Spatio-temporal diffusion of residential land prices across Taipei regions

ABSTRACT: Past studies have shown that changes in the house price of a region may transmit to its neighbouring regions. The transmission mechanism may follow spatial and temporal diffusion processes. This paper investigates such regional housing market dynamics and interactions among local housing sub-markets in Taipei. The analysis is based on a panel data framework and spatial panel models using annual data on median residential land prices from 41 Taipei sub-markets over the period from 1992 to 2010. The empirical analysis suggests that spatial dependence plays a significant role in interactions among regional housing markets. The results are strongly robust across several model specifications and regions controlling for time fixed effects and space-time covariance. These findings have significant implications for urban spatial planning and efficient use of public resources in mega-urban areas. JEL CLASSIFICATIONS: C21; C23; R12; H5

Les fonctions sociales du commérage et le modèle Leviathan

International audienceIn this paper, we are interested in understanding the relation between gossip and two social phenomena: consensus and the positivity bias. These two properties of a population together seem somewhat counterintuitive: a population needs some consensus to act as a group, at the same time the positivity bias is said to be quite universal and it means people diverge. This paradox can perhaps be solved by the understanding of its links to gossip and its social functions (Foster, 2004). Deffuant, Carletti, and Huet (2013) have shown that the Leviathan model is able to exhibit these two social phenomena. They emerge from the individual’s need to form a valuation of themselves (i.e. self-valuation), as well as defining the value of others, through direct interaction and gossip. The particular role of gossip in their emergence and maintenance has not been exhaustively investigated in this model. That is the purpose of this paper, which starts from four hypotheses: gossip leads to consensus which increases with its intensity; gossip decreases the strength of the positivity bias and can suppress it; positivity bias and disagreement are linked to each other; the positivity bias and bias to negativity occurring in the Leviathan model appear conjointly whatever the level of gossip (they have been conjointly diagnosed in the first investigations of Deffuant et al. 2013). Overall, our hypotheses are confirmed. We especially find that consensus is almost never reached without gossip. We also show how an asymmetrical level of openness to the influence of others, depending on how high they are held in esteem, is important for the positivity bias, as well as for the bias to negativity. The number of peers discussed during a meeting is also shown to be essential for consensus

Dealing With Spatial Heterogeneity in Entrepreneurship Research

In quantitative research, analyses are generally made using a geographically defined population as the study area. In this context, the relationships between predictor and response variables can differ within the study area, a feature that is known as spatial heterogeneity. Without analyzing spatial heterogeneity, a global model may not be correct, and there may be unclear spatial boundaries in the generalizability of the findings. The authors discuss how the method of geographically weighted regression (GWR) can be used to identify the study area, and illustrate the utility of GWR for empirical analyses in entrepreneurship research. Future entrepreneurship research can benefit from analyzing whether conflicting evidence may be due to spatial heterogeneity and from applying GWR in an exploratory way

Spatial panel models and common factors

This chapter provides a survey of the existing literature on spatial panel data models. Both static, dynamic, and dynamic models with common factors will be considered. Common factors are modeled by time-period fixed effects, cross-sectional averages, or principal components. It is demonstrated that spatial econometric models that include lags of the dependent variable and of the independent variables in both space and time provide a useful tool to quantify the magnitude of direct and indirect effects, both in the short term and long term. Direct effects can be used to test the hypothesis as to whether a particular variable has a significant effect on the own dependent variable, and indirect effects to test the hypothesis whether spatial spillovers affect the dependent variable of other units. To illustrate these models, their effects estimates, and the impact of the type of common factors, a demand model for cigarettes is estimated based on panel data from 46 U.S. states over the period 1963 to 1992.<br/