International tourism and economic growth in New Zealand

This paper examines whether the tourism-led growth hypothesis holds for the New Zealand economy. Using unit root tests, cointegration tests and vector error correction models, and annual data over the period 1972-2012 on international tourism expenditure, real gross domestic product (GDP) and the exchange rate for New Zealand, it finds that the tourism-led growth hypothesis holds for New Zealand. The long-run elasticity of real GDP with respect to international tourism expenditure is estimated to be 0.4, meaning that a 1% growth in tourism will result in a 0.4% growth of the NZ economy. This finding implies that the New Zealand Government’s policy to promote New Zealand as a preferred tourism destination in the key international tourism markets may boost economic growth

Energy modelling in a general equilibrium framework with alternative production specifications / Mohammad Jaforullah

Bibliography: leaves 240-248xi, 248 leaves ; 30 cm.Thesis (Ph.D.)--University of Adelaide, Faculty of Economics, 198

International tourism and economic growth in New Zealand

This paper examines whether the tourism-led growth hypothesis holds for the New Zealand economy. Using unit root tests, cointegration tests and vector error correction models, and annual data over the period 1972-2012 on international tourism expenditure, real gross domestic product (GDP) and the exchange rate for New Zealand, it finds that the tourism-led growth hypothesis holds for New Zealand. The long-run elasticity of real GDP with respect to international tourism expenditure is estimated to be 0.4, meaning that a 1% growth in tourism will result in a 0.4% growth of the NZ economy. This finding implies that the New Zealand Government’s policy to promote New Zealand as a preferred tourism destination in the key international tourism markets may boost economic growth

Sensitivity of technical efficiency estimates to estimation approaches: an investigation using New Zealand dairy industry data

Using data from the New Zealand dairy industry for the year 1993, this paper estimates farm-specific technical efficiencies and mean technical efficiency using three different estimation techniques under both constant returns to scale and variable returns to scale in production. The approaches used are the econometric stochastic production frontier (SPF), corrected ordinary least squares (COLS) and data envelopment analysis (DEA). Mean technical efficiency of the industry is found to be sensitive to the choice of estimation technique. In general, the SPF and DEA frontiers resulted in higher mean technical efficiency estimates than the COLS production frontier.UnpublishedAfriat, P. (1972). Efficiency estimation of production functions. International Economic Review 13: 568-598. Aigner, D.J., Lovell, C. A. K. and Schmidt, P. (1977). Formulation and estimation of stochastic frontier production function models. Journal of Econometrics 6: 21-27. Battese, G. E. (1992). Frontier production functions and technical efficiency: A survey of empirical applications in agricultural economics. Agricultural Economics 7: 185-208. Battese, G. E. and Tessema, G. A. (1993). Estimation of stochastic frontier production functions with time-varying parameters and technical efficiencies using panel data from Indian villages. Agricultural Economics 9: 313-333. Bravo-Ureta, B. E. and Rieger, L. (1990). Alternative production frontier methodologies and dairy farm efficiency. Journal of Agricultural Economics 41: 215-226. Charnes, A., Cooper, W. W. and Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operations Research 2: 429-444. Coelli, T. J. (1995). Recent developments in frontier modelling and efficiency measurement. Australian Journal of Agricultural Economics 39: 219-245. Coelli, T. J. (1996a). A Guide to FRONTIER Version 4.1: A Computer Program for Stochastic Frontier Production and Cost Function Estimation. CEPA Working Paper 96/07, Department of Econometrics, University of New England. Armidale. Coelli, T. J. (1996b). A Guide to DEAP Version 2.1: A Data Envelopment Analysis (Computer) Program. CEPA Working Paper 96/08, Department of Econometrics, University of New England. Armidale. Coelli, T. J. and Perelman, S. (1999). A comparison of parametric and non-parametric distance functions: With application to European railways. European Journal of Operational Research 117: 326-339. Farrell, M. J. (1957). The measurement of productive efficiency. Journal of the Royal Statistical Society A120: 253-290. Greene, W. H. (1980). Maximum likelihood estimation of econometric frontier functions. Journal of Econometrics 13: 27-56. Harris, R. I. D. (1993). Measuring efficiency in New Zealand manufacturing in 1986/87 using a frontier production function approach. New Zealand Economic Papers 27: 57-79. Jaforullah, M. (1999). A comparison of production frontier methods using Bangladesh handloom textile industry data. Economics Discussion Papers 9911, University of Otago. Dunedin, Otago. Jaforullah, M. and Devlin, N. J. (1996). Technical efficiency in the New Zealand dairy industry: A frontier production function approach. New Zealand Economic Papers 30: 1-17. Kopp, R. J. and Smith, V. K. (1980). Frontier production function estimates for steam electric generation: A comparative analysis. Southern Economic Journal 47: 1049-59. Livestock Improvement Corporation (1993). 1993 Economic Survey of Factory Supply Dairy Farmers. Statistics Section, Livestock Improvement Corporation Limited: Hamilton. Meeusen, W. and van den Broeck, J. (1977). Efficiency estimation from Cobb-Douglas production functions with composed error. International Economic Review 18: 435-444. Neff, D. L., Garcia, P. and Nelson, C. H. (1993). Technical efficiency: A comparison of production frontier methods. Journal of Agricultural Economics 44: 479-489. New Zealand Dairy Board (1996). A Survey of the New Zealand Dairy Industry. Trade Strategy Section, New Zealand Dairy Board: Wellington. Statistics New Zealand (2001). New Zealand Official Yearbook. 102nd Edition, GP Publications. Wellington. Sharma, K. R., Leung, P. and Zaleski, H. M. (1999). Technical, allocative and economic efficiencies in swine production in Hawaii: A comparison of parametric and non-parametric approaches. Agricultural Economics 20: 23-35. Wadud, A. and White, B. (2000). Farm household efficiency in Bangladesh: A comparison of stochastic frontier and DEA methods. Applied Economics 32: 1665-1673. Yin, R. (2000). Alternative measurements of productive efficiency in the global bleached softwood pulp sector. Forest Science 46: 558-569

