 1). As both approaches can result in slightly different contributions of responsiveness and dispersion to differences in wage and productivity growth, the results of both counterfactual approaches are averaged.

5.2.2. The role of workforce ageing

This sub-section explores the influence of workforce ageing for growth-enhancing job reallocation as well as aggregate wage and productivity growth.14

Older workers are less likely to transition to better firms

The lower mobility of older workers was used in Section 5.1 of this chapter to draw out the implications of workforce ageing for the long-term evolution of gross worker mobility. In this section, the attention is instead placed on the extent to which an ageing workforce exhibits lower rates of net job mobility toward higher-paying and more productive firms. This is analysed by focusing on the responsiveness of employment of a given age group to differences in firm wages or productivity (see Box 5.3). A positive correlation between firm employment growth and lagged firm quality suggests that the structure of employment shifts towards higher quality firms and hence that reallocation is growth-enhancing. This approach is preferred in the present case for expositional purposes as it is very synthetic. The results are very similar when focusing on employment growth by quintile of the firm-quality distribution as in the previous section.

Older workers are not only less mobile but also less likely to transition to better firms in terms of average wages or and productivity (Figure 5.10). An increase of 10% in average firm wages (conditional on observed worker characteristics) is associated with an increase in firm-level employment growth of workers aged 15‑24 of 1.0 percentage points, while a similar increase in labour productivity is associated with an increase of 0.7 percentage points in firm-level employment growth of young workers. This confirms the importance of job-to-job mobility for the careers of young workers and growth-enhancing job reallocation (Topel and Ward, 1992[36]; BLS, 2017[37]). The employment-growth response monotonically decreases with age. For workers aged 65‑74, it even turns slightly negative due to the tendency of older workers to retire from high quality firms. Differences in the employment-growth response mainly reflect differences in the role of job-to-job mobility in job reallocation, suggesting that the “job ladder”, through which workers move from lower to higher quality firms, operates more slowly for older workers. As a result, workforce ageing could have important implications for the speed of efficiency-enhancing reallocation and its contribution to aggregate wage and productivity growth.15

Several factors may explain the lower responsiveness of employment growth to firm quality at older ages. First, the structure of older-worker employment across firms may be less sensitive to differences in firm quality because older workers face more important barriers or constraints to changing employers (e.g. home ownership, job security, social ties, family responsibilities; see Chapter 3). Older workers may also place a higher value on flexible working environments in terms of working time and location (OECD, 2024[38]).16 Second, a lower responsiveness of employment growth to firm quality at older ages also may stem from higher costs of adjusting employment levels of older workers for employers, or because high quality employers prefer to hire younger workers (see Chapter 3 for a discussion of hiring discrimination against older workers). Third, older workers may also feel that they have little to gain from changing employers either because they have already climbed the job ladder, or because they have fewer working years remaining in their careers.

Figure 5.10. Growth-enhancing job reallocation is most important for young workers
Copy link to Figure 5.10. Growth-enhancing job reallocation is most important for young workers

Growth-enhancing reallocation among incumbent firms as measured by the employment-weighted impact of firm-quality on net employment growth across firms, by age group, percentage points (p.p.)

Ageing is associated with a slowdown of growth-enhancing job reallocation

Next, the impact of demographic change on the evolution of between-firm wage and productivity growth between 2001 and 2019 is analysed. This is done using a shift-share decomposition on changes in between-firm wage and productivity growth within and across age groups (Box 5.7).17 The shift-share analysis accounts for the effects of demographic change through its impact on the age structure of employment (changes in age‑specific employment shares), as well as changes in the mobility behaviour of older workers relative to younger workers in response to, for example, changes in retirement age or life expectancy (age‑specific shifts in growth-enhancing reallocation). Figure 5.10 shows that in some countries between-firm growth rates due to job mobility to better firms have become somewhat more important for older workers relative to younger ones.

Box 5.7. Methodology IV: The role of ageing in the evolution of aggregate growth

Copy link to Box 5.7. Methodology IV: The role of ageing in the evolution of aggregate growth

The role of workforce ageing in the evolution of between-firm wage and productivity growth ( $∆ y_{t}$ ) is analysed using a shift-share decomposition. The share component captures changes in between-firm growth due to changes in the age structure of employment – which, in turn, arise from changes in age‑specific employment rates and population shares (composition). The shift component instead measures the change in between-firm growth rates within age groups due to changes in age‑specific mobility behaviour (growth rates). Formally, this can be written as:

$(4) ∆ y_{t} = \underset{S h a r e (c o m p o s i t i o n)}{\underset{⏟}{\sum_{a} {\bar{y}}_{a} ∆ s_{t, a}}} + \underset{S h i f t (g r o w t h r a t e s)}{\underset{⏟}{\sum_{a} {\bar{s}}_{a} ∆ y_{t, a}}}$

where $∆ s_{t, a}$ measures the change in the share of employment of age group $a$ between time $t$ and $t - 1$ and $∆ y_{t, a}$ measures the change in between-firm wage or productivity growth rates of age group $a$ between time $t$ and $t - 1$ due to age‑specific shifts in growth-enhancing job reallocation. The average employment share of age group $a$ over the entire period is given by ${\bar{s}}_{a}$ and the average growth between-firm wage or productivity growth rate of age group $a$ due to growth-enhancing job reallocation is given by ${\bar{y}}_{a}$ .

The shift component in turn can be decomposed as the sum of an average shift component related to general changes in labour market dynamism that affect all age groups equally, and of a differential shift component that differs across age groups. One factor that may give rise to differential changes in between-firm growth due to growth-enhancing job reallocation across age groups may be ageing as longer working lives may change the incentives for job mobility to better firms disproportionately among older workers. Formally, the decomposition can therefore be extended as follows:

$(5) ∆ y_{t} = \underset{S h a r e (c o m p o s i t i o n)}{\underset{⏟}{\sum_{a} {\bar{y}}_{a} ∆ s_{t, a}}} + \underset{D i f . s h i f t (g r o w t h r a t e s)}{\underset{⏟}{\sum_{a} {\bar{s}}_{a} (∆ y_{t, a} - {∆ \bar{y}}_{t})}} + {∆ \bar{y}}_{t}$

where ${∆ \bar{y}}_{t}$ measures the common shift component. For the present purposes, the differential shift component is entirely attributed ageing. The total effect of ageing on changes in between-firm growth rates is then given by the sum of the share component and the differential shift component. Note that, even though this chapter only considers the sum of these two components as the role of ageing, it might increase mobility and growth-enhancing reallocation at any age – for youth, who will be in short supply, and for the old, who will have longer time working lives. The common shift component might therefore also contain some effect of ageing among many other factors.

Demographic developments have tended to weaken growth-enhancing job reallocation (Figure 5.11). More specifically, demographic change is associated with slower wage and productivity growth due to reallocation by respectively 0.13 and 0.10 percentage points between 2001 and 2019, which represents a considerable part of the average annual aggregate growth rate of, respectively, 0.6 and 1.0% among the set of countries considered. This is driven by the change in the age structure of employment towards older workers and the lower responsiveness of their mobility choices to between-firm differences in pay or productivity. The shift of the structure of employment to older workers mainly reflects the impact of workforce ageing, but to a lesser extent also declining employment rates among youth due to the long-term reduction in fertility and the tendency of youth to study longer (see Annex Figure 5.A.5). The adverse effects of changes in the age composition of employment are partially offset by differential trends in age‑specific growth rates due to job reallocation.18^,19

Figure 5.11. Ageing weakens aggregate between-firm wage and productivity growth
Copy link to Figure 5.11. Ageing weakens aggregate between-firm wage and productivity growth

Difference in between-firm wage and productivity growth rates based on a shift-share decomposition on the evolution of age‑composition of employment and age‑specific trends in between-firm wage and productivity growth rates, early 2000s to late 2010s, percentage points

From a policy perspective, a key question is why workforce ageing tends to slow aggregate wage and productivity growth. If this reflects a lower responsiveness of firm-level employment growth to differences in firm quality among older workers, this raises concerns about the adaptability of the workforce to structural change and there may be a case for developing policies that can support job mobility among such workers. For example, the structure of older-worker employment across firms may be less sensitive to differences in firm quality because older workers face more important constraints to changing employers (e.g. home ownership, job security, social ties, family responsibilities), because adjusting employment levels of older workers is more costly for employers, or because high quality employers prefer to hire younger workers (see Chapter 3 for a discussion of hiring discrimination against older workers). By contrast, if workforce ageing slows aggregate wage and productivity because older workers feel that they have little to gain from changing employers either because they have already climbed the job ladder, or because they have fewer working years remaining in their careers, this would require policies to increase the returns to mobility at older ages via training and career guidance.

