A Microsimulation Model for Projections of Japanese Socioeconomic Structure

The Integrated Analytical Model for Household Simulation (INAHSIM) is a microsimulation model for the Japanese population that was first developed in the 1980s as a tool for household simulation. This study attempted to improve the conventional INAHSIM in order to construct a more comprehensive alternative that incorporates a larger number of social and economic elements. It also overcame the problem for simulation—the lack of kinship relations in the initial population—by imputing parent-child relationships between those parents and children who do not live together. This paper provides an overview of INAHSIM and adds certain details of the imputation that is essential for simulating the life event of “adult children resettling to care for their aged parents.” In addition, I will show the importance of the said life event in Japan by comparing the difference in the family type of elderly people between its future distribution with or without this life event. Finally, I will discuss the future possibilities for research on microsimulation models that will play an important role in policy making.     

A Taxi Zoning Analysis Using Large-Scale Probe Data: A Case Study for Metropolitan Bangkok

Taxis are considered one of the most convenient means of transportation, especially when people have to travel off-route, where public transportation is not a feasible option, and also when they need to reach a destination according to what is most convenient for them. However, many issues exist about taxi services, such as the problems of passengers who are unable to get taxi service at the location of their choice, or problems concerning when they need the taxi service to arrive. These problems may be due to the unavailability of the taxi at that particular location or due to the taxi driver not wanting to provide service. A taxi driver may not want to provide service to a potential passenger, because they may have preferences on the direction and areas they want to go or because of the different types of service zoning. Understanding the behaviors of taxi drivers and the characteristics of the trip/travel might be helpful to solving such issues. In this study, we conducted an analysis from a questionnaire survey and large-scale taxi probe data to understand taxi service behavior, travel characteristics, and to discover taxi service zoning characteristics. As a result, four types of taxi service zones including isolated zones, interactive zones, special service zones, and target zones were encountered. Travel characteristics were calculated and analyzed at different criteria, such as weekdays, weekends, and various time windows in a single day. The result of these characteristics was explained according to their similarities and dissimilarities in each type of zone. The discovery of the different zones and their respective definitions might be a good initiative for further development of a policy for taxi drivers to provide better service for passengers.     

A Conjoint Analysis of Demand for the Japanese Mobile Phone Market

The Japanese mobile phone market is 55.8% of the total market in the telecommunications industry. This paper is a conjoint analysis on Japanese consumer preferences for Japanese mobile phones. The analysis in this paper is presented after a questionnaire survey was given to 3,412 Japanese mobile phone users over 15 years old. The results of this analysis show that a decline in the price of mobile phone sets, an increase in transmission speed, the application of family discounts, and the continuation of contracts with the same telecommunication companies, all raise demand for mobile networks. In addition, the author found that user groups of NTT DOCOMO, au (KDDI), Softbank, and MVNO have different utility functions. According to the estimation of Mean Willingness to Pay (MWTP) in this paper, suppliers of mobile phone networks may be able to optimize and maximize demand by adjusting attributions and their levels.     

The Boosting Effect of E-WOM on Macro-level Consumption: A Cross-Industry Empirical Analysis in Japan

In this study, we carry out an empirical analysis on how electronic word-of-mouth (hereinafter “e-WOM”) marketing on e-WOM websites and electronic-commerce websites on the Internet boosts consumption on a macro-level. In our analysis, we conduct a model analysis of consumer behavior using data composed of more than 30,000 questionnaire surveys and quantitatively find the elasticity coefficient of the boost to consumption by performing a two-step GMM (generalized method of moments), which uses instrumental variables. The results of the analysis show e-WOM significantly increased expenditures in six fields: computers, electrical appliances, etc.; music; hobbies; clothing, accessories, etc.; beauty products, etc.; and goods for everyday life, etc. Furthermore, there was no field that had a significantly negative value. These results showed that, in the majority of the target fields, e-WOM had not only the effect of winning customers from the competition, but also the effect of boosting consumption on a macro-level. In addition, even from people’s subjective evaluations, there were many in all the generational groups who said that e-WOM boosted expenditures.     

A Statistical Analysis Between Consumer Behavior and a Social Network Service: A Case Study of Used-Car Demand Following the Great East Japan Earthquake and Tsunami of 2011

When a large-scale disaster hits a community, especially a water-related disaster, there is a scarcity of automobiles and a sudden increase in the demand for used cars in the damaged areas. This paper conducts a case study of a recent massive natural disaster, the Great East Japan Earthquake and Tsunami of 2011 to understand those car scarcities and demand in the aftermath of the catastrophe. We analyze the reasons for the increase in demand for used cars and how social media can predict people’s demand for used automobiles. In other words, this paper explores whether social media data can be used as a sensor of socio-economic recovery status in damaged areas during large-scale water-related disaster-recovery phases. For this purpose, we use social media communication as a proxy for estimating indicators of people’s activities in the real world. This study conducts both qualitative analysis and quantitative analysis. For the qualitative research, we carry out semi-structured interviews with used-car dealers in the tsunami-stricken area and unveil why people in the area demanded used cars. For the quantitative analysis, we collected Facebook page communication data and used-car market data before and after the Great East Japan Earthquake and Tsunami of 2011. By combining and analyzing these two types of data, we find that social media communication correlates with people’s activities in the real world. Furthermore, this study suggests that different types of communication on social media have different types of correlations with people’s activities. More precisely, we find that social media communication related to people’s activities for rebuilding and for emotional support is positively correlated with the demand for used cars after the Great East Japan Earthquake and Tsunami. On the other hand, communication about anxiety and information seeking correlates negatively with the demand for used cars.     

The Contents-Based Website Classification for the Internet Advertising Planning: An Empirical Application of the Natural Language Analysis

This study proposes a model for website classification using website content, and discusses applications for the Internet advertising (ad) strategies. Internet ad agencies have a vast amount of ad-spaces embedded in websites and have to choose which advertisements are feasible for place. Therefore, ad agencies have to know the properties and topics of each website to optimize advertising submission strategy. However, since website content is in natural languages, they have to convert these qualitative sentences into quantitative data if they want to classify websites using statistical models. To address this issue, this study applies statistical analysis to website information written in natural languages. We apply a dictionary of neologisms to decompose website sentences into words and create a data set of indicator matrices to classify the websites. From the data set, we estimate the topics of each website using latent Dirichlet allocation, which is fast and robust method for sparse matrices. Finally, we discuss how to apply the results obtained to optimize ad strategies.