H388 Presentations 11/28/06
1.
2.
3.
4.
5.
6.
7.
8.
Catie Broussard
Kris Van Voorhis
Jessica Bruno
Elizabeth Schlossberg
Amanda Graham
Melissa Teixeira
Brian Kelly
Yuehong Lei (no slides)
9. Michael Franklin
10. Manisha Thapa
11. Susan Krissel (absent
with illness)
12. Ashlyn Murphy
REFUGEES
Dependent, Un-Free, Homeless, Unequal
Catie Broussard, 28 November 2006
What is a Refugee?
•
International Law and Normative Practice dictates that a refugee is a person
who “owing to a well-founded fear of being persecuted for reasons of race,
religion, nationality, membership in a particular social group, or political
opinion, is outside of the country of his nationality, and is unable to, or,
owing to such fear, is unwilling to avail himself of the protection of that
county. “ ( UN Conventoin,1951)
•
By concentrating on the large refugee populations in Africa—those refugees
that have being displaced within their home region and are hosted by a
neighboring state—we can target the greatest unequal and dependent
populations to determine how their living.
•
Policy Questions for Hosts and International Community: Integration vs.
Segregation and Repatriation vs. Assimilation
– Decisions based on the situations of Refugee creation
– Host –or contracting – country’s government acceptance of the international
treaties and norms vs. indigenous “acceptance” of new and inherently needy
populations
Source : UN Statistics Report 2005: Global Refugee Trends
Refugee Status is by nature
UNEQUAL to nationals of host
countries.
• Uprooted to another
country
• Persecuted
• Homeless
• Dependent on Host
and International
Community
Refugees in Africa
Obtained from
ReliefWeb
DEPENDENCE OF
REFUGEES
QUESTIONS GET COMPLICATED…
Who is the “Refugee”?
Do only the poor become refugees?
How does the extreme poverty of refugee populations
compare to the state of indigenous populations?
How can we understand and combat the problems of these
dependent, homeless, unequal, “un-free” populations, that
is only further complicated by the difficulty of obtaining
statistics and measures of their plight?
Understanding The
Affordable Housing Crisis
Kris Van Voorhis
History 388
Hunger, Poverty and Market Economy
Professor Ludden
November 28, 2006
The Problem
•
The American Planning Association has dubbed the
affordable housing crisis as a “silent killer,” likening
it to high blood pressure – acute, growing, deadly,
and yet largely unknown for most Americans
•
According to the U.S. Department of Housing and
Urban Development, more than 11 million
households fall within HUD’s "worst-case" category,
forced to pay more than one-half their incomes for
housing, endure overcrowded conditions and/or live
in structures with severe physical deficiencies.
•
More than 3.5 million Americans are considered
homeless, 1.35 million of them being children
Homelessness is a Poverty Issue
The Homeless Population
Demographic Factors:
Race
Demographic Factors:
Geography
An Underlying Cause:
The Lack of Affordable Housing
An Underlying Cause:
The Lack of Government Spending
An Underlying Cause:
The Lack of
Public Awareness and Support
Assessment and Analysis of
Nutritional Status in Bangladesh
Jessica Bruno
History 388
November 28th, 2006
Findings
• Comparisons of food intake vs. education
level, location (urban/rural), gender,
occupation, NGO (benefited/nonbenefited)
• Improvements in intake with primary
education completed, urban location,
female gender, and cultivators
Example
Daily Food Intake per
capita (gm)
Education v Total Daily Food Intake per capita
820
800
780
760
740
720
700
680
660
Illiterate
Can Read
and Write
Primary
Class VI- X S.S.C- H.S.C
Educational Status
Graduate
The Double Burden of
Malnutrition
Exploring the link between obesity
and poverty and why the
correlation exists…
Elizabeth Schlossberg
The Evidence
• NHANES Survey 1971-2004 revealed a 50%
increased chance of becoming overweight in
poor versus non poor families (Miech et al 2006)
• “The prevalence of obesity is significantly higher
in poor communities than in affluent
communities” (Journal of Youth and
Adolescence)
– Variables include age and race
Why the Link?
• Focus: Availability of healthy food
• Healthcare
• Adequate education about nutrition and a
healthy lifestyle
• A safe environment for physical activity
Case Study: Washington DC
Focus on the Availability of Fresh Food
“Residents in Wards 7 & 8 where poverty is high and grocery stores are scarce are more likely to
suffer from diet-related diseases than residents of the District’s other wards (Hunger Solutions).