The segregation of employment by age across firms of different quality has tended to deepen slightly over time

An interesting question is the extent to which workforce ageing affects the careers of younger workers and their contribution to efficiency-enhancing job reallocation. To the extent that older workers are concentrated in better firms and older workers continue to work longer, this may dampen opportunities for younger workers for moving up the firm job ladder. For the most recent year, older workers aged 55‑74 account for 17% of employment in the bottom quintile of the employment-weighted firm distribution of wages and 19% of employment in the top quintile (Figure 5.12). However, young workers aged 15‑34 account for 43% of employment in the bottom quintile and 27% in the top quintile. This age‑based employment segregation has increased slightly since the early 2000s and reflects the importance of the job ladder for integrating new cohorts of young workers in the labour market. The empirical literature does not appear to have directly addressed the growing segregation of employment by age across firms nor the impact of workforce ageing on the opportunities of younger workers for upward mobility between firms. Recent evidence instead has focused on the effects of delayed retirement on the employment opportunities of young workers within firms,20 while earlier research clearly dismissed the idea that older workers crowd out younger ones in employment at the aggregate level (the “lump-of-labour fallacy”, i.e. the incorrect premise that there is a fixed quantity of aggregate employment).

Figure 5.12. Age‑segregation across the firm-distribution of wages has deepened slightly
Copy link to Figure 5.12. Age‑segregation across the firm-distribution of wages has deepened slightly

Age composition across quintiles of the firm-distribution of wages, first and last year

5.3. Policy implications

Copy link to 5.3. Policy implications

Policies have a key role to play in supporting growth-enhancing job reallocation by promoting flexibility for firms and supporting job mobility between firms for workers. Special emphasis is placed in policies that can promote job mobility among mid-career and older workers.

5.3.1. Policies that provide flexibility to firms

The OECD has consistently emphasised the importance of flexibility for firms for well-functioning labour markets, including the process of efficiency-enhancing job reallocation. However, flexibility can take different forms, and some forms of flexibility are better than others (OECD, 2018[39]). This sub-section focuses on the role of employment protection and job retention schemes for efficiency-enhancing job reallocation.

Employment flexibility is important for efficiency-enhancing job reallocation

Job security provisions, whether in the form of employment protection or job retention schemes, can have important implications for broadly shared productivity growth. They can contribute to stronger productivity growth by strengthening incentives for the accumulation and preservation of firm-specific human capital in the workplace by promoting long-term employer-employee relationships (Belot, Boone and Van Ours, 2007[40]) and the use of high-performance work and management practices (Bloom and Van Reenen, 2010[41]). However, they can also undermine productivity growth. For example, by reducing the tendency of firms to adjust employment in line with changing business conditions and weakening the incentives of workers to move to more productive firms, they may undermine efficiency-enhancing job reallocation across firms.

The literature suggests that overly strict employment protection against the dismissal of workers on open-ended contracts reduces efficiency-enhancing job reallocation. In particular, overly strict employment protection legislation can slow job reallocation by reducing vacancy creation and hence opportunities for job mobility (Bassanini, Nunziata and Venn, 2009[42]; Andrews and Cingano, 2014[43]; Bartelsman, Haltiwanger and Scarpetta, 2013[44]).21 The challenge for policy makers is therefore to provide sufficient employment flexibility to firms to allow jobs being reallocated to their most productive uses, while providing incentives for learning and innovation in the workplace and protecting workers against abuse, for which employment protection legislation remains essential.

If not well designed, employment flexibility can increase turnover without supporting growth-enhancing job reallocation

Net job mobility only represents a small fraction of gross worker mobility as worker flows often take place between similar firms or offset each other. In fact, net job flows that shift the structure of employment between quintiles of the firm-quality distribution only account for about 5% of gross worker mobility in the linked employer-employee data used in this Chapter.22 While this share increases when segmenting the firm-quality distribution more finely, the qualitative pattern remains unchanged.23 This suggests that gross worker flows are predominantly driven by factors unrelated to differences in firm wages, including personal considerations, but also policies and institutions.

One factor that may contribute to gross worker mobility rates, but not growth-enhancing job reallocation, is a strong reliance on overly flexible work arrangements, including short-duration fixed-term contracts. While a moderate use of temporary work can help to provide additional job opportunities to unemployed workers by enhancing the matching process between firms and workers, an excessive use comes at the expense of permanent contracts and results in low opportunities for contract conversion and high job turnover (OECD, 2018[39]). In some countries with an excessive reliance on temporary work, this results in part from a strict protection of open-ended contracts which creates incentives for the use of flexible work arrangements, resulting in labour market duality (Hijzen, Mondauto and Scarpetta, 2017[45]; Bassanini and Garnero, 2013[46]; OECD, 2018[39]). To avoid providing incentives for an excessive use of temporary work, employment protection should remain moderate, be balanced across contract types and effectively protect workers against unfair dismissal, including dismissal based on age discrimination – see Chapter 3.

Another factor affecting the relationship between worker mobility and reallocation relates to the role played by unemployment insurance and job retention schemes during deep economic crises (Giupponi, Landais and Lapeyre, 2022[47]). To the extent that economic crises represent episodes of creative destruction during which jobs in low quality firms are destroyed followed by the creation of jobs in high quality firms in the recovery, no attempt should be made to preserve worker-firm matches that are under pressure and support should focus on providing income and job-search support to unemployed workers. However, to the extent that economic crises only temporarily put worker-firm matches at risk that remain viable in the medium term, there is a case for policies that promote labour hoarding, including job retention schemes.24 The optimal response is likely to vary depending on the nature of the crisis and a country’s policies and institutions. Evaluations of job retention schemes in European countries during the COVID‑19 crisis tend to suggest that their negative impact on growth-enhancing job reallocation was limited (Calligaris et al., 2023[48]).

5.3.2. Policies that support mobility of workers to better firms

The analysis in this chapter demonstrates that, while employment flexibility for firms is important for growth-enhancing job reallocation, barriers to the mobility of workers may be even more important. Consequently, more attention should be given to addressing barriers to job mobility and strengthening incentives for job mobility. This sub-section focuses on policy barriers to job-to-job mobility in the form of occupational licencing and non-compete agreements, and the role of wage‑setting policies in shaping incentives for it (see Chapter 3 for additional discussion in relation to older mid-career and older workers).

Professional licensing regulations are under increased scrutiny amid growing concerns about their consequences for growing-enhancing job reallocation

Licensed or regulated professions must follow rules on entry and conduct in their field. Licensing regulations may include administrative procedures, qualification requirements and mobility restrictions (von Rueden and Bambalaite, 2020[49]; OECD, 2022[4]). As credence goods, the quality of professional services is difficult for consumers to assess. Professional licencing aims to correct market failures caused by information asymmetries between consumers and service providers. However, by creating entry barriers, licensing regulations can curtail competition, restrict business dynamism and slow growth-enhancing job reallocation.

The share of professions subject to licensing regulations is significant and has expanded over time, covering up to 30% of jobs in some countries. While their use was traditionally limited to liberal professions like lawyers and engineers, it has tended to expand to include occupations, such as dockers, driving school instructors, transporters, and hairdressers (Bambalaite, Nicoletti and von Rueden, 2020[50]; Kleiner and Krueger, 2010[51]). Evidence on their broader economic consequences is limited. There is some indication that licensing slows firm-level productivity growth, harms entry of innovative firms and lowers employment growth in the most productive ones (Bambalaite, Nicoletti and von Rueden, 2020[50]). For example, in the United States, separation and hiring rates tend to be lower in states with a higher share of occupations under professional licensing regulations and job mobility across states is generally lower towards states with more stringent licensing requirements (Hermansen, 2019[52]). Licensing may also curtail employment growth and reduce earnings in occupations with similar skill-profiles (Dodini, 2023[53]).

