World Institute for Development Economics Research

About: World Institute for Development Economics Research is a facility organization based out in Helsinki, Finland. It is known for research contribution in the topics: Poverty & Population. The organization has 110 authors who have published 525 publications receiving 17316 citations.

Measuring Agricultural Policy Bias: General Equilibrium Analysis of Fifteen Developing Countries

Abstract: The measurement issue is the key issue in the literature on trade policy-induced agricultural price incentive bias. This paper introduces a general equilibrium effective rate of protection (GE-ERP) measure, which extends and generalizes earlier partial equilibrium nominal protection measures. For the 15 sample countries, the results indicate that the agricultural price incentive bias, which was generally perceived to exist during the 1980s, was largely eliminated during the 1990s. The results also demonstrate that general equilibrium effects and country-specific characteristics – including trade shares and intersectoral linkages – are crucial for determining the sign and magnitude of trade policy bias. The GE-ERP measure is therefore uniquely suited to capture the full impact of trade policies on agricultural price incentives. A Monte Carlo procedure confirms that the results are robust with respect to tradability assumptions.

Informed selection of future climates

Abstract: Analysis of climate change is often computationally burdensome. Here, we present an approach for intelligently selecting a sample of climates from a population of 6800 climates designed to represent the full distribution of likely climate outcomes out to 2050 for the Zambeze River Valley. Philosophically, our approach draws upon information theory. Technically, our approach draws upon the numerical integration literature and recent applications of Gaussian quadrature sampling. In our approach, future climates in the Zambeze River Valley are summarized in 12 variables. Weighted Gaussian quadrature samples containing approximately 400 climates are then obtained using the information from these 12 variables. Specifically, the moments of the 12 summary variables in the samples, out to order three, are obliged to equal (or be close to) the moments of the population of 6800 climates. Runoff in the Zambeze River Valley is then estimated for 2026 to 2050 using the CliRun model for all 6800 climates. It is then straightforward to compare the properties of various subsamples. Based on a root of mean square error (RMSE) criteria, the Gaussian quadrature samples substantially outperform random samples of the same size in the prediction of annual average runoff from 2026 to 2050. Relative to random samples, Gaussian quadrature samples tend to perform best when climate change effects are stronger. We conclude that, when properly employed, Gaussian quadrature samples provide an efficient and tractable way to treat climate uncertainty in biophysical and economic models.

Export Productivity and Specialisation: A Disaggregated Analysis

Abstract: The paper analyses export productivity and trade specialisation in China, Brazil, India and South Africa. The investigation calculates a time-varying export productivity measure using highly disaggregated product categories. The modelling shows that export productivity is determined by real income and human capital endowments. But the empirical analysis also reveals significant differences in export productivity and specialisation for countries with comparable per capita income levels.