Essays on the general determinants of consumption and savings
| dc.contributor.author | Song, Zhongchen | |
| dc.date.accessioned | 2021-12-14T20:24:43Z | |
| dc.date.available | 2021-12-14T20:24:43Z | |
| dc.date.issued | 2021 | en |
| dc.description.abstract | This thesis consists of 4 studies linked together by my attempts to study the determinants and behavior of consumption and savings. Chapter One provides an introduction and background for this thesis. Chapter Two replicates Fiorito and Kollintzas (2004). This paper examines the crowding-out effect between government consumption and private consumption. My replication confirms their original findings by re-creating their dataset and estimation methods using the same sources listed in Fiorito and Kollintzas’ appendix. Furthermore, I concluded that their results are robust when employing more recent data. Chapter Three investigates why savings are so high in China from the perspective of the One-Child Policy (OCP). Using data from the 2014 Gallup World Poll and Global Findex database. I compare the saving behavior of Chinese people with people from regions that do not have restrictive population policies. These regions share many cultural, demographic, and economic characteristics with China, suggesting they can be used as a counterfactual for China. The rich dataset also enables me to adopt the Blinder-Oaxaca decomposition procedure to disentangle the different channels by which the OCP could affect savings. My results suggest that there is little difference in the savings behaviour of Chinese people with their regional counterfactuals, and my estimates are generally small. Therefore, I find no evidence to support that the OCP can explain China’s high saving rate. My findings also suggest that the relaxation of the OCP is unlikely to increase Chinese consumption significantly. Chapter Four focuses on using search engine data from Baidu and Google to predict consumption-related aggregates in China. Over the last 15 years, researchers have used search intensity data like Google Trends to analyze whether the volume of internet searches can help predict consumption and consumer behavior, while limited attention has been put on economies where other search engines like Baidu dominates the market. In Chapter Four, I investigate whether Baidu and Google can help to forecast total retail sales of consumption goods in China. I estimate both the baseline models and the models augmented with Baidu/Google search term series, using both OLS and Lasso methodologies. My results show that adding information from Baidu search intensities to the baseline model can improve the accuracy of the predictions. Furthermore, the improved performance from the Baidu data is greater than that from Google Trends or Chinese Consumer Confidence surveys. Chapter Five investigates whether the forecasting procedures I used for Chinese consumption would also be effective in the New Zealand context. To achieve this goal, I adopt a similar estimation procedure as Chapter Four to nowcast and forecast quarterly household consumption using data from Statistics New Zealand for the period 2005 Q1 to 2020 Q4. My results indicate that models with Google Trends reduce prediction errors by 18% for nowcasting and up to 45% for forecasting over a baseline OLS model with AR terms. Chapter Six concludes this thesis. It provides an overview of my chapters, as well as a summary of my main findings. | en |
| dc.identifier.uri | https://hdl.handle.net/10092/103170 | |
| dc.identifier.uri | http://dx.doi.org/10.26021/12302 | |
| dc.language | English | |
| dc.language.iso | en | en |
| dc.publisher | University of Canterbury | en |
| dc.rights | All Rights Reserved | en |
| dc.rights.uri | https://canterbury.libguides.com/rights/theses | en |
| dc.title | Essays on the general determinants of consumption and savings | en |
| dc.type | Theses / Dissertations | en |
| thesis.degree.discipline | Economics | |
| thesis.degree.grantor | University of Canterbury | en |
| thesis.degree.level | Doctoral | en |
| thesis.degree.name | Doctor of Philosophy | en |
| uc.bibnumber | 3107128 | |
| uc.college | UC Business School | en |