Amid growing concerns over the unintended consequences of professional licensing regulations in a context of weak productivity growth, the design and implementation of such regulations is receiving increased scrutiny from policy makers. While they remain crucial in reducing information asymmetries, care should be taken that they do not unwittingly create barriers to growth-enhancing job mobility and remain pertinent as technological developments are rapidly changing the delivery of professional services (OECD, 2023[54]). To limit unintended consequences, policy makers should weigh the use of compulsory licensing measures against voluntary certification approaches that can be combined with consumer information systems (e.g. service quality comparison platforms) (OECD, 2022[4]). Shifting the emphasis from input quality (ensuring professional service providers possess adequate qualifications) to output quality (ensuring the services provided meet certain minimum standards) may also have more limited negative side effects on competition and job reallocation (OECD, 2018[55]).

There is growing evidence that non-compete and other restraint clauses are not just used to protect legitimate business interests but also to limit competition.

Non-compete and related restrictive clauses are contractual provisions that limit employees’ activities after they leave their current job. Such clauses can prevent former employees from working for a competitor, starting a competing business, or competing firms from hiring each other’s employees (“poaching”). In most OECD countries, these restraints are permitted and governed by law to safeguard legitimate employer interests, such as trade secrets, client information or specific investment in the employment relationship (e.g. training) (Andrews and Garnero, 2025[56]). However, these clauses can also be used to reduce competition in product and labour markets, potentially hindering growth-enhancing job reallocation.

Surveys from selected countries indicate that the prevalence of non-compete clauses is high, potentially covering up to a quarter of the workforce and used by as many as half of firms, with usage possibly on the rise (Andrews and Garnero, 2025[56]; OECD, 2022[4]; OECD, 2019[57]). While non-competes are especially common in knowledge‑intensive fields, protecting legitimate business concerns, they are increasingly found in sectors where access to proprietary information and trade secrets is highly unlikely, such as among entry-level fast-food workers in the United States (Andrews and Garnero, 2025[56]). Non-compete clauses are often packaged with other restraint clauses that directly impede worker mobility, including non-disclosure, non-solicitation, and non-recruitment agreements. Where the use of non-competes is formally limited, they can also be substituted with clauses that do not explicitly restrict worker mobility but effectively serve as functional equivalent.

Importantly, non-compete clauses can stifle job mobility, growth-enhancing job reallocation and technology diffusion. As such, they may have contributed to the slowing of productivity growth and the decline in the labour share (Buckley, Rankin and Andrews, 2024[58]; FTC, 2024[59]). For example, non-compete clauses have negatively impacted competitive labour market conditions in the United States by hindering efficient matching between workers and employers, as well as in product and service markets by inhibiting new business formation and innovation (FTC, 2024[59]).25 The 2024 Draghi report on EU competitiveness echoes these concerns, recommending that competition policy address practices that limit labour mobility between companies, including non-compete agreements (Draghi, 2024[2]). Recent evidence from Austria on the introduction of a ban on non-competes for low-wage workers in 2006, suggests that the ban of non-competes had only a modest positive impact on overall job mobility, somewhat in contrast with the evidence for the United States, although several context-specific factors can explain the result (see Box 5.8).

Given the growing concerns about the excessive use of non-compete clauses and their consequences, there are multiple options to restrict their use, many of which are already in place in several OECD countries, e.g. Andrews and Garnero (2025[56]) and OECD (2022[4]; 2019[57]). These measures include, among others, complete bans, partial bans targeting low-wage workers or those on fixed-term contracts, limitations on their application in terms of time or geographic scope or requirements related to compensation and notification. Additionally, competition authorities in some countries have also acted against other restraint clauses (e.g. a ban on non-solicitation clauses). Nevertheless, the evidence suggests that regulations limiting the enforceability of non-compete clauses may not suffice to prevent their unlawful use in the absence of significant penalties for their abuse by employers (Starr, Prescott and Bishara, 2020[60]; Boeri, Garnero and Luisetto, 2024[61]).

Box 5.8. The economic effects of restrictions on non-compete agreements and the role of policy design: The case of Austria

Copy link to Box 5.8. The economic effects of restrictions on non-compete agreements and the role of policy design: The case of Austria

Evidence for the United States suggests that non-compete agreements can have large negative effects on job mobility, wages and innovation – see Starr (2024[62]) for an overall review. However, empirical evidence outside of the United States is scarce (Andrews and Garnero, 2025[63]). A study of a 2006 reform in Austria which banned non-compete clauses for low-wage workers finds a modest negative effect of non-compete clauses on job mobility, and no effect on earnings growth (Young, 2024[64]). Beyond the inherent differences in their labour markets, several design factors may play a role in explaining the differing effects of non-compete regulations in Austria and the United States.

First, non-compete clauses are not the only post-employment restraint that matters. While the 2006 reform in Austria restricted the use of non-compete agreements, it also expanded the use of training repayment clauses, allowing firms to recover training costs from up to five years before a worker’s departure (previously three years). While training repayment clauses do not directly restrict worker mobility, they add costs to workers who may want to leave (Harris, 2021[65]). Considering all the clauses that may limit workers’ mobility (e.g. non-recruitment/non-solicitation of colleagues, non-solicitation of clients, repayment of training costs or repayment of benefits) is therefore key to ensure that limitations to the use of non-compete clauses do not simply translate into a higher use or higher enforcement of other clauses.
Second, lack of clarity about the applicability of non-compete agreements hinders the effect of reforms aimed at reducing their use. The Austrian reform introduced a threshold below which non-competes are not enforceable. The threshold changes every year and only certain wage components are included in its definition. Moreover, what matters in the case of Austria is not the wage at the moment of signing the contract but the wage at the end of the employment relationship. This may result in considerable uncertainty by the workers concerned about the actual enforceability of non-compete agreements hindering the intended effects of the reform.
Third, even unenforceable clauses can stifle job mobility. Banning or restricting non-compete agreements is not enough if they are still used in employment contracts. Research for the United States and Italy shows that even unenforceable non-compete agreements can have a chilling effect on worker mobility, as workers often do not realise that they are unenforceable and cite them as a reason for not taking a job offer from a competitor (Starr, Prescott and Bishara, 2020[60]; Prescott and Starr, 2024[66]; Boeri, Garnero and Luisetto, 2024[61]). Since the use of non-compete agreements in Austria was made merely non-enforceable among workers with incomes below the threshold but applicable to workers whose incomes increase beyond the threshold, employers in Austria have a clear incentive to include “dormant” non-compete agreements that become enforceable in case the threshold is passed. However, and as discussed above, workers may think that they are enforceable even if they fall below the threshold.

Another reform in 2015 in Austria, which, differently from the 2006 reform, was done in agreement with social partners, significantly increased the threshold and clarified the wage components to be taken into account. The reform also brought back the time period for repayment to three years. The effects of the 2015 reform have not yet been evaluated and hence it is not clear how effective it has been in restricting the use of non-compete agreements and promoting job mobility.

Note: This box was prepared with contributions from Sindri Engilbertsson, Andrea Garnero and Sergio Pinto.

A better understanding of the role of wage‑setting institutions for efficiency-enhancing reallocation is needed

The role of wage‑setting institutions, such as minimum wages and collective bargaining, in efficiency-enhancing job reallocation remains a topic of debate. While potentially suppressing incentives for job mobility, minimum wages may ensure fairer wages for workers with a vulnerable bargaining position in labour markets where employers have significant wage‑setting power, and therefore enhance rather than impede efficient labour allocation and worker mobility (Manning, 2020[67]). Descriptive evidence suggests that a stronger compression of wages is not associated with growth-enhancing reallocation (Figure 5.13). When measuring growth-enhancing reallocation in terms of average wages, there is no significant relationship between the responsiveness of net employment to wages, through either job-to-job or employment mobility, and wage compression across countries, as measured by the ratio of dispersion in firm-level productivity to dispersion in firm-level wages.

