Improving Economic
Self-Sufficiency:
Current Status, Future Goals, and
Intervention Strategies Project
Counties: Genesee, Livingston, Monroe, Ontario, Orleans,
Wayne and Yates
Contractor: B.E.C.S., Inc. of Sayre Pennsylvania
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Women’s Foundation of Genesee Valley Copyright © 2004
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Summer, 2004
On behalf of the Board of Directors and the Alumnae Board I am pleased to present this
comprehensive report, Improving Economic Self-Sufficiency: Current Status, Future Goals, and
Intervention Strategies.
At the Women's Foundation we know that women work both in the home and out of the home. We
also know that women are an economic asset. We have always known -- at least anecdotally -- that
a change in a woman's economic life determines her future. In 2003 we decided we needed the data.
Our goal in undertaking this project was to determine the obstacles that keep women from becoming
assets to the community. Our anecdotes were not only confirmed, the situation is far more precarious
for women than we thought.
When the Women's Foundation of Genesee Valley was created in 1994 the mission was to fund and
advocate for programs that encourage economic growth and promote women's economic selfsufficiency. Working with Kathryn Bowen of B.E.C.S. Inc. we uncovered and analyzed the most
recent census data for quantitative information. In addition Dr. Bowen's team conducted focus
groups with service providers and interviewed numerous clients/recipients of agency services for
qualitative information.
The data, outcomes, and insights are all included. We encourage everyone to take the time to read
this report.
On behalf of the Board of Directors I want to first thank Dr. Jessie Drew-Cates who got this entire
project off the ground with her preliminary work in 2001 and then served as the primary grant-writer
to secure the funding for this project. In addition the Board recognizes the Research Team of the
Women's Foundation of Genesee Valley for their overtime work on this project:
Kathleen B. King, Ph.D. Chair
Jessie Drew Cates, Ph.D.
Kijana Crawford, Ed. D.
Lt. Cynthia Herriott Sullivan
Elizabeth Kellogg Walker, Ph.D.
Anne Wilder
J. Christine Wilson
Beverly Hettig, staff
The Board of Directors was able to undertake this project because of the generous support from The
Daisy Marquis Jones Foundation, Eastman Kodak Company, Mary Mulligan Trust, Fred &
Floy Willmott Foundation, and JPMorgan Chase. The printing of this document was made
possible with the generous support of Xerox Corporation.
The Women's Foundation offers this comprehensive report as a starting point for discussion, ideas,
and long-term solutions for women and girls in our community. We invite you to join us in our work
to Improve Lives by Funding Change.
Rosemary C. Mitchell
Executive Director
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Improving Economic
Self-Sufficiency:
Current Status, Future Goals, and
Intervention Strategies Project
B.E.C.S., Inc.
Women’s Foundation
Kathryn A. Bowen
Kathleen B. King, Ph.D.
Tristi C. Nichols
Jessie Drew Cates, Ph.D.
Michelle Scott-Pierce
Kijana Crawford, Ed.D.
Denise Siegart
Lt. Cynthia Herriott Sullivan
Camille Tischler
Elizabeth Kellogg Walker, Ph..D.
Anne Wilder
J. Christine Wilson
Economic Self-Sufficiency (range of definitions)
“Being able to take care of yourself and your family, you can pay the rent, you have a car for
transportation, you have a job and you can pay your bills. You don’t need to depend on anyone
for anything; you are off all assistance programs. You can pay for daycare for your children, you
can buy groceries and you can pay for life necessities.”(Service Recipient)
“Self-sufficiency is on a continuum, everyone wants to be self-sufficient, to have money to care for
themselves; having food, decent housing, transportation, clothes and childcare. Being off public
assistance should not necessarily be the goal, economic self-sufficiency is a process. The do-ityourself mentality only works when women are on the ball, many fall through the cracks.”(Service
Provider)
Please note that the photographs included in this report have been selected from a general database
and do not depict actual participants.
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TABLE OF CONTENTS
CHAPTER 1: OVERVIEW
11
Summary of Quantitative Data
11
Summary of Qualitative Data
13
CHAPTER 2: METHODS
18
Research Design
18
Definition of Terms
18
Poverty
18
Welfare
19
Income Adequacy
20
Working Poor
23
Low Income Women
23
Sources of Quantitative Data
24
Sources of Qualitative Data
24
Sampling Criteria
25
Data Analysis
27
CHAPTER 3: RESULTS
30
Demography of Women in the Genesee Valley Region: Quantitative Data 31
31
Demographic Overview
Gender Distribution
31
Age
31
Race/Ethnicity
34
Households and Families
35
Educational Attainment
37
Pregnancy by Age and Marital Status
40
Place of Residency
41
Economic Status Women
41
Working Women
41
6
Distribution of Income by Households
41
Distribution of Earnings by Gender for Individuals
44
Demographics of Poverty
46
Individuals
46
Households
49
Temporary Assistance
53
Beyond Poverty to Economic Self-Sufficiency
55
A Glimpse of How Poverty Affects Women: Qualitative Data
59
Description of Participants
59
Needs to Attain Economic Self-Sufficiency
60
Barriers to Economic Self-Sufficiency
62
Topics Most Commonly Identified
64
Economic Factors: Jobs, A Living Wage &
Making Ends Meet
64
Housing
68
Transportation
68
Healthcare and Health Insurance
69
Job Training
69
Education
70
Self-Esteem
73
Cultural Barriers
74
Child-Care
74
Domestic Violence Shelter
75
Access to Services and Accessing Public Subsidies
75
Support, A Voice and Respect
78
Life Skills
80
Social Support
80
Cash
81
Definitions of Economic Self-Sufficiency
81
Summary of Barriers, Needs and Definitions of
Economic Self-Sufficiency
83
7
REFERENCES
88
APPENDIX A: TABLES
93
APPENDIX B: SELF SUFFICIENCY STANDARD FOR 7 COUNTIES
124
APPENDIX C: SURVEY
129
LIST OF TABLES AND FIGURES
Tables
Table 3.1 Numbers and Percentages of Females by Age: By Town
32
Table 3.2 Numbers and Percentages of Females by Race/Ethnicity: By Town
34
Table 3.3 Numbers and Percentages of Households by Family Type: By Towns 35
Table 3.4 Numbers and Percentages of Female Heads of Households in
Families by Children’s Age: By Town
36
Table 3.5 Numbers and Percentages of Female Heads of Households by
Age: By Town
37
Table 3.6 Educational Attainment for Women 25 Years of Age and
Older: By Town
38
Table 3.7 Educational Attainment for Females 25 Years of age and
Older by Race/Ethnicity
39
Table 3.8 Number and Percentages of Live Births to Teenagers
from 1997 to 2000
40
Table 3.9 Percentage of Women Working by County and Race/Ethnicity
41
Table 3.10 Median Income in Dollars by Selected Households
42
Table 3.11 Distribution of Annual Income <$20,000 and > $100,000
43
Table 3.12-1 Median Earnings for Men and Women, 16 years of age and
older, in Dollars FULL-TIME, YEAR ROUND
44
Table 3.13 Traditional Female Employment (Annual Occupational Wages)
45
Table 3.14 Numbers of Women in Selected Jobs
46
Table 3.15 Numbers and Percentages of Females in Poverty by Age: By Town
47
Table 3.16 Numbers and Percentages of Children in Poverty by Age: By Town
48
Table 3.17 Numbers and Percentages of Females in Poverty by Race/Ethnicity
48
Table 3.18 Percentage of Women in the Population Compared to Women in
Poverty/Ethnicity
49
8
Table 3.19 Percentages of Households At or Below the Federal
Poverty Threshold
50
Table 3.20 Percentages of Families With and Without Children Under
the Age of 18 At or Below the Federal Poverty Threshold
51
Table 3.21 Number of Recipients of Temporary Assistance
54
Table 3.22 Self-Sufficiency Standard for Monroe and Yates Counties
Compared to Federal Poverty Thresholds
56
Table 3.23 Estimates of Percentages of Female Heads of Households Living
At or Below the Self-Sufficiency Standard
58
Table 3.24 Median Household Incomes for Female Heads of
Households Not in Families: By Town
59
Table 3.25 Budget Record of a Female Wayne County Senior
67
Table 3.26 Cross Comparison of Barriers, Definitions of ESS, Needs, and
Supports Reported by Service Providers (N=81) and Service Recipients
and Survey Respondents (N=130)
84
Figures
Figure 2.1 Percentage of Income to Meet Basic Needs for a Family with
One Parent, One Preschool-age Child and One School-age Child in
Monroe County, NY
21
Figure 2.2 Comparison of Economic Benchmarks for Monroe County, NY
22
Figure 3.1 Percentages Female Population for Study Counties by Age
32
Figure 3.2 Female Population Trends (US Census Bureau, 2000)
33
Figure 3.3 Educational Attainment for African American, Hispanic and
White Women in the Seven Counties
39
Figure 3.4 Percent Earnings of Women Compared to Men by Education
45
Figure 3.5 Numbers of All Households and FHH Living in Poverty
50
Figure 3.6 Distribution of Household Type Among Households in Poverty
51
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Figure 3.7 Percentages of Female Heads of Households in Families Living in
poverty With and Without Children under 18 Years of Age
52
Figure 3.8 Percent of Female Heads of Households in Families in Poverty of
all Families in Poverty
52
Figure 3.9 Percentage of Female Heads of Households (in families) in Poverty
who have Children under 18 Years of Age
53
Figure 3.10 Recipients of Temporary Assistance for the Year 2003
54
Figure 3.11 Percentage of Individuals Receiving Temporary Assistance from
2000 to 2003
55
Figure 3.12 Self-Sufficiency Standard for the Seven Study Counties for One
Adult, One School-aged Child, and One Teenager
57
Figure 3.13 Percentage of Survey Respondents Identifying Each Need (N=93)
61
Figure 3.14 Needs Identified Most Frequently by Service Recipients
61
Figure 3.15 Frequency of Needs Cited Among Providers of Services
62
Figure 3.16 Frequencies of Barriers to ESS Identified by Service Recipients
63
Figure 3.17 Frequencies of Barriers to ESS Identified by Service Providers
63
Figure 3.19 Education Levels Reported by Survey Respondents (N=93)
71
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Chapter 1 Overview
T
he purpose of this research study is to help the Women’s Foundation of Genesee Valley
develop goals and intervention strategies that support women’s progress toward economic
self-sufficiency (ESS). As well, results of the study will be used to strengthen the grant-making
focus on programs and initiatives that have the highest likelihood of positively impacting the
economic status of women.
The study focuses on the seven counties served by the Women’s Foundation of Genesee
Valley. These counties are part of what is defined as “Upstate New York” and include
Genesee, Livingston, Monroe, Ontario, Orleans, Wayne, and Yates counties.
The framework of this investigation centered upon answering the following questions:
♦ What does ESS mean for women living in the seven counties being studied?
♦ What are the various definitions of ESS for women in each area within our seven
counties?
♦ What are the basic elements of women’s economic security and under what
circumstances do women feel economically secure?
♦ What external factors contribute to women’s economic security?
♦ What assets do women bring to the economy of our community?
♦ What threatens women’s economic security?
♦ What prevents women from achieving ESS?
♦ What limits women’s understanding of ESS?
Improving Economic Self-Sufficiency: Current Status, Future Goals, and Intervention
Strategies Project, commissioned by the Women’s Foundation of Genesee Valley was
initiated in January of 2003. Secondary data analysis of the U.S. Census 2000, New York
State Department of Health Vital Statistics and the New York State Office of Temporary and
Disability Assistance supplied the majority of quantitative, statistical information. This
information provides an overview of women in terms of age, race and ethnicity, family
composition, economic status, income, educational attainment, pregnancy, and residency.
Qualitative inquiry methods also were used to increase knowledge and awareness related to
life experiences of women struggling to achieve economic self-sufficiency.
Summary of the Quantitative Data
According to the U. S. Census 2000, there are 576, 906 females in the seven counties,
representing 51.4% of all residents. There are 429, 541 households in this region, of which
131,045, or 30.5%, are headed by women. Of households headed by women, 51,665 or 12%,
are headed by women in families, and 79,380, or 18.5% are headed by women not in
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Overview Chapter 1
families. The majority of women heads of households in families have children under the
age of 18 (32,972 or 63.8%).
Across the seven counties, 11.4% of females are living at or below the Federal Poverty
Threshold, compared to 9.3% of males. While 10% of all households are living at or below
the poverty threshold, 25.9% of households headed by females in families, and 16.4% of
households headed females not in families are living at or below the poverty threshold.
Among female head of households (FHH) in families who are living in poverty, 93.1% have
children under the age of 18.
A total of 15% of children are living in poverty, ranging from 9.3% in Genesee County to
21.9% in Yates County. In the city of Rochester, 37.9% of children are living in poverty.
The percentage of FHH living in poverty is disparate by race and ethnicity. Among white
families in poverty, 54% are headed by women. Among African American families in
poverty, 79% are headed by women. Among Hispanic families in poverty, 71% are headed
by women. Among all other ethnic groups in poverty, 37% are headed by women. However,
the percentages of female head of households with children under the age of 18 who are in
poverty are fairly consistent across racial and ethnic groups. These percentages are 91% for
white FHH, 95% for African American FHH, 93% for Hispanic FHH, and 77% for all other
ethnic groups.
These percentages are consistent with income data. The median annual income for FHH in
families ranged from $20,022 in Yates County to $27,271 in Ontario County. The median
annual income for FHH not in families ranged from $16,675 in Yates County to $22,796 in
Monroe County. Among FHH not in families who are 65 years of age and older, the median
annual incomes range from $12,839 in Wayne County to $16,237 in Monroe County. The
higher median incomes in Monroe County are attributable to those living in the suburbs.
Compared to all other groups median incomes of women living in the city of Rochester are
the lowest for FHH in families and third lowest for FHH not in families.
While 10.4% of individuals were living at or below the
poverty threshold in 2003, 3.2% were receiving public
Temporary Assistance in cash (formerly known as welfare)
and between 4.8% (in Ontario County) and 9.8% (in Monroe
County) were receiving food stamps.
A total of 48,600 women (12%) have less than a high school
education. Ontario County has the lowest (11.5%), and
Orleans County has the highest (21.3%), percentage of
women without a high school diploma. A total of 97,891
women (25.4%) earned a 4 year college degree or higher.
Examining ESS in terms of federally defined poverty
thresholds takes into account only one level of economic
insufficiency. Federal Poverty Guidelines’ main purpose is to
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Overview Chapter 1
establish eligibility for public (and often private) assistance. By definition income at these
levels is not enough to adequately meet basic needs. To fully understand ESS, women’s
economic status must be examined relative to a level of income necessary for a given family
to meet their basic needs, that is, independent of Temporary Assistance for Needy Families
and/or other public or private subsides.
The Self-Sufficiency Standard (SSS), developed by the Family Economic Self-Sufficiency
Project of Wider Opportunities for Women1 is one means of estimating income adequacy.
The SSS is based on the proportion of income spent in seven categories of needs: housing,
childcare, food, health care, transportation, taxes, and miscellaneous expenses. This standard
is unique in that it takes into account geographical differences in cost of living, as well as
differences among families of varying composition. For example, the SSS for a household
with one adult, one school aged child, and one teenager ranges from an annual income of
$19,716 in Yates County to $24,288 in Monroe County.
The estimated percentage of a FHH with two children between the ages of 6 and 17 living
between the federal poverty threshold and the SSS for ranges from 20.1% in Monroe County
to 29.1% in Yates County. Over 60% of FHH in Yates County and in the city of Rochester
are estimated to be living at or below the SSS. In the other counties, with the exception of
Monroe County excluding Rochester, close to 50% of FHH in families are living at or below
the SSS.
Summary of the Qualitative Data
Qualitative data were collected from participants in three groups. They were 81service
providers, 34 women receiving services, and 93 lower income women who completed a
survey in the general community. The second two groups will be referred to as service
recipients. Separate focus groups were held with service providers and women receiving
services.
Direct contact with a cross section of women in need of assistance as well as individuals
implementing programs and providing services provided insight into the life experiences of
women besieged by poverty, stigmatization, discrimination, inadequate education and few
supports. These interactions lead to a greater understanding of how economic self-sufficiency
is defined, the barriers or threats to economic security among women, as well as supports and
services that help to move them toward economic self-sufficiency.
Focus group data identified many women who had no employment or were working at
minimum wage paying jobs, often part time with no benefits. These women were unable to
afford basic needs and economic self-sufficiency was an elusive goal.
“I’m on the edge of going to work at Burger King, I feel like the little bit of money will help
me go farther, so it will make you feel like you are doing something. Right now I can’t afford
a pair of socks for my daughter, this makes me feel very bad. I ain’t lookin’ forward to it, I
know they [Burger King] will work you like an animal for no pay.” (Service Recipient)
1
Six Strategies for Family Economic Self-Sufficiency. (http://www.sixstrategies.org)
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Overview Chapter 1
“We have to deal with it [the system], because we have children; we have to go through it.
Life is not fair, I just don’t know,…where does the struggle end? I try to get a job, seems you
are just not getting in, the harder you try, the worse it is. They say you don’t care, that you
are worthless.” (Service Recipient)
These women were dealing with abject poverty on a daily basis. They identified instability
and insecurity in every aspect of their lives. They were barely meeting expenses each month
because they were unemployed or their wages were too low. Opportunities for advancement
few and benefits were scarce. When asked about actions taken when funds are insufficient to
cover expenses a young woman participating in a focus group replied,
“I have to suffer it out when I run out of money, I have no one
to call. I really budget my food stamps so we don’t run out of
food. When you have no one, it is more harder, you can’t
borrow money. I don’t receive no cash money at all.” (Service
Recipient)
The difficulties women in need experienced were innumerable.
The words of the following women gave just a glimpse:
“My kids were hungry and thirsty over the 4th of July, I went
from Thursday to the following Friday with no money and no
food because something was wrong with the computer at DSS.
My one-year-old was saying, Mommy, I hungry, I thirsty, this
made me feel very bad because I could not feed my babies.”
(Service Recipient)
A theme that frequently arose was that education does not equal employment with a living
wage. This was observed not only among women with a high school education but also
higher levels of education (Associates and Bachelors Degrees). While a high school diploma
is an adequate education level for a wide variety of minimum wage jobs in the Genesee
Valley, these jobs do not provide sufficient income, especially among women who are heads
of households with children. Even in jobs that pay more than minimum wage, many women
were unable to make enough money to pay for child-care, housing, food, health care, taxes
and transportation.
Some women, while perhaps not living below official Federal Poverty Guidelines, struggled
daily to keep their heads above water. They may have even greater need than women below
the poverty level and be more vulnerable as they are often ineligible for services and
typically are not enrolled in social programming.
The quote below reflects the data regarding women who live above the poverty threshold but
below the Self-Sufficiency Standard.
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Overview Chapter 1
“The distinction between classes and poverty are becoming blurred, you can have it
together, however not really be economically self-sufficient. Suburban hunger is on the rise,
27% of the hungry identified in our Hunger Study were people from the suburbs. Their hours
at work get cut back, they have no benefits, and small business cannot carry the burden
indefinitely.” (Service Provider)
Significant barriers exist for poor women. Barriers include no transportation or inadequate
transportation, large geographic distances to services and providers, isolation, low literacy,
feelings of inadequacy, addiction, dependency, low self-worth, all of which can exacerbate
conditions of poverty. Further, according to reports from service providers and service
recipients gender discrimination in terms of job segregation into the lower paying job market
frequently occurs as does racial, social, and class discrimination. This occurs not only in the
job market, but also the community in general to include some service providers.
“They need to hire Black people, we are pushed to the side, give us a job, people don’t give
us a chance.” (Service Recipient)
“Classification of people is a problem. If you can’t write and read English good, you are no
good. Right now I am classified a ‘Zero’ by DSS because I am not proficient in English, a…
‘Zero’; this does not make me feel good?” (Service Recipient)
Services providers identified numerous barriers to economic self-sufficiency. However, the
most common barrier identified was lack of employment with a living wage that women can
access or have the appropriate skills for. Service recipients, on the other hand, most
commonly spoke of barriers due to public policy that they perceived to be punitive and did
not provide the assistance they needed to survive. The second most common barrier
discussed among service providers was related to women who feel unsuccessful because of
past failures, resulting in low self-esteem. Service recipients perceived that one of their
greatest barriers was the lack of advocacy and respect they received from some service
providers.
“They are rude, they treat you like a ball of dirt, they don’t return your calls, they get
frustrated. They take it out on us. They have a lot of people they are dealing with, they get
mixed up, we have to consider this.” (Service Recipient)
Interestingly, it was difficult for any of the
women in the study to specifically point to
“what works.” Data invariably shifted from
“what works” to what women need, barriers,
and interventions they believed in but had no
solid outcome data to support. Several themes
repeated by service providers related to
strategies that they believed assisted women on
their quest toward economic self-sufficiency
and included the following: 1) establishment of
caring relationships that last over time,
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Overview Chapter 1
2) excellent referral knowledge (high levels of collaboration with other service providers so
that referrals are appropriate and comprehensive), 3) assistance with advancing education, 4)
consideration for family to include child care, 5) hand-holding, encouragement, validation, “a
hand up rather than a hand out,” 6) respect, 7) advocacy, and 8) life skills.
Service recipients spoke of: 1) easier access to the services (less red tape and regulations), 2)
more cash, 3) stable, safe, livable housing, 4) drug and alcohol addiction treatment, 5)
transportation, 6) a good job that pays a living wage, and 7) having a goal while being
supported in achieving it, like owning a car or buying a home.
“When I graduated from high school, got my degree, I thought that I would make something
of myself. Just has not happened. We need better paying jobs; teenagers in NY City make the
same as what we make here. My husband works at a salt-brick plant he makes $6.25 an hour,
it is very hard, heavy work, we have no insurance, no benefits. If he hurts his back we are
screwed. I am working for $8.56 an hour for ARC but I need $10-$12.00 an hour. I need to
get out of this County.” (Service Recipient)
“With the C.W.E.P., a lot of places they send you to that don’t give you job experience that
will lead to a higher paying job. They make you do things like cooking, cleaning, putting
away clothes, that is all they make you do. When I asked to do secretarial stuff they say I
can’t.” (Service Recipient)
Remedies for complex problems that occur at the individual, environmental, systems level
are multifaceted. According to service providers and service recipients, women need access
to jobs that pay a living wage. Some women need individual interventions such as
counseling/training on how to develop healthy relationships, alcohol and drug rehabilitation
and/or self-esteem boosting strategies, mental health, and diagnosis and treatment of learning
disabilities. Others need a reliable car, better/safer housing, education, higher quality
education and job training.
Service providers stated that women need support, a
voice and respect. They also believed that women
needed connection with the community as well as a
working knowledge of existing services, life skills, and
more collaboration among services providers in order
to achieve economic self-sufficiency. Service
recipients perceived that they needed education,
tutoring and competency in English, employment that
pays well, transportation and training.
Considerable effort was directed toward defining ESS.
A difference of worldview and interpretation between
service providers and recipients was expected thus the
study purposefully considered multiple meanings
throughout the project realizing that there is no single
answer to the questions posed. Rather, a range of
16
Overview Chapter 1
possibilities based upon input from those closest to and/or directly influenced by economic
instability was explored. The environment of this study was particularly complex,
contextually contingent and mediated by individual interpretations of it, making it crucial to
the validity of the study to triangulate data, methods, analysis techniques, and researcher
interpretations.
Interestingly, service providers defined ESS most frequently at the individual level where
women who are economically self-sufficient “feel self-worth, have confidence, positive selfesteem and self-efficacy.” Service recipients defined ESS most frequently at the
environmental level, “having enough money to cover expenses, to have assets and have
money in the bank and to be able to save.” The second most common definition among
service providers was that ESS included having enough income for adequate, safe housing,
while service recipients perceived that economic self-sufficiency meant that they would no
longer have to depend on welfare.
“In reference to economic self-sufficiency, some of the short sightedness exists both at the
government level and at the private level. What does it really take for substantive change in
an individual? Very cursory examinations occur typically, where people say, just get them a
job; it is not that easy. If a person cannot fill out an application, how can they get a job?
Poverty relates to social isolation, classes…people just don’t have a concept the degree of
stress that poverty has on a woman and her family.” (Service Provider)
17
Chapter 2 Methods
Research Design
A combination of quantitative and qualitative methods was used to address the research
questions outlined in the Overview, Chapter 1. Quantitative data primarily were obtained
from the United States Census 2000, describing the demographic characteristics of women
living in poverty including age, race, place of residence, education level, family composition,
and economic status. Qualitative data were obtained via focus groups and one-on-one
interviews and surveys. Qualitative data include observation, interviewing, focus groups, and
document review. These data informed the study through real life stories of how women
struggled to access the services they need, pay their expenses, find employment, and connect
with the support they needed for stable, productive lives.
Each method provided its own strength to broaden the study. A blend of multiple methods
provides a deeper insight into the nature of poor women’s lives and the extent of their
struggles to become economically self-sufficient. As well, descriptions of the perceptions of
those providing social services are enlightening. Each method compliments the other to
elaborate, enhance, illustrate, and clarify the information.
Definition of Terms
Poverty has been the object of extensive research and study, thus there are multiple viewpoints on
how to measure it and what a person needs in order to escape it. For this study, poverty levels
primarily were examined using data from the U.S. Census Bureau.
Historically, poverty has been measured in two key ways. First, poverty thresholds are used by the
U.S. Census Bureau. Second, poverty guidelines are used by the U.S. Department of Health and
Human Services. The Poverty Thresholds are used for all official poverty population figures. They
were originally derived in 1963 using the U.S. Department of Agriculture food budgets designed
for families under economic stress and data about what portion of family income is spent on food.
Although the thresholds in some sense reflect families’ needs, they are intended for use as a
statistical yardstick, not as a complete description of what people and families need to live. The
Poverty Guidelines, which are essentially the same as the Poverty Thresholds, were designed for
administrative use and are a simplification of the Poverty Threshold calculations. The Federal
Poverty Guidelines main purpose is to establish eligibility for public (and often private) assistance.