Obesity prevalence in Wards 7 & 8 is about four times higher than in Wards 2 and 3, which have the
most grocery stores and many of the highest community food security rankings in the District.”
(Hunger Solutions)
QuickTime™ and a
TIFF (LZW) decompressor
are needed to see this picture.
Grocery Stores and Poverty
The
darker
colors
represent
higher
rates of
poverty
The dots
represent
grocery
stores that
sell fresh
food
Availability and Race
• Ratio of grocery stores to residents revealed a ratio of 1:3,816 in
chiefly white neighborhoods as opposed to 1:23,582 in chiefly
African American neighborhoods (Journal for Preventative Medicine
2002)
Why?
• Similar problems occur in Philadelphia, Chicago
and New York, but why?
• The RISK is greater than the REWARD
– Cost is too great to maintain security and to train
reliable employees in lower income areas
– Smaller profit margin due to sales of cheaper goods
First Obesity, Then Diabetes
The Upper East Side vs. East Harlem
• Upper East Side grocery stores were three times as likely to
stock diet soda, low-fat or fat-free milk, high fiber bread, fresh
fruit and fresh vegetables
• “Those living in East Harlem die of diabetes at twice the
rate of people in the city as a whole” (New York Times
2006)
• Sub par health care available in East Harlem leaves residents
unable to afford medication for diabetes
No Such Thing as an Easy
Solution
• While the availability of fresh, healthy food
in lower income areas is one contributing
factor to the problem, other factors include
education, healthcare and a safe
environment. Until all of these factors,
along with government support come
together, the problem can not be fixed.
Dynamics of Poverty Among the
Indigenous Population of Bolivia
Amanda Graham
November 28, 2006
Introduction
• 72% of population below poverty line
• Same proportion of those people are
indigenous
• 60% of population indigenous
• Social ladder “whitens in accordance with
class privilege”
• Why? Social Exclusion-denied access to
resources
What defines indigenous?
• 36 Indian tribes recognized by government
• 2 Main Groups
– Aymara (20-25%)
– Quechua (35-40%)
Historical Factors
• Spanish Conquistadors
– Exploitation and Slavery
• Liberalism (19th Century)
– Biological Category of Slaves
– Poverty dates from here
– Serfdom until 1950s
• Can’t escape
– Continue to live in rural highlands
Geo-Economic Factors
• Rural/Western Provinces
– Home to Indigenous population
• Eastern Provinces
– Local white/foreign business control
– Control of natural gas resources/GDP
Mobility Factors
• Geographic landscape creates obstacles
for adequate construction
• Lack of adequate roads that link Eastern
and Western provinces
Discrimination Factors
• White Persona v. Indigenous Persona
• Deprivation of basic human rights by
Government
• Low Paying Jobs
Educational Factors
• Low investment in
education
• Indians don’t realize
situation
• Dropout rate
• Attendance disparity
between rich and
poor
Educational Factors (Cont.)
• School attendance’s relationship with child labor
Health Factors
• Vulnerable to communicable diseases like
cholera and tuberculosis
• Diseases preventable by vaccines lower in
rural population
• Risks to women during child birth
Improvements
•
•
•
•
Increase in education
Political activism
New government leadership
Government recognition of demands of
indigenous population
• Improvement slow-needs continued
activism
I n e q u a l i t y in B r a z i l
Melissa Teixeira
National Statistics:
•Population: 188,078,227
•Infant Mortality: 28.6 deaths/1000 live
births
•Life expectancy at birth: 71.97 years
•Literacy rate: 86.4%
•GDP per capita (PPP): $8,300
North
Northeast
Centre-West
1.
8.
9.
10.
11.
17.
18.
19.
2.
3.
4.
5.
6.
7.
Rorai
ma
Amap
á
Amazo
nas
Pará
Tocant
ins
Acre
Rondô
nia
12.
13.
14.
15.
16.
Maranhão
Piauí
Ceará
Rio Grande do
Norte
Paraíba
Pernambuco
Alagoas
Sergipe
21.
Bahia
22.
23.
24.
20.
•Percentage below the Poverty Line: 22%
•Gini Index: 0.59
Mato Grosso
Goiás
Distrito Federal
(Brasília)
Mato Grosso do Sul
Southeast
Minas Gerais
Espírito Santo
Rio de Janeiro
São Paulo
South
25.
