AN ANALYSIS OF EQUILIBRIUM RELATIONSHIP BETWEEN PRICE ELASTICITY AND EXPENDITURE LEVEL: A CASE STUDY OF KOREAN MOBILE MARKET DATA

In most developing countries, telecommunications industry has been grown fast and still has more growth potential than in the developed countries. Clearly the telecommunications industry contributes to foster economic developments and also to narrow the communication gaps among countries. Among many components relating to the success of quick developments of telecommunication services, an appropriate and optimal pricing strategies is the most vital element. In this view point, this paper examines the optimal price discrimination strategy for firms in a monopolistically competitive market. The primary interest is the theoretical relationship between price elasticity and the average expenditure level of consumers. Our equilibrium analysis shows that the relationship can go either way (positive or negative) depending on the prevailing price level of the product in concern. As an empirical example, using a hierarchical Bayes model we find that heavy user of mobile service are substantially more elastic to the price of calls in Korea. A discussion of the optimal pricing scheme and market structure is in order.Price Discrimination, Price Elasticity, Price Sensitivity, Mobile Telecommunications, Hierarchical Bayes Model

DDP-GCN: Multi-Graph Convolutional Network for Spatiotemporal Traffic Forecasting

Traffic speed forecasting is one of the core problems in Intelligent Transportation Systems. For a more accurate prediction, recent studies started using not only the temporal speed patterns but also the spatial information on the road network through the graph convolutional networks. Even though the road network is highly complex due to its non-Euclidean and directional characteristics, previous approaches mainly focus on modeling the spatial dependencies only with the distance. In this paper, we identify two essential spatial dependencies in traffic forecasting in addition to distance, direction and positional relationship, for designing basic graph elements as the smallest building blocks. Using the building blocks, we suggest DDP-GCN (Distance, Direction, and Positional relationship Graph Convolutional Network) to incorporate the three spatial relationships into prediction network for traffic forecasting. We evaluate the proposed model with two large-scale real-world datasets, and find 7.40% average improvement for 1-hour forecasting in highly complex urban networks

HABIT FORMATION AND PRECAUTIONARY SAVING: EVIDENCE FROM THE KOREAN HOUSEHOLD PANEL STUDIES

We examined the evidence of precautionary saving and Constantinides (1990) type habit formation model by using the Korean Household Panel Studies, which has six years of time series observations and thousands of cross section observations. Employing the dynamic panel data estimation method, we found that the estimates of parameters corresponding to the degree of habit formation and precautionary saving are statistically insignificant for food consumption, but statistically significant or at least marginally significant for nondurables and services consumption.Habit Formation, Precautionary Saving, Korean Household Panel Studies, Dynamic Panel Data Estimation

A Characterization of Optimal Feasible Tax Mechanism

This paper is motivated by a practical income (or wealth) taxation problem: For a public good economy where the provision of public goods is to be financed by income taxes collected from individuals, what is the optimal feasible tax mechanism when a social planner is relatively uninformed of the incomes or endowments of the individuals? This kind of problem, the optimal private provision of public goods, is a typical Bayesian mechanism design question for a small economy such as a club. In this case, the social planner has to take into account not only the individuals' incentive to report their income truthfully, but also the (individual) feasibility of the designed tax mechanism in the sense that each individual's tax payment should be consistent with their ability to pay. We employ the feasible implementation model used in Hurwicz, Maskin, and Postlewaite [1995] to study such an optimal taxation problem. It has been assumed in the standard model of optimal labor income taxation literature, pioneered by Mirrlees [1971], that there is a continuum of individuals and the (labor) income is observable to avoid the feasibility problem. Also, the literature on private provision of public goods has paid little attention to the continuous provision of public goods and the constrained efficiency under incomplete information. This paper considers a finite economy where public goods are provided continuously. Using a simple Bayesian model, we provide the full characterization of the two-agent, two-type optimal feasible tax mechanism and its properties. We find that (i) when the total endowment of the economy is relatively low enough or high enough, the first best feasible taxation can be obtained; (ii) the second best feasible tax mechanism requires a poor agent to pay relatively more than a rich agent, that is, it is regressive; and (iii) the tax mechanism is increasing in the sense that the agent's tax payment increases with his endowment. We also provide a comparative statics analysis. For the case of more than two agents, under certain mild assumptions we give some partial results similar to (i) and (ii) above. In addition, we find the optimal feasible tax mechanism for the corresponding infinitely large economyoptimal taxation, feasibility, informational rent, second best