The federal poverty thresholds used in the U.S. Census 2000 are based on a formula defined in
1965 by Orshanski for the Social Security Administration. Orshanski’s original study aimed at
defining “income inadequacy.” She used data from the Department of Agriculture’s 1955
Economy Food Plan (Fisher, 1977) and multiplied it by three to come up with the federal
guidelines for measuring poverty. In the mid-1950s, about one third of income went towards food.
18
Methods Chapter 2
No other categories of goods were utilized in establishing the original guidelines. However,
changing lifestyles has lead to food accounting for one-sixth of a family’s income today.
The federal poverty guidelines are still based on the original formula, although some
modifications have occurred. Inflation is used as the major tool to determine guidelines on an
annual basis. The 2003 Federal Poverty Thresholds2 for the contiguous United States were:
Size of Family Unit
1
2
3
4
5
6
7
8
Earned Income Yearly
$ 8,980
$12,120
$15,260
$18,400
$21,540
$24,680
$27,820
$30,960
Currently, the only adjustments in determining in the thresholds are family size and ages of family
members. The guideline is based on any income received from working or other forms of
compensation such as Social Security, unemployment benefits, and cash public assistance of any
type.3 Various federal and state programs use multiples of the guidelines to determine eligibility.
For example, in New York State a family unit must have an income below 131% of the federal
poverty threshold to establish eligibility for food stamps.
In addition to income, poverty has other psychological, social, and cultural meanings. Shipler’s
(2004) text on the working poor also defines poverty as a feeling of hopelessness and helplessness.
Although people can get jobs, measuring poverty only as annual income ignores the debts a family
may incur from school loans, car loans, and credit card debt. Being poor also can mean not
owning a home, having no money saved, and no health insurance for the individual or family.
Welfare The Personal Responsibility and Work Opportunity Reconciliation Act of 1996 was
passed by Congress and the Temporary Assistance for Needy Families (TANF) was born.4 This
legislation took the place of two former bills, Aid to Families with Dependent Children (AFDC)
and Job Opportunities and Basic Skills Training (JOBS) programs.
In a simplified version, the general federal guidelines for TANF are:
(a) Single parents with children under age six must be in a work activity 20 hours per week to
qualify for benefits.
(b) Unwed minors with children now required to live in a home under the supervision of an adult
qualify for benefits.
(c) Recipients other than single mothers must allot 30 hours per week in a work activity approved
by the local DHHS qualify for benefits.
2
HHS Poverty Guidelines from the Federal Register, Vol 68, No. 26, February 7, 2003, pp. 6456-6458.
U.S. Census Bureau, 2004
4
U.S. Department of Health and Human Services, 2004
3
19
Methods Chapter 2
(d) Any individual may qualify for TANF is a total of five years. Thereafter, it is solely up to
individual states to determine eligibility for continued funding.
(e) States who move welfare recipients into jobs get additional federal monies if the number of
out-of-wedlock births and abortions is reduced.
(f) States are penalized if they do not comply with the stated outcomes of the program.
In New York State the Family Assistance Program operates under the TANF guidelines. The
NYS Safety Net Assistance Program is available to individuals or families who do not qualify for
TANF or other assistance programs. 5
Income adequacy is defined as the level of income necessary for a given family to
adequately meet basic needs. An emerging benchmark for estimating income adequacy,
rather than income inadequacy, is the Self-Sufficiency Standard (SSS). This standard was
developed by Pearce6 for the Family Economic Self-Sufficiency Project of Wider
Opportunities for Women.7 Assumptions made in constructing the SSS Guidelines are:8
a) “The Self-Sufficiency Standard assumes that all adults (whether married or single)
work full-time and includes the costs associated with employment, specifically,
transportation and taxes, and for families with young children, child care.
b) The Standard takes into account that many costs differ not only by family size and
composition, but also by the age of children. While food and health care costs are
slightly lower for younger children, child care costs are much higher, particularly for
children not yet in school and are a substantial budget item not included in the official
poverty measure.
c) The Standard incorporates regional variations in cost.
d) The Standard includes the net effects of taxes and tax credits.
e) While the poverty standard is based on the cost of a single item, food, and assumes a
fixed ration between food and non-food, the Standard is based on the costs of each
basic need, determined independently, which allows each cost to increase at its own
rate. Thus, the Standard does not assume that food is always 33% of a family’s
budget, or constrain housing to 30%.”
The SSS is calculated for 70 different family types in geographic regions across the United
States, including each county in New York State.9 The SSS measures seven major
components. First is housing and assumes that adults have their own bedroom and
children share a bedroom between two siblings. Second is child care and uses the current
market rate for pre-school children. Currently, the SSS does not acknowledge the cost of
child care for working parents with school age children. Third is food, using the low cost
5
New York State Office of Temporary and Disability Assistance, 2004 (http://www.otda.state.ny.us/otda/ta)
Pearce & Brooks, 2000
7
Six Strategies for Family Economic Self-Sufficiency. (http://www.sixstrategies.org)
8
http://www.sixstrategies.org/files/Resource-StandardReport-NY.pdf, page 3.
9
http://www.sixstrategies.org/files/Resource-StandardReport-NY.pdf
6
20
Methods Chapter 2
food plan from the USDA. Fourth is transportation, assuming public transportation is
available and necessary for one adult to get to and from work on a regular basis, as well as
two trips per week for groceries and other errands. Fifth is health insurance and assumes
that the employer pays 24% of individual health care or 38% of a family’s health care.
Sixth area is miscellaneous and includes clothing, shoes, paper products, diapers, cleaning
products, personal hygiene items, telephone, recreation, entertainment, and savings.
Seventh is taxes and includes federal and state payroll taxes and sales taxes. It assumes the
use of the Child Care Tax Credit, the Earned Income Tax Credit, and the Child Tax Credit.
Figure 2.1 illustrates the proportion of income spent on each basic need for a single parent family
with one preschooler and one school-age child in Monroe County. Housing and child care are the
largest routine expenditures for working families with children. Single parent families with two
children, one of whom is under school-age generally spend more than half their incomes on these
two expenses alone.
The next largest expenses are food at 13% and taxes at 11%. Even though taxes are reduced by tax
credits, they still account for about 1/9th of expenses. Health care is a relatively small share,
however this calculation assumes that the employer has a health insurance plan and pays a portion
of the premium.
Figure 2.1 Percentage of Income to Meet Basic Needs for a Family with One Parent, One Preschool-age Child and One
School-age Child in Monroe County, NY10
32%
21%
13%
8%
Miscellaneous
Health Care
8%
7%
11%
Housing
Transportaiton
Child Care
Taxes-Net
Food
“Pearce and Brooks found that a single parent living in Monroe County with two children, one
infant and one in daycare, would need a gross income of $3,078 per month (or $36,936/year) to
meet basic needs. Even working full time, the parent would have to earn $17.49 per hour, much
more than the typical entry-level wage for someone without specialized skills. Further, many low
wage jobs do not provide health insurance, so working poor families are often forced to choose
between health care and other necessities” (Mutari, E., 2001, p. 3).
To place the SSS in context, it is useful to compare it to other commonly used measures of income
adequacy. Figure 2.2 compares the annual SSS for one adult, one infant, and one school aged
10
http://www.sixstrategies.org/files/Resource-StandardReport-NY.pdf, page 13.
21
Methods Chapter 2
child to other economic benchmarks in Monroe County. Minimum Wage is calculated at
$5.15/hour for full time work. The Median Household Income is the median income for all
households, regardless of family size. The Median Female Income is the median income of all
women working full-time, year-round. The Median FHH Income is the median income for
Female Heads of Households in Families.11
$50,000
$44,891
$40,000
$36,936
$29,553
$30,000
$25,265
$20,000
$15,264
$10,180
$10,712
Welfare &
Food Stamps
Full-Time
Minimum
Wage
$5.15/hr.
$10,000
$0
Federal
Median FHH
Poverty Line
Income
Median
Female
Income
SelfSufficiency
Standard
Median
Household
Income
The SSS is a ground-breaking alternative to the federal guidelines currently used to determine
financial eligibility for public assistance as well as most other social programs. This standard is
relevant to a range of issues and arenas providing crucial information about wage adequacy. The
standard can assist the federal government, social programs, philanthropists, state and local
agencies, and many others to understand crucial information about wage adequacy. Possessing
this knowledge will result in being better equipped to design strategies for economic selfsufficiency as well as fund programs that have the highest likelihood of assisting women in
achieving it.
The SSS can be used in a variety of settings, from the welfare client choosing the best means out
of poverty for herself and her family to organizations weighing investment in various education
and training initiatives to state-level policy makers facing critical policy choices on tax policy
subsidies, welfare to work programs, economic development plans, education, and training. The
standard also has implications for employers who are interested in providing living wages for
workers in Upstate New York.
11
The U.S. Census 2000 definitions are used in this report. This is, Head of Household in Families is a householder (female [FHH] or
male [MHH]) living with one or more individuals related to her or him by birth, marriage, or adoption. Head of Household Not in
Families is a householder (female or male) living alone or with non-relatives only.
22
Methods Chapter 2
One caveat must be included. The use of income thresholds should not be interpreted to mean that
a woman can achieve economic self-sufficiency only by being employed and bringing home a
wage that covers her monthly expenses at one point in time. “True self-sufficiency involves not
just a job with a certain wage and benefits, rather income security for a family over time. Thus, for
many the Self-Sufficiency Wage represents a larger goal toward which they are striving and is a
process that they are engaged in, not a one-time achievement” (Pearce & Brooks, 2000). Tables
showing specific calculations of the SSS for each of the seven study counties in the study can be
found in Appendix B.
Working poor. The working poor are defined as individuals who work but whose incomes do not
put them above the poverty guidelines for their family size. Sixth-three percent of U.S. families
living in poverty have one or more family members who work. Jobs held by the working poor
may pay more than minimum wage but usually offer no health-care benefits, no sick pay, and no
pension. Currently, many of these individuals work 35 hours per week, now accepted as full time
employment. Five of the ten fastest growing jobs, retail clerks, janitors, cashiers, health care
workers, and maids, are low paying jobs (Business Week, May 24, 2004).
Female heads of households are most likely to be poor. Nationally, 30% of black women heads of
households are among the working poor (U.S. Department of Labor, Bureau of Labor Statistics,
Report 957, page 2). The majority of workers in low wage jobs are women and 60% are white.
“The low wage workforce is disproportionately young, less educated, and single” (Kim, 2000,
page 27).
Low Income Women. For the purposes of this study, low income women are defined as those
above the federal poverty guidelines but do not make enough monies to reach the SSS.
Consequently, they are ineligible for many state and federal programs. Examples of these
programs are: Community Services Block Grant, Head Start, Low-Income Home Energy
Assistance, Children’s Health Insurance Program, Medicaid, Food Stamps, Special Supplemental
Nutrition Program for Women, Infants, and Children (WIC), National School Lunch and School
Breakfast program, Weatherization Assistance, Job Corps, Senior Community Service
Employment Program, National Farm Worker Jobs Program, and Legal Services for the Poor
(Fisher, 1996).12
Sources of Quantitative Data
The quantitative data used for this study comes from three databases, the U.S. Census 2000,
the New York State Office of Temporary and Disability Assistance, and the New York State
Department of Health, Vital Statistics. Quantitative data are presented in these data sets as
frequencies of raw numbers. Raw numbers were used to calculate percentages within
categories.
The U.S. Census was chosen as a key source of data because it provides a wealth of social
and economic data that are widely used by social scientists in both the public and private
12
Major means-tested programs that do NOT use the poverty guidelines in determining eligibility include Temporary Assistance for
Needy Families (TANF), Supplemental Security Income (SSI), Earned Income Tax Credit program, HUD Housing Assistance
Program, and Social Services Block Grant.
23
Methods Chapter 2
sectors. U.S. Census 2000 data are available online.13 Data are available at the county and
county subdivision level. Because of its long history (since 1940), the U.S. Census Bureau
and the U.S. Bureau of Labor Statistics have refined their survey methods, conducted
numerous tests of validity, and created specific criteria for assessing accuracy of the data
collected.14 The survey instrument used to collect census data is a completely computerized
document that is administered by Census Bureau field representatives across the country
through both personal and telephone interviews. Because of the survey’s large sample and
broad population coverage it is the single most comprehensive source of data available to
social scientists. The census data include sample data on gender, race/ethnicity, age, marital
status, households by type, education, poverty level, geographic location, and workforce
assessment. Data from the U.S. Census Summary File (SF) 3 were used for this report. SF 3
data are estimates based on the sample of households reporting in the long form, weighted to
reflect the entire population. This summary file was used because of the large number of
descriptive statistics available within the file.
The New York State Office of Temporary and
Disability Assistance data were used to assess
the numbers of percentages of individuals and
households receiving temporary assistance in
the study area. Summary tables are available
online15 for the years 2001 through 2003
regarding the number of adults and children
receiving Temporary Assistance via the Family
Assistance and Safety Net Assistance
Programs, and Food Stamps. Data are available
at the county level.
The New York State Department of Health, Vital Statistics data were to assess the numbers
and percentages of teenaged pregnancies in the seven counties. Summary tables are available
online16 for the years 1997 through 2000 for the total number of pregnancies and live births
in women within age groups, and by married status within age groupings. Data are available
at the county level.
Sources of Qualitative Data
The qualitative data are interpretive in nature, with the assumption that access to reality
(given or socially constructed) is only through social constructions such as language,
consciousness, and shared meanings. The philosophical base of this qualitative study is
ecological and phenomenology where focus is placed on understanding phenomena through
the meanings that people assign to them. Interpretive methods are expected to lead to a
greater understanding of the experiences of women who are struggling economically in the
Greater Genesee Valley. Further, the environment plays a key role in determining women
13
http://www.census.gov
http://data.bls.gov/servlet
15
http://www.otda.state.ny.us
16
http://www.health.state.ny.us/nysdoh/vital_statistics
14
24
Methods Chapter 2
and girls’ abilities to become economically self-sufficient. Dependent and independent
variables intentionally were not identified for collection of qualitative data. Rather, focus was
placed on the full complexity of the human experience based upon context/environment, life
history, and the developmental stages of women.
Sampling Criteria. During the first phase of qualitative data collection, a total of 328 nonprofit agencies were selected as the sampling frame. The list of non-profit agencies was
drawn from a national database entitled Data Disk held in the Circulation Records Division
of the Rochester Public Library. This database provided county-by-county lists of non-profit
agencies. Data Disk was used because up-to-date, comprehensive lists of non-profit agencies,
specific audiences served, and agency missions were not available. Random sampling was
not feasible because of the wide array of non-profit organizations’ functions, missions, and
our sampling parameters. Researchers specifically sought to understand service providers and
service recipients perspectives of the day-to-day economic challenges women and girls face
in the Genesee Valley Region. These perspectives were based upon participants’ knowledge,
experience, and insight.
Researchers used a maximum variation sampling technique where agencies whose missions
included providing opportunities to, or services for, women and/or girls were included (this
was determined by reviewing Guide Star, an online registry for non-profit organizations).
Generalization to all women or all groups was not the intent of this sampling strategy.
Rather, the focus was on identifying information that elucidated service, support, interaction,
and contact variation as well as significant common patterns within this variation across nonprofit organizations in the sample.
After reviewing all non-profit agencies listed in the Data Disk database, a letter on Women’s
Foundation of Genesee Valley letterhead was mailed to a total of 143 non-profit agencies
fitting the sampling criteria. The letter explained the study and extended an invitation to
participate in focus groups. Letters were followed by telephone calls from B.E.C.S, Inc. to
schedule focus groups in all seven counties.
The second stage of qualitative data collection
included interview and focus groups with women who
receive assistance. The sampling strategy used for this
aspect of the research project encapsulated a snowball
sample where researchers asked key people from the
different communities to recommend individuals who
might feel comfortable talking with members of the
research team.1 Some study participants were enrolled
in social service programs while others were not.
These included women using soup kitchens, food
pantries, and Laundromats. Intentional focus was
placed on areas identified as having a high incidence
1
Snowball Sampling: Approach for locating information-rich key informants by asking service providers and participants, “Who has
experience with economic insufficiency and is comfortable talking about their struggles?”
25
Methods Chapter 2
of Female Heads of Households with children living below the Federal Poverty Guidelines.
Three major methods were used to collect qualitative data: a) focus groups (both service
providers and service recipients), b) one-on-one interviews with 93 women using a structured
interview guide, and c) observations. Four researchers collaborated during qualitative data
collection.
The focus groups were conducted with no more than 15 people and focused specifically on
the definition of economic self-sufficiency (ESS), barriers to attaining and sustaining ESS,
supports that helped women achieve ESS, and the primary conditions or circumstances that
women needed in order to become economically self-sufficient. These focus groups were
designed as an interview rather than a discussion or problem-solving session. Service
providers and service recipients participated in separate focus groups. Diversity was sought
in race, age, experience, and perspective. Participants were consistently asked to reflect on
four key areas. These areas varied slightly depending upon whether participants were service
providers or service recipients.
Key areas that service providers were asked to address were:
a) Please describe the meaning of economic self-sufficiency according to your own
perspective and experiences.
b) What are the primary barriers that you observe in your day-to-day practice that
interferes with a woman’s ability to achieve economic self-sufficiency?
c) What do you believe positively impacts a woman’s capacity to achieve economic selfsufficiency in terms of a social service/program/ and/or community support?
d) What do you think women need most in order to achieve economic self-sufficiency?
Key areas that service recipients were asked to address:
a) What do you think it means to be economically self-sufficient?
b) Describe what keeps you from becoming economically self-sufficient or being able to pay
for your living expenses.
c) What helps you in the community to become economically self-sufficient?
d) What do you think you need most to become financially stable?
All focus group dialogue was recorded and transcribed and/or thoroughly documented.
Interviews were guided using a tool for consistency (Appendix C). Transcripts and
documentation of focus group events were reviewed for commonalities (i.e., internal
consistency and conceptual distinctiveness) as well as discontinuities across counties
(Strauss, 1987). Categories or themes that were conceptually distinct and internally
consistent were identified. A peer review procedure was implemented whereby at least two
researchers analyzed data independently prior to comparing interpretations of the data. Final
interpretations were drawn with input from the entire B.E.C.S., Inc. research team.
26
Methods Chapter 2
A distinct advantage of this particular methodology is its efficiency and cost effectiveness.
Eight-one service providers were interviewed in the same amount of time that it typically
would take to interview twelve individuals. Focus groups also provided researchers a level of
reliability of the data as participants provided checks and balances on each other that helped
to identify false or extreme views. The group dynamics contributed to focusing on the most
important topics and issues relating to ESS. Areas of consistency and inconsistency were
identified by comparing responses within, as well as across, focus groups.
In order to gain insight into the lives of women struggling with ESS, one-on-one interviews
were done with women who were not necessarily associated with an assistance program, but
most likely received services or were in the process of applying for services. An interview
guide was designed to make inquiries in a non-threatening way. This interview guide
provided a uniform and consistent way to query. The most convenient access for researchers
was soup kitchens, food pantries, and places of business in areas identified as having a high
concentration of female heads of households with children. As an incentive, each woman
received a $10 gift certificate from Wegmans or Tops.
Data Analysis. Data analysis incorporated Urie Bronfenbrenner’s Ecological Theory as the
conceptual framework. This model was selected because it provided a way to think about the
data in terms of how the landscape of human ecology influences personal development,
interaction, and ultimately economic self-sufficiency among the study population. The
ecological environment is conceived topologically as a nested arrangement of structures,
each contained within the next.
The data from focus groups were considered in terms of four key areas: a) the definition of
economic self-sufficiency, b) barriers to economic self-sufficiency, c) what women need in
order to achieve economic self-sufficiency, and d) the supports and/or resources that women
require from their community in order to achieve economic self-sufficiency.
Data were further separated into four key environmental levels and included:
(a) Microsystem is the complex of relations between the developing person and environment
in an immediate setting containing that person (e.g., home, school, workplace). It is
defined as an activity rather than a behavior. A setting is defined as a place with
particular physical features in which the participants engage in particular activities in
particular roles (e.g., daughter, parent, teacher, employee) for particular periods of time.
The factors of place, time, physical features, activity, participant, and role constitute the
elements of a setting.
(b) Mesosystem comprises the interrelations among major settings containing the developing
person at a particular point in her life and typically encompasses interaction among
family, work place, and peer group. A mesosystem is a system of microsystems.
(c) Exosystem is an extension of the mesosystem embracing other specific social structures,
both formal and informal, that do not themselves contain the developing person but
impinge upon or encompass the immediate settings in which that person is found and
thereby influence, delimit, or even determine what goes on there (e.g., Department of
Social Services, community-based agencies, health care system, educational system).
27
Methods Chapter 2
Included are the major institutions of the society both deliberately structured and
spontaneously evolving as they operate at a concrete local level (e.g., world of work,
neighborhood, mass media, agencies of government, the distribution of goods and
services, communication and transportation facilities, and informal social networks).
(d) Macrosystem is a culture/subculture that sets the pattern for the structures and activities
occurring at the concrete level. Known as the blueprints, the macrosystems represent
laws, regulations, and rules but most are informal and implicit, carried often unwittingly
in the minds of the society members as ideology made manifest through custom and
practice in everyday life (e.g., culture, beliefs, values). These are the overarching
institutional patterns of the culture or subculture, such as the economic, social,
educational, legal, and political systems of which micro-, meso- and exosystems are
concrete manifestations. Macrosystems are conceived and examined not only in structural
terms but as carriers of information and ideology that both explicitly and implicitly
endow meaning and motivation to particular agencies, social networks, roles, activities,
and their interrelations.
Analysis of words, phrases, and statements to determine themes that emerged in response to
each of the four key questions was completed by two methods:
(a) Ethnography: Each observation from focus groups and one-on one interviews with
service recipients and service providers was entered into a qualitative software package
28
Methods Chapter 2
known as Ethnograph 5.0. Ethnograph allowed the assignment of codes to the data that
helped to summarize, synthesize, and sort many observations in an iterative and
progressive cycle. Ethnograph is an analytic tool that allows researchers to code at
different levels and in considerable detail. It is straightforward to use yet a powerful
management tool for dealing with large amounts of qualitative data. Using Ethnograph,
codes were assigned to themes and functioned as labels for assigning units of meaning to
data in order to retrieve and organize the data. Coded data were then categorized,
subcategorized, and cross-referenced according to themes, patterns, and occurrences.
(b) Hands-on Analysis: Subsequent analysis concentrated on forming explanations,
speculations, and hypotheses by highlighting themes within focus group documents.
Further, the data were reviewed for alternative explanations, minority opinions, and
disagreements. Explanations and causality were arrived at through a preponderance of
evidence, strength of association, consistency, specificity, plausibility, and coherence.
Triangulation of data sources, methods, analysis, and researchers was used to increase the
trustworthiness and validity of the analysis/interpretation of the data. The goal was to find
corroboration in as many places as possible so that the data/analysis/interpretation was
stronger with higher levels of credibility.
Using multiple methods, researchers, and data is intended to enhance understanding and shed
light on the meaning and nature of ESS among women in the Genesee Valley Region. It
includes the ways in which women interpreted the circumstances that they believe leads to a
life of ESS. Interviewing service providers was intended to consider a different facet of
economic self-sufficiency, that is, from the perspective of experience and observation of
numerous women over a period of time.
29
Chapter 3 RESULTS
T
his study focuses on seven counties that are part of what is defined as “Upstate New
York.” The counties included are Genesee, Livingston, Monroe, Ontario, Orleans,
Wayne, and Yates. This region comprises 23% of the entire Upstate New York region, which
is defined as the 40 counties that stretch from the Albany area westward to the state border at
Niagara Falls and Lake Erie and northward to Lake Ontario, excluding the six most northerly
counties that encompass much of the Adirondack State Park.18
This chapter describes the results of a secondary analysis of data obtained from the United
States Census 2000, the New York State Office of Vital Statistics, and the New York State
Office of Temporary and Disability Assistance. Pertinent demographic and economic
information about females in the seven counties are examined. A description of key
demographic characteristics and issues related to achieving and maintaining ESS among
women living in the study counties is the primary focus of this work.
This chapter also shares the results of a qualitative analysis of focus groups and one-on-one
interviews with women in the seven study counties. These data are used to elucidate the
perspective of women, including their definition of ESS and their attempts to achieve and
maintain ESS.
Each of the questions listed below are addressed. Most of these questions are answered by
using a combination of both quantitative and qualitative data. A detailed description of
background and methodology is provided in Chapter 2.
1. What does “ESS” mean for women living in the seven counties being studied?
2. What are the various definitions of ESS for women in each area within our seven
counties?
3. What are the basic elements of women’s economic security and under what
circumstances do women feel economically secure?
4. What external factors contribute to women’s economic security?
5. What assets do women bring to the economy of our community?
6. What threatens women’s economic security?
7. What prevents women from achieving ESS?
8. What limits women’s understanding of ESS?
18
The entire Upstate area’s population is 5.5 million.
30
RESULTS CHAPTER 3
The Demography of Women in the Genesee Valley Region:
Quantitative Data
The United States Census 2000 is the source of a significant amount of the quantitative data
contained in this report. Much of the data in the U.S. Census 2000 are available by individual and
by household. Subdivisions of the raw census data by individual and by household differ and
equivalent subdivisions of data for individuals and households are not always available. Census
data by individual and household were used as appropriate to examine the variables of interest. In
addition, data from the New York State Office of Vital Statistics and the New York State Office
of Temporary and Disability Assistance databases were used.
The quantitative data are organized in the following general categories: 1) demographic overview
of women (gender distribution, age, race/ethnicity, households and families, educational
attainment, and pregnancy); 2) demographics of economic status among women (distribution of
income by households, distribution of earnings by gender, demographics of poverty by
households); and 3) ESS.
