Assessing Time-Varying Oligopoly and Oligopsony Power in the U.S. Paper Industry

The U.S. paper industry has become increasingly concentrated and therefore been suspected of imperfect competition. In this study, the new empirical industrial organization approach is employed to measure the degree of oligopoly and oligopsony power in the U.S. paper industry simultaneously. The model is estimated by iterative three-stage least squares using annual data from 1955 to 2003. The results reveal that there has been significant oligopoly and oligopsony power in the U.S. paper industry, and the oligopoly power has been consistently lower than the oligopsony power.market power, NEIO, three-stage least squares, time-varying parameters, Agribusiness, Environmental Economics and Policy, Land Economics/Use, Production Economics, L13, Q23,

HORIZONTAL AND VERTICAL INTRA-INDUSTRY TRADE IN THE U.S. FOOD PROCESSING INDUSTRY

Intra-industry trade (IIT) in the U.S. food processing industry is evaluated in this study. The IIT index is disentangled into horizontal and vertical components and evaluated at 6-digit HTS product levels. The degree of IIT varies across different trading partners and sub-industries, and, for the U.S. food processing industry as a whole, it has been steadily increasing since 1989. Most of the IIT in the U.S. food processing industry is vertical in nature. However, horizontal IIT has been increasing faster than vertical IIT. The determinants of horizontal and vertical IIT are examined in a multiple-industry and multiple-country model. Industry characteristics show more significant effects on IIT than country characteristics. Product differentiation, market structure, and scale economies are all relevant in explaining the variation of IIT.Industrial Organization, International Relations/Trade,

PRODUCTIVITY SPILLOVERS FROM INWARD FOREIGN DIRECT INVESTMENT IN THE U.S. FOOD PROCESSING INDUSTRY

Productivity spillovers in the U.S. food processing industry resulting from inward foreign direct investment (FDI) were examined for the time period of 1988 to 1992. Both Caves-type (unidirectional) and simultaneous (bidirectional) spillover models were considered in the analysis. Using the Caves-type spillovers model, foreign investment was found to have significantly negative spillovers. The technology gap between U.S. firms and foreign firms in the food processing industry was small, and it was positively related to the productivity growth. The simultaneous equation model revealed that spillovers were bi-directional in the U.S. food processing industry. The demonstration effect from foreign presence was negative, but the competition effect had even larger positive spillovers for U.S. domestically-owned firms. As a whole, the U.S. food processing industry has benefited from the competition brought by inward FDI.International Relations/Trade,

THE EFFECT OF FOOD-SAFETY RELATED INFORMATION ON CONSUMER PREFERENCE: THE CASE OF THE BSE OUTBREAK IN JAPAN

This paper uses a nonparametric approach for testing whether there is a structural change in the meat demand of Japanese consumers due to the BSE (mad-cow disease) outbreak in the country. The axiom of revealed preference is utilized to test the stability of preference in Japanese meat consumption. The matrix of weak form of revealed preference (WARP) is partitioned and Kruskal-Wallis statistics are derived to evaluate whether the switches of preference are transitory or due to a structural change. Empirical results show that Japanese meat demand is currently unstable and has undergone a structural change, synchronized with the BSE outbreak in Japan in mid-September 2001.BSE (Mad-Cow Disease), the Revealed Preference Test, WARP, structural change, Food Consumption/Nutrition/Food Safety,

ANALYSIS OF THE WORLD OIL CROPS MARKET

The world of oilseeds, meals, and oils has been evolving. New issues have emerged for researchers and policymakers. The United States is still the largest player, mainly due to its dominant position in soybeans and soybean meals. Nevertheless, the position of the United States has been challenged by several countries. The growth by Brazil and Argentina in the production of soybeans and soybean meal has especially eroded the market share of the United States in recent years. Malaysia and Indonesia have been aggressively marketing their palm oil, intensifying the competition for U.S. soybean oil exports. Other issues include changing trade policies and demand in major importing countries, trade liberalization, and genetically modified crops.oilseeds, soybeans, Brazil, Argentina, trade, Crop Production/Industries,

THE EFFECT OF EXCHANGE RATE VOLATILITY ON WHEAT TRADE WORLDWIDE

A modified gravity-type model was employed to evaluate the effect of exchange rate volatility on wheat exports worldwide. Special attention was given to the econometric properties of the gravity model within panel framework. Short and long-term measures of exchange rate volatility were constructed and compared. Both measures of exchange rate volatility have exhibited a negative effect on world wheat trade and the long-term effect was even larger. This result implies that exchange rate volatility is an important factor in explaining the trade pattern of wheat trade worldwide. Keywords: wheat, export, exchange rate, volatility, gravity model, and panel data.wheat, export, exchange rate, volatility, gravity model, and panel data., International Relations/Trade,

THE EFFECT OF EXCHANGE RATE VOLATILITY ON WHEAT TRADE WORLDWIDE

A modified gravity-type model was employed to evaluate the effect of exchange rate volatility on wheat exports worldwide. Special attention was given to the econometric properties of the gravity model within a panel framework. Short and long-term measures of exchange rate volatility were constructed and compared. Both measures of exchange rate volatility exhibited negative effects on world wheat trade, with even greater effects in the long-term measure. This result implies that exchange rate volatility is an important factor in explaining the trade pattern of wheat worldwide.wheat, export, exchange rate, volatility, gravity model, and panel data., International Relations/Trade,

Prompt Tuning based Adapter for Vision-Language Model Adaption

Large pre-trained vision-language (VL) models have shown significant promise in adapting to various downstream tasks. However, fine-tuning the entire network is challenging due to the massive number of model parameters. To address this issue, efficient adaptation methods such as prompt tuning have been proposed. We explore the idea of prompt tuning with multi-task pre-trained initialization and find it can significantly improve model performance. Based on our findings, we introduce a new model, termed Prompt-Adapter, that combines pre-trained prompt tunning with an efficient adaptation network. Our approach beat the state-of-the-art methods in few-shot image classification on the public 11 datasets, especially in settings with limited data instances such as 1 shot, 2 shots, 4 shots, and 8 shots images. Our proposed method demonstrates the promise of combining prompt tuning and parameter-efficient networks for efficient vision-language model adaptation. The code is publicly available at: https://github.com/Jingchensun/prompt_adapter

Fed-CBS: A Heterogeneity-Aware Client Sampling Mechanism for Federated Learning via Class-Imbalance Reduction

Due to limited communication capacities of edge devices, most existing federated learning (FL) methods randomly select only a subset of devices to participate in training for each communication round. Compared with engaging all the available clients, the random-selection mechanism can lead to significant performance degradation on non-IID (independent and identically distributed) data. In this paper, we show our key observation that the essential reason resulting in such performance degradation is the class-imbalance of the grouped data from randomly selected clients. Based on our key observation, we design an efficient heterogeneity-aware client sampling mechanism, i.e., Federated Class-balanced Sampling (Fed-CBS), which can effectively reduce class-imbalance of the group dataset from the intentionally selected clients. In particular, we propose a measure of class-imbalance and then employ homomorphic encryption to derive this measure in a privacy-preserving way. Based on this measure, we also design a computation-efficient client sampling strategy, such that the actively selected clients will generate a more class-balanced grouped dataset with theoretical guarantees. Extensive experimental results demonstrate Fed-CBS outperforms the status quo approaches. Furthermore, it achieves comparable or even better performance than the ideal setting where all the available clients participate in the FL training