Production technology, elasticity of substitution and technical efficiency of the handloom textile industry of Bangladesh

A number of translog and Cobb-Douglas frontier production models were estimated for the Bangladesh handloom textile industry to investigate its production technology and technical efficiency in production. It was found that the technical efficiency of the industry in producing cloth was only 41%. It was concluded that the industry might improve its technical efficiency by increasing its male/female labour ratio and yarn/capital ratio and decreasing its hired/family labour ratio and labour/capital ratio. The production technology of the industry was found to be characterized by a linearly homogeneous Cobb-Douglas function. The elasticity of substitution between labour and capital for the industry was found to be unity.

Is New Zealand's economy vulnerable to world oil market shocks?

We assess New Zealand’s vulnerability to oil shocks by estimating its price and income elasticities of demand for imported oil and by testing for Granger causality between oil imports, their price and GDP. Based on data for the period 1987Q2–2012Q4, we find the short-run price and income elasticities to be statistically insignificant. However, the long-run price and income elasticity estimates are significant and equal to −0.34 and 1.61, respectively. We also find that oil imports, and to some extent oil prices, Granger-cause real GDP, indicating that the New Zealand economy is vulnerable to shocks in the world oil market

Is New Zealand's economy vulnerable to world oil market shocks?

We assess New Zealand’s vulnerability to oil shocks by estimating its price and income elasticities of demand for imported oil and by testing for Granger causality between oil imports, their price and GDP. Based on data for the period 1987Q2–2012Q4, we find the short-run price and income elasticities to be statistically insignificant. However, the long-run price and income elasticity estimates are significant and equal to −0.34 and 1.61, respectively. We also find that oil imports, and to some extent oil prices, Granger-cause real GDP, indicating that the New Zealand economy is vulnerable to shocks in the world oil market

Scale Efficiency in the New Zealand Dairy Industry: A Non-Parametric Approach

The objective of this paper is to measure the scale efficiency of the New Zealand dairy industry and to examine the relationship between farm size and efficiency. Data envelopment analysis (DEA) is applied to a sample of 264 dairy farms. The results suggest that 19 per cent of these farms are operating at optimal scale, 28 per cent at above optimal scale, and 53 per cent at below optimal scale. On average the optimal size for New Zealand dairy farms is estimated at 83 hectares with a herd of 260 animals. Average technical efficiency is estimated at 89 per cent.Data envelopment analysis (DEA), benchmarking partnerships, technical efficiency, optimal, supra-optimal and sub-optimal scale

Scale efficiency in the New Zealand dairy industry: a non-parametric approach

The aim in this article is to measure the scale efficiency of the New Zealand dairy industry and to examine the relationship between farm size and technical efficiency. Data envelopment analysis (DEA) is applied to a sample of 264 dairy farms. The results suggest that 19 per cent of these farms are operating at optimal scale, 28 per cent at above optimal scale, and 53 per cent at below optimal scale. On average, the optimal size for New Zealand dairy farms is estimated at 83 hectares with a herd of 260 animals. Average technical efficiency is estimated at 89 per cent