Figure 5.13. Wage compression does not have major effects on growth-enhancing job reallocation
Copy link to Figure 5.13. Wage compression does not have major effects on growth-enhancing job reallocation

Wage compression and growth-enhancing reallocation among incumbent firms as measured by the employment-weighted impact of firm-quality on net employment growth across firms, net job-to-job mobility and net employment mobility

The ambiguous effects of wage floors for growth-enhancing job reallocation could be the result of two competing mechanisms. By compressing the wage distribution between firms and reducing incentives for voluntary job mobility, wage floors may weaken the job ladder and reduce net job-to-job mobility, making it more difficult for highly productive firms to attract workers (OECD, 2018[39]). For example, Hijzen, Lillehagen and Zwysen (2024[27]) show that higher wage compression in Norway leads to a weaker job ladder as compared to the United States, even though the overall pace of growth-enhancing reallocation is similar in both countries.26 At the same time, wage floors can force the least productive firms to reduce employment or exit the market (Aaronson et al., 2018[68]; Chava, Oettl and Singh, 2023[69]). If workers from exiting firms find jobs in more productive ones, overall employment may remain unaffected while aggregate productivity increases. Recent evidence from Germany (Dustmann et al., 2022[70]) and the United States (Rao and Risch, 2024[71]) confirms the importance of this firm-driven process of efficiency-enhancing job reallocation. These effects are more pronounced when less productive firms that employ a relatively large share of minimum-wage workers experience greater declines in profitability, employment, and survival, while more productive firms expand employment (Dube and Lindner, 2024[72]).

To promote productivity growth through job reallocation, wage‑setting institutions that limit wage dispersion should be complemented with policies that enhance voluntary job mobility and foster innovation in low-productivity firms. The consequences of wage floors for growth-enhancing reallocation depend crucially on the extent to which they shift job opportunities for workers from less to more productive firms and the ability and willingness of workers to move to more productive firms. The former is likely to require flexible product and labour markets while the latter can be supported through comprehensive activation policies and career guidance services. To promote worker-driven job mobility between firms, activation and career guidance services should also be made available to workers who are stuck in low-quality jobs and would like to make a career change. In addition, pay transparency measures might incentivise worker driven-job mobility by making the benefits of potential transitions more salient (see Box 5.9).

Box 5.9. The role of pay transparency measures in growth-enhancing job reallocation

Copy link to Box 5.9. The role of pay transparency measures in growth-enhancing job reallocation

Pay transparency measures mandate wage posting in job ads or making firm-level statistics on median/average pay by occupation public, often also by gender OECD (2021[73])). Such measures may not only help to promote gender equality at work but also may enhance growth-enhancing job-to-job mobility by making outside options with wage gains more salient to workers. This type of transparency could complement wage floors that compress wage differences by making remaining differences more visible.

Workers often face substantial information barriers when assessing external opportunities. Most notably, workers tend to anchor their expectations about potential earnings elsewhere on their current wages, leading to systematic underestimation of the potential wage gains of outside job opportunities, particularly among lower-paid workers who tend to underestimate the potential wage gains of outside job opportunities (Cullen, 2024[74]; Jäger et al., 2024[75]). Mandated wage posting in job ads and public wage statistics may be able to correct these misconceptions.

Recent evidence on mandating public disclosure of median employee pay in the United States shows that employees are more likely to depart firms introducing the disclosure of median employee pay. This suggests that pay transparency can increase job mobility by facilitating pay comparisons both within and across firms, with companies experiencing higher employee churn (Dambra et al., 2025[76]). Similarly, when the Slovak Republic mandated salary information in job postings, workers applied to a more diverse range of positions across sectors, and those hired after implementation earned approximately 3% higher wages than pre‑reform hires (Skoda, 2022[77])). Similar effects were observed in Denmark, where information about external job offers flowing through networks facilitated transitions to higher-paying positions (Caldwell and Harmon, 2019[78]).

With the EU Pay Transparency Directive, companies in EU countries are required to inform job seekers about starting salary or pay range of advertised positions by 2026. Several US states – notably California, Colorado, Connecticut, Maryland, Rhode Island and Washington – have introduced similar requirements in recent years (OECD, 2023[79]).

5.3.3. Policies directed at securing and supporting the mobility of mid-career and older workers

Workforce ageing tends to reduce the contribution of growth-enhancing job reallocation to aggregate wage and productivity growth. To some extent, this might reflect the fact that older workers have already progressed up the job ladder to better firms and contributed to growth-enhancing job reallocation. However, some older workers may face specific barriers to job mobility that keep them locked in low-quality firms or undermine their job-finding prospects following job displacement. Addressing these barriers requires effective policy interventions that tackle barriers to job mobility among mid-career and older workers.

Securing transitions of mid-career and older displaced workers through targeted support

The consequences of job loss tend to be particularly severe for mid-career and older workers. For instance, workers aged 46‑ 50 lose about half of their annual pre‑displacement earnings on average over the first 5 years following job loss, while workers aged 18 to 20 only lose about 20% on average (OECD, 2024[3]). Older workers face larger earnings losses in part because they are more likely to exit the labour force permanently following job loss, reducing effective labour supply and potentially undermining economic growth and the sustainability of public finances.

The risk of older workers leaving the labour force following job loss can be mitigated amongst others by early intervention measures targeted at workers at risk of dismissal or those who have been given notice of dismissal, as well as measures to adapt to structural transformation. For example, in Germany, the Qualifizierungsgeld allows for training and adaptation towards new roles within the same firm to support those who are affected by strong transformation pressures, while “basic support” for continuing vocational education and training (with subsidies depending on company size) is also available. Irrespective of transformation pressures, there is support for vocational education and training for those who have been given notice of dismissal. Further, Transfergesellschaften, which are part of a social plan during mass layoff, can temporarily employ displaced workers while offering support in finding new jobs and opportunities for skill development. Such programmes are particularly beneficial for workers who face a higher risk of long-term unemployment, many of whom are older workers (Fackler, Stegmaier and Upward, 2024[80]).

While labour force exits explain a part of the earnings losses among mid-career and older workers, they also tend to experience a decline in job quality when returning back to work. For example, mid-career and older workers aged 46‑50 experience a 28% decline in wages relative to their job before displacement compared with practically zero average wage losses for workers aged 18 to 30 (OECD, 2024[3]). By providing an in-work benefit partially covering the difference in a worker’s current and pre‑displacement wages, wage insurance can be a particularly useful instrument to mitigate wage losses among older workers following job loss. Importantly, this not only mitigates wage losses, but also tends to speed up the return to work by strengthening incentives for job search and lowering reservation wages.

So far, wage insurance has been primarily targeted at older workers OECD (2024[31]), for example through the Reemployment Trade Adjustment Assistance (RTAA) programme for trade‑displaced older workers in the United States. Evidence on this programme suggests that wage insurance significantly accelerated re‑employment and resulted in higher long-run cumulative earnings (Hyman, Kovak and Leive, 2024[81]). The reduction in unemployment duration was sizeable, and as a result, the costs of the programme were largely offset by reduced expenditures on unemployment insurance and increased tax revenues. There is no evidence that wage insurance led to lower re‑employment wages or a faster wage progression up the job ladder upon re‑employment. This may, to some extent, reflect the programme’s focus on older workers.

A wider availability of employment services can support mobility of employed workers

Employment services should be available for mid-career and older workers who have been displaced as well as those who have become stuck in low-quality jobs. Job-search assistance and career guidance can be particularly important for older workers who do not have recent experience with job mobility (OECD, 2024[3]). While public employment services traditionally focus on the unemployed, expanding these services to include employed job seekers, particularly those in dead-end jobs, could help prevent involuntary job losses and early labour market exit. For example, Switzerland’s Viamia programme offers free career guidance to employed workers aged 40 and over, helping them identify development opportunities and supporting job transitions (OECD, 2024[3]).