DEMOGRAPHIC OVERVIEW
Gender Distribution
The seven counties included in this study area are comprised of 1,122,822 people. Slightly
over one-half, 51.4% (576,906), are female.
Age
The median age of females in the seven counties are: Genesee, 38.6 years; Livingston, 35.6
years; Monroe, 37.6 years; Ontario, 38.5 years; Orleans, 37.1 years; Wayne, 37.8 years; and
Yates, 38.5 years. This is similar to the median age of females in New York State, 37.3 years,
and slightly higher than the median age of females in the US, 36.0 years (US Census 2000,
Summary File 4, Table PCT 4). Similar to the Upstate region, the majority of females the
seven study counties are 18-64 years of age. Sixty-two percent of the women are between the
ages of 18 and 64 years, 23% were under 18 years and 15% were over 65 years of age.
Monroe County is home to the largest number of females (380,858) and Yates the fewest
(12,589).
The distribution of age is very similar to all of New York State. Table 3.1 and Figure 3.1
reflect the variations across the seven study counties. Monroe County has the highest
percentage of females, 52%, with all other counties at 50% or 51%. Orleans County has the
highest percentage of children and youth; Livingston County has the largest percentage of
adults (18-64 years).
A higher proportion of senior women reside in Genesee and Yates Counties compared to
New York State percentages as well as to other individual study county percentages. As is
typical in most regions of the United States elder women outnumber elder men. For the seven
31
Results Chapter 3
counties, women represent 59.5% of the population 65 years of age and older and 71.7% of
the population 85 years of age and older.
Table 3.1 Numbers and Percentages of Females by Age 19,20
Total
Female
Population
% of
Total
population
<18
Years
% of
females
< 18 yrs
18-64
Years
% of
females
18-64 yrs
65+
Years
% of
females
65+ yrs
Genesee
30,554
51%
7,645
25%
17,850
58%
5,059
17%
Livingston
31,999
50%
7,208
23%
20,535
64%
4,256
13%
Monroe
380,858
52%
91,172
24%
231,819
61%
57,867
15%
Ontario
51,301
51%
12,497
25%
31,150
61%
7,654
15%
Orleans
22,255
50%
5,528
26%
13,552
61%
3,175
14%
Wayne
47,350
51%
12,474
25%
28,335
60%
6,541
14%
Yates
12,589
51%
3,165
24%
7,291
58%
2,133
17%
576,906
51%
139,689
24%
350,532
61%
86,685
15%
9,829,709
52%
2,276,045
23%
6,089,406
62%
2,448,352
13%
7 COUNTIES
NY State
Source: US Census 2000, Summary File (SF) 3, Table P8 (http://www.census.gov)
(SF 3 data are estimates based on sample of households reporting on the long form, weighted to reflect the entire population.)
Figure 3.1 Percentages Female Population for Study Counties by Age.
70%
Genesee
Livingston
60%
Monroe
50%
Ontario
40%
Wayne
Orleans
Yates
30%
20%
10%
0%
19
20
<18 YRS.
18-64 YRS.
65+YRS.
Accompanying tables containing data for each township within each county are found in the Appendix A. Tables in Appendix A
are labeled with the same number as those found within this chapter, with the addition of the suffix “A.” For example, the table in
the Appendix accompanying Table 3.1is Table 3.1A.
Table 3.1, Columns 3, 5, 7, & 9 are the percentages of women in each age range, out of the total population in the county.
32
Results Chapter 3
Female Population Trends For Age - 1990-2000
Figure 3.2 illustrates population trends among females over the past 10 years. Overall there
has been the least amount of growth observed among women age 19-64 years and the most
amount of growth among children and youth and senior citizens. While the numbers of
women over the age of 65 has increased in all counties, the largest increase occurred in
Wayne County. The most growth among children and youth was observed in Yates County.
The slowest growth in population observed in Genesee County where there has been a
decline in numbers of children under the age of 18 years over the past 10 years. Livingston
County has experienced a decline in children and youth, and to a greater extent, adult women
between the ages of 19 and 64 years. Monroe County has experienced the smallest growth
among women age 19 to 64 years as well, while Orleans County has experienced the largest
percent of growth among adult women.
Figure 3.2 Female Population Trends (US Census Bureau, 2000).
33
Results Chapter 3
Race/Ethnicity
As shown in Table 3.2, the racial and ethnic distribution of females differs among the
counties. Genesee, Livingston, Ontario, Wayne, and Yates Counties are comprised of mostly
white females, with fewer African American, Hispanic, Native American/Native Alaskan,
and Asian females. Monroe and Orleans counties are more heterogeneous. However, the
heterogeneity in Monroe County is primarily attributable to the city of Rochester. Rochester
is comprised of 43% white, 39% African American, and 12.5% Hispanic females, whereas
all other areas of Monroe County combined is comprised of 91% white, 3% AfricanAmerican, and 2% Hispanic females.
The Tonawanda Reservation is located in Genesee County, accounting for the highest
percentage of Native American females living in the seven counties. However, the largest
numbers of Native American females live in Monroe County. The Mennonite and Amish
populations are thriving in the Genesee Valley. These groups are quite self-contained and
self-sufficient, choosing to refrain from using services or participating in social
programming. The U.S. Census data does not disaggregate data to reflect Mennonite females
alone, however they were included in the U.S. Census 2000. While Mennonite children do
not attend public schools, their families do pay taxes, which make their presence a “boon for
the county” as they require little in terms of services and supports (Travis, 2003).
Table 3.2 Distribution of Women by Race/Ethnicity21
White
%
White
Black
%
Black
29,230
95.2%
378
1.2%
Livingston
30,631
95.3%
276
Monroe
292,839
74.4%
52,862
Genesee
Monroe-ROC
22
Rochester
243,023
49,816
90.5%
8,217
39.8%
%
Native
% Native
Hispanic Hispanic American American
248
0.8%
0.9%
402
13.4%
19,600
3.1%
5,193
14,407
Asian
%
Asian
Other
%
Other
102
0.3%
471
1.5%
270
0.9%
1.3%
89
0.3%
288
0.9%
462
1.4%
5.0%
1,154
0.3%
9,022
2.3%
18,115
4.6%
1.9%
528
11.5%
626
0.2%
44,645
35.7%
Ontario
48,882
93.3%
998
1.9%
1,098
2.1%
60
0.1%
0.5%
Orleans
19,632
86.7%
1,547
6.8%
787
3.5%
116
Wayne
44,343
92.8%
1,193
2.5%
892
1.9%
141
Yates
12,217
96.9%
31
0.2%
102
0.8%
7 COUNTIES
770,613
78.2%
110,147
11.2%
42,729
4.3%
6,658
2,364
2.5%
1.9%
4,899
13,216
1.8%
10.6%
358
0.7%
1,003
1.9%
0.5%
42
0.2%
516
2.3%
0.3%
227
0.5%
974
2.0%
47
0.4%
52
0.4%
163
1.3%
3,031
0.3%
19,113
1.9%
21,704
2.2%
Source: US Census 2000, SF 3, Table P145
21
22
Table 3.2, Columns 3, 5, 7, 9, 11, & 13 are percentages of women in each racial/ethnic group out of all women in the county.
Monroe-ROC = Monroe County excluding the city of Rochester. Data for Monroe County are split into Monroe County excluding
the city of Rochester and the city of Rochester for factors where the data on the county as a whole do not accurately represent the
distribution on the variable of interest.
34
Results Chapter 3
Households and Families
Households are divided into two major categories: families and non-families. Families
include three subdivisions: 1) married couples, 2) female heads of households (FHH), and 3)
male heads of households (MHH). FHH and MHH within families are further divided into
those a) with, and b) without children under the age of 18. Non-families are divided into
FHH and MHH.23 Table 3.3 shows the distribution of households by family type across the
seven counties. Among the 429,541 households in the seven counties, 12% are headed by
females in families and 18.5% are headed by females in non-family households. The highest
percentage of FHH is in the city of Rochester.
Table 3.3 Numbers and Percentages of Households by Family Type24
HOUSEHOLDS
FAMILIES
COUNTY
TOTAL
HHs
Married
Couples
% of all
HH
Genesee
22,804
12,724
Livingston
22,149
12,255
Monroe
FHH
% of all
HH
55.8%
2,105
55.3%
Non-FAMILIES
MHH
% of all
HH
FHH
% of all
HH
MHH
% of all
HH
9.2%
1,076
4.7%
3,932
17.2%
2,967
13.0%
2,167
9.8%
1,012
4.6%
3,646
16.5%
3,069
13.9%
286,820
138,129
48.2%
37,437
13.1%
10,252
3.6%
56,132
19.6%
44,870
15.6%
Monroe-ROC
197,727
115,213
58.3%
16,856
8.5%
6,036
3.1%
35,315
17.9%
24,307
12.3%
Rochester
89,093
22,916
25.7%
20,581
23.1%
4,216
4.7%
20,817
23.4%
20,563
23.1%
Ontario
38,392
21,350
55.6%
3,810
9.9%
1,377
3.6%
6,563
17.1%
5,292
13.8%
Orleans
15,350
8,318
54.2%
1,756
11.4%
806
5.3%
2,351
15.3%
2,119
13.8%
Wayne
34,970
19,930
57.0%
3,545
10.1%
1,662
4.8%
5,295
15.1%
4,538
13.0%
Yates
9,056
5,039
55.6%
845
9.3%
432
4.8%
1,461
16.1%
1,279
14.1%
429,541
217,745
50.7%
51,665
12.0%
16,617
3.9%
79,380
18.5%
64,134
14.9%
TOTAL
Source: US Census 2000, SF 3, Table P92
23
24
Head of household in families is a householder (female or male) living with one or more individuals related to her or him by birth,
marriage, or adoption. Head of household not in families is a householder (female or male) living alone or with non-relatives only.
Table 3.3, Columns 4, 6, 8, 10, & 12 are the percentages of households within each category, out of all households in the county.
35
Results Chapter 3
Of FHH in families, 32,972 (63.8%) have children under the age of 18. FHH with children
between the ages of 6 and 17 (20,551, 62.3%) is the largest subset of FHH in families. As
shown in Table 3.4, the highest percentage of FHH with children under the age of 18 is found
in the city of Rochester and the lowest percentage of FHH with children under the age of 18
is found in Monroe County excluding Rochester. A highest percentage of FHH in families
with young children (< 6 years of age) is found in the city of Rochester, followed closely by
Livingston, Ontario and Wayne Counties.
Table 3.4 Numbers and Percentages of Female Heads of Households in Families by Children’s
Age25
FHH with own children < 18 years of age
FHH
with
children
< 18
% of all
FHH
FFH
with
children
<6
2,105
1,241
59.0%
226
18.2%
COUNTY
Genesee
FHH
% of all
% of FHH
FHH
% of all
with
FHH with
with
with
FHH with
children
children
children
children
children between 6 & between 6 &
<6
0 to 17 years 0 to 17 years 17 only
17 only
All
FHH
in families
171
13.8%
844
68.0%
Livingston
2,167
1,444
66.6%
292
20.2%
154
10.7%
998
69.1%
Monroe
37,437
23,821
63.6%
4,660
19.6%
4,448
18.7%
14,713
61.8%
Monroe-ROC
16,856
9,483
56.3%
1,365
14.4%
1,177
12.4%
6,941
73.2%
Rochester
20,581
14,338
69.7%
3,295
23.0%
3,271
22.8%
7,772
54.2%
Ontario
3,810
2,398
62.9%
489
20.4%
434
18.1%
Orleans
1,756
1,131
64.4%
195
17.2%
251
Wayne
3,545
2,382
67.2%
481
20.2%
424
845
555
65.7%
109
19.6%
87
51,665
32,972
63.8%
6,452
19.6%
5,969
Yates
7 COUNTIES
1,475
61.5%
22.2%
685
60.6%
17.8%
1,477
62.0%
15.7%
359
64.7%
18.1%
20,551
62.3%
Source: US Census 2000, SF 3, Table P15
25
Table 3.4, Columns 4 is the percentage of FHH in Families with children under the age of 18, out of all FHH in Families in the
county. Columns 6, & 8, & 10 are the percentages of FHH in Families with children in each age category, out of all FHH in
Families with children less than 18 years of age in the county.
36
Results Chapter 3
Not surprisingly, the majority of FHH in families are 44 years of age or younger (60.9%).
Conversely, the majority of FHH in non-families are 45 years of age and older (81.5%) with
42.7% being 65 years of age and older (Table 3.5).
Table 3.5 Numbers and Percentages of Female Heads of Households by Age26
FHH in Families
FHH in non-Families
Total
< 45 yrs
%
FHH
45 + yrs
%
65 + yrs
%
Genesee
2,105
1,191
56.6%
3,932
3,040
77.3%
2,000
65.8%
Livingston
2,167
1,287
59.4%
3,646
2,671
73.3%
1,652
61.8%
Monroe
37,437
22,888
61.1%
56,132
38,500
68.6%
22,288
57.9%
Ontario
3,810
2,267
59.5
6,563
4,923
75.0%
3,003
61.0%
Orleans
1,756
1,093
62.2%
2,351
1,915
81.5%
1,260
65.8%
Wayne
3,545
2,248
63.4%
5,295
4,220
79.7%
2,620
62.1%
Yates
845
469
55.5%
1,461
1,217
83.3%
859
70.6%
51,665
31,443
60.9%
79,380
56,486
71.2%
33,682
59.6%
TOTAL
Source: US Census 2000, SF 3, Table P92
Educational Attainment
Table 3.6 shows the distribution of educational attainment for women 25 years of age or
older.27 There are 48,600 women who have not completed high school across the seven
counties. This represents 12.6% of the total population of women 25 years of age and older
for whom educational status is known (386,081 women). Orleans County has the highest
percentage (21.5%) of women without a 12th grade education. Ontario County has the lowest
percentage of women without high school diplomas (11.5%).
The absolute numbers of women without a high school education is striking. Monroe County
has the largest number of women without a high school education, 40,053, followed by
Wayne County with 5,157. Of all women in the seven counties, only 30.4% or 117,328 have
high school diplomas or a General Education Degree (GED).
26
27
Table 3.5, Columns 4, 7, & 9 are the percentages of FHH within each age group, out of the total FHHs within each of the two types
of households in the county.
Data also are available showing educational attainment for women 18 years of age and older. However, the 25 years of age and
older data are used in this report to account for women who attained a General Education Degree (GED) or took more than 4 years
to complete high school.
37
Results Chapter 3
Table 3.6 Educational Attainment for Women 25 Years of Age and Older28
Bachelor’s
degree
Graduate or
Professional
degree
< 9th grade
High School
graduate29
#
%
#
%
#
%
#
%
#
%
#
1,042
5.0%
1,879
9.1%
7,865
38.0%
3,740
18.1%
2,766
13.4%
638
3.2%
1,994
10.1%
6,738
34.0%
4,066
20.5%
2,335
11.8%
11,712
4.6%
28,341
11.1%
71,193
27.8%
45,067
17.6%
26,649 10.4% 42,927 16.8% 30,024 11.7%
Monroe-ROC
6,260
3.9%
14,395
11.5%
50,643
31.3%
33,318
19.7%
20,756
8.7%
34,621 15.3% 24,505
9.8%
Rochester
5,452
7.6%
13,946
19.5%
20,550
28.8%
11,749
16.5%
5,893
8.3%
8,306
11.6%
5,519
7.7%
Ontario
1,158
3.3%
2,835
8.2%
10,952
31.6%
6,945
20.0%
4,447
12.8%
4,829
13.9%
3,498
10.1%
Orleans
960
6.4%
2,225
14.9%
5,985
40.0%
2,431
16.2%
1,409
9.4%
1,230
8.2%
734
4.9%
Wayne
1,664
5.2%
3,493
11.0%
11,669
36.7%
5,919
18.6%
3,453
10.9%
3,374
10.6%
2,236
7.0%
Yates
510
6.2%
988
12.1%
2,926
35.7%
1,457
17.8%
739
9.0%
789
9.6%
784
9.6%
17,684
4.6%
30,916
8.0%
117,328
30.4%
69,625
18.0%
Genesee
Livingston
Monroe
7 counties
Some college, no
degree
Associate’s
degree
9-12
no diploma
%
#
%
2,042
9.9%
1,385
6.7%
2,246
11.3%
1,793
9.1%
41,798 10.8% 57,437 14.9% 40,454 10.5%
Source: US Census 2000, SF 3, Table P37
Variation exists in the distribution of educational attainment by race and ethnicity (Table 3.7
& Figure 3.3). White women have the lowest percentage of women without a high school
education at 12.8% and Hispanic women have the highest percentage of women without a
high school diploma at 38.4%. A higher percentage of Hispanic women earned a Bachelors
or graduate degree compared to Black women. The percentage of Asian women earning a
Bachelor’s or graduate degree was higher than any other group.
28
Table 3.6, Columns 3, 5, 7, 9, 11, 13, & 15 are percentages of women within each educational level, out of the total number of
women for whom educational level is known in the county.
29
High school graduate includes equivalency or GED.
38
Results Chapter 3
Table 3.7 Educational Attainment for Females 25 Years of Age and Older by Race/
Ethnicity.30,31,32
White
%
White
%
Native % Native
Black % Black Hispanic Hispanic Am.
Am.
Asian % Asian Other
%
Other
Less than 9th grade
12,182
3.2%
2,461
7.6%
1,919
17.2%
55
4.7%
866
14.5%
1,398
14.4%
9th to 12th, no diploma
30,273
9.9%
7,885
24.4%
2,367
21.2%
253
21.5%
497
8.4%
2,013
20.7%
Less than high school
42,455
12.8%
10,346
32.1%
4,286
38.4%
308
26.2%
1,363
22.9%
3,411
35.1%
High school graduate
102,490
34.4%
9,907
30.7%
2,865
25.7%
386
32.8%
893
15.0%
2,561
26.4%
Some college, no degree 60,843
20.4%
5,607
17.4%
1,678
15.0%
312
26.5%
536
9.0%
1,496
15.4%
Associate degree
37,325
11.8%
2,735
8.5%
886
7.9%
58
4.9%
404
6.8%
905
9.3%
Bachelor's degree
52,437
11.4%
2,349
7.3%
895
8.0%
70
6.0%
1,231
20.7%
778
8.0%
Graduate or professional 36,689
9.0%
1,314
4.1%
555
5.0%
42
3.6%
1,525
25.6%
563
5.8%
Source: US Census 2000, SF 3, Table P148
Figure 3.3 Educational Attainment for African American, Hispanic and White Women in the
Seven Counties.
G r a d u a te
H is p a n ic
A fric a n A m e ric a n
B a c h e lo r 's
W h ite
A s s o c ia te 's
S o m e c o lle g e
HS g rad
< HS
0%
5%
10%
15%
20%
25%
30%
35%
40%
< HS
H S g ra d
Some
c o lle g e
A s s o c ia te 's
B a c h e lo r's
G ra d u a te
H is p a n ic
3 8 .4 %
2 5 .7 %
1 5 .0 %
7 .9 %
8 .0 %
5 .0 %
A fric a n A m e ric a n
3 2 .1 %
3 0 .7 %
1 7 .4 %
8 .5 %
7 .3 %
4 .1 %
W h ite
1 2 .8 %
3 4 .4 %
2 0 .2 %
1 1 .8 %
1 1 .4 %
9 .0 %
30
The total number of females included in Table 3.7 (N = 392,504) is 6,423 higher than in Table 3.6 (N = 386,081). This is
attributable to women’s ability to report being a member of more than one racial and ethnic group (for example, Black and
Hispanic). In addition, because Summary File 3 reports estimated data, exact totals can differ slightly from table to table.
31
Table 3.7, Columns 3, 5, 7, 9, 11, & 13 are the numbers of women within each racial/ethnic group having achieved the specified
educational level in the county. Columns 4, 6, 8, 10, 12, & 14 are the percentages of women within each racial/ethnic group having
achieved the specified educational level, out of the total number of women in each racial/ethnic group in the county.
32
Because the number of individuals in some racial/ethnic groups are very small in many of the townships, there is the possibility that
individual women could be identified. Thus, there is no accompanying table for Table 3.7 in the Appendix.
39
Results Chapter 3
Pregnancy by Age and Marital Status
The ability to control ones fertility plays a significant role a woman’s ability to achieve ESS.
It is well known that women who give birth to a child at an early age and have rapid repeat
pregnancies are more vulnerable to poverty. As shown in Table 3.8, the number of teenagers
who gave birth has decreased from 1997 to 2000 in most of the seven counties, the
exceptions being Livingston and Ontario counties.
Table 3.8 Number and Percentages33 of Live Births to Teenagers from 1997 to 2000
1997
1998
1999
2000
#
%
#
%
#
%
#
Genesee
%
65
8.6%
55
7.9%
70
9.5%
61
8.9%
Livingston
53
7.5%
56
8.4%
55
7.9%
71
10.6%
Monroe
982
10.2%
976
10.1%
982
10.6%
947
10.0%
Ontario
87
7.4%
95
8.0%
76
7.1%
98
8.5%
Orleans
81
14.7%
79
14.3%
68
12.8%
62
11.7%
Wayne
154
12.2%
140
11.0%
115
9.2%
105
9.0%
Yates
37
11.2%
30
8.7%
33
10.4%
22
8.1%
Source: New York State Department of Health Vital Statistics (http://www. health.state.ny.us/nysdoh/vital_statistics)
The large majority of teenagers who give birth are not married, which increases the
likelihood that they will not be economically self-sufficient. Across the seven counties,
among girls 14 years of age and younger who gave birth in 2000, 100% were not married.
Among girls 15 to 17, 97.4% were not married. Among girls 18 and 19 years of age, 89.4%
were not married. Although, the percentage of single women giving birth decreases with age,
more than half of women 20 to 24 years of age (63.8%) who gave birth in 2000 were not
married. This percentage decreases dramatically thereafter, with 27.1% of women 25-29,
14.7% of women 30-34, 13.3% of women 35-39, and 18.4% of women 40-44 being single
when they have a child. All women 45 years of age or older were married when they had a
child.
Place of Residency (Urban vs. Rural)
Density of females living in rural versus urban settings is not disaggregated from the male
population so a precise statement about distribution of females in rural versus urban settings is not
possible. However, since more than 50% of the total population are female, we can extrapolate
from the aggregated data in order to identify residency, rural versus urban.
When considering the entire study population, there are significantly more urban residents (75%
or 846,946 people) than rural residents (25% or 275,876). However, in Genesee County, 60% of
33
Percentages of live births to teenagers (19 years of age and younger) out of all live births in the county. These data are not available
by towns.
40
Results Chapter 3
residents live in rural areas (35,939) and 40% live in urban areas (24,441). In Ontario County,
49% of residents live in urban areas and 51% live in rural areas (49,288 and 50,936 people
respectively). This higher population density in rural areas held true for all study counties except
Monroe County where only 7% reside in rural areas (51,139) and 93% people live in urban areas
(684,204).
ECONOMIC STATUS OF WOMEN
Working Women
Over the seven counties, approximately 55% of all women 16 years of age and older are
employed. By comparison, approximately 62% of men are employed. The percentages of
women 16 years of age and older who are working in the seven counties differs slightly by
race/ethnicity (Table 3.9).
Table 3.9 Percentage of Women Working by County and Race/Ethnicity1,2
Genesee Livingston Monroe Mon-ROC Rochester Ontario Orleans Wayne Yates
TOTAL
White
Black
58.2%
59.4%
58.5%
59.0%
56.3%
60.6%
53.6%
60.0%
53.8%
58.6%
51.2%
48.3%
53.9%
63.1%
52.0%
52.5%
17.2%
49.9%
92.3%
53.2%
Hispanic
50.4%
56.3%
50.4%
59.9%
46.9%
53.7%
32.1%
58.0%
54.0%
50.3%
American Indian 61.8%
45.5%
46.3%
56.1%
37.5%
25.0%
43.6%
45.8%
80.0%
47.3%
Asian
26.9%
49.3%
53.1%
53.8%
51.3%
45.4%
60.9%
59.9%
17.8%
52.8%
Other
44.5%
55.5%
53.0%
63.3%
49.3%
58.8%
61.3%
64.8%
56.7%
53.4%
Source: US Census 2000, SF 3, Table P150
The U.S. Census 2000 also reports the numbers of women “in the labor force” who are
working. This includes only women working and looking for work. It does not include
women who are not looking for work or have chosen not to work. The percentages of women
in the labor force who are working across all counties are 96.1% for white women, 88.8% for
black women, 87.1% for Hispanic women, 85.7% of Native American women, 92.1% of
Asian women, and 88.8% of all other women.
Distribution of Income by Households
The median household incomes of the seven study counties, as well as the median income for
New York State and the United States, are shown in Table 3.10. The median income for all
households in Genesee, Livingston, Orleans, and Yates Counties are below the median
income for New York State. Genesee, Orleans, and Yates Counties’ median income also are
below the median income for the US.
1
Table 3.9 are percentages of women who are employed, out of all women over the age of 16 years in each racial/ethnic group in the
county. The percentages do not include women who are in the Armed Forces.
2
Because the number of individuals in some racial/ethnic groups are very small in many of the townships, there is the possibility
that individual women could be identified. Thus, there is no accompanying table for Table 3.9 in the Appendix.
41
Results Chapter 3
The median annual income for Monroe County is higher than all others. However, the
medians for the city of Rochester are substantially lower than all others. Median household
incomes for all but 2 of the 19 townships in Monroe County, excluding Rochester, are higher
than those in all other counties. Total family and married couples’ median incomes are
substantially below the NYS and US medians in Genesee, Orleans, and Yates Counties.
Median income in female heads of households is the lowest of all households. Within
families, FHH have the lowest median incomes across all counties.36 FHH not in families,
which includes FHH of all ages and living alone or with another, have lower median incomes
than FHH in families. Those with the lowest annual income are FHH 65 years of age and
older who are living alone.
