中國大陸恩格爾係數的變動因素分析

Abstract

自2002年中共十六大訂定的2020年「全面建設小康社會」之後，恩格爾係數在中國大陸社會經濟領域的地位就大為升高，儼然成為確定生活水平最常用的標準，各地區、省市無不爭相報導本地生活水平已到了「小康」或者「富裕」階段。隨著恩格爾係數受到人民以及地方政府、媒體關注程度提高，更多的質疑也伴隨而來。本碩士論文的研究目的有二：第一，尋找影響中國大陸城鄉恩格爾係數起伏的因素；第二，這些因素影響程度的多少。\n\n就實證結果而言，食品價格指數、依賴比與城鎮恩格爾係數成正向關係；實質收入、物價指數、耐用品消費支出、房地產價格指數、醫療、衣著、教育消費支出與恩格爾係數成負向關係。農村居民方面，實質收入、食品價格指數、房地產價格指數與恩格爾係數成正向關係；物價指數、依賴比、耐用品、醫療、教育、衣著支出與恩格爾係數成負向關係。\n\n由於影響城鄉恩格爾係數的因素各不相同，如果要優化城鎮的消費結構、提高人民生活水平，則應從耐用品消費、教育等支出項目著手，收入已經不是影響城鎮居民恩格爾係數的重要因素；而要提升農村的消費結構及生活水平，則應更注意提高農民收入、注意食品價格指數的起伏，這比成天宣稱恩格爾係數降到多低、達到甚麼階段，更能幫助提升人民的生活水平。然目前中國大陸仍以恩格爾係數最為影響人民生活水平唯一指標，如果沒有輔以其他指標，這對於評價人民生活質量，將有如以管窺豹，無法窺得全貌。

The importance of Engel`s coefficient in Mainland China, has been put on the table since 2002. After the The Sixteenth National Congress of the CPC , ‘Build a Well-off Society in an All-Round Way’has become the main goal in socio-economic area in Mainland China. The position in the area would greatly increase, becoming the most commonly used criteria for determining standard. City are all competing to coverage of local standard of living has come to a \"well-off\" or \"off\" stage. As the Engel`s coefficient by the people, as well as local governments, higher levels of media attention, more questions are followed. The purposes of this paper are: looking for the factors in Mainland China of urban and rural Engel’s coefficient ups and downs; and how much impact these factors.\n\nThe empirical result shows: FCPI, dependent ratio, have positive effects on Engel’s Coefficient in the urban; and real income, CPI, PIRE, household facilities, medicine, clothing, education expenditure, have the negative effect. In rural area, real income, FCPI, PIRE, have the positive effect on Engel’s Coefficient; and CPI, dependent ratio, household facilities, medicine, clothing, education expenditure, have the negative effect.\n\nTo sum up, there are different factor to affect Engel’s Coefficient in urban and rural area. And Engel’s Coefficient is not the only indicate showing the level of people’s living. We should use more indicates to estimate living standard.