A Study of the Spatial Distribution of Suicide Rates

A Study of the Spatial Distribution of Suicide Rates Ferdinand DiFurio, Tennessee Tech University Willis Lewis, Winthrop University With acknowledgements to Kendall Knight, GA, Tennes

A Study of the Spatial

Distribution of Suicide Rates

Ferdinand DiFurio, Tennessee Tech University

Willis Lewis, Winthrop University

With acknowledgements to Kendall Knight, GA, Tennessee Tech University

Introduction

Suicide is a very personal and sensitive issue

-Tragedy, both direct and indirect -Personal

Conduct research that may be helpful with

prevention

This research mixes economics with sociology

Suicide Determinants

The assumption taken in this paper

-micro-level characteristics matter, but macro- level indirect

Risk factors (there are many) and the pathways

-mental health  suicide -other factors directlysuicide

*bypass mental health? A mentally healthy individual could experience significant trauma and commit suicide -other factors indirectlymental healthsuicide

Suicide Determinants

Other factors?

Trauma, substance abuse, genetics?, age, previous attempts, family size, family dynamics, social

dynamics (i.e change in societal status), trauma (divorce, death of family/friends), religious

involvement, length of time from trauma, physical illness, business cycle

Suicide Determinants

Business cycle?

As Snipes et al (2012) point out, Durkheim’s theory “asserted that the suicide rate varies inversely with the stability and durability of

social relationships and that any economic

change, positive or negative, disrupts that status quo and contributes to an increased incidence of suicide”

What our paper does

Objective

-to identify relationships in aggregated suicide rates From here, we can gain more information on how external factors

influence these micro-level characteristics, and ultimately improve prevention

Our basic question

-Does a relationship exist between county-level suicide rates and socioeconomic

characteristics?

How is our paper different?

Rich literature in regional variation of suicide rates

-Snipes et al (2012), Lester (1985) look at cities -Churchill (1999) look at VT counties

-Gruenewald et al (1995) found higher suicides in regions with higher

alcohol sales, and higher in states with higher relative rates of

divorce, interstate migration, crime, and lower levels of church attendance (Lester 1988)

-Lester (1998) Kaplan and Geling, Molina and Duarte (2006) find higher suicides in regions with looser handgun control and higher incidence of

gun ownership

-Snipes also goes to cite Cebula and Zelenskaya (2006) that find a link bw

violent rates of crime

Few to none mention spatial dependence (we’ll explain later)

Some things to consider

Aggregation

-the best we can say

Causation

-classic errors -cannot be implied without theory

Age

-our data does not measure -risk vary with age

What does the literature say about

aggregation and causality?

Lester and Yang (2003), Preti (2003)

-individual level

-“Does unemployment increase the risk of of suicidal behavior, or, alternatively, are those with psychiatric

problems more likely to become unemployed and also more likely to engage in suicidal behavior?”

-time-induced problem

E Agerbo (2003)

-Karl Pearson, who said that “only correlation and not causation can be estimated from observational data.”

Blakely (2003)

-mental illness is the significant factor rather than other factors such as financial inadequacies

General lit review

Snipes (2011), Viren (2005) and Ruhm (2000), among others: the business cycle

-Snipes et al suggest that higher unemployment rates increase the chance of

suicide for a one month lag

-They also report suicide is greater with males

-unemployment appears to play a significant role in female suicide This finding contrasts the accepted belief that females are less prone to suicide due to workplace downturns because of their traditional role in the household

Viren: is a statistical relationship between aggregate suicides and business cycles

Ruhm: “suicide rates are predicted to rise with unemployment rates”

Molina and Duarte identify depression along with other key factors : adolescents who have suffered educational failure, who possess a gun, or who are often

distressed by their physical appearance are more sensitive to the possibility of

attempting suicide

-at the aggregate level, this is hard to verify, but we will obtain data on number of mental health facilities per county per capita

Our paper’s implicit function

Macro model at the county-level: suicide rateit =

consistent with an economic downturn, lower

unemployment consistent with an economic

upswing], unemployment it-1 , crime it , (county or

adjacent county), divorce rate it , religious

unemployment)….with most of these variables, we will investigate lags….particularly with

unemployment, divorce, and crime

As opposed to…

• Micro model at the individual level: suicide risk = f(mental health, mental health access, quality of care for mental health condition, substance abuse, genetics?, trauma (death or illness), religion, age, family size, family

structure, family strife, social life,

demographics, gun inside home?)

What is Spatial Autocorrelation?

-An important component of the data analysis in

our paper is controlling for spatial dependence

-Anselin & Bera: Because counties in the state are contiguous to one another, it is necessary to control

for spillover effects that may bias the empirical

results and cause spatial measurement errors

-the problem of serial correlation in cross-sectional data, but applied to space

-widely used technique that’s expanding into

different fields

How does it apply to this paper?

-Intuitively, there is the possibility that what’s going on

in surrounding counties (contiguous to) could be

impacting suicides in local counties

-For instance, employment shocks in a group of

metropolitan counties could be impacting suicides in a contiguous rural county

-If we don’t identify this, we could miss a large part of

the story

-If we ignored the issue, what would happen? Bias in

the estimated coefficients This would impact the

interpretation and understanding of what the data is

saying

Preliminary results

To test for the presence of spatial dependence,

and thus, for the appropriate model, a series of

steps is required

-To avoid intolerable boredom, we skip the details

here

-Standard Ordinary Least Squares Regression

(OLS) is the “go to” model in most studies (or

some variation of)

-Our tests with OLS indicate unemployment is

significant, and this is consistent with most

“regional” studies

Preliminary results

-But the significance disappears in the spatial fixed

effects model (the appropriate one for our data)

-This raises the question on whether past estimates

could have benefitted from spatial analysis: parsing

out local effects from spillover is crucial

*Caution: we are not saying that job loss is

insignificant to the suicide decision

-Why care? Resource allocation: In a world of

scarcity, putting resources into A involves an

opportunity cost: they were taken away from B When policy is developed to funnel resources into

“unemployment” programs based on biased results, something else could’ve suffered

Preliminary results

-Additional findings: our results find no relationship

between the local economy and suicide rates in

Tennessee (no spatial dependence present so far)

-But, there is a relationship between local crime rates

and local divorce rates

-That is, suicide increases as crime rates goes up and decreases as divorce rates goes up

-Since the variables are contemporaneous, no

interpretation will be made regarding the crime rate

as we are not sure if suicide is reported as a crime, we have to dig deeper

OLS Space FE Time FE FE Spatial lag

UR 0.63 (1.88)* 0.28 (0.57) 0.53 (1.56) 0.003 (0.006)

Crime -0.06 (-3.92)*** 0.09 (1.89)* -0.06 (3.82)*** 0.09 (1.85)*

Divorce -0.13 (-1.007) -0.70 (-2.24)** -0.09 (-0.67) -0.80 (-2.38)**

Notes: t-values in parentheses; Significance levels * = 0.1 ** = 0.05, *** = 0.01

What’s next?

-more work on the model

-foreclosures

-mental health access

-rural vs urban

-lags

Thank you

OLS Space FE Time FE FE Spatial lag

UR 0.63 (1.88)* 0.28 (0.57) 0.53 (1.56) 0.003 (0.006)

Crime -0.06 (-3.92)*** 0.09 (1.89)* -0.06 (3.82)*** 0.09 (1.85)*

Divorce -0.13 (-1.007) -0.70 (-2.24)** -0.09 (-0.67) -0.80 (-2.38)**

Notes: t-values in parentheses; Significance levels * = 0.1 ** = 0.05, *** = 0.01