26.
27.
Paraná
Santa
Catarina
Rio Grande
do Sul
I n e q u a l i t y
“Inequalities in power and wealth
translate into unequal opportunities,
leading to wasted productive
potential and to an inefficient
allocation of resources”
Average Monthly Salary by Region in Brazil 1995-2004 (Real$)
1200
800
Northern Brazil
Northeastern Brazil
Southern Brazil
Southeastern Brazil
Central-Western Brazil
Brazilian Average
600
[World Bank 2006]
400
Gini Index by Region in Brazil 1995-2004
200
0.625
0
1995
Northern Brazil
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
Northeastern
Brazil
Southern Brazil
Year
0.6
The top ten percent of the
Brazilian population control
fifty percent of salaried income and
the bottom fifty percent account for
a mere twelve percent of income.
Gini Index
Average Monthly Salary (Real$)
1000
Southeastern
Brazil
Central-Western
Brazil
Brazilian
Average
0.575
0.55
0.525
0.5
1994
1995
1996
1997
1998
1999
2000
Year
2001
2002
2003
2004
2005
E d u c a t i o n
Level of Education for Students of Five Years or Older, by
Region in 2005
Level (and type)
of Education
(%)
Average Years of Education by Region in Brazil, 2004
Region
Brazil
Northeastern
Region
Southeastern
Region
Average Years
6.6
5.3
7.3
Pre School
9.42
10.19
9.98
Private School
24.29
25.49
23.80
Public School
75.68
74.51
76.15
61.63
66.04
57.40
Private School
11.02
10.27
13.30
Public School
88.98
89.73
86.67
17.75
15.32
19.88
Private School
15.00
13.55
16.83
Public School
84.98
86.45
83.12
Higher Education
8.86
5.14
10.90
Private School
73.92
58.49
81.40
Public School
26.08
41.51
18.60
8.0
7.0
Average Years
6.0
5.0
Primary
Education
4.0
3.0
2.0
1.0
0.0
Secondary
Education
North
Northeast
Southeast1
South
Region
Central-West
Brazil
E d u c a t i o n
Literacy Rates for Brazil by Region
100.00
90.00
Percentage of Population more Educated than their Father and
Level of Education Attained,
Southeast Brazil [% more
educated than father]
by Region
Northeast Brazil [% more
Literacy Rate (%)
70.00
educated than father]
Southeast Brazil [level
attained]
Northeast Brazil [level
attained]
100
90
80
70
61.78%
60.00
50.00
38.20%
40.00
30.00
60
20.00
50
10.00
40
0.00
11.32%
Literate in Southeast
Illiterate in Southeast
30
Literate in Northeast
Illiterate in Northeast
Literacy and Region
20
10
0
Uneducated
Primary Education
Uncompleted
Primary Education
Completed
Secondary
Education
Completed
Literacy Rate, by Literacy of Father and Region
Higher Education
Completed
100%
8.29
Level of Education
9.44
13.47
80%
11.83
17.7
25.55
Percentage (%)
Percentage (%)
88.67%
80.00
56.82
60%
71.31
40%
70.91
61
20%
33.73
20.58
0%
Literate in Southeast
Illiterate in Southeast
Literate in Northeast
Literacy and Region
Illiterate in Northeast
Literacy of Father Unknown
Literate Father
Illiterate Father
M i g r a t i o n
Demographic Composition of Southeastern Brazil
Immigrants from
other regions in Brazil
19%
Immigrants from
Northeastern Brazil
9%
Foreign-born
Immigrants to
Southeastern Brazil
1%
Native of
Southeastern Brazil
71%
Demographics of Northeastern Brazil
Immigrants from
Southeastern Brazil
Immigrants from other
2%
regions of Brazil
1%
Foreign-born
Immigrants to
Northeastern Brazil
0.05%
Native of Northeastern
Brazil
97%
Social Mobility
Employment in Northeast by Sector
Public Administration
5%
Social Services
5%
Other
1%
Transportation and
Communication
5%
Economic Services
2%
Percentage of Employed Persons Over the Age of 15 Still Employed in the Industry in which They First Started:
Comparison Between Sao Paulo and La Bahia
Agriculture
40%
100%
Services
12%
90%
Percentage Working in Same Industry (%)
80%
70%
Commercial Services
13%
60%
Industrial Production
9%
Construction
8%
50%
Industry:
1. Technicial/Scientific
2. Administrative
3. Agricultural
4. Industrial/
Construction
5. Commerce/Business
6. Transportation/
Communication
7. Service
40%
30%
20%
Employment in Southeast by Sector
Public Administration
5%
Other
2%
Agriculture
14%
Social Services
7%
Transportation and
Communication
7%
10%
0%
1
2
3
4
Industry
5
6
7
Sao Paulo
Industrial Production
20%
Economic Services
4%
La Bahia
Services
18%
Construction
10%
Commercial Services
13%
1
Poverty, Inequality, and
Nigeria’s Oil Economy
Questions:
1) Is oil wealth distributed unevenly in
Nigeria?
