Australian Journal of Agricultural and Resource Economics最新文献

Small and fragmented farmland parcels are widely believed to be major impediments to agricultural activities. In this study, instrumental variables models are constructed to estimate the influence of the farm size and the number of farmland plots on household welfare proxied by income and the asset index in Vietnam. The study exploits the panel data of households collected from the Vietnam Access to Resources Household Survey (VARHS) every second year in the period 2008–2014, capturing cultural, political and socioeconomic dimensions of rural Vietnam. A positive relationship was found between farm size and household economic welfare proxied by household per capita income and a household asset index; however, this relationship was negative for the number of land plots. The main conclusion from our analysis is that household welfare would be aided by land policies towards increasing the size of farmland and decreasing land fragmentation. However, our findings also indicate that to be effective, these land policies should be complemented by rural education, effective community development and encouragement of non-farm employment activities.

There is an emerging strand in the agricultural economics literature which examines the calibration of risk programming models using the principles of Positive Mathematical Programming (PMP). In a recent contribution to this journal, Liu et al. (2020) compare three different PMP approaches and attempt to find the ‘most practical’ method for calibrating risk programming models to be used in policy analysis. In this article, we argue that the comparison design by Liu et al. (2020) is problematic, as it is based on inappropriate metrics and it ignores recent advancements in PMP. This word of caution intends to provide constructive criticism and aims at contributing to the use of risk programming models in policy analysis.

We take advantage of a combination of a severe weather event from 3 to 4 June 2015 and a local policy, to investigate the housing market response to climate change-related flooding hazard. The study focuses on a residential area in a low-lying coastal suburb of Dunedin, New Zealand, where the groundwater level is shallow and close to sea level. An unusually heavy rain event in June 2015 resulted in flooding of a significant portion of land in especially low-lying areas. The city council responded by reviewing processes for storm-water management and by imposing minimum-floor-level [MFL] requirements on new construction in the low-lying areas previously identified as at risk of flooding. Applying a ‘diff-in-diff-in-diff’ strategy in hedonic regression analyses, we find that houses in the MFL zone sell for a discount of about 5 per cent prior to the flood. This discount briefly tripled in the area that flooded, but disappeared within 15 months, indicating either very short memory among homebuyers or no long-run change in perception of hazard.

Rigid price setting and high organic price premiums have been perceived as major purchase barriers to organic meat products. While emerging price and product differentiation have been reported for organic products in other categories, empirical evidence for the organic fresh meat market is lacking. We estimate a hedonic pricing model based on German household scanner data for fresh red meat and poultry purchases from 2012 to 2014. We derive and test for differences in organic price premiums across distribution channels, species and product type. Our results indicate significant variation in organic premiums, which range from 14 per cent for minced beef to 108 per cent for chicken breasts, and are considerably lower than previously reported estimates. We also find substantial overlaps in the distributions of conventional and organic prices for selected products. Our results suggest that high price premiums can no longer serve as the dominant explanation for low market shares of organic red meat. Marketers and policymakers may instead communicate the benefits of organic meat over conventional premium alternatives more clearly or increase the availability of organic meat.

It is possible that COVID will trigger permanent changes in work practices that increase costs in U.S. meat-processing plants. These changes will be beneficial for the safety and economic welfare of meat-processing workers. However, they will have economic costs. In assessing reform options, policymakers seek guidance from analyses based on models embracing micro detail and an economy-wide perspective. In this paper, we use USAGE-Food, a highly disaggregated computable general equilibrium (CGE) model of the United States, to work out how additional processing costs would be distributed between consumers of meat products and farmers. We also calculate industry and macroeconomic effects. Despite modelling farmers as owning fixed factors, principally their own labour, we find that the farmer share in extra processing costs is likely to be quite moderate. Throughout the paper, we support simulation results with back-of-the-envelope calculations, diagrams and sensitivity analyses. These devices identify the mechanisms in the model and key data points that are responsible for the main results. In this way, we avoid the black-box criticism that is sometimes levelled at CGE modelling.

China supplied 4.1% of the global agro-food exports in 2019, which is worth $65 billion in sales. The vulnerability of this sector to climatic factors has been growing with climate change that not only affects the costs of primary products but may also affect the costs of processing by reducing input productivity. Existing studies have examined the impacts of a changing climate on yields of major crops, but the sensitivity of the food processing sector to rising temperatures has not been assessed. Here we show, using a rich firm-level data set of food processing firms and daily weather in China for the 1998–2007 period, that accounting profits of Chinese food processing firms exhibited non-linear responses to temperature changes, peaking at a daily average temperature of 21–24°C and declining sharply at higher temperatures. Higher temperatures have wide-ranging effects – raising final-good inventory levels, hurting innovation activity, and reducing industrial output by decreasing TFP, investment in capital and capital stock, all of which caused the adverse impacts of higher temperatures on profits. If no additional adaptation is undertaken, the total profits and output of Chinese food processing firms are projected to decline annually by 15–25% and 14–22%, respectively, under RCP8.5 of the global climate models HadGEM2-ES and NorESM1-M by 2080.

This paper addresses the problem of testing for persistence in the effects of the shocks affecting the prices of renewable commodities, which have potential implications on stabilisation policies and economic forecasting, among other areas. A robust methodology is employed that enables the determination of the potential presence and number of instant/gradual structural changes in the series, stationarity testing conditional on the number of changes detected and the detection of change points. This procedure is applied to the annual real prices of eighteen renewable commodities over the period of 1900–2018. Results indicate that most of the series display non-linear features, including quadratic patterns and regime transitions that often coincide with well-known political and economic episodes. The conclusions of stationarity testing suggest that roughly half of the series are integrated. Stationarity fails to be rejected for grains, whereas most livestock and textile commodities do reject stationarity. Evidence is mixed in all soft commodities and tropical crops, where stationarity can be rejected in approximately half of the cases. The implication would be that for these commodities, stabilisation schemes would not be recommended.

In the last half-century, development economics has gone from being a fringe field of economics to being at the very centre of the discipline, and the field’s foremost proponents have been elevated to the highest levels of the discipline. At the same time, development economists have gone from being economists who study situations wherein multiple market failures lead to persistent poverty to being ‘development-and-x’ economists, where x is any of agricultural, demographic, environmental, health, labour, economics etc. Yet few economists, if any, would label themselves development-and-industrial organisation (IO) economists. In this keynote, I first speculate as to why that is. I then explain how the time is ripe to celebrate the marriage of development and IO, and why the study of agricultural value chains provides the ideal inception point for that marriage to be consummated.