Externalities in the housing market and agglomeration economies

• Main Author: WU, Yifan
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2022
• Subjects: Econometrics
• Online Access: https://ink.library.smu.edu.sg/etd_coll/421; https://ink.library.smu.edu.sg/context/etd_coll/article/1419/viewcontent/GPEC_AY2017_PhD_Yifan_Wu.pdf
• Institution: Singapore Management University

This thesis studies the externalities in the housing market and agglomeration economies. While knowledge-based externalities, or knowledge spillovers are one of the most important micro-foundations of agglomeration economies, the first chapter studies how knowledge spillovers from universities affect local innovation activities. In the second chapter, we propose a high-order spatiotemporal autoregression approach to study the externalities in the housing market. The third chapter studies another important but under explored aspect of the agglomeration economies – the role that marriage market plays in providing incentives to promote urbanization, along with the unique feminization phenomenon during this process. The first chapter studies the impact of universities on local innovation activity by exploiting a unique university expansion policy in China as a quasi-experiment. In this chapter, we take a geographic approach, empowered by geocoded data on patents and new products at the address level, to identify knowledge spillovers as an important channel. We obtain three main findings. First, university expansion significantly increases universities’ own innovation capacity, which results in a dramatic boom of local industry patents. Second, the impact of university expansion on local innovation activities attenuates sharply within 2 kilometers of the universities. Third, university expansion boosts nearby firms’ new products and the number of nearby industrial patents that cite university patents but not industry patents that cite patents far away from universities. In the second chapter, we propose a high-order spatiotemporal autoregression approach for analyzing large real estate prices data. Real estate prices arrive sequentially on different housing units over time in a large volume. In this paper, we propose a high-order spatiotemporal autoregressive model with unobserved cluster and time heterogeneity. When the numbers of clusters (C) and time segments (T) are finite and the errors are iid, quasi maximum likelihood method is used for model estimation and inference. In the presence of unknown heteroskedasticity, or C and/or T is large, an adjusted quasi score method is proposed for model estimation and inference. Methods for constructing the space-time connectivity matrices are proposed. Monte Carlo experiments are performed for assessing the finite sample properties of the proposed methods. An empirical application is presented using the housing transaction data in Beijing. We find that the estimation of the spatiotemporal interaction effects are largely affected after controlling for cluster heterogeneity at the community level. The third chapter studies the relationship between urbanization and feminization, where the marriage market plays an important role in connecting the two. Previous literature studying urbanization and migration has mainly considered incentives arising from cross-city variation in productivity and the subsequent labour market outcomes. In this paper, we study an important but under explored migration incentives arising from the matching outcomes in the marriage market and the gender differences in responding to such incentives. To achieve identification, we exploit the setup of special economic zones (SEZs) as a pull force and China’s accession to the World Trade Organization (WTO) as a push force that exogenously trigger urbanization across locations, which leads to a unique feminization phenomenon during this process. The paper highlights important distributional implications on gender inequality and spatial disparity during the rapid urbanization process.