2) What agricultural, environmental,
economic, and social effects has oil
extraction had on the local communities
of the Niger Delta?
Brian Kelly
Brian Kelly – Nigeria and Oil, 2
Contribution of Agriculture and
Oil to Nigeria’s GDP
100
90
80
70
Agriculture
50
Mining (including Crude Oil)
40
30
20
10
0
19
65
-1
19 96
66 6
-1
19 96
67 7
19 196
68 8
-1
19 96
69 9
-1
19 97
70 0
-1
19 97
71 1
-1
19 97
72 2
-1
19 97
73 3
19 197
74 4
-1
19 97
75 5
-1
19 97
76 6
-1
19 97
77 7
-1
19 97
78 8
-1
19 97
79 9
19 198
80 0
-1
19 98
81 1
-1
19 98
82 2
-1
98
3
Percent
60
Year
Source: Onyige, P.U. Energy and Social Development in Nigeria
Brian Kelly – Nigeria and Oil, 3
Nigeria’s Principal Agricultural
Export Commodities
900,000
800,000
700,000
Exports (in tons)
600,000
Rubber
Palm oil
500,000
Palm kernel
Groundnuts
400,000
Cotton
Cocoa
300,000
200,000
100,000
0
19741975
19751976
19761977
19771978
19781979
19791980
19801981
19811982
19821983
19831984
Year
Source: Onyige, P.U. Energy and Social Development in Nigeria
Brian Kelly – Nigeria and Oil, 4
Oil Revenue Distribution
1963
Distribution of
Mining Rents and
Royalties, 1963
15%
35%
Federal Government
Regions of Origin
Distributable Pools Account
50%
8%
Distributable Pools
Account Regional
Allocations, 1963
20%
42%
North
East
West
Midw est
30%
Source: Khan, Sarah Ahmad. Nigeria: The Political Economy of Oil
Brian Kelly – Nigeria and Oil, 5
Oil Revenue Distribution
1979
Distribution of Mining
Rents and Royalties, 1979
10%
Federal Government
State Government
Local Government
35%
55%
6%
4%
Breakdown of Allocation to
State Governments, 1979
4%
Directly to States
Derivation
Development of Mineral Producing Areas
Ecological Problems
86%
Source: Khan, Sarah Ahmad. Nigeria: The Political Economy of Oil
Brian Kelly – Nigeria and Oil, 6
Poverty in Nigeria
Percentage of Rural Population Below the Poverty Line
80.00%
Nigerian GNP per Capita has
steadily fallen since 1980.
71.73%
70.00%
60.00%
51.43%
Nigerian GNP per Capita Trends
46%
50.00%
1200
40.00%
28.29%
1000
30.00%
20.00%
GNP per Capita (USD)
800
10.00%
600
0.00%
1980
1985
1992
1996
Year
400
The percentage of Nigeria’s rural
population living below the poverty
line has risen since 1980.
200
0
1978
1980
1982
1984
1986
1988
1990
1992
Year
Source: Khan, Sarah Ahmad. Nigeria: The Political Economy of Oil
Source: Anyanwu, John C. Rural Poverty in Nigeria: Profile, Determinants and Exit Paths
Yuehong Lei
AIDS and TB in South Africa
No slides
Maternal Education and the
Relationship to Children’s Health
Michael Franklin
HIST 388
Topic
• Establishing a link between a mother’s
education and the health of her children
– Difficult, many variables deal with children’s
health
• Community and maternal endowments
– Has research “overstated” the benefits of
improved maternal education?
Personal Findings
• Data comes from the UN Stats website
(http://hdl.library.upenn.edu/1017/7058)
• Took 42 countries and compared:
– GDP per capita in current US$ (1981-2000)
– Children under 5 mortality rate per 1,000 live
births (1980-2000)
– Literacy rates in women 15-24 (1981-2004)
Outline
• Examined data in three ways:
– Countries where GDP per capita declined
• Does a fall in GDP lead to deteriorating conditions
and a rise in child mortality?