Table 3.10 Median Income in Dollars by Selected Households
Families
All
Total
Married
Households
Families
couples
Genesee
$40,542
$47,771
$52,765
Livingston
$42,066
$50,513
$56,057
Monroe
$44,891
$55,900
$66,406
Rochester
$27,123
$31,257
$48,400
Ontario
$44,579
$52,698
$58,627
Orleans
$37,972
$42,830
$49,013
Wayne
$44,157
$51,495
$57,267
Yates
$34,640
$40,681
$46,290
NYS
$43,393
$51,691
$61,995
USA
$41,994
$50,046
$57,345
Source: US Census 2000, SF 3, Tables P53, PCT 40, & PCT 42
FHH
$25,223
$23,639
$25,265
$17,953
$27,271
$22,647
$25,437
$20,022
$26,148
$25,458
Non-Families
FHH 65+ yrs,
FHH
living alone
$19,968
$14,436
$19,634
$15,847
$22,796
$16,237
$18,296
$13,081
$21,370
$15,822
$18,558
$14,953
$18,134
$12,830
$16,675
$13,982
$22,917
$14,395
$21,594
$14,653
Income is unevenly distributed (Table 3.11). Approximately one in five households in the
seven study counties have an annual income of less than $20,000, while approximately one in
ten have an annual income $100,000 and above. The disparities are dramatic comparing FHH
in families and in non-families, with approximately 40% of these groups having an annual
income of $20,000 or less, compared to only 2-3% having an annual income of $100,000 or
more.
36
Median incomes for FHH in families in the townships of Monroe County excluding Rochester range from $23,984 in Parma to
$43,250 in Mendon, and for married couples from $56,371 in East Rochester to $105,980 in Pittsford.
42
Results Chapter 3
Table 3.11 Distribution of Annual Income < $20,000 and > $100,00037
All
Households
Income $20,000 and less
Genesee
19.3%
Livingston
20.5%
Monroe
21.1%
Monroe –ROC
13.3%
Rochester
38.5%
Ontario
18.1%
Orleans
22.0%
Wayne
19.2%
Yates
25.2%
TOTAL
20.6%
Income $100,000 and more
Genesee
6.0%
Livingston
7.3%
Monroe
13.1%
Monroe-ROC
17.0%
Rochester
4.7%
Ontario
10.3%
Orleans
5.2%
Wayne
8.1%
Yates
4.9%
TOTAL
11.3%
Source: US Census 2000, SF 3, Table QT-P32
Total
Families
Families
Married
couples
FHH
Nonfamily
HH*38
10.9%
12.5%
13.1%
6.4%
33.0%
10.2%
14.8%
11.4%
16.8%
12.7%
6.4%
20.8%
5.3%
3.8%
13.3%
5.6%
7.9%
6.0%
10.6%
6.6%
34.5%
69.6%
40.0%
23.0%
54.0%
34.7%
44.7%
36.3%
49.9%
40.7%
41.7%
69.7%
37.7%
30.6%
48.5%
38.3%
42.9%
43.4%
49.8%
40.3%
7.7%
9.2%
17.6%
21.8%
5.7%
12.7%
6.4%
9.8%
6.2%
14.7%
9.0%
11.3%
22.6%
25.2%
10.4%
15.0%
7.9%
11.8%
7.3%
18.5%
1.2%
1.2%
2.3%
3.7%
1.3%
3.5%
4.1%
1.3%
1.5%
2.2%
1.7%
2.0%
4.2%
5.2%
2.9%
3.6%
4.1%
2.5%
1.8%
3.7%
In total, the distribution of income reveals a pattern of lowest median household income in
the city of Rochester. The surrounding Monroe County townships, excluding Rochester, have
the highest median household incomes. The six other counties have median incomes between
the two. Clearly the suburbs of Monroe County have the highest concentration of affluent
households in the seven county region. The median outcomes of the six other counties are
closer to that of the city of Rochester than to the suburbs of Monroe County.
The Woods & Poole Wealth Index (a weighted measure that includes sources of income as
well as assets such as home values) compares different localities using 100 as the U.S.
average. On this scale, Monroe County scores above the national average while all the other
study counties score below 80 on the wealth index (Woods & Poole Economics, Inc., 2002).
37
These data are not available by town. Thus, there is no accompanying table in Appendix A.
38
Non-family HH includes women and men. These data are not available for FHH and MHH separately.
43
Results Chapter 3
Distribution of Earnings by Gender for Individuals
In the seven county area, as shown in Table 3.12, women who work full-time, year-round39
earn approximately 70% of what men earn. The ratio of female to male full-time earnings is
4% to 8% lower than the overall percent for New York State and 0% to 4% lower than the
average for the US. The ratio of female to male earning among those who work part-time is
better. However, all of these incomes are low.
Monroe County has the highest median incomes for individual women and men in the seven
counties. Yates County has the lowest median individual incomes, even lower than the
median incomes found in the City of Rochester. In Rochester the median income for women
and men combined is $27,304. The median for males is $30,521 and for females is $25,139
(full-time, year round earnings). Among men, Yates County is the only county with a median
income lower than the city of Rochester. Among women, median incomes in Genesee,
Orleans, and Yates Counties are lower than in the City of Rochester.
Table 3.12 Median Earnings for Women and Men, 16 Years of Age and Older, in Dollars
Median earnings
Genesee Livingston Monroe
Worked full-time, year-round
Ontario
Orleans
Wayne
Yates
NYS
US
$31,421
$28,818
$31,904
$25,962
$35,949
$32,098
Male
$34,430 $36,599 $41,279 $36,732 $32,450
Female
$23,788 $25,228 $29,553 $26,139 $22,605
% Female/Male ratio
69%
69%
72%
71%
70%
Part-time and/or not year round
Total
$9,182
$ 6,670 $ 9,650 $ 8,945 $ 8,310
Male
$10,733 $ 6,975 $ 9,752 $ 9,831 $10,085
Female
$8,443
$ 6,389 $ 9,589 $ 8,360 $ 7,385
% Female/Male ratio
79%
92%
98%
85%
73%
Source: US Census 2000, SF3, Table PCT 47 (full & part time separated)
Full and Part time (combined)
Total
$21,641 $18,841 $25,602 $23,554 $20,269
Male
$28,327 $24,814 $32,040 $30,037 $26,832
Female
$15,787 $13,703 $20,501 $18,208 $15,034
% Female/Male ratio
56%
55%
64%
61%
56%
$36,825
$26,470
72%
$29,671
$21,566
73%
$40,236
$31,099
77%
$37,057
$27,194
73%
$ 9,280
$10,188
$ 8,761
86%
$ 7,089
$ 8,544
$ 6,436
75%
$10,406
$11,722
$ 9,450
81%
$11,217
$12,232
$10,494
86%
$24,259
$30,652
$18,903
62%
$17,457
$22,528
$13,013
58%
$26,247
$31,096
$21,670
70%
$29,458
$29,458
$18,957
64%
Total
$30,009
$31,316
$35,699
Source: US Census 2000, SF 3, Table P 85 (full & part time combined)
Level of education does not equalize the earnings gap between women and men. National
data show that, at all levels of educational preparation, men earn more than women (Figure
3.4).
39
Full-time, year round earnings are the standard used by the Institute for Women’s Policy Research for reporting comparisons
between earnings for women and men.
44
Results Chapter 3
Figure 3.4 Percent Earnings of Women Compared to Men by Education40
Professional
Doctorate
Masters
Bachelors
Associates
Some college
HS graduate
9th- 12th grade
< 9th grade
0%
10%
20%
30%
40%
50%
60%
70%
80%
The Department of Labor provides a detailed list of jobs and normative salaries for entry
level, median, and experienced workers. Tables 3.13 and 3.14 reflect jobs traditionally held
by women as well as less traditional markets that women are currently entering.
Table 3.13 Traditional Female Employment (Annual Occupational Wages)41
Job Title
Entry
NY
State
Entry
Western
NY
Median
Western
NY
$17,010
$32,070
Median
New
York
State
$42,800
$53,300
$36,480
$42,510
Upperlevel
NY
State
$62,410
$62,720
Upperlevel
Western
NY
$50,720
$49,760
Education
Registered
Nursing
Food
Preparation
Hostesses
Restaurant
Hairdresser
$21,520
$37,650
$12,750
$12,750
$16,160
$14,270
$22,430
$17,890
$13,010
$12,630
$18,790
$13,570
$23,240
$14,730
Maid
$12,990
$12,960
$16,880
$14,680
$25,210
$17,740
$21,360
$14,490
$21,360
$14,490
$27,870
$17,360
Childcare
Provider
Administrative
Assistant
$14,600
$12,810
$20,140
$16,960
$22,680
$20,330
$21,390
$20,000
$25,490
$23,120
$27,020
$24,260
SSS for
Monroe
County42
SSS for
Yates
County
$36,936
$30,348
A comparison of the self-sufficiency standards for the seven study counties clearly
highlighted that traditional female employment (e.g., administrative assistants, housekeepers,
restaurant waitresses) does not pay a wage that meets the self-sufficiency standard in any of
the study counties.
Figure 3.14 illustrates types of employment women in the seven county study area hold that
are both traditional and non-traditional. While percentages differ across the study counties, a
consistent theme is evident. Women are primarily in jobs defined as “traditional female
40
Institute for Women’s Policy Research, 2001 data
NYS Department of Labor (2003)
42
Single mother with one infant and one pre-school aged child
41
45
Results Chapter 3
employment” with very low numbers of women in more non-traditional roles such as
financial specialists, architects and engineers, and construction workers. Women in nontraditional jobs (e.g., engineers, dentists, chief executive officers) are more likely to be
economically self-sufficient. There is a notable paucity of non-traditional jobs held by
women across the study counties. Thus, despite the progress that women have made in terms
of equal rights, these rights do not extend to job markets that have been dominated by men.
Table 3.14 Numbers of Women in Selected Jobs43
Traditional
Female Positions
Genesee
Livingston
Monroe
Ontario
Orleans
Wayne
Yates
Total for
Region
Percent
Held by
Women
Management
Sales & Office
Professional
(teachers, nurses,
RT, OT)
Service (waitress,
hotel staff)
4,532
4,771
3,554
5,211
5,264
3,874
69,988
58,949
49,287
9,401
8,063
6,664
2,686
2,706
1,958
7,521
7,106
5,427
1,680
1,692
1,212
101,089
88,551
71,976
39%
34%
28%
2,961
3,086
28,957
4,520
1,960
3,801
1,348
46,633
18%
Business and
financial
operations (CEO,
CFO)
Transportation
and material
moving
Architecture and
engineering
Construction,
extraction and
maintenance
368
446
818
95
11,457
4%
297
409
2,721
529
414
594
168
5,132
2%
37
101
1,733
146
23
223
2
2,265
1%
72
112
919
85
49
154
38
1,429
1%
Non-Traditional Female Positions
8,307
1,097
326
Demographics of Poverty
Individuals. Across the study region, 10.4% of individuals are living at or below the
federally defined poverty threshold, with the percentages ranging from 7.3% in Ontario to
13.1% in Yates. The percentages of females in poverty, averaging 11.4% across the seven
counties, exceeds that of men, which averages 9.3%.
The total number and percentage of individual women and girls living in poverty and the
distribution of women and girls in poverty by age in the seven study counties in shown in
Table 3.15. There are more women living in poverty than men in all seven counties (Table
3.15, column 3). The highest percentages of girls (less than 18 years of age) in poverty are
found in Yates County and in the city of Rochester. Livingston County has the lowest
percentage of girls in poverty and the highest percentage of women aged 18-64 living in
poverty. Monroe County, excluding Rochester, has the highest percentage of elder women,
65 years of age and older, living in poverty. This is the only indicator of poverty in which
43
US Department of Labor, 2000 http://labor.state.ny.us/publications, Thousand Oaks, CA, 371-389
46
Results Chapter 3
women in Monroe County, excluding Rochester exceeds the percentages of all other counties
and the city of Rochester.
Table 3.15 Numbers and Percentages of Females in Poverty by Age44
% of those
Total # of in poverty
females in who are
poverty
female
Genesee
2,631
58.5%
Livingston
3,459
57.5%
Monroe
44,654
56.3%
Monroe-ROC
14,470
58.8%
Rochester
30,184
55.2%
Ontario
4,168
58.7%
Orleans
2,518
57.5%
Wayne
4,527
57.1%
Yates
1,800
58.6%
7 COUNTIES
63,757
56.8%
Source: US Census 2000, SF 3, Table PCT49
# of
females
< 18 in
poverty
727
721
14,570
3,364
11,206
1,187
887
1,389
674
20,155
% of
females
in
poverty
< 18
27.6%
20.8%
32.6%
23.2%
37.1%
28.5%
35.2%
30.7%
37.4%
31.6%
# of
females
18-64 in
poverty
1,534
2,463
25,136
8,125
17,011
2,397
1,466
2,434
941
36,371
% of
# of
% of
females
in
females females
poverty 65+ in in poverty
18-64 yrs poverty 65+ yrs
58.3%
370
14.1%
71.2%
275
8.0%
56.3%
4,948
11.1%
56.2%
2,981
20.6%
56.4%
1,967
6.5%
57.5%
584
14.0%
58.2%
165
6.6%
53.8%
704
15.6%
52.3%
185
10.3%
57.0%
7,231
11.3%
While a higher percentage of females than males lives in poverty across all ages (56.8%
versus 43.2%), the number and percentages of children in poverty is almost equally
distributed between girls (20,155 - 49.4%) and boys (20,605 - 50.6%). The numbers and
percentages of all children living in poverty in are shown in Table 3.16.
As will be shown in the next section, Female Heads of Households with children comprise a
large percentage of all those living in poverty. Therefore, it is informative to examine the
percentages of individual women and children living in poverty. Across the seven counties
75.1% of those living in poverty are women and children. Percentages range from a low of
70.5% in Livingston County to a high of 81.3% in Yates County. In ascending order, the
percentages of women and children living in poverty in the other counties are: Genesee,
74.4%; Monroe 75% (excluding Rochester, 71.4%; city of Rochester, 76.6%); Wayne,
75.3%; Ontario 76%; and Orleans, 78.8%.
44
Table 3.15, Column 3 is the percentage of females in poverty out of all persons (females and males) in poverty. Columns 5, 7, & 9
are the percentages of females in poverty within that age group out of all females in poverty.
47
Results Chapter 3
Table 3.16 Numbers and Percentages of Children in Poverty by Age
# of
preschool
children
(< 5 yrs)
507
610
9,865
% of
preschool
children
(< 5 yrs)
11.3%
15.1%
17.7%
Genesee
Livingston
Monroe
Monroe1,872
5.3%
ROC
Rochester
7,993
39.9%
Ontario
887
11.7%
Orleans
578
17.3%
Wayne
1,030
14.1%
Yates
493
25.6%
7 COUNTIES
23,835
17.0%
Source: US Census 2000, SF 3, Table PCT49
# of
schoolaged
children
(6-11 yrs)
490
422
11,437
% of
schoolaged
children
(6-11 yrs)
9.5%
8.5%
17.1%
# of
teenagers
children
(12-17 yrs)
443
473
8,075
% of
teenagers
children
(12-17 yrs)
7.7%
8.2%
12.9%
# of all
children in
poverty
1,440
1,505
29,377
% of all
children in
poverty
9.3%
10.2%
15.9%
2,394
9,043
910
691
994
466
26,847
5.4%
39.6%
10.2%
17.7%
11.3%
22.3%
16.0%
2,184
5,891
620
548
811
410
19,455
4.9%
33.4%
7.2%
13.4%
8.9%
18.2%
12.1%
6,450
22,927
2,417
1,817
2,835
1,369
70,137
5.2%
37.9%
9.6%
16.1%
11.3%
21.9%
15.0%
Poverty is not equally distributed by race/ethnicity (Table 3.17). The highest percentages of
poverty are found among Hispanic women in Genesee and Monroe Counties. In Ontario,
Orleans, Wayne, and Yates Counties, African American women have the highest percentages
of poverty. In Livingston County, American Indian women have the highest percentage of
females in poverty. There also are relatively high percentages of poverty among women
included in the “Other” category. This category includes Native Hawaiian/Pacific Islanders,
women declaring two or more races, and women grouped in a category of ‘other’ in the U. S.
Census 2000. The lowest percentages of poverty are found in white women, averaging 7.6%,
and Asian women, averaging 9.8%, across all seven counties. It is notable that among
African American and Hispanic women, rates of poverty are consistently high across all
counties. Thus, whether minority women live in urban, suburban, or rural areas, they have a
higher likelihood of living in poverty compared to white women.
Table 3.17 Numbers and Percentages of Females in Poverty by Race/Ethnicity45,46
%
Native
% Native
% Black Hispanic Hispanic American American Asian % Asian
White
% White
Black
Genesee
2,317
8.1%
130
34.7%
94
37.9%
46
17.2%
21
Livingston
3,169
7.2%
49
20.4%
87
27.1%
37
41.6%
58
Monroe
18,937
6.7%
16,389
31.7%
7,200
37.4%
276
24.5%
888
Other
% Other
20.6%
94
20.0%
20.1%
88
19.0%
9.8%
6,101
33.7%
Mon-ROC
11,738
5.0%
965
12.5%
906
18.0%
97
18.6%
511
4.1%
774
15.2%
Rochester
7,199
15.3%
15,424
35.0%
6,294
44.2%
179
29.7%
377
9.2%
5,327
40.9%
Ontario
3,360
7.2%
329
35.9%
294
28.9%
10
16.7%
7
2.0%
304
30.3%
Orleans
1,890
10.0%
353
47.8%
192
37.4%
25
21.6%
4
9.5%
156
30.2%
Wayne
3,866
8.8%
338
29.1%
180
20.6%
13
9.6%
Yates
1,725
14.9%
11
55.0%
34
38.6%
0
7 COUNTIES
35,264
7.6%
17,599
31.9%
8,081
36.2%
407
22.1%
209
21.5%
10
0
19.2%
28
17.2%
988
9.8%
6,980
32.2%
Source: US Census 2000, SF 3, Table P75
45
Table 3.17, Columns 2, 4, 6, 8, 10, & 12 are the numbers of women in poverty in the county. Columns 3, 5, 7, 9, 11, & 13 are the
percentages of women in poverty out of all women in the county.
46
Because the number of individuals in some racial/ethnic groups are very small in many of the townships, there is the possibility that
individual women could be identified. Thus, there is no accompanying table in the Appendix for Table 3.17.
48
Results Chapter 3
Another way to illustrate the relationship between poverty and race/ethnicity is to compare
the percentage of those living in poverty to the percentage of the total population of women
for each race/ethnicity (Table 3.18). Whereas white females comprise 82.7% of all females in
the seven counties, they comprise only 50.9% of all females in poverty. In comparison,
African American women comprise only 9.9% of all females, yet they account for 25.4% of
females living in poverty. Only white and Asian females have a lower percentage of females
in poverty relative to the percent of females in the population.
Table 3.18 Percentage of Women in the Population Compared to Women in Poverty/Ethnicity47
All races
/ethnicities White
Total
women
576,906
Women in
poverty
69,319
%
White
%
%
Native
% Native
Black Hispanic Hispanic American American
Asian
%
%
Asian Other Other
9.9%
23,129
4.0%
1,877
0.3%
10,091
1.7%
7,357
1.3%
35,264 50.9% 17,599 25.4%
8,081
11.7%
407
0.6%
988
1.4%
6,980
10.4%
Black
477,167 82.7% 57,285
Source: US Census 2000, SF 3, Table P75
Households. Approximately 10% of all households are living at or below the federally
defined poverty threshold, ranging from 7.4% in Ontario to 10.7% in Yates and 10.8% in
Monroe. Again, women disproportionately bear the burden of poverty. As shown in Table
3.19, the highest percent of households in poverty are found in FHH in families, 25.9%,
followed by FHH in non-families, 16.4%, compared to 2.7% of married couples, 12.5% of
MHH in families, and 14% of MHH not in families. Figure 3.5 illustrates the numbers of all
households and FHH living in poverty in each of the seven counties.
The pattern of distribution of poverty within each type of household reflects the median
incomes in the seven study counties. In every type of household, the highest percent of those
living in poverty are in the city of Rochester, followed by the six other counties. Monroe
County, excluding Rochester, has the lowest percent of households living in poverty in every
category.
47
Table 3.18: The percentages in row 2 are the percentages of women within each racial/ethnic group, out of the total number of
women in the population. The percentages in row 3 are the percentages of women within each racial/ethnic group, out of the total
number of women in poverty.
49
Results Chapter 3
Table 3.19 Percentages of Households At or Below the Federal Poverty Threshold
Households in Poverty
% of
% of Married
Total HH
Couples
Genesee
7.8%
2.8%
Livingston
9.8%
2.4%
Monroe
10.8%
2.6%
Monroe-ROC
5.1%
1.5%
Rochester
23.5%
8.2%
Ontario
7.4%
2.1%
Orleans
9.1%
3.3%
Wayne
8.5%
2.8%
Yates
10.7%
4.8%
TOTAL
10.0%
2.7%
Source: US Census 2000, SF 3, Table P92
Families
% of
FHH
20.0%
23.2%
27.3%
12.1%
39.8%
19.4%
26.9%
20.7%
31.8%
25.9%
% of
MHH
11.7%
11.1%
13.7%
5.6%
25.3%
7.4%
11.4%
11.9%
11.3%
12.5%
Non-Families
% of
% of
FHH
MHH
15.4%
9.2%
20.9%
16.9%
16.1%
15.1%
11.2%
8.6%
24.6%
22.7%
15.3%
9.9%
13.0%
11.7%
18.2%
11.1%
20.0%
9.1%
16.4%
14.0%
Figure3.5 Numbers of All Households and FHH Living in Poverty
35,000
30,000
25,000
20,000
15,000
10,000
5,000
0
Total Households
FHH in families
FHH not in families
Genesee
1774
420
606
Livingston
2,181
502
761
Monroe
31,054
10,228
9051
Ontario
2,829
740
1007
Orleans
1,392
473
305
Wayne
2,967
733
2266
Yates
968
269
803
Examination of the distribution of households in poverty shows that among households living
in poverty, 31% are FHH in families, 30.1% are FHH not in families (FHH total of 61.1%),
13.4% are married couples, 4.8% are MHH in families, and 20.7% are MHH not in families
(Figure 3.6).
50
Results Chapter 3
Figure 3.6 Distribution of Household Type Among Households in Poverty
100%
M a le H H*
N ot in Fa m ilie s
M a le H H* in Fa m ilie s
80%
M a r r ie d C ouple s
60%
Fe m a le HH *
N ot in Fa m ilie s
40%
20%
Fe m a le HH *
in Fa m ilie s
0%
To
ta l
Mo
n ro
On
e
ta r
io
Wa
yn e
Ge
ne
see
Liv
in g
s to
O rl
n
ean
Ya
s
te s
* H H = He ad of H ou se h ol d
Further subdivision of FHH in families demonstrates that FHH with children under the age of
18 have the highest percentage of those in poverty compared to any other family group.
(Table 3.20)
Table 3.20 Percentages of Families With and Without Children Under the Age of 18 At or Below
the Federal Poverty Threshold
Families in Poverty
Living WITH children < 18
Living WITHOUT children <18
% of
% of
Married
% of
Married
% of
% of
% of
MHH
FHH
MHH
Couples
Couples
FHH
Genesee
3.7%
16.5%
1.8%
5.4%
2.5%
27.4%
Livingston
2.8%
11.9%
2.0%
3.9%
9.5%
30.4%
Monroe
3.6%
17.9%
1.7%
7.0%
7.4%
35.6%
Monroe-ROC
1.5%
5.6%
11.2%
11.2%
8.6%
12.1%
Rochester
8.2%
39.8%
25.3%
24.6%
24.6%
22.7%
Ontario
2.8%
9.7%
1.5%
5.1%
3.2%
26.1%
Orleans
4.7%
14.8%
2.0%
4.5%
2.7%
36.9%
Wayne
3.2%
15.8%
2.5%
0.0%
1.9%
28.2%
Yates
7.9%
15.4%
2.4%
4.6%
0.0%
42.4%
TOTAL
3.6%
16.2%
1.8%
6.1%
6.1%
34.0%
Source: US Census 2000 Summary File 3, Table P90
By examining only families in poverty that include women, one can further elucidate those
women at greatest risk. Among FHH in families who are living at or below the poverty
threshold 93.1% have children under the age of 18. The distribution across the seven counties
is shown in Figure 3.7. In comparison, among married couples in poverty, 64.3% have
children 17 years of age and younger.
51
Results Chapter 3
Figure 3.7 Percentages of Female Heads of Households in Families Living in Poverty With
and Without Children under 18 Years of Age.
100%
80%
60%
40%
20%
0%
Genesee
Livingston
With Children <18
92%
95%
Without children <18
8%
5%
Monroe
Ontario
Orleans
Wayne
Yates
89%
89%
95%
100%
96%
11%
11%
5%
0
4%
Examination of all families in poverty by race and ethnicity reveals disparities in the
percentages. As shown in Figure 3.8 of all white families in poverty 54% are FHH. Of all
African American families in poverty, 79% are FHH. Of all Hispanic families in poverty,
71% are FHH.
Figure 3.8 Percent of Female Heads of Households in Families in Poverty of all Families in
Poverty.
Other Ethnicities FHH
Hispanic FHH
Black FHH
White FHH
0%
Percent of all families in Poverty
10%
20%
30%
40%
50%
60%
70%
80%
White FHH
Black FHH
Hispanic FHH
Other Ethnicities FHH
54%
79%
71%
37%
The percentages of FHH in families with children under 18 years of age who are in poverty
are fairly consistent across race/ethnicity (Figure 3.10). Among white FHH in families, 91%
have children under the age of 18. Among African American FHH in families, 95% have
children under the age of 18. Among Hispanic FHH in families, 93% have children under the
age of 18. A lower percentage of FHH in poverty categorized as “Other” (Asian, Pacific
Islander, Native American) have children under the age of 18 children (77%).
