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This paper analyzes the productivity effects of structural capital such as research and development (R&D) and organizational capital (OC). Innovation work also produces innovation-labor-biased technical change (IBTC) and knowledge spillovers. Analyses use full register-based dataset of Finnish firms for the period 1994–2014 from Statistics Finland.

Intangibles are derived from the labor costs of innovation-type occupations using linked employer-employee data. The approach is consistent with National Accounting and offered as one method in OECD (2010) and applied in statistical offices, e.g. in measuring software. The EU 7th framework Innodrive project 2008–2011 extended this method to cover R&D and OC.

Methodology is implementable at firm-level and offers way to link personnel reporting to intangible assets. The OC-IBTC as well as total resources allocated to OC are relevant for productivity growth. The R&D stock is relatively higher but R&D-IBTC is smaller than OC-IBTC. Public policy should, besides technology policy, account for OC and OC-IBTC and related knowledge spillovers in the industries that are most important among the SMEs (low market-share-firms).

The data are based on remote access to Statistics Finland; the data cannot be disseminated.

Intangible assets are measured from innovation work that encompasses not only R&D work. IBTC is proxied in production function estimation by relative compensations on IA work. The non-competing nature of IAs is captured by IA knowledge spillovers. The sample sizes are much higher than in earlier studies on horizontal knowledge spillovers (such as for SMEs,) thus bringing additional generality to the results.

The aim of this paper is to examine rent sharing under a heterogeneous workforce using Finnish linked employer‐employee data in 1987‐1998. Rent sharing is one component of the empirically estimated firm‐effect and depends on the sensitivity of firm‐level payments to quasi‐rents. It is shown that rent sharing moderates other forms of firm‐level wages. Thus, the lower the starting wages, the higher rent sharing will be. Alternatively, in many firms new workers are attracted to the job by paying high entry wages, while these new workers do not obtain the full level of rent sharing in the first years of service. Highly educated workers are the main targets of rent sharing and rent sharing is more common in R&D‐intensive firms. All this shows the importance of human capital accumulation and flexible technology in explaining rent sharing. This can also explain why rent sharing is targeted at experienced workers in R&D‐intensive firms. In non‐R&D‐intensive firms, job search is also of importance. Rent sharing is more common when highly educated workers have flexible labour supply.

To analyse productivity effects of performance‐related pay (PRP).

Fixed effect analysis of the productivity effects of the introduction of PRP scheme using linked employer‐employee data from Finland in 1996‐2002 and controlling for the skill structure of the employees.

PRP improves both productivity and profitability by the same magnitude of around 6 per cent, but only if the compensations are substantial enough and exceeding on average 3.6 per cent of salaries for those who receive it. Incentive effects relate to the introduction of PRP, usually accompanied by new human resource management. PRP in Finland cannot, however, be directly linked to an increase in participation of employees in decision‐making. PRP schemes have substantially improved firm performance without creating much wage pressures.

Useful information for the implementation and design of incentive‐based wage schemes.

Very few papers using large data sets have information on exact PRP payments that are separate from bonus pay or piece wages.

This paper introduces the Special Issue on competitive versus non‐competitive wage differentials, a collection of papers originally presented at the 79th Conference of the Applied Econometrics Association held in Brussels in May 2002.

This paper aims to revisit the relationship between intangible capital and labour productivity growth using the largest, up-to-date macro database (2000–2015) available to corroborate the econometric findings of earlier work and to generate novel econometric evidence by accounting for times of crisis (2008–2013) and economic recovery (2014–2015).

To achieve these aims, this paper employs a cross-country growth accounting econometric estimation approach using the largest, up-to-date database available encompassing 16 EU countries over the period 2000–2015. The paper accounts for times of crisis (2008–2013) and of economic recovery (2014–2015). It separately estimates the contribution of three distinct dimensions of intangible capital: (1) computerized information, (2) innovative property and (3) economic competencies.

First, when accounting for intangibles, the paper finds that these intangibles have become the dominant source of labour productivity growth in the EU, explaining up to 66 percent of growth. Second, when accounting for times of crisis (2008–2013), in contrast to tangible capital, the paper detects a solid positive relationship between intangibles and labour productivity growth. Third, when accounting for the economic recovery (2014–2015), the paper finds a highly significant and remarkably strong relationship between intangible capital and labour productivity growth.

This paper corroborates the importance of intangibles for labour productivity growth and thereby underlines the necessity to incorporate intangibles into today's national accounting frameworks in order to correctly depict the levels of capital investment being made in European economies. These levels are significantly higher than those currently reflected in the official statistics.

High-growth firms generate a large share of new jobs and are thus the key drivers of innovation and industry dynamics. As the employees' education supports innovation and productivity, this article hypothesizes that employee competences explain high growth.

The study approaches this by examining intangible capital and specialized knowledge to evaluate how these characteristics support the probability of becoming a high-growth firm. The estimation uses linked employer–employee data from Danish registers from 2005 to 2013.

As the authors measure high growth with the size-neutral Birch index, they can examine the determinants of high growth across different firm size classes. The findings imply that intangible capital relates positively to the firm's high growth.

Previous research on high-growth firms is concentrated on the owners’ education. This article broadens to the high education of all employees and accounts for the employees’ occupation and capitalization of knowledge with intangible capital.