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5
Section 7. Economic security
https://doi.org/10.29013/EJEMS-20-3-64-68
Xie Yifei,
Delaware County Christian School, PA E-mail: [email protected]
FINANCIAL WORRIES OVER NON-EMERGENCY COST AMONG ADULTS IN2017
Abstract
Aim: This study aims to 1) examine the predictors of adults' financial worries over Non-Emergency Cost in 2017 2) build a predictive model for adults' financial worries over Non-Emergency cost using logistic regression model.
Method: The National Health Interview Survey (NHIS) in 2017 was used. All the participants who were eligible were randomly assigned into 2 groups: training sample and testing sample. Logistic regression model was built using the training sample data. Receiver operating characteristic (ROC) were calculated.
Results: A total of6618 (25.42%) participants out of26034 had worried about the Non-Emergency Cost. About 27.70% female participants and 22.67% male participants had worried.
According to the logistic regression, younger population were less likely to worry about monthly non-emergency cost then the elderly population. Male is 22.4% less likely to worried about housing cost. Non-Hispanic population were 58.5% less likely to worry. Compared to the unmarried, married people were 33.2% less likely to worry. Compared to other race while the black population were 72.6% more likely to worry. Compared to people who were looking for a job, the employed and the one not looking for a job were 48.9% less likely to worry about monthly non-emergency cost.
The area under curve was 0.6264. The optional cutoff time is 0.6211. The mis-classification error was 0.2555. the sensitivity rate is about 0.60% and the specificity is 99.87%.
Conclusions: In this study, we identified several important predictors for worries over Non-Emergency Cost in 2017 e.g., age, gender, race and working status.
Keywords: financial worries, Non-Emergency Cost, earing usage
1. Introduction: This study aims to: 1) examine the predictors of
U.S. households earned an average of $74,664 adults' financial worries over Non-Emergency Cost in 2016. Here's how those earnings were used to in 2017; 2) build a predictive model for adults' finan-pay off the following average monthly expenses cial worries over Non-Emergency Cost using logistic (https://www.bls.gov/news.release/cesan.nr0.htm): regression model.
Figure 1
2 Data and Methods:
Data:
The National Health Interview Survey (NHIS) is the principal source of information on the health of the civilian noninstitutionalized population of the United States and is one of the major data collection programs of the National Center for Health Statistics (NCHS) which is part of the Centers for Disease Control and Prevention (CDC).
The National Health Interview Survey (NHIS) Data 2017 was used in this study.
https://www.cdc.gov/nchs/nhis/about_nhis. htm
Optimal Cutoff for Binary Classification maximizes the accuracy.
Mis-Classification Error is the proportion of all events that were incorrectly classified, for a given probability cutoff score.
Sensitivity: probability that a test result will be positive when the disease is present (true positive rate.
Specificity: probability that a test result will be negative when the disease is not present (true negative rate, expressed as a percentage). e, expressed as a percentage).
Models:
We used logistic regression models to calculate the predicted risk. Logistic regression is a part of a category of statistical models called generalized linear models, and it allows one to predict a discrete outcome from a set of variables that may be continuous, discrete, dichotomous, or a combination of these. Typically, the dependent variable is dichoto-mous and the independent variables are either categorical or continuous.
The logistic regression model can be expressed with the formula: ln(P/P-l) = j60 + 01*X1 + fi2*X2 + ... .+ fin*Xn
Variables:
The outcome variable is percentage of How worried are you about.paying monthly bills (ASIN-BILL).
Table 1.- Variables used in this study
SEX 1: male
2: female
ORIGIN_I Hispanic Ethnicity:
1: yes; 2: no
RACRECI3 1: White
2: Black
3: Asian
4: All other race groups*
AGE_P Age <18 years old
0-17
Region 1 Northeast
2 Midwest
3 South
4 West
3. Results
A total of 6618 (25.42%) participants out of 26034 had worried about the Non-Emergency Cost. About 27.70% female participants and 22.67% male participants had worried.
