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This chapter provides an empirical assessment of the effects of infrastructure provision on structural change and aggregate productivity using industrylevel data for a set of developed and developing countries over 1995–2010. A distinctive feature of the empirical strategy followed is that it allows the measurement of the resource reallocation directly attributable to infrastructure provision. To achieve this, a two-level top-down decomposition of aggregate productivity that combines and extends several strands of the literature is proposed. The empirical application reveals significant production losses attributable to misallocation of inputs across firms, especially among African countries. Also, the results show that infrastructure provision has stimulated aggregate total factor productivity growth through both within and between industry productivity gains.

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.

The aim of this paper is to present a methodology for the decomposition of economic growth by industry which allows interindustry comparisons.

The paper uses the growth decomposition methodology developed by Ivanov and Ivanov and Webster for tourism and generalizes it for all industries in the national economy.

The methodology is exemplified with analysis of the contribution of specific industries to economic growth in Bulgaria for the period 2000‐2005. However, the model presents an approach that is general and can be applied to other countries and industries.

The methodology identifies the direct impacts of specific industries on the per capita growth of real gross domestic product/gross value added. Future research might integrate indirect and induced effects in the analysis. The methodology could be further refined by decomposing the gross domestic product/gross value added to their constituent elements.

The paper identifies the industries in the Bulgarian economy that generate economic growth.

The paper introduces a new methodology for measuring the contribution of specific industries to economic growth. It might be of value to both academics and macroeconomic policy makers.

This paper aims to address the thesis that poverty is best alleviated by a policy emphasising the growth of per capita average income, a strategy that affords little room for direct pro-poor interventions or a movement towards a more equal distribution of incomes. This policy prescription is based on the empirical finding that cross-country variations in poverty are largely explained by variations in growth rates of average income.

The paper contends, as has been done in other commentaries on the subject, that inferring the dictum that “growth is [virtually the only thing] good for poverty” from cross-country evidence on poverty, growth and inequality is neither logically plausible nor normatively compelling. This is sought to be established both through conceptual reasoning and (secondary) data-based analysis. In particular, the thesis under review implicitly rejects the value of counter-factual analysis. Such a hypothetical illustrative analysis is attempted here, using evidence relating to urban poverty, growth and inequality in India.

The paper concludes, without undermining the salience of growth, that there is little basis for the pre-eminence accorded to it as the instrument for poverty redress.

This paper has not been published elsewhere. A collaborative paper by one of the present authors with another scholar, on a similar theme is, however, under preparation for publication.

The purpose of this paper is to apply a stochastic Frontier production model to Canadian manufacturing industries, to investigate the sources of total factor productivity (TFP) growth. As productivity (growth) appears to be the single most important determinant of a nation's living standard or its level of real income over long periods of time, it is important to better understand the sources of productivity growth. In Canada, TFP growth is the major contributing factor (relative to changes in capital intensity) to labour productivity growth, particularly in manufacturing sector. However, the TFP gap is also the main source of labour productivity gap between Canada and other industrialized (Organization for Economic Co‐operation and Development) countries in recent years.

In this paper, a stochastic Frontier production model is applied to Canadian manufacturing industries to investigate the sources of TFP growth. Using a comprehensive panel data set of 18 industries over the period 1990‐2005 and the approach proposed by Kumbhakar et al. and Kumbhakar and Lovell, TFP growth is decomposed into technological progress (TP), changes in technical efficiency, changes in allocative efficiency and scale effects.

The decomposition reveals that during the period under study, TP has been the main driving force of productivity growth, while negative efficiency changes observed in certain industries have contributed to reduce average productivity growth. In addition, the empirical results show that research and development expenditure, information and communications technology investment, as well as trade openness exert a positive impact on productivity growth through the channel of efficiency gains.

The author argues that the decomposition carried out in this study may be very helpful to elicit the correct diagnosis of Canada's productivity problem and develop effective policies to reverse the situation, thereby reducing Canada's lagging productivity gap.

This paper aims to analyse the observed “jobless growth” between 1993-1994 and 2011-2012 based on structural transformation to explain why the elasticity of employment generation to gross domestic product growth has declined during this period.

This paper uses the job generation and growth decomposition tool to quantify the effects of inter-sectoral mobility of workers, intra-sectoral productivity changes and demographic changes on per capita value added growth. Alternative scenarios are generated to simulate the effect of higher female labour participation rates.