Career guidance in the form of career counselling and coaching might help older workers fill knowledge gaps and gaining the confidence needed for successful job search. In the Netherlands, the Programme for Sustainable Employability and Early Retirement (MDIEU) provides subsidies to employers and sectors to offer career counselling and coaching specifically designed for older job seekers.

Building professional networks is another critical challenge, with many older workers reporting they needed help finding contacts to “break into” desired jobs (OECD, 2024[3]). Local and national governments can strengthen the access to professional and social networks by creating public spaces that facilitate network building, such as group career counselling sessions specifically, designed for older workers. PES can also assist older workers in growing their networks and fostering intergenerational knowledge transfers through networking events and mentoring programmes.

Removing barriers that disincentivise job and geographic mobility

While direct interventions and employment services play a vital role in supporting job mobility, mid-career and older workers may also face other barriers, beyond the realm of the public employment services, that undermine job transitions.

Mid-career and older workers may hesitate to leave long-held positions due to the risk of losing accumulated severance entitlements and other tenure‑related benefits. An increased portability of social benefits and severance pay entitlements could boost job mobility among mid-career and older workers. For example, in 2003, Austria replaced tenure‑based severance pay by portable savings accounts funded through employer contributions, which workers can access in the case of job loss or transfer when directly moving to another job and convert in a pension at retirement. There is some evidence that this increased job mobility, especially among workers distressed firms (Kettemann, Kramarz and Zweimüller, 2017[82]). A sensible cap on severance entitlements for long tenures may also be an option.

Geographic constraints may create additional obstacles, especially for mid-career and older workers who are more likely to be homeowners. For example, overly restrictive housing policies can make relocation difficult, even when better opportunities exist elsewhere. Housing policy reforms could boost mobility, for example by lowering property transaction taxes, and easing overly strict rental regulations (Causa, Abendschein and Cavalleri, 2021[83]; Causa and Pichelmann, 2020[84]). Making housing assistance and access to social housing portable could also help some workers relocate more easily, particularly those on lower incomes. For example, the removal of queuing or residency requirements, such as the Right to Move in English housing associations, may increase job and geographic mobility (OECD, 2020[85]). While such approaches can benefit workers of all ages that want to relocate for a job, mid-career and older workers may often have stronger preferences for geographic stability and might benefit more from approaches that support local career transitions.

Training and work experience are necessary to keep up with changing labour markets

A changing world of work, with rapid advances in artificial intelligence and other technologies, makes continuous skill development crucial for maintaining high-quality employment at older ages. However, skill obsolescence poses a particular challenge for older workers, who are often less likely to participate in training and whose skills may therefore become obsolete as the skill demand on the labour market changes – see Chapter 4. This is in part related to fewer opportunities for training at older ages, and in part due to worries over whether acquiring new skills will lead to better outcomes (OECD, 2024[3]).

To foster continued learning and upskilling at older ages, governments can implement targeted training initiatives that help older workers leverage their existing skills while developing new competencies – see Chapter 4 for a more extensive discussion. For example, Australia’s Career Transition Assistance Program provides tailored support for workers aged 45 and over, focussing on improving digital literacy and identifying transferable skills. The programme includes skills assessments and the development of career plans, helping participants to navigate transitions into new jobs. However, the success of such approaches often hinges on financial barriers. Latvia targets low-qualified older workers by covering 90‑95% of training costs through the Improvement of Professional Competencies of Employed Person Program. This has increased training participation among workers who might otherwise be unable to afford such upskilling programmes (OECD, 2024[3]). Employers also play an important role in identifying relevant training initiatives. For example, Finland’s competence‑based qualification system for adults in vocational training allows individuals to focus on areas where they need development without attending entire training programmes, with such areas for development assessed by committees of educational institutions and representatives of industry employers.

Work experience programmes can also be important for fostering transitions for mid-career and older workers – see also Chapter 4. For example, in partnership with local employers, the United Kingdom’s Returnership Initiative offers training programmes (Skills Bootcamps), apprenticeships as well as pre‑employment training and work-experience programmes (Sector-based Work Academy Programmes). These approaches allow workers to gain experience in different sectors before committing to enter new occupations and/or industries, while simultaneously helping employers address skills shortages (OECD, 2024[3]).

Recognition of prior learning (RPL) programmes is another important tool for supporting mobility among experienced workers. These programmes can shorten the duration of training by acknowledging existing competencies – e.g. those acquired on the job or outside of formal education – making upskilling more attractive for older workers who may be hesitant to invest in lengthy training programmes – see also Chapter 4. Several European countries have implemented RPL frameworks – for instance, the Dutch Ervaringscertificaat and the French Validation des Acquis de l’Expérience can formally recognise skills acquired through work experience. This can, for example, ensure that access to higher education can be granted without the necessary formal qualifications (OECD, 2024[3]).

5.4. Concluding remarks

Copy link to 5.4. Concluding remarks

This chapter has provided new insights into how growth-enhancing job reallocation contributes to aggregate wage and productivity growth in the context of workforce ageing using linked employer-employee data from 17 OECD countries. It provides two key insights. First, the “job ladder”, i.e. job-to-job mobility to better firms, plays a key role in growth-enhancing job reallocation and hence aggregate wage and productivity growth. Second, workforce ageing tends to slow growth-enhancing job reallocation and may further result in weaker aggregate wage and productivity growth. Each of these findings has important implications for policies.

The importance of the job ladder in efficiency-enhancing job reallocation implies that policies that provide flexibility for firms need to be combined with policies that can facilitate job mobility for workers. In the policy discussion on efficiency-enhancing job reallocation, there has traditionally been a strong emphasis on policies that can provide employment flexibility (e.g. employment protection, product market regulations). This is consistent with the competitive market paradigm, in which firms determine employment given wages and workers are indifferent where they work, all else equal. However, if markets are imperfectly competitive, a more encompassing perspective on policies is needed. In an imperfectly competitive labour market, there are persistent wage gaps between firms, even for workers with the same skills, signalling the importance of frictions to job mobility and shaping the preferences of workers across different firms (OECD, 2022[4]). Consequently, there is a need to combine policies that provide employment flexibility for firms with policies that promote mobility for workers, including by addressing policy-related barriers to job mobility, such as professional licensing or the use of non-compete clauses, as well as policies that can help overcome barriers to job mobility between employers related to differences in skill requirements and location.

Policies that can help to maintain a flexible workforce at all ages and support job mobility among mid-career and older workers are particularly relevant. The chapter shows that since mid-career and older workers are less mobile, workforce ageing tends to slow growth-enhancing job reallocation, leading to lower aggregate wage and productivity growth. Whether lower mobility of mid-career and older workers is a result of larger costs of moving, for example because they face larger moving expenses (e.g. loss in job security, home ownership) or because they may have fewer working years to recover those implicit charges, or whether mid-career and older workers are already significantly better matched than younger workers and therefore have little to gain from job mobility, remains an open question. Irrespective of the origin of lower mobility among mid-career and older workers, policy has an important role to play in supporting job transitions of older workers to higher quality firms. Such policies should include employment and training services targeted at mid-career and older workers but also policies that reduce the costs of moving by making severance pay portable or reforming housing policies by reducing transaction taxes on selling and buying a home or relaxing overly strict rental regulations. There is also a role for increasing the returns to mobility at older ages via training and career guidance.

It is worth pointing out that climbing the job ladder matters not just for growth-enhancing job reallocation, but also for the integration of workers in the labour market and the subsequent advancement in their careers. Indeed, the role of the job ladder for individual wage growth is much larger than that for aggregate wage and productivity growth. In fact, the effect of upward job-to-job mobility on aggregate growth is partly offset by the negative contribution of employment mobility related to the entry of young cohorts in low quality firms. If anything, the potential importance of climbing the job ladder for the integration of the workers in the labour market may be increasing as younger cohorts tend to start their careers in lower quality firms. Consequently, it is even more important to ensure that opportunities for climbing the job ladder are equally shared by preventing that one’s socio‑economic background is a determinant of success in the labour market.

