Annual Review of Resource Economics - Volume 12, 2020

Despite the significant risks and uncertainties that farmers in developing and emerging countries face in their production process, efforts at encouraging them to adopt agricultural insurance to mitigate their production risks have mainly yielded little success. This article reviews the recent literature on the demand for agricultural insurance in developing and emerging countries, by presenting the state of uptake, drivers of the demand for it, and the potential welfare gains from it. Our review reveals that while risk aversion is necessary for the demand for agricultural insurance, liquidity constraints, rates of time preference, basis risk, and trust are equally relevant in explaining the demand for insurance in poor countries. An interesting observation is the increasing number of studies that employ randomized control trials to analyze farmers’ uptake of agricultural insurance in developing and emerging countries. Our comprehensive review finds some information gaps in the literature, and we propose some avenues for further research.

This review covers a nascent literature that experiments with survey design to measure whether the way in which we collect socio-economic data in developing countries influences the data and affects the results of subsequent analyses. We start by showing that survey methods matter and the size of the survey design effects can be nothing short of staggering, affecting basic stylized facts of development (such as country rankings by poverty levels) and conclusions drawn from econometric analyses (such as what the returns to education are or whether small farm plots are more productive than large ones). We describe some of the emerging best practices for conducting survey experiments, including benchmarking against the truth, delving into the error-generating mechanisms, and documenting the costs of different survey approaches.

The aim of this article is to review a number of issues related to uncertain population forecasts, with a focus on world population. Why are these forecasts uncertain? Population forecasters traditionally follow two approaches when dealing with this uncertainty, namely scenarios (forecast variants) and probabilistic forecasts. Early probabilistic population forecast models were based upon a frequentist approach, whereas current ones are of the Bayesian type. I evaluate the scenario approach versus the probabilistic approach and conclude that the latter is preferred. Finally, forecasts of resources need not only population input, but also input on future numbers of households. While methods for computing probabilistic country-specific household forecasts have been known for some time, how to compute such forecasts for the whole world is yet an unexplored issue.

In this review, we attempt to describe the evolution of integrated assessment modeling research since the pioneering work of William Nordhaus in 1994, highlighting a number of challenges and suggestions for moving the field forward. The field has evolved from global aggregate models focused on cost-benefit analysis to detailed process models used to generate emissions scenarios and to coupled model frameworks for impact analyses. The increased demand for higher sectoral, temporal, and spatial resolution to conduct impact analyses has led to a number of challenges both computationally and conceptually. Overcoming these challenges and moving the field forward will require not only greater efforts in model coupling software and translational tools, the incorporation of empirical findings into integrated assessment models, and intermethod comparisons but also the expansion and better coordination of multidisciplinary researchers in this field through better training of the next generation of integrated assessment scholars and expanding the community of practice.