Structural transformation in India between 1993-1994 and 2011-2012 was characterised by increasing labour productivity in most sectors, inter-sectoral mobility of workers and a decline in the employment rate. About 81 per cent of the increase in per capita value added was because of a rise in labour productivity; about 24 per cent was because of inter-sectoral shifts of labour; and about 9 per cent because of demographic changes. The decline in the employment rate had a negative effect of −14.20 per cent. The process of transformation was unconventional. First, labour productivity growth was the highest in the service sector and second, the bulk of the movement of labour was to the construction sector.

This paper focusses only on the quantitative dimensions of employment and offers no new explanations why female labour force participation declined.

This paper offers a new perspective on the debate of jobless growth focussing on structural transformation.

The purpose of this paper is to estimate the degree of technical efficiency, determinants of technical inefficiencies and driving forces behind the production growth for a panel data set collected during the 1998/1999 and 2004/2006 Kharif cropping season, from 452 small-scale rice farming households in the Giridih and Purulia districts of Eastern India.

The estimations of technical efficiency utilize stochastic frontier production function with a sub-model of inefficiency effects at both aggregated farm level and disaggregated plot level where traditional varieties (TVs) and high-yielding varieties (HYVs) are differentiated. The output growth decomposition analysis identifies the main contributor to the total rice production growth.

The results indicate that the sampled farms are operated at moderate levels of technical efficiency. The production of HYV rice is associated with higher technical efficiency compared to TV rice. Farming experience, education attainment, landholding size, the share of non-agricultural income and the share of land in the lower terraces account for the differences in technical inefficiencies across the sampled farms. The decomposition analysis suggests that as technical efficiency decreased, technical change is the main source of production growth during the survey period.

The small sample size applied in the analysis will result in an insufficient representativeness of the study area.

This paper fills the literature gap as estimations of technical efficiency that account for subtle differences in adopted rice varieties are still rare in India.

The purpose of this paper is to provide a quantitative assessment of the factors contributing to India's growth acceleration since 1970, based on the neoclassical growth model.

A feature of neoclassical growth models is that capital accumulation is induced by both productivity growth and increases in investment rates. The paper uses a growth decomposition method based on that of Robertson. The method reconstructs India's actual growth path exactly, then decomposes the growth using counterfactual simulations, holding investment rates constant and productivity growth constant. The role of human capital is also discussed.

An increase in the productivity growth rate from 1970 accounts for 68per cent of India's post 1970s growth and the rise in the investment rate accounts for 30 per cent. Hence an upward trend in productivity growth has been more than twice as important as the doubling of the investment rate. A similar conclusion applies for the post 2000 era, where a rise in investment from 25 per cent to 37 per cent of GDP, only adds about 0.7 percentage points of growth to the 4.5 per cent annual growth rate over this period.

The paper provides quantitative estimates of the role of investment and productivity to India's growth based on the neoclassical growth model. It thus improves upon existing growth accounting studies by allowing for the induced effect of productivity growth on capital accumulation. It also improves upon existing development accounting techniques that rely on steady state restrictions, and which would therefore be inappropriate for evaluating India's recent transitional growth.

The Green Revolution transformed agriculture in the Indian State of Punjab, with positive spillovers to the rest of India, but recently the state’s economy has fallen dramatically in rankings of per capita state output. Understanding the trajectory of Punjab’s economy has important lessons for all of India. Economic development is typically associated with changes in economic structure, but Punjab has remained relatively reliant on agriculture rather than shifting economic activity to manufacturing and services, where productivity growth might be greater.

The authors empirically examine structural change in the Punjab economy in the context of structural change and economic growth across the States of India. The authors calculate structural change indices and map their pattern over time. The authors estimate panel regressions and time-varying parameter regressions, as well as performing productivity change decompositions into within-sector and structural changes.

Panel regressions and time-varying-coefficient regressions suggest a significant positive influence of structural change on state-level growth. In addition, growth positively affected structural change across India’s states. The relative lack of structural change in Punjab’s economy is implicated in its relatively poor recent growth performance. Comparisons with a handful of other states reinforce this conclusion: Punjab’s lack of economic diversification is a plausible explanation for its lagging economic performance.

This paper performs a novel empirical analysis of structural change and growth, simultaneously using three different approaches: panel regressions, time-varying parameter regressions and productivity decompositions. To the best of the authors’ knowledge, it is the only paper we are aware of that combines these three approaches.