Transmission of Rice Prices from Thailand into West African Markets: The Case of Benin,Mali, and Senegal

ABSTRACT

The integration of West African rice market to the world market is assessed in order to derive the implication for food security. To this end, the transmission of rice price changes on the world market to selected markets in West Africa was examined to test for the presence of transaction costs. Using the two-regime threshold cointegration procedure on monthly price data, evidence in support of the hypothesis of asymmetric price transmission was found between Thailand and some West African markets. Price increases on the world market were more quickly transmitted to domestic price than were price decreases in Benin and Mali, suggesting short-run dynamic inefficiencies and the presence of transaction costs. In Senegal, the adjustment was linear, suggesting greater integration with the world rice market. The results suggest that West African governments should design and implement adequate policies to develop the domestic rice sector, improve market infrastructures in order to reduce their country dependency to international markets and ensure food security.     

What do professional forecasters actually predict?

This paper studies what professional forecasters predict. We use spectral analysis and state space modeling to decompose economic time series into trend, business cycle, and irregular components. We examine which components are captured by professional forecasters by regressing their forecasts on the estimated components extracted from both the spectral analysis and the state space model. For both decomposition methods, we find that, in the short run, the Survey of Professional Forecasters can predict almost all of the variation in the time series due to the trend and the business cycle, but that the forecasts contain little or no significant information about the variation in the irregular component.     

“Speculative Influence Network” during financial bubbles: application to Chinese stock markets

We introduce the Speculative Influence Network (SIN) to decipher the causal relationships between sectors (and/or firms) during financial bubbles. The SIN is constructed in two steps. First, we develop a Hidden Markov Model (HMM) of regime-switching between a normal market phase represented by a geometric Brownian motion and a bubble regime represented by the stochastic super-exponential Sornette and Andersen (Int J Mod Phys C 13(2):171–188, 2002) bubble model. The calibration of the HMM provides the probability at each time for a given security to be in the bubble regime. Conditional on two assets being qualified in the bubble regime, we then use the transfer entropy to quantify the influence of the returns of one asset i onto another asset j, from which we introduce the adjacency matrix of the SIN among securities. We apply our technology to the Chinese stock market during the period 2005–2008, during which a normal phase was followed by a spectacular bubble ending in a massive correction. We introduce the Net Speculative Influence Intensity variable as the difference between the transfer entropies from i to j and from j to i, which is used in a series of rank ordered regressions to predict the maximum loss (%MaxLoss) endured during the crash. The sectors that influenced other sectors the most are found to have the largest losses. There is some predictability obtained by using the transfer entropy involving industrial sectors to explain the %MaxLoss of financial institutions but not vice versa. We also show that the bubble state variable calibrated on the Chinese market data corresponds well to the regimes when the market exhibits a strong price acceleration followed by clear change of price regimes. Our results suggest that SIN may contribute significant skill to the development of general linkage-based systemic risks measures and early warning metrics.     

The curse of knowledge: having access to customer information can reduce monopoly profits

We show that a monopolist's profit is higher if he refrains from collecting coarse information on his customers, sticking to constant uniform pricing rather than recognizing customers' segments through their purchase history. In the Markov perfect equilibrium with coarse information collection, after each commitment period, a new introductory price is offered to attract new customers, creating a new market segment for price discrimination. Eventually, the whole market is covered. Shortening the commitment period results in lower profits. These results sharply differ from the ones obtained when the firm can uncover the exact willingness-to-pay of each previous customer.     

Equilibrium Tariff Schedules In Commercial Policy Games Under Uncertainty

Abstract A partial two country equilibrium model is built in which two different exogenous random shocks may occur. the governments simultaneously choose tariff functions relating their specific tariff to the level of an observable variable (volume of trade or international price). In the case of a “volume of trade shock” the Nash equilibria of this game are more protectionist the larger the possible trade swings and autarky is always an equilibrium outcome. In the case of a “terms of trade shock”, constant tariffs, at their Nash equilibrium in specific tariff levels are the only sensible equilibrium outcome.     

PORTFOLIO OPTIMIZATION UNDER TRACKING ERROR AND WEIGHTS CONSTRAINTS

The performance of active portfolio managers who must comply with a weights constraint is often assessed against a benchmark. The weights constraint is common as the funds are committed by their own prospectus to a minimum (or maximum) portfolio concentration. We characterize the optimal asset allocation and analyze the implications of the weights constraint on the manager's performance and on the relevance of the information ratio. We obtain that because of the weights constraint, at the optimum, the information ratio often decreases when the manager is free to deviate more from the benchmark.     

A Case of Mistaken Identity? Measuring Rates of Improved Seed Adoption in Tanzania Using DNA Fingerprinting

Studies of improved seed adoption in developing countries are almost always based on household surveys and are premised on the assumption that farmers can accurately self-report their use of improved seed varieties. However, recent studies suggest that farmers’ reports of seed varieties planted, or even whether the seed is local or improved, are sometimes inconsistent with the DNA fingerprinting results of those crops. We use household survey data from Tanzania to test the alignment between farmer-reported and DNA-identified maize seed types planted. In the sample, 70% of maize seed observations are correctly reported as local or improved, while 16% are type I errors (falsely reported as improved) and 14% are type II errors (falsely reported as local). Type I errors are more likely to have been sourced from other farmers, rather than formal channels. An analysis of input use, including seed, fertiliser, and labour allocations, reveals that farmers tend to treat improved maize differently, depending on whether they correctly perceive it as improved. This suggests that errors in farmers’ seed type awareness may translate into suboptimal management practices. The average yield of seed that is correctly identified as improved is almost 700 kg per hectare greater than that of type I errors. This indicates that investments in farmers’ access to information, seed labelling, and seed system oversight are needed to complement investments in seed variety development.