The Economic Benefits of Nutrition Labeling: A Case Study for Fresh Meat and Poultry Products

New rules issued by the U.S. Department of Agriculture requiring provision of nutrition information on raw meat and poultry products may encourage consumers to make healthier food choices. Reduced intake of fat and cholesterol may prevent future cases of stroke, heart disease, and cancer. The benefits of these rules are estimated to be $62 to $125 million annually.     

Using food prices and consumption to examine Chinese cost of living

This paper considers two approaches to examining potential bias in China's consumer price index: (i) inferring true changes in cost of living from consumer behaviour; and (ii) creating alternative price indices to compare with official indices. For (i), our semi‐parametric estimates agree with the recent finding of a large understatement in increases to the true cost of living. For (ii), we focus on food prices and explore potential causes of bias. We find some evidence of a new‐good bias. China's large‐scale migration also causes changes in the cost of living that are not reflected in the consumer price index.     

Analysis of non-stationary dynamics in the financial system

Novel data-driven analyses, appropriate for detecting economic instability in non-stationary time series, are developed using functional principal component analysis (fPCA) and Synchrosqueezing. fPCA is applied in a new way, aggregating multiple financial time series to identify periods of macroeconomic instability. Synchrosqueezing, a technique which generates a time-series’ time-dependent spectral decomposition, is modified to develop a new quantitative measure of local dynamical changes and structural breaks. The merit of this integrated technique is demonstrated by analyzing financial data from 1986 to 2012 that includes equity indices, securities and commodities, and foreign exchange. Both procedures successfully detect key historic periods of instability. Moreover, the results reveal distinctions between periods of long-term gradual change in addition to structural breaks. These tools offer new insights into the analysis of financial instability.     

Technological evolution and interdependence in China's emerging biofuel industry

This study uses the European Patent Office worldwide patent database and applies two-stage interactive data collection methods to reveal the evolving technological interdependence for China's emerging biofuel industry. Three findings are excerpted from our empirical results. First, due to dominant patterns of business ownership, China's biofuel technology is seen as largely based on the evolutionary strength of the foodstuff and chemical fields. Second, China's biofuel technology development has evolved in the mode of ‘forward engineering’, led by Chinese universities rather than initiated by the public research institutes as in the experience of other East Asian latecomers. Third, our patent map and technology trajectory analyses illustrate that China's biofuel technology tends to be application-oriented and highly intertwined with the pharmaceutical industry since the 2000s, which evidences the development of biofuel industry as reciprocally reinforcing China's innovation capability deriving from its prominent chemical sector. By examining endogenous technology capability embedded in the national innovation capacity, this study uncovers public implications for other technology latecomers attempting to build an emerging industry while facing technology uncertainty in a transitional society.     

Understanding a Two‐Sided Coin: Antecedents and Consequences of a Decomposed Product Advantage*

This paper investigates the antecedents and consequences of two product advantage components: product meaningfulness and product superiority. Product meaningfulness concerns the benefits that users receive from buying and using a new product, whereas product superiority concerns the extent to which a new product outperforms competing products. The present paper argues that scholars and managers should make a deliberate distinction between the two components because they are theoretically distinct and also have different antecedents and consequences. Data were collected through an online survey on 141 new products from high‐tech companies located in The Netherlands. The results reveal that new products need to be meaningful as well as superior to competing products to be successful. This finding is consistent with the prevailing aggregate view on product advantage in the literature. However, the results also show that the effects of the two components on new product performance are moderated by market turbulence. Although each component is important in that it forms a necessary precondition for the other to affect new product performance under circumstances of moderate market turbulence, meaningfulness is most important for new product performance in turbulent markets where preferences have not yet taken shape. In contrast, when markets become more stable, the uniqueness of meaningful attributes decreases, and new products that provide advantage by fulfilling their functions in a way that is superior to competing products are more likely to perform well. In addition, the study shows that the firm's customer and competitor knowledge processes independently lead to product meaningfulness and superiority, respectively. The findings also reveal that under conditions of high technological turbulence the customer and competitor knowledge processes complement each other in creating product meaningfulness and superiority. This implies that the level of technological turbulence puts requirements on the breadth of firms' market knowledge processes to create a new product with sufficient advantage to become successful. The paper concludes that neglecting the distinction between product meaningfulness and superiority when assessing a new product's advantage may lead to an incomplete insight on how firms can improve the performance of their new products.     

An application of rule-based forecasting to a situation lacking domain knowledge

Rule-based forecasting (RBF) uses rules to combine forecasts from simple extrapolation methods. Weights for combining the rules use statistical and domain-based features of time series. RBF was originally developed, tested, and validated only on annual data. For the M3-Competition, three major modifications were made to RBF. First, due to the absence of much in the way of domain knowledge, we prepared the forecasts under the assumption that no domain knowledge was available. This removes what we believe is one of RBF’s primary advantages. We had to re-calibrate some of the rules relating to causal forces to allow for this lack of domain knowledge. Second, automatic identification procedures were used for six time-series features that had previously been identified using judgment. This was done to reduce cost and improve reliability. Third, we simplified the rule-base by removing one method from the four that were used in the original implementation. Although this resulted in some loss in accuracy, it reduced the number of rules in the rule-base from 99 to 64. This version of RBF still benefits from the use of prior findings on extrapolation, so we expected that it would be substantially more accurate than the random walk and somewhat more accurate than equal weights combining. Because most of the previous work on RBF was done using annual data, we especially expected it to perform well with annual data.     

Integrated data-driven analytics to identify instability signatures in nonstationary financial time series

With the objective of identifying instability signatures of the financial system, this article integrates two classes of data-driven techniques. The first class of techniques is utilized to investigate macroeconomic behaviour by aggregating an ensemble of heterogeneous nonstationary time-series data and the second class of techniques examines the local dynamics of the microstructures in each time series. Moving window principal component analysis (PCA) and functional PCA (fPCA) are shown to extract collective signatures of the financial system for understanding macroeconomic behaviour, and the Synchrosqueezing and Markov switching techniques are used to study local dynamics within each individual time series. The integrated data analytics successfully identifies the diverse events from 1986 to 2012. All events, both major and minor, have been identified by fPCA. The major economic events, especially the 2008 Great Recession, along with several minor events, showed a strong leading indicator in the density index derived from Synchrosqueezing. The capability of this integrated analytics suite is demonstrated in this article, and it motivates further studies encompassing data sets from broader sectors. As a complement to existing model-driven approach, this would lead to achieving a robust and reliable method that can help in taking measures to avoid catastrophic collapse in the constantly evolving financial system.