Utilizing Class Information for Deep Network Representation Shaping

Statistical characteristics of deep network representations, such as sparsity and correlation, are known to be relevant to the performance and interpretability of deep learning. When a statistical characteristic is desired, often an adequate regularizer can be designed and applied during the training phase. Typically, such a regularizer aims to manipulate a statistical characteristic over all classes together. For classification tasks, however, it might be advantageous to enforce the desired characteristic per class such that different classes can be better distinguished. Motivated by the idea, we design two class-wise regularizers that explicitly utilize class information: class-wise Covariance Regularizer (cw-CR) and class-wise Variance Regularizer (cw-VR). cw-CR targets to reduce the covariance of representations calculated from the same class samples for encouraging feature independence. cw-VR is similar, but variance instead of covariance is targeted to improve feature compactness. For the sake of completeness, their counterparts without using class information, Covariance Regularizer (CR) and Variance Regularizer (VR), are considered together. The four regularizers are conceptually simple and computationally very efficient, and the visualization shows that the regularizers indeed perform distinct representation shaping. In terms of classification performance, significant improvements over the baseline and L1/L2 weight regularization methods were found for 21 out of 22 tasks over popular benchmark datasets. In particular, cw-VR achieved the best performance for 13 tasks including ResNet-32/110.Comment: Published in AAAI 201

Measuring the Degree of Currency Misalignment Using Offshore Forward Exchange Rates: The Case of the Korean Financial Crisis

This paper proposes a new method of measuring the degree of currency misalignment through the use of offshore forward exchange rates. Using default risk adjusted no­arbitrage conditions for forward exchange contracts, we calculate the spot exchange rates and the domestic interest rates that are implied from the observed forward exchange rates. The difference between the implied and the observed spot exchange rates is our measure of currency misalignment. Our methodology is based on the presumption that, during a currency crisis, offshore forward exchange rates reflect market sentiments more closely than onshore spot and forward exchange rates. The latter are usually tightly regulated and heavily affected by government intervention during a non­normal event such as a financial crisis. We apply the method to the Korean financial crisis in 1997 and discuss its implication for evaluating the IMF adjustment program and explaining foreign capital flows.currency misalignment, covered interest parity, non­deriverable forwards, Korean financial crisis

Heavy hydrogen in the stratosphere

We report measurements of the deuterium content of molecular hydrogen (H2) obtained from a suite of air samples that were collected during a stratospheric balloon flight between 12 and 33 km at 40º N in October 2002. Strong deuterium enrichments of up to 400 permil versus Vienna Standard Mean Ocean Water (VSMOW) are observed, while the H2 mixing ratio remains virtually constant. Thus, as hydrogen is processed through the H2 reservoir in the stratosphere, deuterium is accumulated in H2 . Using box model calculations we investigated the effects of H2 sources and sinks on the stratospheric enrichments. Results show that considerable isotope enrichments in the production of H2 from CH4 must take place, i.e., deuterium is transferred preferentially to H2 during the CH4 oxidation sequence. This supports recent conclusions from tropospheric H2 isotope measurements which show that H2 produced photochemically from CH4 and non-methane hydrocarbons must be enriched in deuterium to balance the tropospheric hydrogen isotope budget. In the absence of further data on isotope fractionations in the individual reaction steps of the CH4 oxidation sequence, this effect cannot be investigated further at present. Our measurements imply that molecular hydrogen has to be taken into account when the hydrogen isotope budget in the stratosphere is investigated