Issues relating to Agricultural Pricing Policies and their Analysis in Developing Countries

Agricultural pricing policies in developing countries are often the result of complex interactions between producer, consumer and merchant groups and their relative effectiveness in influencing government decision making. Even within governments, various ministries often have opposing views. In this environment one of the contributions a policy analyst can make is to attempt to quantify the effects of different policy options. This permits a more informed discussion which hopefully leads to better decision-making and an improved incentive environment. Many analyses of agricultural pricing policies have used the standard partial equilibrium analysis where no linkages between commodity markets were considered. In this paper we have considered cross-price effects. Also, we have discussed issues relating to other adjustments/refinements of the standard method so that a practitioner not familiar with the various methods can form an opinion of what the options are and what adjustments may be appropriate for a particular case in question. The adjustments relate to overvaluation of currencies, input price distortions, differences in the degree of distortions between producers and consumers, and variability of border prices. The inclusion of cross-price elasticities was important for assessing production, consumption and trade effects for Argentina, but for the other countries it resulted in only somewhat improved accuracy. The adjustment for exchange rates had a large impact in Egypt and was important for other countries as well. This underlines the importance of exchange rates as key variables for agricultural pricing policies in general. The numbers show that the traditional taxation policies of agricultural products in the sample of developing countries is somewhat less widespread than in the past. These policies, however, continue to favor consumers over producers, with significant losses for some of the latter. The large size of welfare losses, especially compared to efficiency losses, highlights the importance of correcting distorted prices that adversely affect the poorest sections of society. Also, the usual government objective of taxing producers to raise revenues is frequently defeated by the large subsidies provided to consumers. For the partitioner, for whom time is often of the essence, the assessment of welfare effects using the partial equilibrium method may provide reasonably good ‘first cut’ estimates of the order of magnitude of the impact of distortions. But often, these 'base case estimates' can and should be adjusted for a number of possible factors. The analyst needs to determine how important accurate estimates of key variables are to the policy makers; he or she then needs to compare the costs involved in generating or gathering the data and doing the calculations with the benefits of a broader and more accurate analysis of the distortionary effects of the particular case in question.     

How Does Technological Regime Affect Performance of Technology Development Projects?

In this study, we examine how technological regime affects the performance of technology development projects (i.e., project quality, sales, and profit). Technological regime is defined as the set of attributes of a technological environment where the innovative activities of firms take place. Technological opportunity, appropriability of innovations, cumulativeness of knowledge and capabilities, and closeness of knowledge base to basic sciences (versus applied sciences) are attributes of technological regime. Using data from 381 firms across five industries, we show that high levels of technological opportunity, appropriability, and closeness of knowledge base to basic sciences are associated with higher project performance. Cumulativeness is associated with higher project quality, but not higher sales and profit. We also show that the effect of technological opportunity on project performance is moderated by two other determinants of technological regime: cumulativeness and nature of knowledge base. We find that cumulativeness has a negative moderating effect on the positive relationship between technological opportunity and project performance, while closeness of knowledge base to basic sciences positively moderates the effect of technological opportunity on project performance. We discuss the implications of our findings for new product development research and practice.     

Nonexclusionary input prices under quantity competition: vertical integration,foreclosure and sabotage

A vertically integrated provider is a monopoly supplier of an input essential for its rival to produce downstream output. Market exclusion in the form of inefficient foreclosure or sabotage can arise when input prices are, respectively, ‘too high’ or ‘too low’ relative to the downstream price. The range of nonexclusionary input prices within which neither form of market exclusion arises is determined by displacement ratios. The safe harbour range of downstream-to-upstream ‘price-cost’ margin ratios is decreasing in the degree of product homogeneity and approaches a single ratio in the limit as the products become perfectly homogeneous. The bounds of nonexclusionary input prices are markedly wider under Bertrand–Nash competition than they are under Stackelberg competition.     

Cryptocurrency price prediction using traditional statistical and machine-learning techniques: A survey

Cryptocurrencies are decentralized electronic counterparts of government-issued money. The first and best-known cryptocurrency example is bitcoin. Cryptocurrencies are used to make transactions anonymously and securely over the internet. The decentralization behavior of a cryptocurrency has radically reduced central control over them, thereby influencing international trade and relations. Wide fluctuations in cryptocurrency prices motivate the urgent requirement for an accurate model to predict its price. Cryptocurrency price prediction is one of the trending areas among researchers. Research work in this field uses traditional statistical and machine-learning techniques, such as Bayesian regression, logistic regression, linear regression, support vector machine, artificial neural network, deep learning, and reinforcement learning. No seasonal effects exist in cryptocurrency, making it hard to predict using a statistical approach. Traditional statistical methods, although simple to implement and interpret, require a lot of statistical assumptions that could be unrealistic, leaving machine learning as the best technology in this field, being capable of predicting price based on experience. This article provides a comprehensive summary of the previous studies in the field of cryptocurrency price prediction from 2010 to 2020. The discussion presented in this article will help researchers to fill the gap in existing studies and gain more future insight.