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Annex 5.A. Supplementary material

Copy link to Annex 5.A. Supplementary material

	Average annual growth rates (%)
	Labour productivity GDP per hour				Annual wages FTE employees				Median wage FT employees			Low pay (P10)
	1998‑2002	2002‑10	2010‑19	2019‑23	1998‑2002	2002‑10	2010‑19	2019‑23	2002‑10	2010‑18	2018‑22	2002‑10	2010‑18	2018‑22
Australia	2.44	1.02	1.20	‑0.09	1.70	1.57	0.62	‑0.05	1.42	1.01	0.14	1.08	1.28	‑0.96
Austria	1.68	1.40	0.68	0.93	0.76	0.95	0.31	‑0.19	0.56	0.33	‑0.51	..	..	..
Belgium	1.19	0.97	0.42	0.46	1.43	0.12	0.42	0.20	0.81	0.95	‑0.84	0.77	0.08	‑1.75
Canada	1.75	0.72	0.96	0.36	1.17	1.67	0.78	0.89	1.02	0.34	0.44	1.42	1.21	1.12
Chile	..	2.12	1.46	1.41	4.87	4.76	0.13	..	..	..	..	..	..	..
Colombia	‑0.92	2.01	2.15	2.40	..	..	1.23	..	..	1.22	0.53	..	..	..
Costa Rica	0.61	3.08	2.84	2.71	1.45	1.33	3.41	..	..	1.23	..	..	..	..
Czechia	2.66	2.95	1.92	0.27	4.16	3.13	2.59	‑1.91	2.38	2.45	0.28	1.85	2.81	1.79
Denmark	0.98	1.12	1.32	0.60	1.30	1.76	0.65	‑0.22	1.24	0.95	‑0.29	0.98	0.66	‑0.46
Estonia	..	4.24	2.35	‑1.70	6.30	5.76	3.37	0.43	5.45	4.68	0.61	7.05	5.45	1.28
Finland	2.66	1.34	0.56	‑0.45	1.44	1.72	0.26	‑0.13	2.16	0.41	‑0.10	2.04	0.32	‑0.32
France	1.95	0.62	0.82	‑0.60	1.28	1.26	0.70	‑0.61	0.80	0.39	‑1.31	1.07	0.08	‑3.55
Germany	1.80	0.69	1.06	0.41	0.74	0.21	1.55	‑0.76	‑0.21	0.72	0.66	‑1.07	0.95	2.37
Greece	2.64	1.08	‑1.70	0.42	5.70	0.67	‑2.74	‑0.22	2.50	‑2.80	‑2.58	2.41	‑4.41	1.38
Hungary	3.11	2.98	1.38	1.70	3.17	2.12	1.37	1.74	2.47	4.70	4.54	2.27	6.81	0.46
Iceland	3.33	2.96	1.28	1.55	3.90	‑0.87	3.60	0.46	‑7.31	6.55	‑2.65	‑6.80	6.33	‑1.65
Ireland	5.94	2.91	3.77	4.30	3.62	3.60	0.02	‑1.47	0.43	0.09	‑0.42	0.25	0.78	0.06
Israel	1.48	1.53	1.46	1.91	2.74	‑0.68	1.27	1.54	‑0.31	3.26	2.11	0.28	2.85	1.31
Italy	0.82	0.00	0.14	0.19	0.72	0.63	‑0.28	‑1.46	0.75	‑0.01	‑2.38	0.00	0.84	‑2.58
Japan	2.05	0.94	0.96	0.73	‑0.59	0.23	0.38	‑0.96	‑0.21	‑0.27	‑0.06	‑0.19	0.25	0.71
Korea	..	..	..	2.07	1.46	1.78	1.93	‑0.40	1.59	1.61	1.87	2.05	3.83	3.13
Latvia	5.65	5.84	2.64	3.72	3.39	6.46	4.15	3.28	4.39	5.64	1.10	5.69	6.52	‑0.23
Lithuania	5.42	4.43	3.24	1.15	6.83	4.94	4.51	1.61	3.10	5.17	9.28	4.15	6.71	9.67
Luxembourg	1.11	0.60	‑0.28	‑0.93	1.66	0.95	0.52	2.10	0.39	0.74	0.05	‑0.05	1.78	‑0.86
Mexico	..	..	0.01	‑0.62	3.26	‑1.75	0.70	0.32	..	..	..	..	..	..
Netherlands	1.72	1.07	0.39	0.39	0.40	1.01	‑0.14	‑1.29	0.65	‑0.03	‑0.89	0.40	0.05	‑0.83
New Zealand	1.46	0.95	0.65	0.76	0.81	2.04	1.37	1.46	1.63	1.05	0.52	1.48	1.78	1.70
Norway	2.41	0.16	0.49	0.43	2.78	2.55	1.06	0.28	2.19	0.83	0.42	1.71	0.27	0.74
Poland	5.31	3.12	3.12	1.17	4.32	1.45	3.01	‑0.17	3.67	2.95	2.32	3.80	3.80	3.72
Portugal	1.42	1.30	0.46	2.22	1.81	0.20	‑0.18	1.01	2.19	‑0.45	1.53	2.13	1.49	2.24
Slovak Republic	4.63	4.12	2.00	2.44	4.54	3.48	1.85	‑0.52	2.39	3.20	1.79	1.77	3.87	3.07
Slovenia	3.85	2.49	1.61	1.11	2.88	2.71	1.05	1.98	1.27	1.18	1.85	2.18	1.15	3.67
Spain	0.01	0.91	0.86	0.37	‑0.04	0.99	‑0.49	0.34	0.97	‑0.34	‑0.36	1.21	‑0.42	0.19
Sweden	2.54	1.47	0.80	0.41	2.99	2.03	1.23	‑0.40	1.86	1.48	‑0.57	1.82	0.98	‑1.02
Switzerland	1.70	1.36	0.78	1.10	1.66	0.47	0.53	0.47	0.30	1.10	0.06	0.43	1.13	0.21
Türkiye	1.88	3.69	3.52	..	0.70	2.92	3.80	..	..	‑7.15	..	..	..	..
United Kingdom	2.47	1.04	0.38	0.51	2.87	1.27	0.36	‑0.17	1.05	‑0.33	‑0.16	1.07	0.27	1.50
United States	2.23	2.15	0.76	1.61	2.23	1.00	0.99	1.27	0.17	0.37	0.63	‑0.16	0.70	2.45
European Area	1.83	0.77	0.76	0.30	1.21	0.83	0.65	‑0.74	0.59	0.41	‑0.59	0.20	0.60	‑0.49
OECD 30	2.09	1.44	0.85	0.90	1.63	0.98	0.78	0.21	0.54	0.41	0.16	0.32	0.74	1.09
OECD	1.85	1.42	0.90	0.88	1.66	0.98	0.92	0.18	0.53	0.20	0.21	0.36	0.78	1.05

Note: Aggregates are weighted by the GDP 2015 expressed in PPPs. OECD: average of countries shown. OECD30: Australia, Belgium, Canada, Czechia, Denmark, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Israel, Italy, Japan, Latvia, Lithuania, Luxembourg, the Netherlands, New Zealand, Norway, Poland, Portugal, the Slovak Republic, Slovenia, Spain, Sweden, Switzerland, the United Kingdom and the United States. Euro Area: Belgium, Finland, France, Germany, Greece, Ireland, Italy, Latvia, Lithuania, Luxembourg, the Netherlands, Portugal, the Slovak Republic, Slovenia and Spain.

Source: OECD calculations based on OECD productivity database, http://data-explorer.oecd.org/s/1xl; OECD dataset on average annual wages, http://data-explorer.oecd.org/s/1p0; OECD database on earnings distribution for median wages.

