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This paper explores the pattern of technical change in the Korean economy from 1970 to 2013 and investigates its determinants. We use the Classical growth-distribution schedule to show that the labor-saving and capital-using pattern has predominated. For the rationale behind this Marx-biased technical change, we focus on the relationship between technical change and real wage growth via the evolution of labor and capital productivity, and verify the historical direction of technical change against the rise and fall of the working class. Furthermore, we find that the deviation during the post-crisis period from the long-run trend of Marx-biased technical change is not attributable to the vitality of new technological innovations, but rather the reflection of class dynamics over extracting productivity under weaker capital deepening. The results suggest that the recent deterioration of labor share and labor unions in Korea is closely associated with low incentive for technological progress, which contributes to prolonged stagnation.

This chapter examines explanations for the slowdown of capital accumulation since 1980. Using Bureau of Labor Statistics data on trends on productivity and capital spending, we find that slowing productivity growth accounts for slower capital accumulation. Other explanations for the downturn, such as outsourcing, the “post-industrial” economy, and financialization, do not reflect macroeconomic trends. However, we argue that shareholder value ideology affected decisions about how to balance productivity growth and inputs such as capital and labor. We discuss the consequences of slowing accumulation on American economic hegemony.

This paper deals with some structural indicators and their evolution, in China and its regions, over the period 1981-2010.

The paper uses a quantitative approach. Linear programming and structural growth decompositions were used. The authors first produce estimates of the optimal productivities of incremental capital and the optimal incremental income elasticity of capital by means of a linear programming exercise. They then produce an accounting growth decomposition to assess the changes in the contribution of capital productivity, capital intensity and labour participation to the growth rate of output per capita. Finally, they combine an accounting growth decomposition with a standard production function, growth accounting, decomposition to assess the contribution of both capital productivity and capital intensity to total factor productivity (TFP). They also show in the Appendix the difference in the TFP growth contribution when marginal elasticities are assumed variable over time and when scale returns are assumed to be increasing rather than constant.

The main conclusion of the paper is that capital intensity, rather than capital productivity or labour participation, has been the main growth contributor. Capital productivity has fallen, while capital intensity has increased significantly, but that does not mean that quantity in itself, rather than quality, is behind such growth, as total factor productivity, which is significantly more than engineering technical change, has been relatively important over the period.

Both the use of linear programming to assess the evolution of incremental capital productivity and the decomposition of TFP.

“Economics is a Serious Subject.” Edwin Cannan.

Uses a model of technical change embodied in capital equipment to analyse average labour productivity growth. Determinants of productivity growth identified in this analysis are: (1) the rate of labour‐saving technical change; (2) the differential in the rates of change of wages and the rental price of capital; and (3) the rate of growth of industry productive capacity. Finds evidence that each of the identified factors has a positive and statistically significant relationship to average labour productivity growth in a cross‐section of Australian manufacturing industries.

The purpose of this paper is to provide new evidence, made possible by human capital data that became available after IFRS adoption, on the productivity of intellectual capital and its components. These productivity measures are modelled to determine their value-relevance in the share market, and the modelling is extended to comparative productivity measures for the book-value of assets.

Financial data are sourced from financial databases and company annual reports on a sample of 160 Australian listed firms over a five-year period. Panel regression analysis is used to test five models built from Riahi-Belkaoui's (1999) general price model of the value-relevance of accounting numbers.

The results show that the productivity of human capital, structural capital and intellectual capital are each significantly positively related to share price (i.e. have value-relevance), whereas the productivity of total assets at book-value is non-significant and tangible assets is inversely significant.

This study constructs a new improved method of computing the amount of structural capital, and uses recently available financial statement data to provide first-time evidence on human capital and its inclusion in the determination of the amount of intellectual capital. These new models and data enable a direct comparison to be made between the value-relevance of intellectual and the book-value of assets.

Data on shift‐working, annual hours and employment are used to derive alternative measures of capital utilisation in seven countries. There is a positive relationship between utilisation and capital intensity, and utilisation levels in developing countries are significantly higher than in industrialised countries. Inter‐country comparisons of capital productivity are made by comparing industries with similar levels of capital per production worker. It is shown that output per input of capital services in some developing countries is half that of comparable industries in industrialised countries. However, the higher levels of utilisation in developing countries partially offset these low values for the productivity of capital services. Data on labour productivity and earnings are combined to measure labour costs per unit of output and thus throw light on the overall competitive position of industry in the different countries.

“It should also be noted that the objective of convergence and equal distribution, including across under-performing areas, can hinder efforts to generate growth. Contrariwise, the objective of competitiveness can exacerbate regional and social inequalities, by targeting efforts on zones of excellence where projects achieve greater returns (dynamic major cities, higher levels of general education, the most advanced projects, infrastructures with the heaviest traffic, and so on). If cohesion policy and the Lisbon Strategy come into conflict, it must be borne in mind that the former, for the moment, is founded on a rather more solid legal foundation than the latter” European Commission (2005, p. 9)Adaptation of Cohesion Policy to the Enlarged Europe and the Lisbon and Gothenburg Objectives.

The purpose of this paper is to examine the effect of intellectual capital on bank productivity in an emerging market in Africa.

The Malmquist productivity index (MPI) is employed to estimate productivity growth of 18 banks in Ghana from 2003 to 2011 while the Value Added Intellectual Coefficient (VAIC) is used to measure bank intellectual capital performance. The panel-corrected standard errors estimation technique is used to estimate a panel regression model with MPI as the dependent variable. Bank market concentration and bank size are controlled for in the regression analysis.

The authors find productivity growth to be largely driven by efficiency changes compared to technological changes. The results from the regression analysis indicate that VAIC has a positive effect on the productivity of banks in Ghana. The authors also find human capital efficiency and capital employed efficiency as the components of VAIC that drive productivity growth in the banking industry. Bank size and industry concentration are also identified as significant drivers of productivity in the market.

The study’s findings support investments in intellectual capital as a means of improving the performance of banks in emerging markets.

To the best of the knowledge, this is the first study to empirically examine the relationship between intellectual capital and productivity in an emerging banking market in Africa.