Mental Health: Analysis of Factors Affecting Emotional Stability
I) Mental health is an important topic in today’s society. It includes one’s emotional, social and psychological well-being, affecting how one thinks, feels, and acts.
Being concerned for the wellbeing of one’s self and other’s mental health is important to keeping society functioning smoothly as 1 in 4 Americans suffer from a mental illness.
The monetary cost of mental illness is predicted to be very large ($317 billion in 2014).
Many factors contribute to mental health problems, and this research attempts to identify some of these important factors.
This research examines the nature of mental health and studies how certain socioeconomic variables affect an individual’s emotional stability.
III)
A) A multivariable linear regression was used for my hypothesis test.
Significant factors influencing emotional stability will include income, age, ethnicity, gender, location, family structure, and risky behavior. Increased income, living in an urban area, and having children will have a significantly positive impact on emotional stability (Newport, 2007). Increasing age and number of drinks per day will both have a negative causal relationship with emotional stability (Manaf, 2016). Gender and ethnicity could not be assigned a priori.
There is still omitted variable bias in this model, but using so many variables hopefully decreased much of the OVB.
B) The model I came up with ended up looking like:
IV). Data: This research uses data from the 1979 National Longitudinal Survey of Youth. Data points that the answer could change for over time such as income, age, urban, any children, and drinks per day were all taken from a 2014 survey.
The dependent variable emotional stability is from a survey question asking how well the respondent thinks a pair of personality traits applies to themselves. This pair is calm and emotionally stable. Using a scale of 1-7 where 1 means “strongly disagree” and 7 equals “strongly agree,” the respondent rated themselves on this scale.
The Income variable came from the total net family income in the previous calendar year of the respondent.
The age variable is the age of the respondent at the time of the survey question. Age might not be that significant because there is a small range of age asked in this survey question. The respondent was anywhere between 49 and 58 at the time of this question.
Race is a variable that was turned into a binary variable for the regression: the respondent is classified as either white or non-white.
Urban is a variable that describes where the respondent lives. The variable for the regression was turned into a binary variable with the respondent being classified as living in either an urban or non-urban area.
The Variable Any children is a binary variable: 0 meaning the condition doesn’t apply and the respondent has no biological children, 1 meaning the condition applies and the respondent has biological children.
The drinks per day variable measures how many drinks on average the respondent has per day. Drink is defined as a can of beer or the equivalent, a glass of wine, or a shot glass of hard liquor. The scale went from 1,2,3,4,5,6,7,8,9, 10 or more.
MODEL ESTIMATION
(1)
VARIABLES
Effect on Emotional Stability
Income
.00074***
(0.00024)
Age
-0.00035
(0.0123)
White
-.209***
(0.064)
Female
-0.120**
(0.056)
Urban
.0938
(0.0702)
Any Children
.0287
(.0686)
Drinks per Day
-.0247
(.0174)
Constant
5.542***
(.675)
Observations
3,174
R-squared
0.0090
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
V) Explain Each Result
The preliminary results show that having a higher income positively affects emotional stability and it is significant at 1% level, holding all else constant.
Age and number of drinks per day appear to have a slightly negative impact on emotional stability, but neither one of these outcomes are significant, holding all else constant.
Whites, compared to other races, on average are less stable and this coefficient is significant at a 1% level, all else constant.
Female has a negative affect on emotional stability at a 5% significance level, other things constant.
Having a child or children positively but not significantly impacts emotional stability holding other things constant.
Living in an urban area appears to have a slightly positive impact on emotional stability holding all else constant, but it is not significant.
Number of drinks per day appears to have a slightly negative affect on emotional stability like hypothesized, but it is not a significant affect.
I used a Breusch-Pagan test and the errors turned out to be homoskedastic. The variances in the standard errors were all the same.
VI) Conclusion
Reiterate research question, why is interesting
I found that … given the methods I have used a multivariable linear regression.
Future steps
· To check the robustness of my results, I will use other proxy’s for mental health.
· I intend to include nonlinearities in my model.
· Further research into potential causal factors of mental illness is needed.
VII) References:
· Manaf, M. Rizal Abdul, et al. “Factors Influencing the Prevalence of Mental Health Problems among Elderly Residing in a Rural Community.” PLOS ONE, Public Library of Science, 9 June 2016.
· Newport, Frank. “Strong Relationship Between Income and Mental Health.” Gallup.com, 26 Nov. 2007.
· “What Is Mental Health?” MentalHealth.gov, 29 Aug. 2017.