52
Results Chapter 3
Figure 3.9 Percentage of Female Heads of Households (in families) in Poverty who have
Children Under 18 Years of Age.
Other Ethnicities FHH
Hispanic FHH
African American FHH
White FHH
0%
FHH with Children
10%
20%
30%
40%
50%
60%
70%
80%
90% 100%
White FHH
African American
FHH
Hispanic FHH
Other Ethnicities
FHH
91%
95%
93%
77%
Clusters of poverty were noted across the study counties and included Batavia, North Dansville,
Rochester, Greece, Geneva, Albion, Arcadia, and Milo. These areas had distinctive concentrations
of women and children in poverty. Interestingly, six towns had no documentation of women and
children in poverty and included Pavilion, Stafford, Ossian, York, Ogden, and Seneca.
The demographics of poverty among women shift from those with young children to elder
women, depending on whether or not they live in families. In the majority of FHH in families
(83.4%) and married couples (53.9%), the householder is under the age of 45. On the other
hand, among FHH not in families, the large majority of women are 45 years of age or older
(60.6%), with 42.7% of FHH not in families being over the age of 64 (refer to Table 3.5).
Although the distribution of poverty by gender and race/ethnicity is consistent with national
data, this report quantifies the extent of poverty among women and children over the seven
counties under study. Clearly, the burden of poverty is shouldered in large part by women,
especially female heads of households with children under the age of 18. The disparities in
the distribution of poverty across race and ethnicity also are significant.
Temporary Assistance. Another indicator of the struggle toward ESS is the extent to which
individuals rely on temporary public assistance.48 Figure 3.10 shows the number of recipients
of Temporary Assistance in the seven counties for 2003. Temporary Assistance includes
Family Assistance and Safety Net Assistance.
48
Source: New York State Office of Temporary and Disability Assistance. Data available from the New York State Office of
Temporary and Disability Assistance are not provided by gender. Therefore, data about Temporary Assistance is presented for all
recipients, female and male.
53
Results Chapter 3
Figure 3.10 Recipients of Temporary Assistance for the Year 2003
35,000
30,000
25,000
20,000
15,000
10,000
5,000
0
Genesee
Total
Adults
Children
616
358
258
Livingston
1,157
558
599
Monroe
30,593
18,539
12,054
Ontario
1,280
673
607
Orleans
877
496
381
Wayne
1,068
580
488
Yates
197
108
89
From 2000 to 2003, the number of recipients of Temporary Assistance substantially increased in
Livingston and Ontario Counties and substantially decreased in Monroe and Orleans Counties
(Table 3.21). However, as shown in Figure 3.11, the percentage of the population receiving
Temporary Assistance in each county has remained relatively stable since 2000, ranging from 2%
to 5% of all households. This information provides only an estimate of numbers of households
headed by females who receive Temporary Assistance.
Table 3.21 Number of Recipients of Temporary Assistance
Year
2000
2001
2002
Genesee
607
590
551
Livingston
980
1,038
1,107
Monroe
37,313
31,984
30,263
Ontario
1,019
1,075
1,186
Orleans
1,069
1,054
993
Wayne
1,074
844
957
Yates
187
147
183
Total
42,249
36,732
35,240
Source: NYS Office of Temporary and Disability Assistance
54
2003
616
1,157
30,593
1,280
877
1,068
197
35,788
% change
from
2000-03
1.5%
18.1%
-18.0%
25.6%
-18.0%
-0.6%
5.3%
14.0%
Results Chapter 3
Figure 3.11 Percentage of Individuals Receiving Temporary Assistance from 2000 to 2003.
G enesee
L iv in g sto n
M o n ro e
O n ta rio
O rle a n s
W ayn e
Y a te s
6%
5%
4%
3%
2%
1%
0%
2000
2001
2002
2003
It should be noted that averaged over the seven counties, 10.4% of individuals were living at or
below the federal poverty threshold. Yet, only 3.2% of individuals were receiving Temporary
Assistance (cash) in 2003. Over the same period of time, 2000-2003, the percentages of
individuals receiving Food Stamps increased in every county, ranging from 20.2% in Monroe
County to 45.3% in Wayne County. By 2003, 4.8% of the population in Ontario County to 9.1%
of the population in Monroe County was receiving Food Stamps. These figures demonstrate that a
significant proportion of persons living at or below the poverty threshold do not receive public
assistance.
Beyond Poverty to Economic Self-Sufficiency
Examining poverty in terms of federally defined thresholds takes into account only one level
of economic insufficiency. Federal Poverty Guidelines’ main purpose is to establish
eligibility for public (and often private) assistance. By definition income at these levels is not
enough to adequately meet basic needs. To fully understand ESS it is essential to go beyond
federal thresholds and evaluate women’s economic status relative to a level of income
necessary for a given family to meet their basic needs, that is, independent of Temporary
Assistance for Needy Families (formerly known as welfare) and/or other public or private
subsides.
The Self-Sufficiency Standard (SSS), developed by the Family Economic Self-Sufficiency
Project of Wider Opportunities for Women49 is one means of estimating income adequacy.
The SSS is based on the proportion of income spent in seven categories of needs: housing,
childcare, food, health care, transportation, taxes, and miscellaneous expenses (e.g., clothing,
shoes, paper products, diapers, cleaning products, personal hygiene items, and telephone). It
is unique in that it takes into account geographical differences in cost of living, as well as
49
Six Strategies for Family Economic Self-Sufficiency. (http://www.sixstrategies.org)
55
Results Chapter 3
differences among families of varying composition. This standard assumes that adults in the
household are working full-time and are independent of private and/or public subsides.
The SSS has been calculated for families of varying composition in geographic regions
across in the United States, including each county in New York State.50 Table 3.22 presents
examples of SSS calculations for the most expensive (Monroe) and least expensive (Yates)
counties in the study area. Three household compositions were chosen for illustration. One
adult with two children, one of whom is an infant, was chosen to highlight the cost of childcare. One adult with two children between the ages of 6 and 17 was chosen because this
grouping comprises the largest percent of FHH in families with children in all seven counties
(refer to Table 3.4). One adult living alone was chosen to represent the majority of elder
women. As can be seen, the federally defined poverty thresholds are significantly less than
the income indicated by the SSS as necessary to cover basic needs.
Table 3.22 Self-Sufficiency Standard for Monroe and Yates Counties Compared to Federal Poverty
Thresholds
Housing
Child
Care
Food
Transport- Health
ation
Care
Misc.
Taxes51 Monthly
income
Annual
income
Monthly expenses for one adult, one infant and one school aged child
Monroe
$609
$1,093
$325
$190
$242
$246
$374
$3,078
$36,936
Yates
$480
$881
$325
$190
$242
$212
$200
$2,529
$30,348
Federal poverty level for three persons
$1,272
$15,264
Monthly expenses for one adult, one school aged child and one teenager
Monroe
$609
$368
$429
$190
$274
$184
-$3
$2,024
$24,288
Yates
$480
$281
$325
$190
$247
$163
-$147
$1,643
$19,716
Federal poverty level for three persons
$1,272
$15,264
Monthly expenses for one adult
Monroe
$501
0
$164
$185
$103
$95
$224
$1,272
$15,264
Yates
$400
0
$164
$185
$103
$85
$183
$1,120
$13,440
Federal poverty level for one person
$748
Figure 3.12 illustrates annual income deemed adequate by the NYS Self-Sufficiency
Standard for a family of three that includes one adult, one school-age child and one teenager
in each of the seven study counties.
50
51
http://www.sixstrategies.org/files/Resource-StandardReport-NY.pdf
Include earned income tax credit, child-care tax credit and child tax credit
56
$8,976
Results Chapter 3
Figure 3.12 Self-Sufficiency Standard for the Seven Study Counties for one adult, one
school-aged child, and one teenager
$0
$5,000
$10,000
$15,000
$20,000
Yates
$19,716
Wayne
$24,288
Orleans
$22,284
Ontario
$24,288
Monroe
$24,288
Livingston
$22,428
Genesee
$22,284
$25,000
Column 3 of Table 3.23 illustrates the estimated percentage of FHH living between the
federal poverty threshold52 and the Self-Sufficiency Standard53 for a FHH with two children
between the ages of 6 and 17. Rochester has the highest percentage of FHH in families living
at or below the SSS, while the townships that comprise Monroe County excluding Rochester
have the lowest percentage. Over 60% of FHH in Yates County and in the city of Rochester
are estimated to be living at or below the SSS. In the other counties, with the exception of
Monroe County excluding Rochester, close to 50% of FHH in families are living at or below
the SSS. Given that approximately 90% of FHH in families have children, it is likely that
these estimates closely reflect the economic living conditions of that group.
52
53
The actual poverty threshold for a family of 3 is $15,264. A bottom end cut-off of $15,000 was used in this illustration
The actual Self-Sufficiency Standard for this family composition varies as shown in Figure 3.14. A top end cut-off of $25,000 was
used in this illustration as the SSS for six of the seven counties is between $20,000 and $25,000. Census data are available only in
$5,000 groupings up to $50,000.
57
Results Chapter 3
Table 3.23 Estimates of Percentages of Female Heads of Households Living At or
Below the Self-Sufficiency Standard
FHH in families
% living
% living BETWEEN
at or below
≈ poverty threshold
≈ poverty threshold
($15,000/yr)
(< $15,000/yr)
& ≈ SSS ($25,000/yr)
Genesee
22.7%
26.7%
Livingston
28.4%
24.7%
Monroe
29.4%
20.1%
Monroe-ROC
14.2%
17.9%
Rochester
42.0%
22.1%
Ontario
22.7%
23.2%
Orleans
29.8%
24.8%
Wayne
25.0%
24.2%
Yates
33.3%
29.1%
Source: US Census 2000, SF 3, QT-P 32
TOTAL % living
at or below
Self-Sufficiency
Standard
49.4%
53.1%
49.5%
32.1%
64.1%
45.9%
54.6%
49.2%
62.4%
Although much attention is focused on those living at or below the federal poverty threshold,
it is noteworthy that approximately 25% of FHH with 2 children aged 6 to 17 are living
above the poverty threshold but below the SSS (refer to Table 3.22 Column 3). Women
living between the federally defined poverty threshold and the SSS are not eligible for most
social service assistance and must struggle to make ends meet.
Data are not available to examine the percentages of women not in families who are living
between the poverty threshold and the SSS. However, median income is available to examine
self-sufficiency in FHH not in families with a focus on elder women. The data in Table 3.24
show that the median income for FHH living alone is close to the SSS. Thus, approximately
50% of FHH living alone are living at or below the SSS.
58
Results Chapter 3
Table 3.24 Median Household Incomes for Female Heads of Households Not in Families
Non-Families
FHH all ages
Genesee
$19,968
Livingston
$19,634
Monroe
$22,796
Rochester
$18,296
Ontario
$21,370
Orleans
$18,558
Wayne
$18,134
Yates
$16,675
NYS
$22,917
USA
$21,594
Source: US Census 2000, SF 3, Table PCT42
FHH 65+ yrs living alone
$14,436
$15,847
$16,237
$13,081
$15,822
$14,953
$12,830
$13,982
$14,395
$14,653
FHH 65+ yrs living with
another
$22,500
$24,107
$33,654
$38,333
$15,795
$34,479
$36,250
$23,571
$38,547
$35,808
Taken as a whole, the analyses reveal that ESS is a significant challenge for women who are
heads of households, especially if they are living with children under the age of 18, or elder
women living alone.
A Glimpse of How Poverty Affects Women: Qualitative Data
The aim of the qualitative data is to ascertain how women who receive services, are in poverty, or
who work with women in need of services determine what they think will help them become more
self sufficient. In all three groups, women were asked to identify their primary needs. In the focus
groups comprised of service recipients and service providers, participants were asked to identify
their needs and barriers to fulfilling these needs, as well as services that could help them achieve
ESS. Women in all three groups were asked to define ESS.
Similar topics are consistently mentioned in all three groups. Therefore, data are first
presented in the tables by needs for, and barriers to, achieving ESS organized by the
responses of the three groups of women. Next, the narrative data are organized by topics.
Last, women’s definitions of ESS are presented along with their perceptions of the means
necessary to achieve ESS.
Description of Participants
The qualitative data were collected from participants in three groups. The first is service
providers. One hundred forty three service providers were invited to participate in focus
groups. Eighty-one, or 57%, representing 68 different non-profit agencies participated. Nine
focus groups were held, with 6 to 11 persons in each group. The majority of participants were
59
Results Chapter 3
female with six males attending. The focus groups were held in Monroe, Livingston, Ontario,
Yates, Genesee, and Wayne Counties. This group is referred to as the service provider group.
The second group is 34 women receiving services in three of the seven counties (Monroe,
Ontario, and Yates Counties). These 34 women met in four focus groups. Because of
concerns for confidentiality, women participating in focus groups were not asked to provide
their names or any other information related to residency, ethnicity, or citizenship. This
group is referred to as the service recipients.
The third group is comprised of women who participated in a survey, using a structured format
and one-on-one interviews. Eighty-seven of the 93 women in the survey data were interviewed
while accessing food pantries, shelters, congregate feeding sites and soup kitchens during the
summer of 2003. Six of the 93 women were not accessing services and were interviewed in
Laundromats. Women completing the survey were from Monroe, Genesee, Ontario, and
Livingston Counties. This group is referred to as the survey respondents.
Women from Monroe, Orleans, Genesee, Ontario, and Livingston Counties were represented
among the survey respondents. Characteristics of survey respondents are as follows:
48% urban and 22% rural
61% apartment rental, 18% rented house, 9% owned homes
50% single, 17% married, 18% divorced, 19% separated, and 5% widowed
69% had children (1 = 25%, 2 = 18%, 3=15%, 4=9%, 5=2%)
51% unemployed, 13% seeking employment
22% full time employment, 12% part time employment
29% owned personal vehicle
54% participated in U.S. Census 2000
58% received Cash Assistance and Food Stamps
66% received NY Medicaid
13% lived in Section 8 Housing
19% received Child-care Subsidies
Survey respondents had many characteristics that matched the demographics reported in the U. S.
Census 2000 for single female heads of households and women in male heads of households in
poverty residing in the seven-county area.
Needs to Attain Economic Self-Sufficiency
The rank order of needs identified by survey respondents and the percentage of participants who
identified each need are presented in Figure 3.13. A copy of the survey is found in the Appendix.
60
Results Chapter 3
Figure 3.13 Percentage of Survey Respondents Identifying Each Need (N = 93)
Domestic Violence Shelter
Addiction Tx
Childcare
Self-Esteem
Legal Services
HS Diploma
Job Training
Health Insurance
Health Care
Transportation
Housing
Living Wage
0%
10%
20%
30%
40%
50%
60%
Service recipients also identified what they believed was needed in order to achieve and
maintain ESS. Responses were based upon their experiences and observations during
implementation of programming as well as interactions with women and the community.
Needs identified by members of this group are presented in Figure 3.14.
Figure 3.14 Needs Identified Most Frequently by Service Recipients
Cash
Training
Transportation
Employment
Education
0
5
10
15
20
25
30
35
Service providers also identified what they believed women need in order to achieve and
maintain ESS. Service providers identified at least 40 different needs. The needs most
frequently identified by service providers are presented in Figure 3.15.
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Results Chapter 3
Figure 3.15 Frequency of Needs Cited Among Providers of Services
Health & preventive services
Advocacy
Case management
Education
Training
Child care
Collaboration among providers
Life skills
Connection with community
Support, voice, respect
0
5
10
15
20
25
Barriers to Economic Self-Sufficiency
Service providers and service recipients identified ways in which the social structure creates
barriers to ESS. The survey group was not asked specifically about barriers. Barriers
comprised almost one-half of data identified during qualitative data analysis. Regardless of
the questions asked, service recipients and providers consistently came back to discussing
barriers. This dynamic was as strong among providers as it was among women in need of
assistance. Frequencies of barriers to self-sufficiency identified by the service recipients are
presented in Figure 3.16. Frequencies of barriers to ESS identified by the service providers
are presented in Figure 3.17
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Results Chapter 3
Figure 3.16 Frequencies of Barriers to ESS Identified by Service Recipients
No Transportation
Classism & racism
Complex social system
Assumption of morally delinquency
Stereotypical jobs that pay less
No control over finances
No family support
No affordable housing
Lack of advocacy and respect from DSS
System does not allow assistance needed
0
5
10
15
20
25
30
Figure 3.17 Frequencies of Barriers to ESS Identified by Service Providers
No control over finances
No daycare
Mental Illness
Poor Collaboration
Cultural barriers
Lack of advocacy from DSS
Low self-esteem
Employment with inadequate wage
0
2
4
6
8
10
12
14
16
Services recipients and service providers saw barriers differently. Recipients of services felt
that the “system” does not provide the assistance women need. They also voiced problems
with advocacy and respect from the Department of Social Services (DSS). Housing and the
lack of family support were the second and third most commonly identified barriers.
Service providers saw the issue primarily as one of employment that does not provide an
adequate, or “living” wage, followed by low self-esteem. The lack of advocacy and cultural
barriers were the third and fourth most common barriers identified.
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Topics Most Commonly Identified
This section is organized by topics most commonly identified by study participants.
Economic Factors: Jobs, A Living Wage, And Making Ends Meet
Of the 93 survey respondents, 29% described their financial situation as extremely bad, and 26%
stated it was somewhat bad. Twenty-nine percent said that their financial situation was much
worse this year when compared to last year at the same time. When informed of the income
deemed necessary to be economically self sufficient by the Self-Sufficiency Standard (SSS), 71%
of women said their income was not even close to the SSS and 20% stated their income was far
below the SSS. Fifty-seven percent of women thought that the SSS sounded “about right” in order
to assure ESS, while 22% thought that it was too low.
“I have to suffer it out when I run out of money, I have no one to call. I really budget my
Food Stamps so we don’t run out of food. When you have no one, it is more harder, you can’t
borrow money. I don’t receive no cash money at all.” (Service Recipient)
Some service recipients reported that wages were too low in the jobs they were qualified for
or could get. Therefore, they needed to work at more than one job to support themselves and
their children. Service recipients and survey respondents also mentioned the wage gap
between men and women.
“I need a decent job with a good salary; I don’t know why I don’t get jobs. I apply for 10 a
week. It gets you down. I’m 38 years old and I don’t need more training.” (Service Recipient)
“I’m on the edge of going to work at Burger King, I feel like the little bit of money will help
me go farther, so it will make you feel like you are doing something. Right now I can’t afford
a pair of socks for my daughter, this makes me feel very bad. I ain’t lookin’ forward to it, I
know they [Burger King] will work you like an animal for no pay.” (Service Recipient)
“A lot of women need to work two jobs to make ends meet; this affects the whole family.”
(Service Provider)
“With me and my husband, my husband makes $4.00 too much for us to be eligible for cash
assistance, we have no health insurance. My daughter has partial health care because she
has some problems but we don’t get cash assistance. I do get food stamps. My husband was
in the military. Since getting out we are struggling. My husband makes $174/month at this
one job; he works his butt off part time nights and works during the day at a salt-brick plant
making $6.25 an hour. We tried to get assistance. I was pregnant, had the baby and
immediately got pregnant with another. Birth control pills are $30.00 a month; DEPO is
$75.00. This comes out of our pay every three months. It is too expensive to keep a car on the
road. Because my husband lost his license, our insurance is $217, telephone is $50.00 a
month. I went for 2½ years without telephone or cable, no car, I walk everywhere.” (Service
Recipient)
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Service providers partially attributed the conditions of poverty to lack of jobs that paid a living
wage. Because of this, women who are employed full time were still in need of childcare, medical,
rent, and transportation subsidies. They also identified the lack of employment opportunities as the
primary barrier to achieving ESS among women (this barrier was coded 15 times in provider
focus group data and 17 times in the service recipients’ data).54 Service providers who were living
and working in the more rural counties spoke of lack of transportation to jobs that pay a living
wage and lack of employment opportunities close to home. Urban service providers spoke of
women being segregated into stereotypical jobs that hold little promise to pay a wage that could
result in ESS.
“We see 15,000 women each year; most are 19-34 years of age. They are classified as working
poor. They don’t have health insurance, they don’t have benefits, they cannot afford services, and
they do not qualify for Medicaid.”(Service Provider)
“The distinction between classes and poverty are becoming blurred, you can have it
together, however not really be economically self-sufficient. Suburban hunger is on the rise,
27% of the hungry identified in our Hunger Study were people from the suburbs. Their hours
at work get cut back, they have no benefits, and small business cannot carry the burden
indefinitely.” (Service Provider)
How one felt about a job influenced the perception of ESS. ESS was viewed as depending
upon having work for which one can feel proud, enjoy going to, and feel like a valued
member of the work environment. Like many individuals who work, one participant noted,
Job advancement was never mentioned by service recipients or providers. Recipients and
providers were focused on accessing jobs that pay a living wage. Most conversations were
about the absence of work opportunities that have the potential to raise women out of
poverty. Many service providers shared that they themselves were not economically selfsufficient without the income of a partner or spouse.
“When I graduated from high school, got my degree, I thought that I would make something
of myself. Just has not happened. We need better paying jobs; teenagers in NY City make the
same as what we make here. My husband works at a salt-brick plant he makes $6.25 an hour,
it is very hard, heavy work, we have no insurance, no benefits. If he hurts his back we are
screwed. I am working for $8.56 an hour for ARC but I need $10-$12.00 an hour. I need to
get out of this County.” (Service Recipient)
One need identified by service providers and recipients was child support. Women heads of
households often reported that they experienced erratic or no child support from the fathers
of their children and efforts to “go after” men for support were inconsistent. This was a
source of frustration and stress. As well, it is very time consuming because of the numerous
demands made by the court system.
54
The average number of times a single code was assigned to a particular theme was 5 times in the provider data and 6 times in the
recipient data.
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“This county does not pursue deadbeat fathers. I have never received child support, he might
go to jail in September for 5 days, people give up, they don’t have the time, it is ridiculous. I
would like to meet with the assemblymen.” (Service Recipient)
“This may sound stupid… because he don’t pay child support, he loses his license, he loses
his job because he can’t drive to work, it don’t make sense, I sure won’t get any support if he
loses his license or goes to jail. This is a waste of my time, nothing is ever done.” (Service
Recipient)
One focus group of service recipients (n = 6) was primarily made up of elderly women from
a low-income housing residence. These women spoke of many financial concerns. One
woman defined ESS as, “being able to sleep at night without worrying about paying the
bills.” The women in this focus group were all white. One woman was in her 30s and the
others were elders. All were widowed or divorced. Most had children who provided them a
level of emotional support. However, their children did not have the means or desire to assist
financially. All were receiving Social Security,55 four were receiving food stamps, one
received SSI, and two received small amounts of money from their husband’s pensions (very
small; one shared she received $25.00 a month).
Five of the women in this focus group said that they had little fiscal knowledge and poor
financial decision-making. They stated that they had to depend upon “Free Food Day”,
FoodLink, food stamps, consignment stores for clothing, and doing without things they
enjoyed, like baking and buying Christmas presents for their grandchildren, because there
was “no money left after you pay the bills.”
“Social Security and SSI simply do no provide enough money, if we had money, we would not
be here, that’s for sure!” (Service Recipient)
“The cost of living is more than what Social Security covers, they [government] need to raise
Social Security to match with what it costs to live. We only got a 2% raise when there was a
15% or 20% raise in the cost of living.” (Service Recipient)
One elder woman shared a story about her husband who had been very “controlling” over the
money in their household stating, “When I asked for money for a cup of coffee, he wanted the
change back. He bought everything, all the groceries, even my sanitary pads. He kept all our
financial business in a safe. I got the combination as he was in the hospital dying with cancer
when my son was nine.” (Service Recipient)
This well-spoken woman who was in her 70’s drove her own car and often provided
transportation for other residents to doctors’ appointments and grocery stores. She kept (what
55
Social Security provides 90 percent of income for 41 percent of all older women; 25 percent have no other source of
income. Women represent 60% of all Social Security recipients at age 65 and by 85, 71% of recipients are women (Wilde
and Dagata, 2002).
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appeared to be) meticulous records of every item purchased, its cost, and her current balance.
She remarked that July had been a good month as she had no co-payments to make at her
doctor’s office, she did not need medicine, and her gas and electric bill was low. She was
very proud of her budget book, stating that it helped her to “feel more in control.” Table 3.24
reflects one month of income and expenses reported by this woman and compares it to the
Self-Sufficiency Standard established for one adult in her county. She voiced concern about
experiencing an emergency, particularly something mechanical with her car or a health issue
like getting pneumonia as she has asthma and often experienced breathing problems. With
only $171.79 remaining after the bills were paid, her concern is understandable.
Table 3.25 Budget Record of a Female Wayne County Senior56
Source of Monthly Income
Monthly
Income
Food Stamps
SSI
Social Security
$100.00
$289.25
$292.00
TOTALS
Self-Sufficiency Standard
Monthly Income
Self-Sufficiency Standard Annual
Income
$681.25
$1,242
Source of
Monthly
Expenses
Groceries
Rent
Electric
Phone
Car Maintenance
Car Insurance
Laundry
Hair Appointment
Amount
$141.34
$100.00
$83.00
$30.00
$50.00
$68.12
$16.00
$21.00
$509.46
$ Remaining
at the end of
the month
Annual
Income
$171.79
$8,175
$14,904
Several women spoke of high cost of medicines and worried about meeting their copayments. They also worried about having enough money to cover costs should they
experience an extended illness.
Some states subsidize the costs of prescription drugs according to income level and provide
varying amounts of cost sharing. In the year 2000, New York extended subsidies to single
persons with incomes up to $35,000 and to couples earning up to $50,000. Enrollees are
assessed a premium, and higher-income enrollees pay an annual deductible (Holahan, J,
2002). This subsidy was not mentioned during this focus group. Women spoke of using their
Medicare to cover part of their medical bills. Some mentioned using a prescription card for
medications or having co-payments on some categories of drugs.