	Differences in annual growth rates
	Difference labour productivity and annual wage				Difference annual wage and median wage			Difference median wage and low pay
	1998‑2002	2002‑10	2010‑19	2019‑23	2002‑10	2010‑18	2018‑22	2002‑10	2010‑18	2018‑22
Australia	0.74	‑0.54	0.58	‑0.04	0.14	‑0.39	‑0.19	0.35	‑0.26	1.09
Austria	0.92	0.45	0.37	1.12	0.39	‑0.02	0.32	..	..	..
Belgium	‑0.24	0.85	0.00	0.27	‑0.69	‑0.53	1.04	0.04	0.87	0.91
Canada	0.58	‑0.95	0.18	‑0.53	0.65	0.44	0.45	‑0.40	‑0.88	‑0.68
Chile	..	‑2.64	1.33	..	..	..	..	..	..	..
Colombia	..	..	0.93	..	..	0.01	..	..	..	..
Costa Rica	‑0.84	1.75	‑0.56	..	..	2.18	..	..	..	..
Czechia	‑1.50	‑0.18	‑0.67	2.18	0.75	0.14	‑2.19	0.53	‑0.36	‑1.52
Denmark	‑0.32	‑0.64	0.67	0.82	0.52	‑0.30	0.06	0.26	0.29	0.17
Estonia	..	‑1.53	‑1.02	‑2.13	0.31	‑1.30	‑0.19	‑1.60	‑0.77	‑0.66
Finland	1.22	‑0.38	0.30	‑0.32	‑0.44	‑0.16	‑0.03	0.12	0.09	0.22
France	0.67	‑0.64	0.12	0.01	0.46	0.32	0.70	‑0.27	0.31	2.24
Germany	1.07	0.47	‑0.49	1.17	0.43	0.82	‑1.42	0.86	‑0.23	‑1.71
Greece	‑3.06	0.41	1.04	0.64	‑1.82	0.07	2.35	0.08	1.61	‑3.96
Hungary	‑0.06	0.87	0.01	‑0.04	‑0.35	‑3.33	‑2.80	0.20	‑2.10	4.08
Iceland	‑0.57	3.83	‑2.32	1.10	6.44	‑2.95	3.11	‑0.51	0.22	‑1.00
Ireland	2.32	‑0.69	3.75	5.77	3.17	‑0.08	‑1.05	0.18	‑0.68	‑0.48
Israel	‑1.26	2.20	0.19	0.37	‑0.37	‑1.98	‑0.57	‑0.59	0.41	0.80
Italy	0.09	‑0.63	0.42	1.65	‑0.12	‑0.26	0.92	0.75	‑0.85	0.20
Japan	2.64	0.70	0.58	1.69	0.45	0.65	‑0.90	‑0.02	‑0.52	‑0.76
Korea	..	..	..	2.47	0.19	0.32	‑2.27	‑0.46	‑2.22	‑1.26
Latvia	2.27	‑0.62	‑1.51	0.44	2.07	‑1.49	2.18	‑1.30	‑0.89	1.34
Lithuania	‑1.41	‑0.52	‑1.27	‑0.46	1.84	‑0.66	‑7.67	‑1.05	‑1.54	‑0.39
Luxembourg	‑0.55	‑0.35	‑0.80	‑3.03	0.56	‑0.22	2.05	0.44	‑1.04	0.91
Mexico	..	..	‑0.69	‑0.94	..	..	..	..	..	..
Netherlands	1.32	0.06	0.53	1.67	0.36	‑0.12	‑0.39	0.25	‑0.08	‑0.06
New Zealand	0.65	‑1.09	‑0.72	‑0.70	0.42	0.32	0.94	0.15	‑0.73	‑1.18
Norway	‑0.37	‑2.39	‑0.57	0.15	0.37	0.23	‑0.13	0.47	0.57	‑0.33
Poland	0.99	1.67	0.12	1.34	‑2.21	0.05	‑2.49	‑0.14	‑0.85	‑1.40
Portugal	‑0.39	1.10	0.64	1.21	‑1.99	0.27	‑0.52	0.06	‑1.94	‑0.71
Slovak Republic	0.09	0.64	0.15	2.96	1.09	‑1.35	‑2.31	0.62	‑0.66	‑1.28
Slovenia	0.97	‑0.23	0.55	‑0.86	1.44	‑0.12	0.13	‑0.91	0.02	‑1.83
Spain	0.06	‑0.08	1.35	0.03	0.02	‑0.15	0.70	‑0.25	0.08	‑0.55
Sweden	‑0.45	‑0.56	‑0.42	0.81	0.17	‑0.26	0.17	0.03	0.51	0.45
Switzerland	0.04	0.89	0.25	0.63	0.17	‑0.56	0.41	‑0.13	‑0.04	‑0.14
Türkiye	1.17	0.77	‑0.28	..	..	10.95	..	..	..	..
United Kingdom	‑0.40	‑0.23	0.02	0.68	0.22	0.69	‑0.01	‑0.02	‑0.60	‑1.66
United States	0.00	1.15	‑0.23	0.34	0.84	0.62	0.63	0.33	‑0.34	‑1.81
European Area	0.62	‑0.07	0.11	1.04	0.25	0.24	‑0.15	0.38	‑0.19	‑0.10
OECD 30	0.46	0.46	0.07	0.69	0.44	0.37	0.06	0.22	‑0.33	‑0.93
OECD	0.19	0.44	‑0.03	0.69	0.45	0.73	‑0.03	0.17	‑0.58	‑0.84

Country	Name	Source	Sample	Period
Austria	AMS-BMASK Arbeitsmarktdatenbank	Social security administration	Universe	2000‑19
Belgium	Sample from data warehouse – Kruispuntbank Sociale Zekerheid	Social security data, linked with census information	10% random sample of workers in private sector employment	2002‑19
Canada	Canadian Employer-Employee Dynamics Database	Tax administration	Universe	2001‑19
Denmark	Integrerede Database for Arbejdsmarkedsforskning (IDA) and other data from Statistics Denmark	Tax administration	Universe	2000‑19
Estonia	Data from the Tax and Customs Board Register	Tax administration	Universe	2003‑19
Finland	FOLK employment data from Statistics Finland, Employer Payroll Report from Tax Admin.	Tax administration	Universe	2000‑19
France	Panel DADS	Social security administration	8.5% random sample of workers	2002‑1927
Germany	Integrierte Erwerbsbiographien (IEB)	Social security administration	10% random sample of workers	2000‑19
Hungary	ADMIN – I – Panel of administrative data (OEP, ONYF, NAV, NMH, OH)	Social security administration	50% random sample of workers	2003‑17
Italy	INPS-INVIND Panel	Social security administration	8.6% random sample of firms	2002‑19
Lithuania	SoDra microdata	Social Security administration	25% random sample of Social Security population	2000 – 2019
Netherlands	CBS Microdata from Statistics Netherlands	Tax administration	Universe	2006‑19
New Zealand1	Integrated Data Infrastructure (IDI) and Longitudinal Business Database (LBD) from Stats NZ	Tax administration	Universe	2000‑19
Norway	Arbeidsgiver- og arbeidstakerregister (Aa-registeret), Lønns- og trekkoppgaveregisteret (LTO)	Tax administration	Universe	2008‑19
Portugal	Quadros de Pessoal	Mandatory employer survey	Universe	2002‑19
Spain	Muestra Continua de Vidas Laborales con Datos Fiscales (MCVL-CDF)	Social security and tax administration	4% random sample of workers	2006‑19
Sweden	Longitudinell integrationsdatabas för sjukförsäkrings- och arbetsmarknadsstudier (LISA), Företagens ekonomi (FEK), Jobbregistret (JOBB)	Social security administration	Universe	2002‑18

1. Access to the data used in this study was provided by Stats NZ under conditions designed to give effect to the security and confidentiality provisions of the Data and Statistics Act 2022. The results presented in this study are the work of the author, not Stats NZ or individual data suppliers. These results are not official statistics. They have been created for research purposes from the Integrated Data Infrastructure (IDI) and Longitudinal Business Database (LBD) which are carefully managed by Stats NZ. For more information, please visit www.stats.govt.nz/integrated-data/. The results are based in part on tax data supplied by Inland Revenue to Stats NZ under the Tax Administration Act 1994 for statistical purposes. Any discussion of data limitations or weaknesses is in the context of using the IDI for statistical purposes, and is not related to the data’s ability to support Inland Revenue’s core operational requirements.