– Correlation
• GDP per capita vs. Mortality Rates
• Female literacy vs. Mortality Rates
– Countries with similar GDP per capita
• How do female literacy and child mortality rates
compare?
Decline in GDP Per Capita
• 17 countries experienced a fall in their GDP per
capita
– Decrease in GDP per capita indicates living standards
did not improve, and potentially worsened
• Despite drop in GDP per capita, child mortality
rates decreased everywhere, except Zimbabwe
(increased) and Liberia (did not change)
• Literacy rates among women improved in each
country
GDP Per Capita and Female Literacy
• GDP per capita vs. Mortality rates
– R² = 0.3822
• Female literacy v. Mortality rates
– R² = 0.749
• Stronger association between female
literacy and child mortality than GDP per
capita
Vi
et
na
m
L
Et ao
hi s
o
Ca Er pia
m itre
b a
Ug od
Sr an ia
iL d
a a
G nka
ha
Tona
Ni go
K ge
Se en r
y
Pa neg a
ki a
Ye sta l
m n
Li en
Th be
a ria
G ilan
uy d
Zi Djibana
m o
ba ut
El C bw i
S on e
Ni alva go
c
Ca ara dor
m gu
e a
Ni roon
g
CoEcu eria
st ad
a or
R
Tu ica
Ro rk
m ey
an
Ch ia
Cu ile
Pa Br ba
ra az
Uk gua il
ra y
in
Re
Sy e
pu Jo ria
b. rd
Ko an
re
Si
ng Iraa
Sa apo n
ud Om re
iA a
r n
Kuabia
w
Q ait
at
ar
GDP/Per Capita (in current US$ for 1981)
40000
35000
GDP/Per Capita
Child Mortality
Linear (Child Mortality )
Country
300
30000
250
25000
200
20000
150
15000
10000
100
5000
50
0
R = 0.3822
2
Under 5 Child Mortality per 1,000 Live Births (1980)
GDP Per Capita vs. Mortality Rates for Children Under 5
350
0
N
Ye ige
Se me r
Pa neg n
k a
Et ista l
hi n
o
Li pia
be
r
To ia
g
O o
m
Er an
i
Ni trea
ge
Dj ri
i a
Ug bou
an ti
d
La a
Ca S os
y
Ca mb ria
o
Sa m d
ud er ia
i A oo
ra n
bi
Ni
a
ca Ira
ra n
G gua
ha
Ke na
Ku nya
El Tu wai
S rk t
Zi alva ey
m d
ba or
b
Co we
n
Q go
J at
Sr ord ar
i L an
an
Br ka
Vi a
Pa etn zil
ra am
Ec gua
Th uad y
Si ail or
n a
Co ga nd
st p o
a re
Ri
c
Ch a
i
Ro Cu le
b
m a
G ani
Re U uya a
pu kr na
b. ain
Ko e
re
a
Female Literacy Rate ages 15-24 (1981)
Child Mortality
250
80
200
60
150
40
100
Child Mortality per 1,000 Live Births
1980
Lit Rate
Female Literacy Rate vs Mortality Rates for Children Under 5
Linear (Child Mortality )
120
350
100
300
20
50
0
0
Country
R = 0.749
2
Er
it
ya
Country
ile
Ro
m
an
ia
Uk
ra
in
e
Ch
rk
ey
Zi
m
ba
bw
e
Q
at
ar
Sr
iL
an
ka
Vi
et
na
m
Ec
ua
do
Si
r
ng
ap
or
e
Tu
Ke
n
gu
a
ia
di
a
ra
b
ra
Ni
ca
di
A
m
bo
La
os
ou
ti
re
a
go
To
Dj
ib
Ca
Sa
u
r
eg
al
Et
hi
op
ia
Se
n
Ni
ge
Child Mortality per 1,000 Live Births
Child Mortality
Child Mortality
Linear (Child Mortality )
350
300
250
200
150
100
50
0
R2 = 0.749
GDP Per Capita
Country
GDP
Female Lit (%)
Child Mortality
Cameroon
1,011 (5)
57.4 (4)
173 (4)
Nigeria
1,167 (4)
45.3 (5)
216 (5)
Ecuador
1,207 (3)
93.7 (2)
57 (2)
Costa Rica
1,381 (2)
96.7 (1)
26 (1)
Turkey
1,487 (1)
79.8 (3)
133 (3)
GDP Per Capita
Country
GDP
Female Lit (%)
Child Mortality
Pakistan
443
21.5
153
Yemen
444
11.0
205
• Vietnam has the lowest GDP per capita in 1980
yet one of the lowest rates of child mortality and
highest of female literacy
Conclusion
• Education = Good
• Importance of maternal education
– Present inequality between men and women
– Improving a mother’s level education has
been found to yield greater results than
improving her husband’s level of education
Manisha Thapa
Female Education and Child
Health in Nepal
Variables in Women’s education versus Child
health relationship
Under 5 Child Mortality Rate Ratios (Rural : Urban
and Uneducated : Educated Mothers)
5
4.5
4
3.5
rural to urban
3
2.5
2
1.5
Uneducated to
Educated
Mothers
1
0.5
bo
ng
la
d
es
ts h
w
an
bu a
ru
nd
i
c.
