Analysis of Relationship Between Alcohol Consumption and People’s Unemployment

Qiyu Chen, Zidong Ji, Lan Chen, Qi Huang
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Abstract

This paper examines the correlation between unemployment in the labor market and individual consumption of alcohol. It uses the data from the National Longitudinal Survey of Youth (NLSY). It includes information on labor market results, alcohol consumption, and assorted individuals' demographics every two years in 1989 and 1994. The data are restricted to young adults between the ages of 24 and 32 in 1989 (and hence 29-32 in 1994). Each person has a unique identifier (variables named id), and the year is represented by the specified variable. The variable names of these data use STATA software for data processing and variable analysis. The standardized data were used to analyze the main components of years, including 1989 and 1994. The PCA method needs to evaluate the Eigenvalue of the result, which reflects the degree to which the main component affects the original variable. And KMO is used to measure the strength of the correlation between variables by comparing the correlation coefficients of the variables with the coefficients of bias correlation. By analyzing these variables' data, the number of days in the last month the individual has at least 1 drink is significant with the unemployment rate. One interesting phenomenon in other possible variables is the impact of the number of years of education the individual's father has on the unemployment rate is not significant in 1989, but the dad's education back to significant variables list.
酒精消费与失业的关系分析
本文考察了劳动力市场失业与个人酒精消费之间的相关性。它使用了来自全国青年纵向调查(NLSY)的数据。它包括1989年和1994年每两年一次的劳动力市场结果、酒精消费和各类个人人口统计信息。这些数据仅限于1989年24至32岁的年轻人(因此1994年为29至32岁)。每个人都有一个唯一的标识符(变量名为id),年份由指定的变量表示。这些数据的变量名采用STATA软件进行数据处理和变量分析。标准化数据用于分析年份的主要组成部分,包括1989年和1994年。PCA方法需要对结果的特征值进行评价,特征值反映了主成分对原变量的影响程度。KMO通过比较变量的相关系数与偏差相关系数来衡量变量之间的相关强度。通过分析这些变量的数据,一个人在上个月至少喝了1杯酒的天数与失业率有显著关系。在其他可能的变量中,一个有趣的现象是,个人父亲受教育年限对失业率的影响在1989年并不显著,但父亲的受教育程度又回到了显著变量列表中。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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