Production Structure, Technical Change, and Productivity Growth in Albanian Agriculture

1996 ◽  
Vol 22 (3) ◽  
pp. 295-310 ◽  
Author(s):  
Michalis Hatziprokopiou ◽  
Giannis Karagiannis ◽  
Kostas Velentzas
Keyword(s):  
Productivity Growth ◽  
Technical Change ◽  
Production Structure

Production structure and input substitution in Canadian sawmill and wood preservation industry

2006 ◽  
Vol 36 (11) ◽  
pp. 3007-3014 ◽  
Author(s):  
Rao V Nagubadi ◽  
Daowei Zhang
Keyword(s):  
Growth Rate ◽  
Cost Function ◽  
Productivity Growth ◽  
Technical Change ◽  
Wood Preservation ◽  
Production Structure ◽  
Factor Productivity ◽  
Translog Cost Function ◽  
Input Substitution ◽  
Power Inputs

We use a translog cost function to analyze the sawmill and wood-preservation industry in Canada from 1958 to 2003. The estimated cost function is homothetic and Hicks neutral. Allen elasticities of substitution indicate that significant substitution possibilities exist. According to Morishima elasticities of substitution, substitution of labour by other inputs is easier than the substitution of other inputs by labour, and substitution of other inputs by materials is easier than the substitution of materials by other inputs. The demand for production labour, nonproduction labour, and electric power inputs is elastic. In contrast to the previous findings of zero or negative rate of technical change, we find technical progress at the rate of 0.57% per annum and a total factor productivity growth rate of 0.54% per annum in the Canadian sawmill and wood preservation industry.

Investment, Profitability, and Structural Change

2021 ◽  
pp. 113-130
Author(s):  
John Weiss ◽  
Hossein Jalilian
Keyword(s):  
Structural Change ◽  
Productivity Growth ◽  
Technical Change ◽  
Technological Advance ◽  
Historical Overview ◽  
Resource Reallocation ◽  
Dynamic Economy ◽  
And Shifts ◽  
Productivity Gains ◽  
Investment Choices

This chapter explores the links between profitability, investment, and structural change. This is examined for the case of India, using both a historical overview and regression analysis. In a Kaldorian dynamic economy, the reallocation of investment provides the driver for resource reallocation, allowing more productive and profitable activities to expand and less productive and less profitable activities to contract. Thus, investment choices drive structural change, productivity growth, technological advance, and ultimately profits. Investment in turn responds to expected profits, which are driven by technical change, related productivity gains, and shifts in demand. The relationships are thus circular and cumulative, with this chapter testing for the presence of such relationships.

Inventive Activity in the British Textile Industry, 1700–1800

1992 ◽  
Vol 52 (4) ◽  
pp. 881-906 ◽  
Author(s):  
Trevor Griffiths ◽  
Philip A. Hunt ◽  
Patrick K. O'Brien
Keyword(s):  
Eighteenth Century ◽  
Total Factor Productivity ◽  
Productivity Growth ◽  
Technical Change ◽  
Consumer Goods ◽  
Textile Industry ◽  
Industrial Development ◽  
Industrial Revolution ◽  
Factor Productivity ◽  
Inventive Activity

An analysis of innovations in the eighteenth-century British textile industry is the basis for an evaluation of aggregate studies of invention during the Industrial Revolution, derived from patent evidence alone. Disaggregation of the data challenges recent generalizations concerning the pace and pattern of technical change over the period. Discontinuities in the nature of invention, promoting an acceleration in total factor productivity growth, are traced to the 1790s. Prior to that date, industrial development conformed to a pattern of Smithian growth, as manufacturers diversified their output in response to an expanding domestic market for consumer goods.

An intraregional economic analysis of production structure and factor demand in major Canadian softwood lumber producing regions

1988 ◽  
Vol 18 (8) ◽  
pp. 1036-1048 ◽  
Author(s):  
J. K. Meil ◽  
J. C. Nautiyal
Keyword(s):  
Technical Change ◽  
Time Series Data ◽  
Decomposition Analysis ◽  
Production Capacity ◽  
Series Data ◽  
Variable Cost ◽  
Production Structure ◽  
Cross Sectional ◽  
Behaviour Factor ◽  
Factor Demand

Cross-sectional time-series data were employed to estimate four intraregional models of production structure and factor demand over the time period 1968–1984. Lumber, tie, and pulp chip information was incorporated into the restricted, single-output, variable cost transcendental logarithmic function. Results indicate that aggregate sectoral studies do not adequately reflect regional production behaviour in the industry. Additional tests for aggregation bias demonstrated that different mill sizes within a region also portray differing production behaviour. Factor demand decomposition analysis indicated that demand for production inputs is not static, but is governed by offsetting dynamic effects. With few exceptions, all mills across regions exemplify material- and energy-using and labour-saving biases in technical change. Larger mills consistenly registered the greatest labour-saving technical change, which countered their lack of attaining significantly large cost-reducing scale economies. Mid-sized mills consistently exhibited the largest returns to scale. The data suggest that small mills are leaving the industry in some regions and production capacity is becoming concentrated in the larger mills.

Simulating the Two Views of the British Industrial Revolution

2000 ◽  
Vol 60 (3) ◽  
pp. 819-841 ◽  
Author(s):  
C. Knick Harley ◽  
N.F.R. Crafts
Keyword(s):  
Productivity Growth ◽  
Technical Change ◽  
Industrial Revolution ◽  
Economic Structure ◽  
Trade Data ◽  
Trade Model ◽  
Computational General Equilibrium ◽  
Cge Modeling ◽  
Food Imports ◽  
New View

This study examines technical change, trade, economic structure, and growth during the British Industrial Revolution by means of computational general equilibrium (CGE) modeling. It rejects Peter Temin's contention that our “new view” of sectorally concentrated productivity growth is inconsistent with industrial export data. A CGE trade model with diminishing returns in agriculture and realistic assumptions about consumer demand shows that while technical change in cottons and iron were major spurs to exportation of those specific goods, the need for food imports also stimulated exports generally. Incorporating trade data thus enriches our “new view.”

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