Annex Figure 5.A.1. Annualised change in hourly productivity and wages within and between industries
Copy link to Annex Figure 5.A.1. Annualised change in hourly productivity and wages within and between industries

Annex Figure 5.A.2. Accounting for the role of mobility for cross-country differences in aggregate growth rates
Copy link to Annex Figure 5.A.2. Accounting for the role of mobility for cross-country differences in aggregate growth rates

Share of the cross-country variances in growth rates in aggregate wages and productivity explained by the covariance of each mobility component (beta values)

Annex Figure 5.A.3. Time trends in responsiveness
Copy link to Annex Figure 5.A.3. Time trends in responsiveness

Age‑specific between-firm aggregate wage and productivity growth rates, by age group, first vs. last year, percentage

Annex Figure 5.A.4. Accounting for the role of dispersion and responsiveness for cross-country differences in aggregate growth rates
Copy link to Annex Figure 5.A.4. Accounting for the role of dispersion and responsiveness for cross-country differences in aggregate growth rates

Share of the cross-country variances in between-firm growth rates in aggregate wages and productivity explained by the covariance of dispersion in firm quality and responsiveness to firm quality (beta values)

Annex Figure 5.A.5. The age-composition of employment has undergone significant change since 2000
Copy link to Annex Figure 5.A.5. The age-composition of employment has undergone significant change since 2000

Historical and projected evolution of age‑composition of employment, 17 OECD countries, 2000‑60, percentage

Notes

Copy link to Notes

← 1. The analysis using linked employer-employee data is based on contributions by Eliana Viviano (Bank of Italy), Patrick Bennett (University of Liverpool and IZA), Cesar Barreto (OECD and FAU), Felipe Bento Caires (European University Institute), Lucas Chen (Reserve Bank of New Zealand), Jose Garcia-Louzao (Bank of Lithuania, Vilnius University and CESifo), Dogan Gülümser (Uppsala University), Salvatore Lattanzio (Bank of Italy and Bocconi University), Benjamin Lochner (FAU, IAB and IZA), Stefano Lombardi (VATT, IFAU, IZA and UCLS), Tahsin Mehdi (StatCan), Jordy Meekes (Leiden University and IZA), Balázs Muraközy (University of Liverpool), Marco G. Palladino (Banque de France), Kjell Salvanes (NHH and IZA), Oskar Nordström Skans (Uppsala University, UCLS, IZA and IFAU), Rune Vejlin (Aarhus University and IZA), and Wouter Zwysen (ETUI). This chapter is part of a broader OECD project that mobilises linked employer-employee data for cross-country research and policy analysis (LinkEED 2.0). For more details, please visit: www.oecd.org/en/about/projects/linkeed-200.html.

← 2. This is to an important extent driven by large countries, including the United States. When taking the simple average across countries, there is no evidence of a decoupling between wage and productivity growth.

← 3. This approach keeps age‑specific mobility fixed in 2017 and assumes no change in this measure over time. However, it is likely that the mobility of older workers increases over time as careers become longer. For example, there is some indication that the contribution of job mobility to aggregate wage and productivity growth among older workers has increased between the early 2000s and late 2010s (Annex Figure 5.A.3).

← 4. Employment refers to private sector non-agricultural dependent employment.

← 5. In contrast to the concept of job reallocation discussed in Section 5.2, efficiency-enhancing job reallocation takes account of both the amount of job reallocation and its direction.

← 6. In perfectly competitive labour markets, firms are wage‑takers and there is no link between wages and productivity across firms. In that case, wage differences between firms reflect “compensating differentials” for differences in non-wage working conditions.

← 7. Since the model used for Figure 5.6 can only be estimated for incumbent firms, entering firms are excluded from the analysis.

← 8. Gross worker mobility is extensive, with about 58% of workers hired or separating from their employer each year. On average, job-to-job mobility accounts for 53% of this gross worker mobility, while employment mobility accounts for 47%.

← 9. As noted by Bertheau and Vejlin (2022[93]) annual data may overstate the relative importance of gross job-to-job mobility in overall gross worker mobility. It is not clear, a priori, how aggregation bias affects the role of net job-to-job mobility in efficiency-enhancing job reallocation. The role of net job-to-job mobility only captures the direction of job mobility across firms that differ in their wages and productivity and does not depend on the importance of gross worker flows. We find, using quarterly data for Austria for 2018 and 2019, that it does not significantly alter the role of net job-to-job mobility in growth-enhancing job reallocation, i.e. its direction across the firm distribution in productivity and wages.

← 10. Cohort effects can also play a role in explaining declining responsiveness of employment growth to firm quality. For example, more recent cohorts may be less mobile because they tend to be more skilled and more specialised, rendering the matching process between firms and workers more complex (Baksy, Caratelli and Engbom, 2024[30]).

← 11. To some extent, this reflects the larger negative contribution of employment mobility in countries with low wage growth.

← 12. “Beta values” are calculated by the covariance of within-firm growth and total growth across countries divided by the variance of total growth and the covariance of between-firm growth and total growth across countries divided by the variance of total growth.

← 13. Based on quarterly data for the United States in Hahn, Hyatt and Janicki (2021[28]), on-the‑job wage growth contributed about 0.3 percentage points to the average quarterly to earnings growth between 1996 and 2017. The contribution of job-to-job mobility was about 0.4 percentage points and employment mobility ‑0.5 percentage points.

← 14. All exercises in this section exclude evidence for Belgium, as the source data does not contain workers beyond the age of 69 and is therefore not comparable to data used for other countries in this chapter.

← 15. Differences in the responsiveness of employment growth to firm quality by gender are negligible.

← 16. This is also reflected by a willingness of older workers to accept a decline in hourly wages in exchange for fewer working hours (Ameriks et al., 2020[86]).

← 17. In practice, this exercise is restricted to countries with uninterrupted time‑series between at least 2005 and 2018.

← 18. Under a strong assumption of unchanged trends in between-firm growth rates across age groups, projections on demographic developments between 2020 and 2060, following Fluchtmann, Keese and Adema (2024[19]) as well as UN (2024[20]) (not shown here), only marginally depress annual aggregate wage and productivity growth further. This is mainly a result of more muted projected changes in the age composition of employment over future decades.

← 19. The common shift, which may also capture some effects of ageing, is positive. How much of this can be attributed to ageing itself is unclear.

← 20. Boeri et al. (2022[91]), and Bianchi et al (2023[92]) find that delaying retirement reduces opportunities for younger workers in the same firm, while Carta et al. (2021[5]) find the opposite, possibly due to the benefits of age diversity for productivity (OECD, 2020[94]). Hernæs et al. (2023[87]), who find reduced demand for young workers through delayed retirement, also find a small positive effect on labor productivity in the short run.

← 21. Recent evidence from Sweden on the role of employment protection for older workers demonstrates that overly strict employment protection can preserve jobs that firms would prefer to terminate as they are no longer sufficiently productive. While this affects a relatively small fraction of older workers, most of which remain sufficiently productive, it can lower firm productivity (Saez, Schoefer and Seim, 2023[88]).

← 22. Gross worker mobility is somewhat over 50% as shown in Figure 5.5, while net job mobility is around 2.5% on average across quintiles in Figure 5.6. This suggests that the share of net mobility in overall worker mobility is around 5%.

← 23. The rate of excess job reallocation, based on job creation and destruction as defined in Section 5.1, accounts for about a third of gross worker mobility. However, this is an upper bound since not all changes in net firm employment contribute to changes in the firm quality distribution.

← 24. Job retention schemes are not effective in dealing with structural shocks and indeed risk slowing job reallocation in response to permanent shocks.

← 25. Johnson et al. (2024[89]) show that non-competes in the United States have potentially far-reaching consequences for labour market dynamics. However, they do not find important differences across age groups.

← 26. Similarly, Forth et al. (2025[90]) find that the minimum wage tends to reduce job-to-job mobility in the United Kingdom.

← 27. Due to methodological breaks affecting employment levels and productivity measures, data for France exclude the years 2008‑09 and 2015‑17.

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