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Industry and globalisation

MultiProd: Uncovering the micro drivers of aggregate productivity

 

 

The MultiProd project studies productivity patterns and investes the extent to which different policy frameworks can shape firm productivity. It examines the way resources are allocated to more productive firms.

Because employment patterns and productivity growth play a central role in shaping the welfare of societies and the competitiveness of countries, the analysis aims to be a key input for policy makers as firm-level productivity and allocative efficiency are the engines of future growth.

Drawing on the experience of the OECD ‘DynEmp’ (Dynamics of Employment) project, which provided harmonised micro-aggregated data to analyse employment dynamics, the MultiProd project provides cross-country harmonised micro-aggregated data of paramount importance for understanding productivity performances.

 

 

PROJECT OBJECTIVES

  • Study productivity performance over the entire productivity distribution, with further refinements by size, age, and ownership categories
  • Enable comparative within-industry productivity analysis across countries and over time
  • Examine the effectiveness of various policy frameworks aimed at shaping firm productivity and enhancing resource allocation to more productive firms.
  • 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.
  • Investigate the relationship between productivity and wage dispersion, and whether heterogeneity in productivity has contributed to wage inequality.
  • Better understand the role of digitalisation, technology diffusion and the rise of the intangible economy for firm-level productivity performance.

POLICY IMPLICATIONS

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. 

 

Data and Methodology

The project relies on a distributed microdata methodology, in a similar fashion to the DynEmp project. A software will be sent to countries, and the affiliated researchers in each country will run the code on their confidential microdata ensuring that the aggregated output will respect confidentiality rules. An important aspect of the methodology is to make sure that the data are comparable across countries, subject to data availability. 

The data sources used are production surveys or similar datasets, such as balance sheets and income statements, which contain information on output (production or sales), value added, inputs (employment, capital, intermediates) and labour costs. MultiProd matches them to social security records or business registers, which considerably improves the quality of the analysis as it provides the weights to correct for the under-representation of certain groups of firms due to the sampling in the production surveys, thereby producing statistics based on representative sample and reliable aggregates.

Outputs

The output of the project is a collection of detailed statistics at a sectoral level for the period from 2000 including data availability for different variables, including:

  • labour and multifactor productivity;
  • employment and wages (in both level and growth);
  • capital intensity;
  • investment.

These statistics are collected both at different percentiles of the firm-level productivity distribution and refined by size, age, and ownership categories. The output includes measures of within-industry productivity dispersion and wage inequality; measures of allocative efficiency; decompositions that will make it possible to quantify the contribution to aggregate productivity level, growth and volatility of different types of firms (e.g. entrants; low vs. high productivity firms etc.).

In addition, the software allows countries to perform empirical analysis within a harmonised framework, with the aim of establishing a set of stylised facts for each country regarding the relationship between productivity and firm characteristics (size, age, previous performance, ownership, etc.).

 

Publications

  

multiprod INDICATORS

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

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