The effect of the WIC program on consumption patterns in the cereal category

The Special Supplemental Nutrition Program for Women, Infants, and Children (WIC) is a federally-funded food assistance program for low income participants who are at nutritional risk. Beneficiaries receive vouchers for specific foods and brands, selected for their nutritional value. While the program is designed to improve nutrition, it may also induce changes in consumption behavior that persist beyond participation in the program. In this paper, we study how participation in WIC impacts the consumption patterns and preferences during and after the program. Our analysis focuses on the cereal category, in which the subsidized brands must meet certain nutritional guidelines. As expected, during the program households increase cereal consumption volume and shift their choices towards the WIC-approved brands. More interesting is that once households exit the program, the higher category consumption rate and elevated share of WIC brands persist. To understand the behavioral mechanism underlying these consumption patterns, we estimate a choice model and find an increased preference for WIC brands after controlling for state dependence. The evidence suggests that this targeted food subsidy program is effective in creating behavior change that persists even after the incentive is withdrawn.     

Empirical Analysis of the Market Value Added in the Czech Automotive Industry

International Advances in Economic Research -     

Financial Performance of the Automotive Industry in Selected EU Countries

International Advances in Economic Research -     

Career concerns and mandated disclosure

This paper examines the effects of mandated disclosure on the design of contracts and induced behavior in the presence of career concerns. We analyze the impact of two key properties of a mandated performance measure that is publicly disclosed: its sensitivity to the agent’s effort and its informativeness about the agent’s ability. We show conditions under which the agent’s effort (and the firm’s output) and the pay-for-performance weight critically depend on these two properties. In particular, when the mandated measure is sufficiently noisy, the pay-for-performance weight always decreases relative to a setting with no mandated measure. But when the mandated measure’s noise is close to that of the existing performance measure, the effect of a mandated measure on the pay-for-performance weight depends on the effort-sensitivity and informativeness of the measure. We also characterize settings where a mandated disclosure would be desirable or not; variations arise because mandated disclosures can increase both effort and risk. Our results imply that mandating the public disclosure of performance measures, particularly measures that are relatively informative about ability but are difficult to influence through managerial effort, may have the unintended consequence of generating inefficiencies in firms’ employment contracts.     

Marketing and business intelligence with help of ant colony algorithm

Recently, there is increasing need of banks for targeting and acquiring new customers, for fraud detection in real time and for segmentation products through analysis of the customers. Doing it, they can serve their customers better, and can increase the effectiveness of the company. For this purpose, various data mining methods are used which enable extraction of interesting, nontrivial, implicit, previously unknown, and potentially useful patterns or knowledge from huge amounts of data. Traditional data mining methods include classification rule tasks, for their solution there are a number of methods. Among them can be mentioned, for example, Random forest algorithm or C4.5 algorithm. However, accuracy of these methods significantly reduces in the event that some data in databases is missing. These methods are always not optimal for very large databases. The aim of our work is to verify a possible solution of these problems by using the algorithm based on artificial ant colonies. This algorithm was successful in other areas. Therefore, we tested its applicability and accuracy in marketing and business intelligence and compared it with so far used methods. The experimental results showed that the presented algorithm is very effective, robust, and suitable for processing of very large files. It was also found that this algorithm overcomes the previously used algorithms in accuracy. Algorithm is easily implementable on different platforms and can be recommended for using in banking and business intelligence.     

Modeling different penetration rates of eco-driving in urban areas: Impacts on traffic flow and emissions

Climate change and air quality are two main environmental challenges in metropolitan areas. As road transportation is one of the main contributors, public administrations are facing these problems with a number of complementary policy measures: shift to cleaner modes, new fuels and vehicle technologies, demand management, and the use of information and communication technologies (ICT) applied to transportation. Eco-driving is one of the measures that present large fuel savings at individual level. Although these savings are well documented in the literature, few studies focus on how eco-drivers driving patterns affect the surrounding vehicles and the traffic in general, and more particularly what would be the impact when the number of eco-drivers grows. Using a traffic microsimulation tool, four models in urban context have been built, corresponding to the different types of urban roads. Both the base-case and the parameters setting to simulate eco-driving have been calibrated with real data collected through floating vehicles performing the trips with normal and eco behaviors. In total, 72 scenarios were simulated, varying the type of road, traffic demand, and the percentage of eco-drivers. Then, the CO₂ and NO_x emissions have been estimated through a microscopic emission model. The results show that in scenarios with low or medium demand levels and increasing number of eco-drivers, the effects are positive in terms of emissions. On the other side, with high percentage of eco-drivers and high traffic demand, the emissions rise. Higher headways and smooth acceleration and decelerations increase congestion, producing higher emissions globally.