a
co .r.
m
or
eq os
ua
do
er r
itr
ea
gh
an
a
H
ai
ti
0
ba
Urban-Rural
differences
- access to
health facilities
-access to clean
drinking water
-transportation
Ratio
•
Countries
Variables in Women’s education versus Child
health relationship
5
4.5
4
3.5
3
2.5
2
1.5
1
0.5
0
wealth
(lowest
quintile to
highest)
ba
ng
la
d
bo e sh
tsw
an
bu a
ru
nd
i
c.
a.
r
co
m .
or
eq os
ua
do
er r
i tr
ea
gh
an
a
Ha
it i
ratios
• Wealth as a
variable
-similar relation
with child health
as mother’s
education
-stronger relation
than rural-urban
Under 5 Child Mortality Rates ratios for wealth (lowest
to highest quintile) and education (uneducated to
educated mothers)
countries
uneducated
to educated
mothers
Case for Nepal
WHO data for 2001
• Under-5 mortality rate (per 1 000 live births) - rural to urban ratio) - 1.7
• Under-5 mortality rate (per 1 000 live births) - lowest to highest wealth
quintile ratio)- 1.9
• Under-5 mortality rate (per 1 000 live births) - mother with no to higher
education ratio -2.4
Why does the relationship between women’s education and child health
still hold for Nepal?
• Gradual Urbanization
• Increased per capita GDP undermined by inflation
• Government spending in education relatively higher than in health sector
Programs to improve maternal
and child nutritional status
Susan Krissel
(absent due to illness)
Examples of Interventions
• UNICEF
– Fortification of food (ex. Iodization of salt)
– Supplemental micronutrient formula with RDIs
for pregnant/lactating women
– Education for the empowerment of women
– Baby-Friendly Hospital Initiative
– International Code of Marketing of Breast Milk
Substitutes
• Earthwatch
– Educate women about nutrition and hygiene as
related to disease prevention
– Involve and train community members/leaders
– Make community self-sufficient
• Canada Prenatal Nutrition Program
–
–
–
–
–
–
–
Supplementation
Community gardens
Gift certificates to buy healthy food
Cooking demonstrations and shopping tours
Nutrition and Health Awareness Education
Budgeting workshops
Breastfeeding incentives
Conclusions
• Various types of organizations are taking
action to improve maternal/child nutrition
• These organizations are mainly focused
on improving malnutrition through
nutrients, rather than targeting its causes
Poverty & Female Mental
Health
Ashlyn Murphy
Final Project
Hist 388
Issue of Causation
• Is there a causal relationship between
poverty and mental illness?
• Which came first: the poverty or the
mental illness?
Selection Hypothesis
• Emotional problems that are preexisting
predispose a woman to poverty.
• Mental illness/emotional problems precede
poverty.
Social Causation Hypothesis
• Stresses of poverty and the environment
of poverty lead to mental illnesses.
• Poverty precedes mental illness.
Previous Findings & Links
•
1.
2.
3.
4.
The following circumstances have been
pre-established as common among
clinically depressed women:
Recent entry in welfare program.
Dependent upon welfare.
“Inadequately” employed: marked by
unfavorable hours and/or wages.
Nonunion employment position.
5.
6.
7.
8.
Residing in low-income neighborhoods.
Residing in neighborhoods marked by drug trade.
Having no health benefits.
Spending large amount of income on child care.