56
These figures are based solely upon the self-reported costs for the month of July of one participant in a focus group among seniors living in a lowincome residence. These figures serve as an example to illustrate common income and expenses, not as figures that are can be generalized to the
entire elderly population.
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Housing
Housing and assistance with heating bills were commonly identified needs cited by women
in this study. Women shared numerous stories of having few choices related to where they
live. Affordable housing in safe and healthy areas are very scarce according to services
providers and women in search of homes for their families. Rental housing that is covered by
subsidies is often barely inhabitable, with little in the way of enforced regulations on keeping
housing up to code. Landlords were said to ignore requests for repairs. Pests and unsafe
environments with housing located in areas that are ravished with violence, drugs,
prostitution, and alcoholism were cited as problems.
“DSS does not give you a choice of where you live. As it stands, women are forced to live in
death trap areas where there is no maintenance and they have no power to make requests for
repairs. Drugs and violence are a problem too. With the housing assistance we get from DSS,
the money is inadequate to get into a home that is safe and a decent place to life. If women
don’t get into a place that DSS subsidy covers, no matter how bad it is, you will lose your
benefit from DSS and have to start all over again with another landlord.”(Service Recipient)
“Landlords are not cooperative, we have to fight with them constantly. Big things are wrong
with houses around here, children become ill and sick with lead poisoning. We have
electricity running through lights, plumbing, heating, gas. We are supposed to have
inspections several times a year but it don’t happen. My heater under the sink is not working,
it smells really bad, it could catch on fire. We had constant gas leaks in our basement. Every
time it would rain, we would have to vacate the house. I had to sleep in the car when I was
pregnant and had two kids in the winter. We are always calling the police and fire
department but they never fix anything.”(Service Recipient)
“We have very few housing options out here. I am afraid of being evicted. It is very
frustrating. I cry because I am frustrated. There is no room for my kids’ toys. Everything is in
one room. They make it too difficult.”(Service Recipient)
Transportation
Transportation was one of the most significant needs cited by service recipients. Women who
live in urban areas depend upon the bus system. Late buses, long bus routes, bad weather,
frequent transfers, and inconvenient bus stops cause stress and consternation. Long periods of
time waiting at the bus stop or riding on long routes were often necessary. Other hardships
were related to the cost of bus fare and the burden that bus travel imposed on mothers with
children.
Women in rural areas needed to depend on others for rides. They did not own vehicles or
could not afford to keep their car on the road because of high costs of maintenance,
insurance, and gasoline. Lack of transportation also was cited as a factor in access to jobs in
rural areas where no buses were available.
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“Transportation is a barrier, not having a car; you don’t always get bus passes from DSS.
This makes you at the mercy of DSS for your transportation because they don’t always give
you the bus passes you need. Buses do not run when you need them too, sometimes you get
to an appointment two hours early, or go to a meeting to find that it has been cancelled and
you have to wait a long time to catch a bus to get home.” (Service Recipient)
Health Care and Health Insurance
Women and their children eligible for services are also eligible for NY Medicaid so their
health care visits are covered. In addition, there are children eligible for services whose
parents are not. The population of women with the gravest concern in this area is those that
fall into the range of having an annual income of $10,000 to $35,000. These women are not
eligible for government subsidized health care coverage. Many do not have employers that
provide this benefit and they need to either provide their own health insurance or be
uninsured. Women can accumulate overwhelming debt with one illness. Even if insured,
most have a co-payment. For women who are not earning incomes that meet the selfsufficiency level, these costs can lead to mounting debt.
Job Training
Service providers and recipients identified that money for job-training programs has
decreased. There also was concern about the mismatch between job training and available
jobs. Service recipients cited many instances where they were forced to enroll in a training
program that they did not perceive added to their cache of skills and did not make them
marketable for jobs that were meaningful and paid well. The Community Work Experience
Program (C.W.E. P.) and the Volunteers of America Program are perceived particularly as
useless by a group of young African American women. Not only did these women see this
type of training as a waste of time, they also believed that these types of job preparation
increase the stigma of poverty. They said employers would not offer them jobs even if they
had interned with the business during this program. They sensed that the employer would
rather hire someone other than a person with C.W.E.P. training.
“With the C.W.E.P., a lot of places they send you to that don’t give you job experience that
will lead to a higher paying job. They make you do things like cooking, cleaning, putting
away clothes, that is all they make you do. When I asked to do secretarial stuff they say I
can’t.” (Service Recipient)
“Employers, they don’t hire you. I do what I am supposed to do. They think they are too good
for the C.W.E.P. worker.”(Service Recipient)
Interestingly, according to Oscar Best, the Deputy Commissioner of the Division of Income
Maintenance from the NYS Office of Temporary Assistance and Disability, “The
Community Work Experience Program is a program in which participants are assigned to
public or non-profit agencies that provide them with opportunities to develop, demonstrate or
maintain basic work habits while performing useful tasks.”57
57
Trans. No. 91 INF-13. (2001).
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Women service recipients did not feel that the tasks they are assigned to do are useful.
Instead, they see them as a “place” where they are assigned to work only to meet their
eligibility requirements for assistance. Service recipients perceived the training or programs
to which they are assigned as just something to “do” with women who need help. Their
perception was that such service programs do not really have to deal with the complex issues
that face many women.
“I am a product of programs, I was raised in institutions. I was a functional alcoholic for
years. My life was one day at a time. I ended up in prison, I don’t need a program.” (Service
Recipient)
Service providers talked about the change in welfare provisions that force women to work
rather than go to school. Even those women who qualify for Temporary Assistance for
Needy Families (TANF) must work in government supervised jobs or lose their subsidy.
Therefore, encouraging and assisting women to enroll in educational programs is not
considered a viable option.
“Young women are being encouraged to work rather than go to school. Girls get pregnant at
16. They have the baby. They have to find an apartment in order to get help from DSS, then
they go to work at McDonalds. They live from day to day. She has to work 30 hours a week
so she does not get sanctioned. A legacy is being perpetuated because fewer girls are getting
an education. The more education women have, the higher the likelihood for her children to
go to school. This reinforces keeping women down.” (Service Provider)
“I wanted to go to school but they threatened to take my benefits if I did not work, they would
sanction me. It seems that they want to keep a person stuck. We need child-care, treatment
for addiction, school, and work.” (Service Recipient)
Education
The 93 survey respondents answers to questions about their own educational attainment
reflect the U. S. Census 2000 data. Seven percent (6 women) reported that they had only
completed grade school, 25% (23 women) reported that they had some high school but did
not complete and 31% (29 women) had completed high school. Sixty-six percent of this
convenience sample had high school education or less. Eleven percent had completed
college. Figure 3.18 illustrates levels of education among the women interviewed.
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Figure 3.19 Education Levels Reported By Survey Respondents (N=93)
Completed College
Some College
Completed Trade School
Some Trade School
Completed High School
Some High School
Completed Grade School
Some Grade School
0
5
10
15
20
25
30
The most commonly cited need among service recipients was education, tutoring, and
competency in the English Language. Service providers named education as one of the
primary needs of women if they are to achieve and maintain ESS over time. The education
theme occurred 15 times in transcripts from provider focus groups and totaled at least 49
lines of information.
“Education is HUGE!! Change in the educational system is needed for moms at home. How
can we get the educational institutions to do distance learning? Education is the ladder out
of the basement. Moms will no longer be chained to their houses if properly educated.”
(Service Provider)
“Can you be economically self-sufficient if you are not linguistically self-sufficient?
Empowered women are linguistically competent.” (Service Provider)
“Education is the equalizer.” (Service Provider)
Availability of GED programs, particularly in Monroe County, were reported “easy” to
access as there were numerous opportunities for women to participate in programs.
Community Colleges provided GED classes in the rural counties.
“It is really easy to get your GED; there are lots of schools.” (Service Recipient)
The barriers to education most often identified by service providers were lack of desire on
the women’s part, poor timing in terms of stress and chaotic life circumstances, undiagnosed
learning disabilities, lack of accessible, convenient, consistent transportation to classes, and
lack of safe child-care during the hours required to attend class. Time to complete the course
work, child-care, cost of materials, and transportation were barriers identified by service
recipients. Additionally, some women felt unable intellectually. One woman expressed it this
way:
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“I tried to get my GED but everything had changed so much since I went to school. The
questions were so confusing, I didn’t know which answer to put down. I just could not
understand what they were asking me.” (Service Recipient)
Providers and recipients had different opinions about the level of education perceived to be
necessary for gainful employment. Providers most commonly talked about college or trade
school as the point of entry into occupations that pay well and provide benefits. Participants
debated the credibility of the GED and the likelihood that it would open doors to jobs that
pay a living wage. Some women perceived that there was stigma attached to having a GED
rather than a high school diploma. However, most saw them as essentially the same.
“If you get a GED, you don’t necessarily need to get an Associate Degree. You need to learn
a skilled trade. This can lead to a $20 an hour job.” (Service Recipient)
“You need to get a high school diploma; people realize that you got off track if you have a
GED.” An older woman replied, “I don’t think it makes a difference, life is what you make
it.” (Service Recipient)
“High school diploma is better but the GED can get you to college. I got a GED. It all
depends on what you do after you get your GED. I know people that have GEDs that go on
and get their Master’s. I feel that, if you want a good job, you at least need to have an
Associate Degree.” (Service Recipient)
During collection of the survey data, an interesting discontinuity was observed. Some women
responded “no” to the yes/no survey question related to needing a high school education. Yet,
when asked, “What kinds of help do you think you need in order to get to the place that you
can take care of yourself and your family financially?” education ranked equally with a job
that pays a living wage. Examples of things women said that they needed are a GED,
computer training, a college degree, training in budgeting, and job training.
Service recipients actually defined ESS as “having enough education and skill to be
successful.” They strongly agreed with providers that education was key in achieving ESS.
Education was mentioned 32 times under the “What Women Need in Order to Achieve ESS”
category and totaled at least 81 lines of data in transcripts of the interviews and focus groups.
Some women felt strongly about the importance of being competent in English. There also
were women who perceived a disconnect between education and jobs. As discussed
previously, women felt that education alone was not enough in that a woman can be educated
and still not get a job that pays a living wage.
“You need increased educational levels to get a better job. Even with higher degrees, women
make 77 cents on the male dollar. Women want jobs that are rewarding, need to encourage
them to get at least a two-year degree.” (Service Provider)
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Self-Esteem
Service providers perceived that women don’t always access the services available or are not
successful in acquiring and keeping jobs because they have low self-esteem. It appeared that
providers understand this problem at the individual level, that is, a problem of personal
inadequacy. Their perception was that women feel unsuccessful because of past failures and
because they “look” different (e.g., bad teeth, poor clothing).
However, most of the service recipients rejected the idea that self-esteem was an issue. They
saw the material barriers to survival (e.g., lack of living wage, punitive policies, etc.) as the
major problem. While some women did cite low self-esteem as a problem, these women
tended to have histories of abuse and drug and/or alcohol addiction.
One of the primary service provider definitions of ESS involved having a feeling of selfworth, being confident, and having positive self-esteem and self-efficacy. Providers believed
that having the perception that you are worthy and were an asset [to the community] defined
what it meant to be economically self-sufficient. This attitude was a strong theme.
Over 25% of service providers from both urban and rural settings stressed the need for selfesteem building among women struggling economically. However, given the information
shared by women experiencing the daily struggles of being poor, and providers’ observations
of this process, the need for self-esteem building was questioned. Lack of self-esteem may
not be an accurate characterization of the problem.
Based upon the numerous conversations with providers and recipients, the barriers that poor
women confront every day, from lack of subsistence to demeaning social attitudes, may be a
more fundamental problem. It is difficult for women to feel good about themselves, and have
a sense of well being, if they worry about being evicted, or if there is no one to watch their
children when they are sick, or that health care bills are going to swallow up their entire
monthly salaries. According to women in the focus groups, the lives of the working poor may
be considered economically insufficient and do not
promote healthy self-esteem.
“My kids were hungry and thirsty over the 4th of
July, I went from Thursday to the following Friday
with no money and no food because something was
wrong with the computer at DSS. My one-year-old
was saying, Mommy, I hungry, I thirsty, this made
me feel very bad because I could not feed my
babies.” ((Service Recipient)
“Classification of people is a problem. If you can’t
write and read English good, you are no good. Right
now I am classified a ‘Zero’ by DSS because I am
not proficient in English, a… ‘Zero’; this does not
make me feel good?” (Service Recipient)
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Survey respondents were asked whether they perceived a “need” for self-esteem building in
order to achieve ESS. Most, 84%, responded “no.”
“Self-esteem ain’t got nothing to do with it. How much self-esteem do you think it takes to do
what kids do? Ask any of them in gangs, they will tell you they have lots of esteem.” (Service
Recipient)
Cultural Barriers
Cultural differences between service providers and recipients were cited as problem areas.
Providers and recipients cited Service providers’ lack of knowledge about what it means to
live in poverty, misunderstandings or lack of knowledge about African American and
Hispanic culture, discrimination, and the scarcity of bilingual service providers as barriers.
However, sensitivity to the difficulties that women in need encounter and respect and
compassion was highly evident among service providers participating in focus groups.
“They need to hire Black people, we are pushed to the side, give us a job, people don’t give
us a chance.” (Service Recipient)
“Migrant women, 90% of whom are Mexican, 10% are Guatemalan and Haitian, there are
8,000 workers, among these women, the notion of economic self-sufficiency is not even
considered. Women do not leave their husbands and they do not expect to be independent.
They have significant language barriers. Migrant workers are NOT recognized/considered in
any type of economic decisions.” (Service Provider)
Child-Care
For reasons that are not wholly explained by the data, many women do not use child-care
despite the fact that many of the agencies assist women in finding child-care. This could be
because finding child-care that is responsive to one’s situation may be very difficult or
impossible. Some possible reasons for the difficulties with child-care include:
a)
b)
c)
d)
e)
having a job that requires working hours outside the dominant work day (9-5),
needing to juggle two or three part time positions,
needing to arrange for the care of sick children,
needing to arrange for the care of school age children, and
lack of incentives for child-care providers to accommodate erratic and unpredictable
schedules.
“Day care is too full or over priced. I cannot find an opening. You cannot pick and choose
the days you work. You have to pay all week long. There is little availability for the night
shift hours.” (Service Recipient)
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Domestic Violence Shelter
At several points in the discussion of housing, the subject of a need for more shelters for
victims of domestic violence arose. This was particularly true among women with children.
Service providers mentioned that the difficulties with navigating the social service system, as
well as lack of respect from some providers, resulted in women choosing to tolerate abuse
rather than experience the humiliation and shame associated with asking for assistance.
“Women are afraid of homelessness and not being able to care for their children. Fiftypercent will go back to their abusers.” (Service Provider)
Access to Services and Accessing Public Subsidies
Accessing services was a topic discussed in all three groups. Data from service providers
suggest that poverty is commonly experienced among female heads of households with
children. Dialogue during focus groups affirmed that there were many women in need of
assistance and services during 2003. There were no observations shared that programs were
having difficulty with enrollment, although attrition was a problem cited by recipients and
providers. Service providers frequently talked about extraordinary caseloads, increased
paperwork, and inadequate time, money, and energy to meet the high demand.
Service recipients spoke of up to a 12 month waiting period for Section 8 housing, long lines
on food stamp recertification day, crowded waiting rooms, brusque case managers, lost
paper-work, and few phone messages returned.
“Navigating the system is difficult. When the welfare system changed, DSS got consolidated,
offices closed, and people lost their jobs. Now there are fewer workers to do the same amount of
work.” (Service Provider)
“We have to deal with it [the system], because we have children; we have to go through it.
Life is not fair, I just don’t know,…where does the struggle end? I try to get a job, seems you
are just not getting in, the harder you try, the worse it is. They say you don’t care, that you
are worthless.” (Service Recipient)
“DSS is a waste of time. I leave a message and maybe after 17 days they will call you back.”
(Service Recipient)
Services were described as fragmented. Most women need multiple services. For example, a
women receiving TANF also may need help with heating bills, transportation, clothing for
job interviews, and/or child-care at odd hours. One agency will typically address only a few
of these needs. As a consequence, women often are not aware of the multiple services that
may be available. Agencies do not always have the ability or awareness of all the possible
sources of help. Even when women are aware of multiple resources, the logistics of arranging
the time to visit three to four different sites can be problematic. Service organizations can be
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far apart, and often women have children they must either take with them or find child-care.
Limited collaboration and communication among service providers and lack of information
and/or willingness to share information about services can keep women from accessing all
the services available to them.
When asked about whether or not they utilized community-based programs or services provided
by Catholic Charities, Cornell Cooperative Extension, the Family Resource Center, and Legal
Services of the Finger Lakes, women in the survey overwhelmingly women responded “no.” Even
the well known programs of Women, Infants, and Children (WIC) and Planned Parenthood were
used by only 25% and 10% of the women, respectively. Survey respondents reported that they
most commonly clipped coupons, skipped meals, and did without things that they needed in order
to make ends meet. Service recipients also said they used these types of money saving techniques.
Women went without under-garments, clothes that fit properly, make-up, and personal hygiene
items, such as deodorant and shampoo, when money was inadequate to cover living expenses.
Forty-one percent of the survey respondents perceived that the assistance and resources available
in their communities were somewhat inadequate in assisting them meet expenses and provide for
basic needs. Nine percent thought assistance and resources were extremely inadequate and 42%
perceived that their community’s assistance and resources were adequate. Only 20% of survey
respondents perceived that accessing needed assistance and services in their communities was
“fairly easy”, 68% found accessing services “somewhat to very difficult”, while 7% thought
access was “impossible.”
Most of the survey respondents said that they
would not call the Department of Social
Services or a community based organization for
assistance. They preferred calling a family
member or a friend if a crisis occurred or
assistance was needed. Seventy-three percent
stated that they would not look to their
community church for assistance. Recipients
felt that accepting help from DSS meant
surrendering all personal privacy. They “tell
you who you can be friends with and who you
can’t be friends with.”
Problems cited by service recipients were that there is frequent turnover in case managers, that
staff are over-worked and burned out, that staff have bad attitudes towards recipients, and that
their focus is not on the needs of women. Women felt that they have to fight for what they get.
Forty-three percent of survey respondents perceived that they were treated hostilely or rudely by
workers at the Department of Social Services. Only 8% perceived that providers in community
based organizations treated them disrespectfully. No data are available directly from DSS staff.
“I gave up calling DSS workers. I had four workers in a week. I am at the point of you
contact me. Workers get changed a lot, you don’t know who your worker is.”
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“The Department of Social Services demeans and talks with disrespect to women. Women
would rather go home to their abuser than be abused by people in the service delivery
business.” (Service Recipient)
“You are not taken seriously at DSS. They belittle you. There needs to be more work on the
workers. Women go out there and get discouraged. If you bring your worker it all changes. If
you don’t, there is no empowerment, it is like you are dumb, you can’t do it.” (Service
Recipient)
“They are rude, they treat you like a ball of dirt, they don’t return your calls, they get
frustrated. They take it out on us. They have a lot of people they are dealing with, they get
mixed up, we have to consider this.” (Service Recipient)
The extent to which non-profit providers help women access other subsidies was not
addressed. However, the fragmentation of services, lack of networking between services,
unawareness of other services on the part of providers, and the reluctance on the part of some
providers to inform women about alternate services would indicate that women can not be
assured of coordinated referrals.
Most of the non-profit organizations and their service providers participating in this study
work with individuals seeking assistance or in the immediate environments of woman where
they have a measure of control. The data show that there are only a few agencies that work at
the systems level and to actively advocate for changes in policy at the local, state, or federal
level. These agencies advocate for jobs that pay a living wage or a change in the organization
of work and family. Similarly, there were little data that indicated that providers thought
changes in societal beliefs, values, or cultural structures (e.g., the organization of work and
family) needed change or were within their scope of influence.
Providers frequently spoke of the need for resources that assure longevity of programming,
including funds for operational costs. They spoke of needing more opportunities to
collaborate on grant writing. Providers suggested that what is needed is more funding
directed toward strategies that “work” as opposed to “new hair brained ideas.”
When providers were asked about “what works”, there was a lack of clarity on specific
interventions. Most service providers acknowledged that because of limited funding and
program evaluation, implementation strategies are not consistent or carried out over a long
enough period of time to measure long-term outcomes. The extent to which these services
and supports actually impact women’s ability to achieve ESS has not been consistently
measured in the community. Providers speak of success stories, sharing instances where
women furthered their education, got out of debt, bought their own home, found work, and
became better mothers. However, outcome data that measures the extent to which a collection
of community resources impact ESS specifically are not readily available.
Most service providers were confident that assistance and resources were available in most
communities, although providers from the larger counties had a more comprehensive list of
services and supports for women. However, the complexity and number of service providers,
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attrition of staff, and large caseloads interfered with providing a seamless connection with the
services that women need in the more densely populated areas. Connecting with community
services was a geographic and transportation challenge in the rural counties.
Service providers shared concerns about lack of communication, trust and turf issues, and the
inability for most providers to stay knowledgeable about the variety of resources and
assistance that is available. Service providers realized the importance of referral and
recognized that helping women connect to the community was a true challenge as they had
difficulties keeping abreast of agencies, services available, and the ever changing faces and
names.
“Agencies need to know what each other are doing. They need to get information to other
agencies and remind front line staff so they are aware of up-to-date information.” (Service
Provider)
Connection to community and knowledge of community services was a topic addressed
primarily by service providers. Their perspectives were at the individual level, with a belief
that being connected to the community is vital if a woman is to be supported as she works
toward becoming economically self-sufficient.
Support, a Voice, and Respect
Most of the service providers focused on what women need at the personal or individual
level.
“The do it yourself mentality only works if the woman is on the ball. Many fall through the
cracks.” (Service Provider)
Several statements indicating what providers thought about the importance of support and
respect include:
“Women need to be internally empowered, to be able to give back, have a feeling of positive
self-esteem and self-worth.” (Service Provider)
“Women need to have caring, supportive relationships with service providers.” (Service
Provider)
“Service providers need better training, specifically strength-based, that helps them to work
with poor people, those with disabilities. They need training to enhance staff ability to
empower women.” (Service Provider)
Providers from rural and urban counties shared concerns related to the lack of respect that
women in need of assistance endured. They stated that discrimination, stigmatization,
excessive workloads, and lack of training in strength-based approaches lead to interactions
where women in need of assistance are belittled, disempowered, and treated rudely and
unfairly.
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“Women are reluctant to ask for help because they are intimidated. They have pride. They
have very few skills and knowledge and a lot of fear and pride. When women go for help,
often they can’t get it.” (Service Provider)
“Social services’ offices need people skills. They treat poor women very badly, increases
their sense of worthlessness.” (Service Provider)
“The Department of Social Services is very judgmental. They believe that being poor is a
way of life so their expectations are low so women can’t see the point in trying.” (Service
Provider)
Observation of service providers during the focus group meetings indicated that supporting
and respecting women was strongly valued. Many of their programs supported women in a
variety of ways, from holistic case management and relapse prevention to education on
reproductive health, financial management, interpersonal relationships, parenting, childcare,
and transportation. Providers spoke of meeting women where they are and supporting them
in their efforts to meet their goals. This philosophy could be summed up as “giving them a
hand up rather than a hand out.”
“Support, start where they are and then work with them, assist them to make goals, keep
their expectations high, help them to learn about proper hygiene and how to fix a meal, eat
breakfast, do dishes and to develop their social skills.” (Service Provider)
An attitude shared by several service providers was that they expected success from their
clients as opposed to assuming that they ultimately will fail. A particularly nurturing program
spoke about starting with the woman herself, “working from
the inside out,” and confronting internal factors that might
predispose the woman to poor decision making. The term
“advocacy” was mentioned frequently and perceived as a
core need for most women struggling with ESS.
“We do not get money from major funders. What we do does
not fit into education, job readiness, doesn’t fit, what we do…
what we do cuts to the core. We help women find their
identity, that identity, choice, the very essence of who she is.”
(Service Provider)
“These women need support over a lifetime.” (Service
Provider)
They believed that establishing caring relationships with women, supporting women over
time, and using a holistic approach that considers women’s developmental stages and life
circumstances make a positive difference among some of their clients.
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Life Skills
Life Skills was a topic addressed by service providers. A number of the agencies and
organizations implemented their programs around developing life skills. These skills
included parenting, budgeting, grocery shopping, planning meals, running a household, and
dealing effectively with people in the community, such as their caseworkers and their
landlords.
Providers believed that, while incorporating life skills’ development into programming is an
excellent idea, the feasibility and cost sometimes prohibit its inclusion in all programming
scenarios. Providers also identified concrete needs such as education, housing, work
preparation and job training, healthcare, transportation, a job with a living wage, and
affordable, safe child-care. However, these needs were not emphasized at the same level
among service providers as they were among service recipients and survey respondents.
According to service providers, other services provided included assistance with accessing
supports and services such as child-care subsidies, emergency services, shelters, housing,
health care, treatment of addiction, mental health services, emergency food sources, legal
services and transportation.
Social Support
Social support is a critical factor in achieving self-sufficiency. However, social instability is a
major issue for women in their struggle for ESS. Illness (self or family member), loss of
child-care, family members in trouble (e.g., parent, child, or other relative addicted to drugs,
alcohol or in trouble with the law), returning husbands or boyfriends, the necessity of moving
due to job loss or unsafe living conditions were all cited as threats to stability and thus threats
to ESS. There is so little margin in income versus expenses that any change in women’s
social environments threatens their ability to make ends meet. It also can activate a domino
effect in that it takes months or longer to recover. Social supports that provide sick childcare, a loan, safety from abusive family members, cash for medicine, etc., are just some of
the things that can be the difference between staying afloat or sinking.
Informal social supports, such as free child-care by
a family member or financial assistance in times of
emergencies, can significantly affect the ongoing
economic stability of the poor family. Families that
have access to these informal social supports seem
to do better than those who do not. Those who do
not live near extended family members, women
who have exhausted or never had informal social
supports, and generations of families who have
little to give each other struggle to provide the
kinds of help that economically “successful”
families can provide.
