Income Group Classification Case Study

Abstract

Energy is the significant resources for global economic growth and development and for human daily life. In recent years, the increasing scarcity of energy and the continuous rise in energy prices have posed more severe challenges to many countries in the current international environment. Residents' energy consumption, which can be categorized into indirect consumption from production driven by consumption and direct consumption for daily living, is intricately linked to their consumption behavior. And as we know, income levels play a decisive role in shaping this consumption behavior. Different income brackets often lead to distinct energy consumption patterns; for instance, higher - income individuals might consume more energy through luxury goods production and larger living spaces, while lower - income groups focus on basic energy needs for survival and daily activities. Given this crucial connection, understanding the factors that influence income becomes paramount. This paper uses a sample size of 65,062 and applies two methods, univariate analysis and bivariate analysis, to comprehensively analyze the impacts of micro - factors such as an individual's years of education and macro - factors such as industry characteristics on personal income. A logistic regression model is constructed to predict the level of personal income based on different income - influencing factors, aiming to identify citizens with a wage level of less than $50,000 and assist the government in formulating relevant policies. The study finds that: The coefficients of some levels of education, workclass, and native country are positive. An increase in these factors will lead to an increase in the probability of a person having a salary of <= 50,000. The coefficients of age, fnlwgt, marital_status, working_hours_per_week, some levels of education, workclass, and native country are negative. An increase in these factors will lead to a decrease in the probability of a person having a salary of <= 50,000. Based on the above research results, this paper puts forward corresponding policy recommendations for enhancing the income level of vulnerable groups and improving their quality of life.