1. Study productivity performance over the entire productivity distribution, with further refinements by size, age, and ownership categories.
2. Enable comparative within-industry productivity analysis across countries and over time.
3. Examine the effectiveness of various policy frameworks aimed at shaping firm productivity and enhancing resource allocation to more productive firms.
4. Identify firms at the ‘frontier’ – the best performers – and understand how they differ across countries, what drives their performance, and how much they contribute to aggregate productivity growth.
5. Investigate the relationship between productivity and wage dispersion, and whether heterogeneity in productivity has contributed to wage inequality.
6. Better understand the role of digitalisation, technology diffusion and the rise of the intangible economy for firm-level productivity performance.

To a large degree, productivity differences explain the differences in income per capita across countries. But as firm-level productivity can vary widely, even in narrowly defined industries, analysing industry-average productivity data does not offer a full picture. Countries might display the same average, for example, but this might be characterised by very different underlying distributions.

This has important policy implications. For instance, low average productivity in a given country might be due to its having too few firms at the top, which would hint at an underlying lack of innovation, or to its having too many firms at the bottom, which would point to weak market selection. These two opposite situations would call for very different policies. Understanding how firm-level productivity patterns translate into aggregate productivity is, therefore, a key challenge for policymakers.

The core MultiProd database provides a detailed portrait of productivity and wage dispersion in 22 countries, with consistent coverage over the period from 1995 to 2012. The data collected include measures of within-industry productivity dispersion and wage inequality; measures of allocative efficiency; and decompositions of productivity growth related to different groups of firms (e.g. entrants; low vs. high productivity firms etc.). Statistics are collected both by size, age, and ownership categories and for different percentiles of the firm-level productivity distribution. The 2020 update of MultiProd is includes a number of additional analyses, including information on mark-ups and intangible capital, measures of concentration, and transition matrices that track the dynamics of productivity growth across time.

MultiProd data is collected by the OECD through a network of affiliated researchers and national statistical offices with access to confidential, firm-level research data for a single country. The OECD provides code to harmonise differences in the data across countries and to generate an analysis dataset capturing the dynamics and dispersion of productivity and wages across highly disaggregated groups of firms. The local researchers and statistical agencies then apply the necessary steps to ensure that outputs meet specific confidentiality requirements and return the secure, micro-aggregated results to the OECD for analysis.

Current research within the MultiProd project focuses on the role of the transition to a digital and knowledge economy in determining productivity dispersion within sectors, and the role of policy in supporting the diffusion of technology and knowledge to low productivity firms.

Indicators based on MultiProd v.1

• Labour productivity in different labour productivity performance groups, by country and 1-digit sector (.xlsx)
• Labour productivity across firm size, by country and 1-digit sector (.xlsx)
• Dispersion of labour productivity (90-10 ratio), by country and 2-digit industry (.xlsx)
• Wages in different labour productivity performance groups, by country and 1-digit sector (.xlsx)
• Wages across firm size, by country and 1-digit sector (.xlsx)

Indicators based on MultiProd v.2

• Indicators on trends in productivity (.xlsx)