The Estimation of Technical Efficiency Effects Models with an Example Applied to the Thai Manufacturing Sector

This paper does two things. First, it presents alternative approaches to the standard methods of estimating productive efficiency using a production function. It favours a parametric approach (viz. the stochastic production frontier approach) over a nonparametric approach (e.g. data envelopment analysis); and, further, one that provides a statistical explanation of efficiency, as well as an estimate of its magnitude. Second, it illustrates the favoured approach (i.e. the ‘single stage procedure’) with estimates of two models of explained inefficiency, using data from the Thai manufacturing sector, after the crisis of 1997. Technical efficiency is modelled as being dependent on capital investment in three major areas (viz. land, machinery and office appliances) where land is intended to proxy the effects of unproductive, speculative capital investment; and both machinery and office appliances are intended to proxy the effects of productive, non-speculative capital investment. The estimates from these models cast new light on the five-year long, post-1997 crisis period in Thailand, suggesting a structural shift from relatively labour intensive to relatively capital intensive production in manufactures from 1998 to 2002.

Technical Appendix-3-Regime asymmetric STAR modeling and exchange rate reversion

The breakdown of the Bretton Woods system and the adoption of generalized oating exchange rates ushered in a new era of exchange rate volatility and uncer- tainty. This increased volatility lead economists to search for economic models able to describe observed exchange rate behavior. The present is a technical Appendix to Cerrato et al. (2009) and presents detailed simulations of the proposed methodology and additional empirical results.

Technical progress and product reliability under competition and monopoly

Technical progress lowers costs and prices but appears to have an ambiguous effect on product reliabilty. This paper presents a simple model which explains this observation.

Native language, spoken language, translation and trade

We construct new series for common native language and common spoken language for 195 countries, which we use together with series for common official language and linguis-tic proximity in order to draw inferences about (1) the aggregate impact of all linguistic factors on bilateral trade, (2) whether the linguistic influences come from ethnicity and trust or ease of communication, and (3) in so far they come from ease of communication, to what extent trans-lation and interpreters play a role. The results show that the impact of linguistic factors, all together, is at least twice as great as the usual dummy variable for common language, resting on official language, would say. In addition, ease of communication is far more important than ethnicity and trust. Further, so far as ease of communication is at work, translation and inter-preters are extremely important. Finally, ethnicity and trust come into play largely because of immigrants and their influence is otherwise difficult to detect.

Native language, spoken language, translation and trade

We construct new series for common native language and common spoken language for 195 countries, which we use together with series for common official language and linguis-tic proximity in order to draw inferences about (1) the aggregate impact of all linguistic factors on bilateral trade, (2) whether the linguistic influences come from ethnicity and trust or ease of communication, and (3) in so far they come from ease of communication, to what extent trans-lation and interpreters play a role. The results show that the impact of linguistic factors, all together, is at least twice as great as the usual dummy variable for common language, resting on official language, would say. In addition, ease of communication is far more important than ethnicity and trust. Further, so far as ease of communication is at work, translation and inter-preters are extremely important. Finally, ethnicity and trust come into play largely because of immigrants and their influence is otherwise difficult to detect.

How do technical change and technological distance influence the size of the Okun’s Law coefficient?

How does technical change influence the size of the Okun’s Law coefficient? Using a nonlinear version of Okun’s Law augmented with technical change and technological distance, we show that the impact of output movements on unemployment variations is influenced by the imitation or innovation origins of technical change