Firms’ pay practices play a key role in shaping wages, wage inequality and the gender wage gap, but this has so far only been reflected to a limited extent in the policy debate. The evidence in this report shows that around one-third of overall wage inequality can be explained by gaps in pay between firms rather than differences in the level and returns to workers’ skills. Gaps in firm pay, in turn, reflect dispersion in productivity, but also disparities in wage-setting power between them. To tackle rising wage inequality, worker-centred policies (e.g. education, adult learning) need to be complemented with firm-oriented policies. This involves notably: (1) policies that promote the productivity catch-up of lagging firms, which would not only raise aggregate productivity and wages but also reduce wage inequality; (2) policies that reduce wage gaps at given productivity gaps without limiting efficiency-enhancing reallocation, especially the promotion of worker mobility; and (3) policies that reduce the wage-setting power of firms with dominant positions in local labour markets, which would raise wages and reduce wage inequality without adverse effects on employment and output.

Over the past few decades, policy makers in many OECD countries have been grappling with low productivity growth and rising income inequality. At the same time, gaps in business performance in the form of productivity have widened, with a small number of high-performing businesses thriving while others falling further behind. High-performing firms have also been pulling away in terms of sales and profitability, and industry concentration is on the rise in many countries. The COVID‑19 crisis could reinforce these trends, as the digitalisation of business models has accelerated in a way that has favoured large tech-savvy firms. However, while there is growing evidence that widening gaps in business performance contribute to low aggregate productivity growth, little is known about its implications for wage and, ultimately, income inequality.

Firms’ pay practices play a key role in shaping wages, wage inequality and the gender wage gap, but their contribution has so far not been well reflected in the policy debate. The evidence in this volume shows that around one‑third of overall wage inequality can be explained by gaps in pay between firms rather than differences in the level and returns to workers’ skills. Gaps in firm pay, in turn, reflect differences in productivity, but also disparities in wage‑setting power. To tackle rising income inequality, worker-centred policies (e.g. education, adult learning) need to be complemented with firm-oriented policies. This involves notably: (1) policies that promote the productivity catch-up of lagging firms, which would not only raise aggregate productivity and wages but also reduce wage inequality; (2) policies that reduce wage gaps at given productivity gaps without limiting efficiency-enhancing reallocation, especially the promotion of worker mobility; and (3) policies that reduce the wage‑setting power of firms with dominant positions in local labour markets, which would raise wages and reduce wage inequality without adverse effects on employment and output.

In many OECD countries, low productivity growth has coincided with rising wage inequality. Widening wage and productivity gaps between firms may have contributed to both developments. This chapter uses harmonised linked employer-employee data for 20 OECD countries to analyse the role of firms in wage inequality. The main finding is that, on average across countries, differences in average wages between firms explain about one-half of overall wage inequality. Two-thirds of between-firm wage inequality (i.e. about a third of overall wage inequality) reflect firms’ wage-setting practices or wage premia, i.e. the part of wages that is determined by the firm rather than the characteristics of its workers. The remaining third (i.e. a sixth of overall wage inequality) can be attributed to differences in workforce composition across firms. The contribution of differences in wage premia to wage inequality tends to be larger in countries with decentralised collective bargaining systems and lower levels of job mobility. Overall, these results suggest that firms play an important role in explaining wage inequality, as wages are to a notable extent determined by firm wage-setting practices rather than being exclusively by workers’ skills.

This chapter investigates the role of cross-firm dispersion in productivity in explaining dispersion in firm wage premia, as well as the factors shaping the link between productivity and wages at the firm level. The results suggest that around 15% of cross-firm differences in productivity are passed on to differences in firm wage premia. The degree of pass-through is systematically larger in countries and industries with more limited job mobility, where low-productivity firms can afford to pay lower wage premia relative to high-productivity ones without a substantial fraction of workers quitting their jobs. Stronger product market competition raises pass-through while more centralised bargaining and higher minimum wages constrain firm-level wage setting at any given level of productivity dispersion. From a policy perspective, the results suggest that the key priority to reduce wage differences between firms while easing the efficient reallocation of workers across them is to promote job mobility.

High labour market concentration (i.e. the concentration of employment or hiring in a small number of firms) may allow employers to suppress wages. This chapter uses linked employer-employee data from seven OECD countries to analyse the extent of labour market concentration across countries, industries, geographical areas and groups of workers, as well as its effects on wages. The main findings are: (1) a significant share of workers (around 20%) are employed in highly-concentrated labour markets, especially in manufacturing and rural areas; (2) high labour market concentration reduces wages; (3) negative wage effects tend to be particularly pronounced for low-qualified workers; and (4) over the past two decades, negative wage effects have become stronger at any given level of concentration, but concentration itself has remained broadly flat. These results imply that labour market concentration is a relevant issue from the perspective of public policies aiming to address inequality but cannot explain broader economic trends related to wage stagnation and the decline in the labour income share experienced by a number of countries over the past two decades.

This chapter contributes to a better understanding of the gender wage gap over women’s professional career by focusing on the gap in pay between similarly-skilled women and men within and between firms at each age. Using linked employer-employee data for sixteen OECD countries, it is shown that about three quarters of the gender wage gap reflects pay differences within firms, due to differences in tasks and responsibilities as well as differences in pay for work of equal value (e.g. bargaining and discrimination). The remaining one quarter reflects differences in pay between firms, due to the concentration of women in low-wage firms. The gender wage gap within and between firms tends to increase over the working life. This reflects gender differences in opportunities for upward mobility between and within firms and the role of career breaks at the age of childbirth for the career progression of women. Tackling the gender wage gap crucially requires promoting access of women to high-wage jobs and firms. This chapter has been written by an OECD team consisting of Antton Haramboure and Alexander Hijzen with contributions of: Antoine Bertheau (University of Copenhagen, DENMARK), Gabriele Ciminelli (OECD), Chiara Criscuolo (OECD), Katarzyna Grabska-Romagosa (Maastricht University, THE NETHERLANDS), Ryo Kambayashi (Hitotsubashi University, JAPAN), Michael Koelle (OECD), Balazs Murakőzy (University of Liverpool, HUNGARY), Vladimir Peciar (Ministry of Finance, SLOVAK REPUBLIC), Andrei Gorshkov and Oskar Nordström Skans (Uppsala University, SWEDEN), Satu Nurmi (Statistics Finland/VATT, FINLAND), Catalina Sandoval and Jonathan Garita (Central Bank of Costa Rica, COSTA RICA), Nathalie Scholl (OECD), Cyrille Schwellnus (OECD) and Richard Upward (University of Nottingham, UNITED KINGDOM). For details on the data used in this chapter please see the standalone Data Annex and Disclaimer Annex.