Worries Over Non Emergency Payment
Figure 2. matrix of correlations between variables Table 2.- Logistic Regression for Non-Emergency Cost
Estimate Std. Error z value Pr(> z )
(Intercept) 0.593 0.135 4.383 0.000 ***
AGE P -0.212 0.085 -2.486 0.013 *
Male -0.253 0.042 -6.026 0.000 ***
HISPAN NO -0.880 0.059 -14.825 <2e-16 ***
MARRIED -0.404 0.043 -9.359 <2e-16 ***
White -0.016 0.082 -0.194 0.846
Black 0.546 0.098 5.592 0.000 ***
Midwest 0.003 0.068 0.045 0.964
South 0.051 0.063 0.812 0.417
West 0.047 0.068 0.694 0.488
Working -0.672 0.083 -8.086 0.000 ***
NWorNLor -0.671 0.088 -7.614 0.000 ***
According to the logistic regression, younger population were less likely to worry about monthly non-emergency cost then the elderly population. Male is 22.4% less likely to worried about housing cost. Non-Hispanic population were 58.5% less likely to worry. Compared to the unmarried, married
people were 33.2% less likely to worry. Compared to other race while the black population were 72.6% more likely to worry. Compared to people who were looking for a job, the employed and the one not looking for a job were 48.9% less likely to worry about monthly non-emergency cost.
Table 3.- Odds Ratio According to Logistic Regression
OR Risk Increase
AGE P 0.809 -19.1%
Male 0.776 -22.4%
HISPAN NO 0.415 -58.5%
MARRIED 0.668 -33.2%
White 0.984 -1.6%
Black 1.726 72.6%
Midwest 1.003 0.3%
South 1.052 5.2%
West 1.049 4.9%
Working 0.511 -48.9%
NWorNLor 0.511 -48.9%
Figure 3. Odds Ratio (blue) and Risk Increase (red) According to Logistic Regression
Cost. About 27.70% female participants and
The area under curve was 0.6264. The optional cutoff time is 0.6211. The mis-classification error was 0.2555. the sensitivity rate is about 0.60% and the specificity is 99.87%. 4. Discussion
22.67% male participants had worried.
According to the logistic regression, younger population were less likely to worry about monthly non-emergency cost then the elderly population. Male is
A total of 6618 (25.42%) participants out of 22.4% less likely to worried about housing cost. Non-26034 had worried about the Non-Emergency Hispanic population were 58.5% less likely to worry.
Compared to the unmarried, married people were Compared to people who were looking for a job, the
33.2% less likely to worry. Compared to other race while employed and the one not looking for a job were 48.9%
the black population were 72.6% more likely to worry. less likely to worry about monthly non-emergency cost.
ROC Curve
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Figure 4. ROC in testing sample for Logistic Regression
The area under curve was 0.6264. The optional factors of the financial worries of the Non-Emergen-
cutoff time is 0.6211. The mis-classification error was cy Cost.
0.2555. the sensitivity rate is about 0.60% and the In this study, we identified several important specificity is 99.87%. predictors for financial worries over Non-Emergen-
There are limitations in this study. For example cy Cost in 2017 e.g., age, gender, race and working
we did not include the health conditions and family status. income inforamtion in this study when examing the
References:
1. Refer to: URL: https://www.bls.gov/news.release/cesan.nr0.htm
2. The National Health Interview Survey (NHIS) in 2017.
3. Peng C. J., Lee K. L., Ingersoll G. M. An Introduction to Logistic Regression Analysis and Reporting. The Journal of Educational Research, 96(1).- P. 3-14.
4. Tabachnick B. and Fidell L. Using Multivariate Statistics (4th Ed.). Needham Heights, MA: Allyn & Bacon, 2001.
5. Stat Soft. Electronic Statistics Textbook. URL: http://www.statsoft.com/textbook/stathome.html; http://www.statsoft.com/textbook/stathome.html.
6. Stokes M., Davis C. S. Categorical Data Analysis Using the SAS System, SAS Institute Inc., 1995.
