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Fiji's agricultural sector has been integral to the Fijian economy in terms of the provision of food and raw materials for the domestic market, the absorption of domestic labour and capital, and generation of export income. It has also supported manufacturing and services sectors. However, research providing a systematic investigation into the effect of trade on agricultural value added and the spillover effects of agriculture into other sectors of the Fijian economy deserve an extended analysis in terms of setting effective agricultural trade and sectoral linkage policies. This paper investigates the effect of trade on agricultural value added and inter-industry spillover effects in Fiji. The analytical procedure involved testing the effect of trade on agricultural value added; testing the effect of trade as well as the investment in agriculture on the manufacturing of food, beverages and tobacco; and testing the spillover effects of agriculture on the industry and services sectors. Our findings reveal the positive but statistically insignificant effect of trade on the agricultural and industrial sectors. However, we find evidence of a positive and statistically significant correlation of agricultural value added on food, beverage and tobacco manufacturing, which is important in its own right and due to its impact on the industrial sector. Our findings do not provide any strong support for a positive and statistically significant spillover effect directly from agriculture to the industrial and services sectors. Fiji's agricultural sector has suffered through poor agricultural policies, institutional failures as well as low levels of public and private sector investment that has led to its gradual decline. While the services sector has emerged as the dominant sector in Fiji, with large concentrations on tourism services, the continued success of the services sector is also dependent on the concurrent emergence of a productive agricultural sector that can supply locally produced food to the tourism industry, reduce food import bills and foster greater linkages with the agricultural sector. We conclude that policy makers need to seriously consider new agricultural policy initiatives that would allow greater connectedness of the agricultural sector to other sectors such as tourism services. JEL Classifications: O13, O56, Q17 Keywords: Fiji, Trade, Agriculture, Spillover Effects, Inter-Industry Linkages Contact Author's Email Address: azmat@squ.edu.om, INTRODUCTION This paper investigates the effect of trade on agricultural value added and the inter-industry spillover effects of the agricultural sector in Fiji. While trade and the agricultural sector have [...]

This paper presents estimates of the value of statistical life (VSL) in rural Thailand using the contingent-valuation (CV) method. These estimates are applied to an economic analysis of landmine clearance. The estimated VSL of US$250,000 suggests that the value of lives saved from landmine clearance is at least an order of magnitude greater than the values used in existing studies. JEL classification--Jl7, 022 Key words--Asia, Thailand, benefit-cost analysis, contingent valuation, landmines, value of statistical life

Models of energy demand in New Zealand have typically been based upon either a partial general equilibrium approach or constructed from spreadsheet models. The results created by such methods predict [...]

The Young Economists' Prize is awarded annually for the best paper submitted to the Editor of New Zealand Economic Papers by an author who is under 30 years of age [...]

Purpose The purpose of this paper is to assess to what extent intraday data can explain and predict long-term memory.Design/methodology/approach This article analysed the presence of long-memory volatility in five Asian equity indices, namely, SENSEX, CNIA, NIKKEI225, KO11 and FTSTI, using five-min intraday return series from 05 January 2015 to 06 August 2015 using two approaches, i.e. conditional volatility and realized volatility, for forecasting long-term memory. It employs conditional-generalized autoregressive conditional heteroscedasticity (GARCH), i.e. autoregressive fractionally integrated moving average (ARFIMA)-FIGARCH model and ARFIMA-asymmetric power autoregressive conditional heteroscedasticity (APARCH) models, and unconditional volatility realized volatility using autoregressive integrated moving average (ARIMA) and ARFIMA in-sample forecasting models to estimate the persistence of the long-term memory.Findings Given the GARCH framework, the ARFIMA-APARCH long-memory model gave the better forecast results signifying the importance of accounting for asymmetric information when modelling volatility in a financial market. Using the unconditional realized volatility results from the Singapore and Indian markets, the ARIMA model outperforms the ARFIMA model in terms of forecast performance and provides reasonable forecasts.Practical implications The issue of long memory has important implications for the theory and practice of finance. It is well-known that accurate volatility forecasts are important in a variety of settings including option and other derivatives pricing, portfolio and risk management.Social implications It could be said that using long-memory augmented models would give better results to investors so that they could analyse the market trends in returns and volatility in a more accurate manner and reach at an informed decision. This is useful to minimize the risks.Originality/value This research enhances the literature by estimating the influence of intraday variables on daily volatility. This is one of very few studies that uses conditional GARCH framework models and unconditional realized volatility estimates for forecasting long-term memory. The authors find that the methods complement each other. [ABSTRACT FROM AUTHOR]