The impact of firm-type dominance on regional manufacturing growth

Availability of financial capital and location decisions are variables that influence regional manufacturing output. This study maintains that a region’s manufacturing growth depends upon the region’s firm-type dominance. That is, the type of firms that dominate the region’s manufacturing output can be classified as non-local (national or foreign - NF) vs. local and large vs. small. Accordingly, for policy analysis, regions can be classified by firm-type dominance. This distinction is important since, invariably, location decision options and availability of financial capital are more favourable for the larger NF firms than for local firms. In an attempt to assess the impact of firm-type dominance, this study draws upon the dominant industry model[Salvary 1987]which has established that, in any given region, there is a dominant industry (the driving force of the region) to which a region’s manufacturing growth is linked. The information on the impact of firm-type dominance on a region's manufacturing output may enable policy-makers to design workable (or revise existing) manufacturing diversification policies

Capacity utilization as a real-time predictor of manufacturing output

In this article, Evan F. Koenig demonstrates that the Federal Reserve Board's initial estimate of manufacturing capacity utilization is helpful in predicting subsequent growth in manufacturing output. Together with lagged real-time output growth and growth in the composite index of leading indicators, capacity utilization explains more than 50 percent of the variation in output growth at a four-quarter horizon. Based on data available at the beginning of the year, the forecasting equation predicts little or no growth in manufacturing output during 1996.Industrial capacity ; Manufactures

The economic effects of oil prices shocks on the UK manufacturing and services sector

This paper investigates the relationship between changes in oil prices and the UK’s manufacturing and services sector performances. Only a few studies have been conducted at the sector level: the goal of this paper is to contribute in that direction. After presenting review of existing literature about oil effects on the UK’s sectors of manufacturing and services, an econometric analysis is carried out. In a more detailed analysis, three sets of vector autoregressive (VAR) models are employed using linear and non-linear oil price specifications among several key macroeconomic variables. From the linear oil price specification VAR model, the impulse response function reveals that oil price movement causes positive effects in both the output of manufacturing and services sectors. The variance decomposition shows that oil prices are quite important as a cause of the variance of the UK services sector output, while they do not have such a large role in the variance of the UK’s manufacturing output. From the asymmetric specification, it has been found that positive oil price changes determine a consistent contraction in manufacturing output, while the services sector does not seem to be affected by increases. Alternatively, negative oil price changes, show that manufacturing output does not increase so much despite a decrease in oil prices. The services sector is much more affected by oil prices decreases than increases. Finally considering the net oil price increase (NOPI) specification, it has been found that the manufacturing sector is much more affected by oil price changes than the services sector.Oil shock; VAR; impulse response function; variance decomposition;

Can confidence indicators be useful to predict short term manufacturing growth?

In this study we investigate the usefulness of business survey data in forecasting Hungarian manufacturing output growth in the short run. We analyse the individual questions of the business surveys, and use models with different flexibility (factor model, best fitting and recursively best fitting model) to estimate the relationship between the business survey indicators and manufacturing output growth. The models are evaluated according to their forecasting performance. We generally find that although confidence indicators can be useful in forecasting manufacturing output in the short run, their forecasting ability is limited to a one-quarter horizon. For this reason their use in forecasting should mainly be restricted to nowcasting purposes.

Comparisons of real output in manufacturing

This study is concerned with the conceptual and measurement problems which arise in comparisons of levels of per capita output and productivity in different countries. The author stresses the reliance of standarized valuations of the different elements of output rather than official exchange rates when making comparisons. Two approaches are noted; (1) the expenditure approach and (2) the production approach. The production approach, discussed here, looks at the industry of origin and provides a basis for growth accounting, comparative structural analysis, studies of technological performance, and work on labor productivity and total factor productivity. This approach provides a sounder base for constructing relative indicators of productivity. It also reveals trade protection policies and their incidence on different sectors of the economy. The approach shows which data are anomalous and which analytically useful in industrial census. It also shows how new insights might be gained by exploiting some official sources which often remain untapped by international agencies.Environmental Economics&Policies,Access to Markets,Markets and Market Access,Economic Theory&Research,Banks&Banking Reform

The impact of Chinese import penetration on the South African manufacturing sector

This article uses a Chenery-type decomposition and econometric estimation to evaluate the impact of Chinese trade on production and employment in South African manufacturing from 1992 to 2010. The results suggest that increased import penetration from China caused South African manufacturing output to be 5 per cent lower in 2010 than it otherwise would have been. The estimated reduction of total employment in manufacturing as a result of trade with China is larger – in 2010 about 8 per cent – because the declines in output were concentrated on labour-intensive industries and because the increase in imports raised labour productivity within industries