Measuring non-use value of environmental goods using the contingent valuation method:problems of information and cognition and the application of cognitive questionnaire design methods

Much interest now focuses on the use of the contingent valuation method (CVM) to assess non-use value of environmental goods. The paper reviews recent literature and highlights particular problems of information provision and respondent knowledge, comprehension and cognition. These must be dealt with by economists in designing CVM surveys for eliciting non-use values. Cognitive questionnaire design methods are presented which invoke concepts from psychology and tools from cognitive survey design (focus groups and verbal reports) to reduce a complex environmnetal good into a meaningful commodity that can be valued by respondents in a contingent market. This process is illustrated with examples from the authors' own research valuing alternative afforestation programmes. -Authors

Exploring divergence between respondent and researcher definitions of the good in contingent valuation studies

In Contingent Valuation studies, researchers often base their definition of the environmental good on scientific/expert consensus. However, respondents may not hold this same commodity definition prior to the transaction. This raises questions as to the potential for staging a satisfactory transaction, based on Fischoff and Furby's (1988) criteria. Some unresolved issues regarding the provision of information to respondents to facilitate such a transaction are highlighted. In this paper, we apply content analysis to focus group discussions and develop a set of rules which take account of the non-independence of group data to explore whether researcher and respondents' prior definitions are in any way similar. We use the results to guide information provision in a subsequent questionnaire.

Latent vs. Observed Variables: Analysis of Irrigation Water Efficiency Using SEM and SUR

In this paper we compare conceptualising single factor technical and allocative efficiency as indicators of a single latent variable, or as separate observed variables. In the former case, the impacts on both efficiency types are analysed by means of structural equation modeling (SEM), in the latter by seemingly unrelated regression (SUR). We compare estimation results of the two approaches based on a dataset on single factor irrigation water use efficiency obtained from a survey of 360 farmers in the Guanzhong Plain, China. The main methodological findings are that SEM allows identification of the most important dimension of irrigation water efficiency (technical efficiency) via comparison of their factor scores and reliability. Moreover, it reduces multicollinearity and attenuation bias. It thus is preferable to SUR. The SEM estimates show that perception of water scarcity is the most important positive determinant of both types of efficiency, followed by irrigation infrastructure, income and water price. Furthermore, there is a strong negative reverse effect from efficiency on perception.

Trends in Approval Times for Genetically Engineered Crops in the United States and the European Union

Genetically engineered (GE) crops are subject to regulatory oversight to ensure their safety for humans and the environment. Their approval in the European Union (EU) starts with an application in a given Member State followed by a scientific step (risk assessment), and ends with a political decision-making step (risk management); and in the United States (US) it starts with a scientific (field trial) step and ends with a ‘bureaucratic’ decision-making step. We investigated trends for the time taken for these steps and the overall time taken for approving GE crops in the US and the EU (traders in these commodities). Results show that from 1996-2015 the overall time trend for approval in the EU decreased and then flattened off, with an overall mean completion-time of 1,763 days. In the US in 1998 there was a break in the trend of the overall approval time: Initially, from 1988 until 1997 the trend decreased with a mean approval time of 1,321 days; from 1998-2015, the trend almost stagnated with a mean approval time of 2,467 days.

Adoption of farm-based irrigation water-saving techniques in the Guanzhong Plain, China

This article analyses adoption of farm-based irrigation water saving techniques, based on a cross-sectional data set of 357 farmers in the Guanzhong Plain, China. Approximately 83% of the farmers use at least one farm-based water-saving technique. However, the traditional, inefficient techniques border and furrow irrigation are still prevalent whereas the use of advanced, more efficient techniques is still rather rare. We develop and estimate an adoption model consisting of two stages: awareness of water scarcity and intensity of adoption. We find that awareness of water scarcity and financial status enhance adoption of more advanced techniques whereas access to better community-based irrigation infrastructure discourages it. We furthermore find both community-based irrigation infrastructure and farm-based irrigation water-saving techniques have mitigating effects on production risk. From the results it follows that adoption can be stimulated via financial support and via extension aimed at enhancing awareness of water scarcity.

Why are you tall while others are short? Agricultural production and other proximate determinants of global heights

We analyze the proximate determinants of the biological standard of living from a global perspective, namely high-quality nutrition and the disease environment during the nineteenth and twentieth centuries. Until the mid-twentieth century, the local availability of cattle, meat, and milk per capita and the local disease environment mainly determined the stature of the population – and, by implication, how long they lived and how healthy they were. During the late twentieth century, the trade of agricultural products and health-promoting technologies increased in relative importance; hence, the local availabilities became less decisive in explaining height differences.