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While a number of providers talked about the importance of social support, only a few
women shared information that describes meaningful help designed specifically to assist
women build social supports. Wilson Commencement Park, Sojourner House, Step-by-Step,
and the Rochester YWCA were examples of agencies that provide significant assistance in
this area. The combination of housing, child-care services, employment services, support
groups, and a variety of other services plus, in the case of the YWCA, a stated goal to “strive
to build support systems” sets the stage for enhancing social support systems.
Cash
Service recipients spoke openly about the need for cash. They needed cash for security
deposits, cash to buy personal hygiene items, cash for the parking meter, and cash to pay
debts. The absence of cash created significant difficulty for women.
Definitions of Economic Self-Sufficiency
Overall, it was difficult for service providers to separate the meaning of Economic SelfSufficiency (ESS) from barriers to ESS and what women “need” in order to achieve it. There
was a wide range of thoughts about what constitutes ESS among providers and recipients. At
one end of the continuum were women who thought that ESS meant having enough money to
live a full life and be able to purchase everything that one needed. To be economically selfsufficient meant not needing to rely on friends or family for anything. At the other end of the
continuum were women who felt that the definition of ESS should be broadened to include
people who would always require government funds, such as people with disabling mental or
physical problems for whom working steadily is impossible.
Service recipients defined ESS as having enough money to cover expenses and no longer
requiring assistance from anyone. They were very clear about aligning their definition of ESS
with independence.
Service providers defined ESS as having a connection to a source of income and knowing
where to send one’s children to school and what kind of health care services to use. ESS also
meant sufficient resources to allow choices about where and how to live. For others, ESS
meant having enough money to get through an emergency, for example, repairing a car or
providing child-care for a sick child.
Many service providers considered ESS more as a process than a steady state. They thought
that we are all striving to be economically self sufficient, with some people being further
along toward meeting that goal than others. With a few mishaps, that is,, job loss, divorce, or
disability, economic instability is just over the horizon for many women. Service providers
commented that they themselves, and others they know in the service field, do not make
enough money to consider themselves economically self-sufficient. It was clear that they
were not exaggerating and were barely paying their bills. Some were using food pantries.
One mishap, such as a car accident, an illness in the family, or having a car repossession
leading to loss of a job, would be enough to make their economic situation very unstable.
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For other women it was impossible to talk about ESS without talking about personal efficacy.
They saw them as inextricably linked. There was a belief that one must have a certain level
of personal efficacy to be ESS and without a certain level of control over one’s choices
personal efficacy is severely compromised. The idea of ESS was inextricably connected to
social structures and personal resources and capabilities.
Some providers spoke eloquently about the complexity of attaining ESS.
“In reference to economic self-sufficiency, some of the short sightedness exists both at the
government level and at the private level. What does it really take for substantive change in
an individual? Very cursory examinations occur typically, where people say, just get them a
job; it is not that easy. If a person cannot fill out an application, how can they get a job?
Poverty relates to social isolation, classes…people just don’t have a concept the degree of
stress that poverty has on a woman and her family.” (Service Provider)
Service providers defined ESS as the ability to pay bills, save money, and pursue goals, such
as owning a home and car, and having a good education. They also saw ESS as the ability to
respond to emergency needs, without falling into a “hole.” Several interviewees suggested
that ESS does not necessarily imply being off TANF or other forms of public assistance, but
managing these sources of income well. Service providers felt that some individuals will
never be able to be ESS in the strictest sense. Having choices, being able to choose where one
lives, being in a safe environment, and having the skills to manage money and life were all
seen as a part of being economically self-sufficient.
Another factor that came up often was the necessity of stability. Attaining ESS was seen as
nearly impossible if one has so little margin that any misfortune would destabilize the
situation. Destabilizing events that where mentioned several times were:
-
loss of job
change in job schedules -- many women have two or three jobs; if the schedule
changes on one they can’t accommodate the other
illness, oneself or another family member
loss of child-care
loss of a car or need for expensive repairs
return of a violent boyfriend or husband
return of a needy older child
a bureaucratic mix-up at the Department of Social Services or Medicaid offices
eviction due to development, rowdy relatives, or uncooperative landlords
A continuum exists from the individual level to the systems level in what is needed to attain
ESS. Personal efficacy only goes so far if the social structure does not cooperate by
providing jobs that organize work to sustain family and community.
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The following is a summary of key definitions of ESS from the view of service providers. In
general, providers defined ESS as a process in which women move away from requiring cash
assistance, however, they retain other benefits such as food stamps and Medicaid.
•
This interpretation varied from retaining some government benefits to being totally free
of benefits. However, it was weighted on the side of retaining some benefits.
•
Having employment that would pay enough to meet basic needs was identified as critical
to self-sufficiency.
•
Change was expected at the individual level where proximal process interventions attend
to the woman and how she interacts with her immediate environment. Proximal processes
are the essence of what occurs in the course of everyday activities by the woman and her
family.
•
Providers saw the need for education, job skills, life skills, parenting skills, and budgeting
and shopping skills. They also talked about the importance of being “self-determined”
where each woman has control over the events of her life and can make her own
decisions and choices. For example, if something breaks, she has a cash reserve to fix it.
Providers also mentioned the need for “willingness” on the part of a woman in need to
make the required life changes and that she builds self-esteem. A barrier was seen as the
“context” of a woman’s upbringing, where she did not have role modeling for strong
work ethics.
•
Providers recognized that consideration must be given to the cultural values’ beliefsystem where culture plays an important role in a woman’s perception of ESS. One
example given was that some Hispanic women may choose to depend on the male for
support and may have few aspirations of independence. Thus, she might think about selfsufficiency in a different way than a woman striving to be totally independent.
Summary of Barriers, Needs, and the Definition of Economic SelfSufficiency
In order to categorize the data, responses were divided into four key areas: 1) Barriers, 2)
Needs, 3) Definitions, and 4) Supports. These four key areas were further synthesized at the
ecological level to reflect a woman’s environment according to her stage of human
development.
Bronnfenbrenner’s theory of ecology of human development describes a series of progressive
accommodations made throughout the life span that involves the individual and the
environments in which she or he lives An assumption of this theory is that people are
affected by relationships within and between their environments as well as the larger social
contexts in which these environments are embedded.
The ecological environment is a nested arrangement of structures, each structure contained
within the next, and includes the following four key ecological levels.
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1)
2)
3)
4)
Individual level: the micro- or personal level;
Environmental level: individuals’ interactions with the immediate environment;
Systems level: the systems within which persons interact to meet their needs;
Cultural, beliefs, and value level: the blueprint of society that determines how persons
are viewed within the society.
The matrix presented in Table 3.25 outlines how service providers and service recipients/
survey respondents responded to questions related to Barriers to ESS, the Definition of ESS,
what women need in order to achieve ESS, and the Supports and Resources that are needed
in order to assure ESS among all women. This matrix is listed in order of priority with the
theme occurring the most frequently listed first.
Table 3.26 : Cross Comparison of Barriers, Definitions of ESS, Needs, and Supports
Reported by Service Providers (n=81) and Service Recipients and Survey Respondents
(n=130)
Ecological
Level
External
Environment
Individual
Systems
Ecological
Level
Service Providers
BARRIERS
No employment with a living wage
that women can access and/or have
the appropriate skills for.
Feeling unsuccessful because of
past failures, low self-esteem.
Systems
Systems
Systems
Individual
Lack of advocacy and respect from
DSS, inconsistent case managers,
needs are not the focus, workers
have poor attitudes and are burned
out, women have to fight for the
services they need.
Cultural Barriers: discrimination,
language barriers, few bi-lingual
workers people are misunderstood.
Lack of collaboration and
communication among service
providers, providers do not know
what is available and/or do not
refer appropriately.
NEEDS
Support, a voice and respect
External
Environment
Level
Individual
Connection with the community
and a working knowledge of
existing services.
Life Skills
Systems
External
Environment
Increased collaboration among
service providers.
Cultural,
Value, Belief
Systems
External
Environment
Individual
Individual
External
Environment
External
Environment
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Service Recipients
BARRIERS
Public Policy that is punitive, does not
allow for the assistance women need to
survive, lack of equity in service delivery.
Lack of advocacy and respect from DSS,
inconsistent case managers, needs are not
the focus, workers have poor attitudes and
are burned out, women have to fight for
the services they need.
No affordable housing that is suitable for
living and/or homelessness.
No employment with a living wage that
women can access and/or have the
appropriate skills for.
No family support, being a single mother,
divorced, widowed and/or parent figures
to grandchildren.
NEEDS
Education & tutoring, competency in
English
Employment that has the potential to
move women toward ESS.
Transportation
Training
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Individual
Individual
Individual
External
Environment
Systems
DEFINITIONS of ESS
Feeling selfworth/confident/positive selfesteem and self-efficacy
Adequate income for housing
Individual
External
Environment
Individual
Feeling at peace, feeling safe
physically and emotionally
No need for TANF
Individual
SUPPORTS
Funding to sustain programs that
actually help women achieve ESS
External
Environment
DEFINITIONS of ESS
Have enough money to cover expenses,
having assets, having savings.
No need for TANF
Money to cover emergency expenses.
Having choices (where to live, where to
work)
SUPPORTS
Resources: a) education, b) training, c)
literacy, d) job readiness, e) prevention
and f) financial literacy.
Responses are listed in order of frequency within each category enabling a comparison of
issues addressed most often by service providers to issues addressed most often by service
recipients regarding ESS. This table highlights where service providers feel they have the
greatest impact, barriers they work most fervently to overcome, and possible bias concerning
what they think women need to achieve economically self-sufficiency. Likewise, it highlights
what service recipients believe are the most significant enablers and barriers to achieving
ESS.
For example, the barrier most frequently identified by service providers was at the external
environmental level, that is, women cannot access and/or lack the skill to access, the services
needed for job training and gainful employment. Alternatively, service recipients perceived
their greatest barrier as beyond their immediate environment, focusing at the systems level,
that is, public policy. Recipients described receiving misinformation or incomplete
information, a slow response to telephone messages, and the inability to form relationships
with caseworkers as case workers were frequently rotated or left their positions. Women
perceived that they were considered “stupid” and that their credibility was often called into
question.
In the “Needs” category, data indicate that service providers most frequently focus on women
in need from an internal or personal perspective, placing humanistic needs, that is, support, a
voice and respect, connection with the community, and life skills. Next, they focus on
programmatic needs that enable the continued delivery of services. Service recipients most
frequently focus on tangible needs, that is, the need for education and competency in English
(referring to adequate literacy levels as well as English as a second language), employment,
transportation, and training.
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NEEDS
Service Providers
• Support, a voice and respect
•
• Connection with the community
and knowledge of services
• Life skills
• More collaboration among
service providers
•
•
•
Service Recipients
Education & tutoring, competency in
English
Employment that has the potential to
pay a living wage
Transportation
Training
Differences also are noted in the second most frequently identified barrier, where providers
perceived recipients as having low self-esteem, an individual level factor. Providers referred
to helping women to become “empowered,” “feel better about themselves,” and “gain
confidence.” Alternatively, the barrier identified second most frequently by service
recipients was at the systems level and stemmed from the lack of respect they received,
particularly among staff at the Department of Social Services. Recipients perceived that
accessing needed services was a demeaning experience that involved “fighting hard for what
you need.” The experience of “constantly being beaten down by the system” did not promote
positive self-esteem or self-efficacy, rather it fostered resentment, frustration, and anger.
“You have to fight social services, really fight a lot and advocate for yourself. It is way above
what most people can do. I don’t have the self-confidence. I struggle because I am not a
fighter. It has to be survival of the fittest. I am not the fittest.” (Service Recipient)
Service providers did perceive that a primary barrier to ESS was the lack of employment that
paid a living wage and the lack of advocacy from the Department of Social Services. These
barriers match in comparing service recipients and service providers.
BARRIERS
Service Providers
• No employment with a living
wage
• Low self-esteem
• Lack of advocacy from DSS
• Cultural barriers
• Poor collaboration among service
providers
Service Recipients
• Poor access to the services
needed
• Lack of advocacy from DSS
• No affordable housing
• No employment with a living
wage
• No family support
The implied priorities may have implications for how directly service providers apply their
energies and influence in ways that service recipients view as most beneficial.
For example, a service provider may see the key barrier to ESS as the lack of employment
(regardless of wage or benefit levels). Energy is placed on getting the woman into a job-
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training program and then into a job. The service recipient sees her primary barriers as lack
of resources to pay for heat and electricity, inadequate cash for a security deposit in a safer
neighborhood, or access to appropriate school supplies and clothing for her children.
The same level of differences in perceived priorities is observed when defining ESS. The
differences in definitions of ESS may be even more powerful as service providers associate
self-esteem with women’s ability to achieve ESS, whereas women in need do not consider
self-esteem in their definition of ESS, but rather mention the tangible aspects of selfsufficiency, that is, to have enough money to cover expenses, have assets such as a home
and/or a car, and being able to save money. This definition is distinctly different from the
need for building self-worth and self-esteem. Fifty percent of the service providers’
definitions had to do with internal, intangible aspects; however service recipients did not
refer to these aspects at all. Both service providers and service recipients perceived being
independent of social services as an important aspect of ESS. Service recipients rated this
indicator much more frequently than did service providers.
DEFINITION of ECONOMIC SELF-SUFFICIENCY
Service Providers
Service Recipients
• Feeling positive self esteem
• Having enough money to cover
expenses, to have assets, money in
the bank
• Adequate income for housing
• No need for TANF
• Feeling at peace, feeling safe
• Money for emergencies
physically and emotionally
• No need for TANF
• Having choices
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92
APPENDIX A
ACCOMPANYING TABLES OF
CHAPTER 3
RESULTS
Tables in the Appendix are labeled with the same number as
the table they accompany in Chapter 3, Results, with the
addition of the suffic “A.” For example, Table 3.1A in the
Appendix accompanies Table 3.1 in Chapter 3.
Not all tables in Chapter 3 have accompanying tables in the
Appendix. Therefore, tables in the appendix are not ordered in
sequential numbers.
93
Table 3.1A
Numbers and Percentages of Females by Age: By Town
Genesee
Alabama
Alexander
Batavia city
Batavia
Bergen
Bethany
Byron
Darien
Elba
Le Roy
Oakfield
Pavilion
Pembroke
Stafford
Tonawanda Res
Livingston
Avon
Caledonia
Conesus
Geneseo
Groveland
Leicester
Lima
Livonia
Mount Morris
North Dansville
Nunda
Ossian
Portage
Sparta
Springwater
West Sparta
York town
Monroe
Brighton
Chili
Clarkson
East Rochester
Gates
Greece
Hamlin
Henrietta
Irondequoit
Mendon
Ogden
Parma
Penfield
Perinton
Pittsford
Riga
Rochester
Rush
Sweden
Webster
Wheatland
Total
Female
Population
30,554
1,032
1,243
8,386
3,033
1,529
872
1,270
1,469
1,203
3,962
1,625
1,209
2,272
1,257
192
31,999
3,397
2,283
1,140
5,538
771
1,125
2,326
3,706
2,271
3,015
1,524
351
421
782
1,169
615
1,565
380,858
18,644
14,164
3,118
3,540
15,275
48,815
4,697
18,525
28,144
4,320
9,352
7,511
17,968
23,843
14,504
2,733
114,874
1,673
7,070
19,414
2,674
% of Total
Population
51%
50%
50%
52%
51%
48%
50%
51%
48%
49%
51%
51%
49%
50%
52%
55%
50%
51%
50%
50%
58%
20%
49%
51%
51%
50%
53%
50%
45%
50%
48%
49%
51%
50%
52%
52%
51%
51%
53%
52%
52%
50%
47%
54%
52%
51%
51%
52%
52%
53%
50%
52%
46%
52%
51%
52%
# females % of females # females % of females # female % of females
< 18 yrs
< 18 yrs
18-64 yrs. 18-64 yrs.
65+ yrs.
65+ yrs
7,645
25%
17,850
58%
5,059
17%
299
29%
601
58%
132
13%
359
29%
708
57%
176
14%
1,917
23%
4,596
55%
1,873
22%
704
23%
1,817
60%
512
17%
412
27%
910
60%
207
14%
233
27%
544
62%
95
11%
357
28%
813
64%
100
8%
400
27%
899
61%
170
12%
300
25%
748
62%
155
13%
874
22%
2,390
60%
698
18%
438
27%
958
59%
229
14%
323
27%
742
61%
144
12%
625
28%
1,335
59%
312
14%
354
28%
697
55%
206
16%
50
26%
92
48%
50
26%
7,208
23%
20,535
64%
4,256
13%
796
23%
2,050
60%
551
16%
628
28%
1,390
61%
265
12%
308
27%
720
63%
112
10%
569
10%
4,509
81%
460
8%
209
27%
477
62%
85
11%
299
27%
670
60%
156
14%
536
23%
1,513
65%
277
12%
1,040
28%
2,263
61%
403
11%
462
20%
1,300
57%
509
22%
717
24%
1,706
57%
592
20%
412
27%
870
57%
242
16%
73
21%
248
71%
30
9%
117
28%
273
65%
31
7%
174
22%
505
65%
103
13%
309
26%
732
63%
128
11%
181
29%
365
59%
69
11%
378
24%
944
60%
243
16%
91,172
24%
231,819
61%
57,867
15%
3,280
18%
11,012
59%
4,352
23%
3,495
25%
8,753
62%
1,916
14%
860
28%
1,790
57%
468
15%
767
22%
2,159
61%
614
17%
3,153
21%
9,058
59%
3,064
20%
11,283
23%
28,936
59%
8,596
18%
1,435
31%
2,924
62%
338
7%
3,906
21%
12,583
68%
2,036
11%
5,626
20%
15,214
54%
7,304
26%
1,167
27%
2,616
61%
537
12%
2,408
26%
6,035
65%
909
10%
2,075
28%
4,568
61%
868
12%
4,331
24%
10,613
59%
3,024
17%
6,017
25%
14,717
62%
3,109
13%
3,394
23%
8,600
59%
2,510
17%
733
27%
1,689
62%
311
11%
30,071
26%
70,899
62%
13,904
12%
383
23%
1,080
65%
210
13%
1,378
19%
5,066
72%
626
9%
4,688
24%
11,905
61%
2,821
15%
722
27%
1,602
60%
350
13%
94
Table 3.1A
Numbers and Percentages of Females by Age: By Town
Ontario
Bristol
Canadice
Canandaigua city
Canandaigua
East Bloomfield
Farmington
Geneva city
Geneva
Gorham
Hopewell
Manchester
Naples
Phelps
Richmond
Seneca
South Bristol
Victor
West Bloomfield
Orleans
Albion
Barre
Carlton
Clarendon
Gaines
Kendall
Murray
Ridgeway
Shelby
Yates
Wayne
Arcadia
Butler
Galen
Huron
Lyons
Macedon
Marion
Ontario
Palmyra
Rose town
Savannah
Sodus
Walworth
Williamson
Wolcott
Yates
Barrington
Benton
Italy
Jerusalem
Middlesex
Milo
Potter
Starkey
Torrey
Total
Female
Population
51,301
1,198
872
5,884
3,861
1,714
5,379
7,257
1,712
1,915
1,729
4,706
1,257
3,553
1,724
1,372
815
5,086
1,267
22,255
3,394
1,026
1,314
1,737
1,856
1,478
3,777
3,568
2,836
1,269
47,350
7,929
970
2,250
1,050
2,869
4,379
2,566
4,847
3,873
1,275
938
4,553
4,176
3,420
2,255
12,589
708
1,274
496
2,446
682
3,636
895
1,817
635
% of Total
Population
51%
49%
47%
52%
50%
51%
51%
53%
52%
51%
52%
51%
51%
51%
50%
51%
50%
51%
50%
50%
42%
48%
45%
52%
49%
52%
60%
52%
52%
51%
50%
53%
43%
51%
50%
50%
50%
52%
50%
50%
52%
51%
51%
50%
50%
48%
51%
51%
49%
45%
54%
50%
52%
49%
51%
49%
# females % of females # females % of females # female % of females
< 18 yrs
< 18 yrs
18-64 yrs. 18-64 yrs.
65+ yrs.
