Analysing Vietnam's rice export policy and recent export ban in the context of rising food prices, this study combines insights from a regionally‐disaggregated or ‘bottom‐up’ CGE model and a micro‐simulation using household data. Three main conclusions are drawn. First, although there is little impact on GDP, there are substantial distributional impacts across regions and households from different export policies and market conditions. Second, both rural and urban households, including poor households, benefit from free trade, even though domestic rice prices are higher. Finally, under free trade, relatively large gains accrue to rural households, where poverty is most pervasive in Vietnam.

No abstract

Agricultural land protection (ALP) is a standard policy response to a desire for food security. However, ALP may result in a misallocation of resources. Examining rice land policy in Vietnam, we determine the optimal level of rice land protected against other crops using a stochastic optimization model built on top of a general equilibrium framework, combined with sequential micro-simulations on household data. We find that converting part of protected rice land enhances economic efficiency. Nonetheless, the policy is relatively pro-rich, implying a trade-off between poverty reduction and economic efficiency, making some households in already poor areas worse off. Our approach can be applied to land-use planning generally, highlighting the relevant tradeoffs and the search for needed optimal land-use policies.

Rural finance has long been an important tool for poverty reduction and rural development by donors and governments, but the impacts have been controversial. Measuring impact is challenging due to identification problems caused by selection bias and governments' targeted interventions, while randomised trial data are scarce and limited to contexts where little to no rural finance exists. Using an author-collected dataset, we provide insights on a large-scale long-lasting subsidised rice credit programme in Myanmar, one of the poorest and, until recently, most economically isolated countries in Asia. Identification relies on a fuzzy regression discontinuity design, exploiting an arbitrary element to the credit provision rule which is based on rice landholding size. Although we find little evidence that rice yield or output is increased, we do see that the programme has some positive effects on total household income, suggesting a positive spillover effect on other farm income activities.

Decisions surrounding the presence of infectious diseases are typically made in the face of considerable uncertainty. However, the development of models to guide these decisions has been substantially constrained by computational difficulty. This paper focuses on the case of finding the optimal level of surveillance against a highly infectious animal disease where time, space and randomness are fully considered. We apply the Sample Average Approximation approach to solve our problem, and to control model dimension, we propose the use of an infection tree model, in combination with sensible 'tree-pruning' and parallel processing techniques. Our proposed model and techniques are generally applicable to a number of disease types, but we demonstrate the approach by solving for optimal surveillance levels against foot-and-mouth disease using bulk milk testing as an active surveillance protocol, during an epidemic, among 42,279 farms, fully characterised by their location, livestock type and size, in the state of Victoria, Australia.