65+ yrs
12,497
24%
31,150
61%
7,654
15%
335
28%
765
64%
98
8%
183
21%
566
65%
123
14%
1,260
21%
3,310
56%
1,314
22%
967
25%
2,377
62%
517
13%
454
26%
1,072
63%
188
11%
1,494
28%
3,489
65%
396
7%
1,562
22%
4,417
61%
1,278
18%
338
20%
987
58%
387
23%
494
26%
1,153
60%
268
14%
413
24%
1,020
59%
296
17%
1,123
24%
2,741
58%
842
18%
303
24%
736
59%
218
17%
909
26%
2,187
62%
457
13%
448
26%
1,067
62%
209
12%
373
27%
798
58%
201
15%
177
22%
525
64%
113
14%
1,365
27%
3,140
62%
581
11%
299
24%
800
63%
168
13%
5,528
25%
13,552
61%
3,175
14%
914
27%
1,860
55%
620
18%
245
24%
660
64%
121
12%
308
23%
860
65%
146
11%
481
28%
1,074
62%
182
10%
429
23%
1,078
58%
349
19%
438
30%
872
59%
168
11%
716
19%
2,792
74%
269
7%
960
27%
2,096
59%
512
14%
687
24%
1,523
54%
626
22%
350
28%
737
58%
182
14%
12,474
26%
28,335
60%
6,541
14%
1,953
25%
4,488
57%
1,488
19%
241
25%
598
62%
131
14%
586
26%
1,273
57%
391
17%
249
24%
627
60%
174
17%
667
23%
1,644
57%
558
19%
1,218
28%
2,776
63%
385
9%
758
30%
1,532
60%
276
11%
1,296
27%
3,029
62%
522
11%
942
24%
2,350
61%
581
15%
398
31%
709
56%
168
13%
275
29%
526
56%
137
15%
1,153
25%
2,725
60%
675
15%
1,251
30%
2,669
64%
256
6%
889
26%
2,064
60%
467
14%
598
27%
1,325
59%
332
15%
3,165
25%
7,291
58%
2,133
17%
231
33%
366
52%
111
16%
367
29%
710
56%
197
15%
124
25%
331
67%
41
8%
436
18%
1,621
66%
389
16%
171
25%
418
61%
93
14%
811
22%
1,983
55%
842
23%
293
33%
521
58%
81
9%
551
30%
984
54%
282
16%
181
29%
357
56%
97
15%
95
Table 3.2A
Numbers and Percentages of Females by Race/Ethnicity: By Town
White
Genesee
Alabama
Alexander
Batavia city
Batavia
Bergen
Bethany
Byron
Darien
Elba
Le Roy
Oakfield
Pavilion
Pembroke
Stafford
Tonawanda Res
Livingston
Avon
Caledonia
Conesus
Geneseo
Groveland
Leicester
Lima
Livonia
Mount Morris
North Dansville
Nunda
Ossian
Portage
Sparta
Springwater
West Sparta
York town
Monroe
Brighton
Chili
Clarkson
East Rochester
Gates
Greece
Hamlin
Henrietta
Irondequoit
Mendon
Ogden
Parma
Penfield
Perinton
Pittsford
Riga
Rochester
Rush
Sweden
Webster
Wheatland
29,230
1,010
1,224
7,811
2,979
1,484
858
1,226
1,441
1,120
3,834
1,590
1,171
2,238
1,225
19
30,631
3,226
2,149
1,090
5,191
743
1,096
2,218
3,624
2,124
2,958
1,496
349
404
748
1,137
600
1,478
292,839
15,965
12,789
2,887
3,336
13,347
45,079
4,545
15,264
25,824
4,183
8,987
7,319
16,617
22,220
13,262
2,617
49,816
1,607
6,441
18,288
2,446
% White
Black
95.2%
97.9%
98.5%
92.5%
97.9%
96.4%
98.1%
96.5%
98.1%
91.0%
96.5%
97.5%
96.5%
98.5%
96.5%
9.9%
95.3%
95.0%
93.9%
95.6%
93.0%
96.4%
97.3%
94.7%
97.6%
92.9%
97.9%
98.2%
99.4%
94.8%
95.7%
96.9%
96.9%
92.0%
74.4%
84.8%
89.5%
90.9%
93.5%
86.9%
91.4%
96.3%
81.2%
90.4%
96.8%
95.5%
96.9%
92.3%
92.5%
91.0%
95.0%
39.8%
96.1%
89.6%
93.3%
90.0%
378
4
4
262
45
0
7
9
0
23
0
9
4
8
3
0
276
49
50
0
48
8
0
24
0
41
21
0
0
6
0
4
0
25
52,862
653
690
62
42
863
1,662
32
1,390
974
57
126
75
309
326
254
30
44,645
4
298
252
118
%
Native % Native
% Black Hispanic Hispanic American American
Asian
% Asian
Other
% Other
1.2%
0.4%
0.3%
3.1%
1.5%
0.0%
0.8%
0.7%
0.0%
1.9%
0.0%
0.6%
0.3%
0.4%
0.2%
0.0%
0.9%
1.4%
2.2%
0.0%
0.9%
1.0%
0.0%
1.0%
0.0%
1.8%
0.7%
0.0%
0.0%
1.4%
0.0%
0.3%
0.0%
1.6%
13.4%
3.5%
4.8%
2.0%
1.2%
5.6%
3.4%
0.7%
7.4%
3.4%
1.3%
1.3%
1.0%
1.7%
1.4%
1.7%
1.1%
35.7%
0.2%
4.1%
1.3%
4.3%
102
0
5
69
0
8
0
0
0
0
0
0
11
9
0
0
288
0
0
5
190
9
10
17
0
0
30
0
0
3
13
0
0
11
9,022
1,296
199
60
46
484
613
28
830
263
34
148
29
612
734
771
29
2,364
29
89
347
17
0.3%
0.0%
0.4%
0.8%
0.0%
0.5%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.9%
0.4%
0.0%
0.0%
0.9%
0.0%
0.0%
0.4%
3.4%
1.2%
0.9%
0.7%
0.0%
0.0%
1.0%
0.0%
0.0%
0.7%
1.7%
0.0%
0.0%
0.7%
2.3%
6.9%
1.4%
1.9%
1.3%
3.2%
1.2%
0.6%
4.4%
0.9%
0.8%
1.6%
0.4%
3.4%
3.1%
5.3%
1.1%
1.9%
1.7%
1.2%
1.8%
0.6%
471
5
7
191
9
22
2
11
14
38
108
17
19
11
17
0
462
45
53
40
79
6
17
45
49
15
6
7
0
8
15
17
9
51
18,115
389
323
72
49
282
812
45
757
659
25
81
76
191
390
91
39
13,216
17
175
364
62
1.5%
0.5%
0.6%
2.3%
0.3%
1.4%
0.2%
0.9%
1.0%
3.1%
2.7%
1.0%
1.6%
0.5%
1.3%
0.0%
1.4%
1.3%
2.3%
3.5%
1.4%
0.8%
1.5%
1.9%
1.3%
0.7%
0.2%
0.5%
0.0%
1.9%
1.9%
1.4%
1.5%
3.2%
4.6%
2.1%
2.3%
2.3%
1.4%
1.8%
1.6%
1.0%
4.0%
2.3%
0.6%
0.9%
1.0%
1.1%
1.6%
0.6%
1.4%
10.6%
1.0%
2.4%
1.9%
2.3%
248
4
0
84
9
14
8
24
9
37
32
5
4
5
13
0
402
71
28
5
75
5
3
33
12
91
6
11
2
5
0
8
6
41
19,600
468
214
89
73
364
1,082
72
541
824
4
68
46
234
321
175
31
14,407
13
185
345
44
96
0.8%
0.4%
0.0%
1.0%
0.3%
0.9%
0.9%
1.9%
0.6%
3.0%
0.8%
0.3%
0.3%
0.2%
1.0%
0.0%
1.3%
2.1%
1.2%
0.4%
1.3%
0.6%
0.3%
1.4%
0.3%
4.0%
0.2%
0.7%
0.6%
1.2%
0.0%
0.7%
1.0%
2.6%
5.0%
2.5%
1.5%
2.8%
2.0%
2.4%
2.2%
1.5%
2.9%
2.9%
0.1%
0.7%
0.6%
1.3%
1.3%
1.2%
1.1%
11.5%
0.8%
2.6%
1.8%
1.6%
270
9
3
30
0
11
0
0
5
13
0
9
4
1
12
173
89
6
9
0
0
0
0
4
28
15
0
10
0
0
6
7
4
0
1,154
62
72
7
22
22
96
0
23
38
17
0
5
44
34
27
9
626
3
3
13
31
0.9%
0.9%
0.2%
0.4%
0.0%
0.7%
0.0%
0.0%
0.3%
1.1%
0.0%
0.6%
0.3%
0.0%
0.9%
90.1%
0.3%
0.2%
0.4%
0.0%
0.0%
0.0%
0.0%
0.2%
0.8%
0.7%
0.0%
0.7%
0.0%
0.0%
0.8%
0.6%
0.6%
0.0%
0.3%
0.3%
0.5%
0.2%
0.6%
0.1%
0.2%
0.0%
0.1%
0.1%
0.4%
0.0%
0.1%
0.2%
0.1%
0.2%
0.3%
0.5%
0.2%
0.0%
0.1%
1.1%
Table 3.2A
Numbers and Percentages of Females by Race/Ethnicity: By Town
% White
Black
%
Native % Native
% Black Hispanic Hispanic American American
48,882 93.3%
1,179 98.4%
843
96.7%
5,627 95.5%
3,725 95.4%
1,680 97.8%
5,177 96.1%
5,756 76.3%
1,577 90.4%
1,835 95.5%
1,646 93.7%
4,572 96.9%
1,234 98.2%
3,480 97.2%
1,716 99.5%
1,324 96.2%
782
95.6%
4,879 95.6%
1,243 97.3%
19,632 86.7%
3,003 86.9%
998
97.1%
1,240 94.4%
1,680 96.7%
1,691 89.4%
1,429 95.4%
2,600 66.4%
3,254 89.8%
2,497 86.3%
1,240 96.6%
44,343 92.8%
7,187 89.0%
939
96.8%
2,118 92.1%
972
91.4%
2,545 86.7%
4,182 95.5%
2,468 95.6%
4,682 96.2%
3,779 97.4%
1,236 96.3%
916
97.6%
3,964 86.1%
4,031 96.5%
3,195 92.8%
2,129 93.5%
12,217 96.9%
701
99.0%
1,274 100.0%
490
98.8%
2,393 97.8%
672
98.5%
3,475 95.4%
869
95.3%
1,725 94.9%
618
97.2%
998
0
2
16
33
3
28
740
81
14
15
20
4
0
0
0
6
36
0
1,547
201
7
31
6
91
6
833
161
211
0
1,193
245
8
45
35
169
41
25
59
20
0
5
368
38
100
35
31
0
0
0
8
6
5
3
9
0
1.9%
0.0%
0.2%
0.3%
0.8%
0.2%
0.5%
9.8%
4.6%
0.7%
0.9%
0.4%
0.3%
0.0%
0.0%
0.0%
0.7%
0.7%
0.0%
6.8%
5.8%
0.7%
2.4%
0.3%
4.8%
0.4%
21.3%
4.4%
7.3%
0.0%
2.5%
3.0%
0.8%
2.0%
3.3%
5.8%
0.9%
1.0%
1.2%
0.5%
0.0%
0.5%
8.0%
0.9%
2.9%
1.5%
0.2%
0.0%
0.0%
0.0%
0.3%
0.9%
0.1%
0.3%
0.5%
0.0%
White
Ontario
Bristol
Canadice
Canandaigua city
Canandaigua
East Bloomfield
Farmington
Geneva city
Geneva
Gorham
Hopewell
Manchester
Naples
Phelps
Richmond
Seneca
South Bristol
Victor
West Bloomfield
Orleans
Albion
Barre
Carlton
Clarendon
Gaines
Kendall
Murray
Ridgeway
Shelby
Yates
Wayne
Arcadia
Butler
Galen
Huron
Lyons
Macedon
Marion
Ontario
Palmyra
Rose town
Savannah
Sodus
Walworth
Williamson
Wolcott
Yates
Barrington
Benton
Italy
Jerusalem
Middlesex
Milo
Potter
Starkey
Torrey
60
8
9
42
73
18
48
553
32
37
65
32
9
40
8
5
10
98
11
787
97
6
0
44
74
20
360
81
91
14
892
379
7
65
23
85
59
33
44
7
14
3
72
0
40
61
102
0
0
0
8
0
40
19
34
1
97
2.1%
0.7%
1.0%
0.7%
1.9%
1.0%
0.9%
7.3%
1.8%
1.9%
3.7%
0.7%
0.7%
1.1%
0.5%
0.4%
1.2%
1.9%
0.9%
3.5%
2.8%
0.6%
0.0%
2.5%
3.9%
1.3%
9.2%
2.2%
3.1%
1.1%
1.9%
4.7%
0.7%
2.8%
2.2%
2.9%
1.3%
1.3%
0.9%
0.2%
1.1%
0.3%
1.6%
0.0%
1.2%
2.7%
0.8%
0.0%
0.0%
0.0%
0.3%
0.0%
1.1%
2.1%
1.9%
0.2%
358
11
3
7
0
0
16
5
0
6
0
0
0
0
0
0
3
6
3
116
25
6
10
0
0
5
53
11
0
6
141
20
0
12
0
10
4
6
18
7
0
5
5
13
12
29
47
0
0
0
5
0
38
0
4
0
0.1%
0.9%
0.3%
0.1%
0.0%
0.0%
0.3%
0.1%
0.0%
0.3%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.4%
0.1%
0.2%
0.5%
0.7%
0.6%
0.8%
0.0%
0.0%
0.3%
1.4%
0.3%
0.0%
0.5%
0.3%
0.2%
0.0%
0.5%
0.0%
0.3%
0.1%
0.2%
0.4%
0.2%
0.0%
0.5%
0.1%
0.3%
0.3%
1.3%
0.4%
0.0%
0.0%
0.0%
0.2%
0.0%
1.0%
0.0%
0.2%
0.0%
Asian
% Asian
Other
% Other
0
0
13
87
7
8
47
67
0
0
0
37
0
8
0
23
9
48
4
42
4
5
15
0
0
4
0
5
9
0
227
27
0
4
0
5
32
0
6
26
3
0
32
71
21
0
52
5
0
3
11
2
14
2
15
0
0.7%
0.0%
1.5%
1.5%
0.2%
0.5%
0.9%
0.9%
0.0%
0.0%
0.0%
0.8%
0.0%
0.2%
0.0%
1.7%
1.1%
0.9%
0.3%
0.2%
0.1%
0.5%
1.1%
0.0%
0.0%
0.3%
0.0%
0.1%
0.3%
0.0%
0.5%
0.3%
0.0%
0.2%
0.0%
0.2%
0.7%
0.0%
0.1%
0.7%
0.2%
0.0%
0.7%
1.7%
0.6%
0.0%
0.4%
0.7%
0.0%
0.6%
0.4%
0.3%
0.4%
0.2%
0.8%
0.0%
1003
0
2
111
66
8
69
427
54
29
31
59
10
51
0
25
8
36
17
516
126
6
18
7
36
34
70
112
84
23
974
216
16
56
33
122
61
49
56
41
30
10
163
23
75
23
163
2
0
3
21
2
69
19
30
17
1.9%
0.0%
0.2%
1.9%
1.7%
0.5%
1.3%
5.7%
3.1%
1.5%
1.8%
1.3%
0.8%
1.4%
0.0%
1.8%
1.0%
0.7%
1.3%
2.3%
3.6%
0.6%
1.4%
0.4%
1.9%
2.3%
1.8%
3.1%
2.9%
1.8%
2.0%
2.7%
1.6%
2.4%
3.1%
4.2%
1.4%
1.9%
1.2%
1.1%
2.3%
1.1%
3.5%
0.6%
2.2%
1.0%
1.3%
0.3%
0.0%
0.6%
0.9%
0.3%
1.9%
2.1%
1.7%
2.7%
Table 3.3A
Numbers and Percentages of Households by Family Type: By Towns
HOUSEHOLDS
Genesee
Alabama
Alexander
Batavia city
Batavia
Bergen
Bethany
Byron
Darien
Elba
Le Roy
Oakfield
Pavilion
Pembroke
Stafford
Tonawanda Res
Livingston
Avon
Caledonia
Conesus
Geneseo
Groveland
Leicester
Lima
Livonia
Mount Morris
North Dansville
Nunda
Ossian
Portage
Sparta
Springwater
West Sparta
York town
Monroe
Brighton
Chili
Clarkson
East Rochester
Gates
Greece
Hamlin
Henrietta
Irondequoit
Mendon
Ogden
Parma
Penfield
Perinton
Pittsford
Riga
Rochester
Rush
Sweden
Webster
Wheatland
TOTAL
HH
22,804
709
878
6,459
2,331
1,172
637
900
1,053
865
3,070
1,156
886
1,672
880
136
22,149
2,614
1,677
852
2,403
525
854
1,551
2,713
1,797
2,333
1,130
275
308
587
889
440
1,201
286,820
15,856
10,229
2,034
2,778
11,752
36,985
3,275
12,831
22,276
3,061
6,513
5,319
13,141
17,600
9,446
1,979
89,093
1,278
4,586
14,766
2,022
Married % of all
Couples
HH
12,724
55.8%
462
65.2%
556
63.3%
2,681
41.5%
1,352
58.0%
741
63.2%
418
65.6%
588
65.3%
764
72.6%
564
65.2%
1,601
52.1%
691
59.8%
598
67.5%
1,048
62.7%
613
69.7%
47
34.6%
12,255
55.3%
1,493
57.1%
1,054
62.9%
549
64.4%
1,051
43.7%
299
57.0%
529
61.9%
944
60.9%
1,607
59.2%
715
39.8%
1,107
47.4%
647
57.3%
198
72.0%
174
56.5%
401
68.3%
529
59.5%
253
57.5%
705
58.7%
138,129 48.2%
7,282
45.9%
6,466
63.2%
1,376
67.6%
1,092
39.3%
6,496
55.3%
20,893
56.5%
2,075
63.4%
7,055
55.0%
11,550
51.8%
2,079
67.9%
4,349
66.8%
3,360
63.2%
8,452
64.3%
11,222
63.8%
6,711
71.0%
1,303
65.8%
22,916
25.7%
902
70.6%
2,143
46.7%
9,206
62.3%
1,201
59.4%
FAMILIES
% of all
HH
FHH
2,105
9.2%
30
4.2%
52
5.9%
921
14.3%
204
8.8%
61
5.2%
53
8.3%
64
7.1%
45
4.3%
67
7.7%
261
8.5%
102
8.8%
62
7.0%
92
5.5%
57
6.5%
34
25.0%
2,167
9.8%
210
8.0%
136
8.1%
48
5.6%
202
8.4%
51
9.7%
69
8.1%
92
5.9%
298
11.0%
313
17.4%
306
13.1%
129
11.4%
16
5.8%
34
11.0%
31
5.3%
64
7.2%
42
9.5%
126
10.5%
37,437
13.1%
1,155
7.3%
823
8.0%
162
8.0%
415
14.9%
1,122
9.5%
3,749
10.1%
321
9.8%
1,103
8.6%
2,138
9.6%
162
5.3%
601
9.2%
360
6.8%
915
7.0%
1,306
7.4%
493
5.2%
186
9.4%
20,581
23.1%
77
6.0%
447
9.7%
1,139
7.7%
182
9.0%
98
MHH
1076
35
36
283
103
58
28
21
64
45
159
78
41
89
36
0
1012
113
91
49
29
25
53
66
109
134
75
66
4
31
20
78
38
31
10252
294
399
61
133
464
1279
160
383
697
93
135
317
297
489
150
55
4216
31
149
387
63
% of all
HH
4.7%
4.9%
4.1%
4.4%
4.4%
4.9%
4.4%
2.3%
6.1%
5.2%
5.2%
6.7%
4.6%
5.3%
4.1%
0.0%
4.6%
4.3%
5.4%
5.8%
1.2%
4.8%
6.2%
4.3%
4.0%
7.5%
3.2%
5.8%
1.5%
10.1%
3.4%
8.8%
8.6%
2.6%
3.6%
1.9%
3.9%
3.0%
4.8%
3.9%
3.5%
4.9%
3.0%
3.1%
3.0%
2.1%
6.0%
2.3%
2.8%
1.6%
2.8%
4.7%
2.4%
3.2%
2.6%
3.1%
FHH
3,932
89
124
1,578
409
132
49
104
104
114
647
156
74
234
86
32
3,646
475
169
110
700
69
100
237
335
361
508
144
25
39
52
106
44
172
56,132
4,281
1,490
214
710
2,314
6,597
414
2,268
5,090
435
812
658
1,874
2,756
1,404
268
20,817
164
874
2,344
348
Non-FAMILIES
% of all
HH
MHH
17.2%
2,967
12.6%
93
14.1%
110
24.4%
996
17.5%
263
11.3%
180
7.7%
89
11.6%
123
9.9%
76
13.2%
75
21.1%
402
13.5%
129
8.4%
111
14.0%
209
9.8%
88
23.5%
23
16.5%
3,069
18.2%
323
10.1%
227
12.9%
96
29.1%
421
13.1%
81
11.7%
103
15.3%
212
12.3%
364
20.1%
274
21.8%
337
12.7%
144
9.1%
32
12.7%
30
8.9%
83
11.9%
112
10.0%
63
14.3%
167
19.6%
44,870
27.0%
2,844
14.6%
1,051
10.5%
221
25.6%
428
19.7%
1,356
17.8%
4,467
12.6%
305
17.7%
2,022
22.8%
2,801
14.2%
292
12.5%
616
12.4%
624
14.3%
1,603
15.7%
1,827
14.9%
688
13.5%
167
23.4%
20,563
12.8%
104
19.1%
973
15.9%
1,690
17.2%
228
% of all
HH
13.0%
13.1%
12.5%
15.4%
11.3%
15.4%
14.0%
13.7%
7.2%
8.7%
13.1%
11.2%
12.5%
12.5%
10.0%
16.9%
13.9%
12.4%
13.5%
11.3%
17.5%
15.4%
12.1%
13.7%
13.4%
15.2%
14.4%
12.7%
11.6%
9.7%
14.1%
12.6%
14.3%
13.9%
15.6%
17.9%
10.3%
10.9%
15.4%
11.5%
12.1%
9.3%
15.8%
12.6%
9.5%
9.5%
11.7%
12.2%
10.4%
7.3%
8.4%
23.1%
8.1%
21.2%
11.4%
11.3%
Table 3.3A
Numbers and Percentages of Households by Family Type: By Towns
HOUSEHOLDS
TOTAL
HH
38,392
Ontario
Bristol
902
Canadice
801
Canandaigua city
4,773
Canandaigua
2,885
East Bloomfield
1,215
Farmington
3,879
Geneva city
4,970
Geneva
1,403
Gorham
1,407
Hopewell
1,256
Manchester
3,607
Naples
990
Phelps
2,641
Richmond
1,289
Seneca
1,021
South Bristol
673
Victor
3,665
West Bloomfield
1,015
15,350
Orleans
Albion
2,367
Barre
730
Carlton
1,092
Clarendon
1,228
Gaines
1,533
Kendall
963
Murray
1,882
Ridgeway
2,618
Shelby
2,016
Yates
921
34,970
Wayne
Arcadia
5,836
Butler
738
Galen
1,666
Huron
812
Lyons
2,155
Macedon
3,243
Marion
1,749
Ontario
3,624
Palmyra
3,002
Rose town
899
Savannah
651
Sodus
3,448
Walworth
2,845
Williamson
2,559
Wolcott
1,743
9,056
Yates
Barrington
474
Benton
880
Italy
407
Jerusalem
1,616
Middlesex
521
Milo
2,874
Potter
595
Starkey
1,242
Torrey
447
Married % of all
Couples
HH
21,350
55.6%
600
66.5%
448
55.9%
1,983
41.5%
1,851
64.2%
756
62.2%
2,292
59.1%
2,028
40.8%
771
55.0%
848
60.3%
718
57.2%
1,982
54.9%
541
54.6%
1,613
61.1%
838
65.0%
669
65.5%
396
58.8%
2,430
66.3%
586
57.7%
8,318
54.2%
1,080
45.6%
490
67.1%
650
59.5%
701
57.1%
797
52.0%
674
70.0%
1,001
53.2%
1,351
51.6%
1,037
51.4%
537
58.3%
19,930
57.0%
2,819
48.3%
413
56.0%
948
56.9%
487
60.0%
1,030
47.8%
1,988
61.3%
1,107
63.3%
2,080
57.4%
1,711
57.0%
524
58.3%
353
54.2%
1,894
54.9%
2,079
73.1%
1,632
63.8%
865
49.6%
5,039
55.6%
347
73.2%
568
64.5%
252
61.9%
939
58.1%
308
59.1%
1,322
46.0%
397
66.7%
639
51.4%
267
59.7%
FAMILIES
% of all
FHH
HH
3,810
9.9%
66
7.3%
54
6.7%
605
12.7%
179
6.2%
94
7.7%
446
11.5%
722
14.5%
88
6.3%
150
10.7%
148
11.8%
355
9.8%
105
10.6%
274
10.4%
96
7.4%
84
8.2%
45
6.7%
224
6.1%
75
7.4%
1,756
11.4%
341
14.4%
52
7.1%
73
6.7%
101
8.2%
224
14.6%
93
9.7%
185
9.8%
344
13.1%
259
12.8%
84
9.1%
3,545
10.1%
821
14.1%
83
11.2%
161
9.7%
64
7.9%
240
11.1%
357
11.0%
174
9.9%
420
11.6%
213
7.1%
88
9.8%
73
11.2%
327
9.5%
146
5.1%
185
7.2%
193
11.1%
845
9.3%
28
5.9%
68
7.7%
19
4.7%
103
6.4%
48
9.2%
309
10.8%
67
11.3%
168
13.5%
35
7.8%
99
MHH
1377
30
42
156
95
71
163
198
27
38
44
155
37
97
21
35
29
64
75
806
139
36
51
87
41
37
139
140
88
48
1662
346
49
90
35
129
86
56
195
99
36
52
171
120
107
91
432
7
21
18
78
30
186
12
55
25
% of all
HH
3.6%
3.3%
5.2%
3.3%
3.3%
5.8%
4.2%
4.0%
1.9%
2.7%
3.5%
4.3%
3.7%
3.7%
1.6%
3.4%
4.3%
1.7%
7.4%
5.3%
5.9%
4.9%
4.7%
7.1%
2.7%
3.8%
7.4%
5.3%
4.4%
5.2%
4.8%
5.9%
6.6%
5.4%
4.3%
6.0%
2.7%
3.2%
5.4%
3.3%
4.0%
8.0%
5.0%
4.2%
4.2%
5.2%
4.8%
1.5%
2.4%
4.4%
4.8%
5.8%
6.5%
2.0%
4.4%
5.6%
FHH
6,563
64
111
1,212
358
141
510
1,175
286
198
137
691
178
418
155
113
74
602
140
2,351
418
69
105
164
259
71
244
466
409
146
5,295
1,106
90
227
90
410
375
252
507
522
149
92
540
241
365
329
1,461
40
108
44
243
61
664
47
188
66
Non-FAMILIES
% of all
MHH
HH
17.1%
5,292
7.1%
142
13.9%
146
25.4%
817
12.4%
402
11.6%
153
13.1%
468
23.6%
847
20.4%
231
14.1%
173
10.9%
209
19.2%
424
18.0%
129
15.8%
239
12.0%
179
11.1%
120
11.0%
129
16.4%
345
13.8%
139
15.3%
2,119
17.7%
389
9.5%
83
9.6%
213
13.4%
175
16.9%
212
7.4%
88
13.0%
313
17.8%
317
20.3%
223
15.9%
106
15.1%
4,538
19.0%
744
12.2%
103
13.6%
240
11.1%
136
19.0%
346
11.6%
437
14.4%
160
14.0%
422
17.4%
457
16.6%
102
14.1%
81
15.7%
516
8.5%
259
14.3%
270
18.9%
265
16.1%
1,279
8.4%
52
12.3%
115
10.8%
74
15.0%
253
11.7%
74
23.1%
393
7.9%
72
15.1%
192
14.8%
54
% of all
HH
13.8%
15.7%
18.2%
17.1%
13.9%
12.6%
12.1%
17.0%
16.5%
12.3%
16.6%
11.8%
13.0%
9.0%
13.9%
11.8%
19.2%
9.4%
13.7%
13.8%
16.4%
11.4%
19.5%
14.3%
13.8%
9.1%
16.6%
12.1%
11.1%
11.5%
13.0%
12.7%
14.0%
14.4%
16.7%
16.1%
13.5%
9.1%
11.6%
15.2%
11.3%
12.4%
15.0%
9.1%
10.6%
15.2%
14.1%
11.0%
13.1%
18.2%
15.7%
14.2%
13.7%
12.1%
15.5%
12.1%
TABLE 3.4 A
Numbers and Percentages of Female Heads of Households in Families by Children’s Age: By Town
Genesee
Alabama
Alexander
Batavia city
Batavia
Bergen
Bethany
Byron
Darien
Elba
Le Roy
Oakfield
Pavilion
Pembroke
Stafford
Tonawanda Res
Livingston
Avon
Caledonia
Conesus
Geneseo
Groveland
Leicester
Lima
Livonia
Mount Morris
North Dansville
Nunda
Ossian
Portage
Sparta
Springwater
West Sparta
York town
Monroe
Brighton
Chili
Clarkson
East Rochester
Gates
Greece
Hamlin
Henrietta
Irondequoit
Mendon
Ogden
Parma
Penfield
Perinton
Pittsford
Riga
Rochester
Rush
Sweden
Webster
Wheatland
FHH with
ALL FHH children <
in families
18
2,105
1,241
30
13
52
22
921
559
204
103
61
48
53
29
64
42
45
15
67
36
261
174
102
62
62
18
92
79
57
28
34
13
2,167
1,444
210
171
136
107
48
28
202
146
51
34
69
43
92
47
298
166
313
172
306
253
129
94
16
10
34
13
31
21
64
46
42
14
126
79
37,437
23,821
1,155
621
823
484
162
100
415
278
1,122
531
3,749
2,203
321
211
1,103
561
2,138
1,082
162
93
601
342
360
232
915
448
1,306
750
493
315
186
94
20,581
14,338
77
38
447
280
1,139
714
182
106
% of all
FHH in
families
59.0%
43.3%
42.3%
60.7%
50.5%
78.7%
54.7%
65.6%
33.3%
53.7%
66.7%
60.8%
29.0%
85.9%
49.1%
38.2%
66.6%
81.4%
78.7%
58.3%
72.3%
66.7%
62.3%
51.1%
55.7%
55.0%
82.7%
72.9%
62.5%
38.2%
67.7%
71.9%
33.3%
62.7%
63.6%
53.8%
58.8%
61.7%
67.0%
47.3%
58.8%
65.7%
50.9%
50.6%
57.4%
56.9%
64.4%
49.0%
57.4%
63.9%
50.5%
69.7%
49.4%
62.6%
62.7%
58.2%
FHH with own children < 18 years of age
% of FHH
FHH with % of FHH
FHH with
with
FHH with % of FHH children with children
children < 6 children < 6 chilren 0 to with chilren between 6 & between 6 &
yrs
yrs
17 yrs
0 to 17 yrs
17 only
17 only
226
18.2%
171
13.8%
844
68.0%
2
15.4%
3
23.1%
8
61.5%
5
22.7%
4
18.2%
13
59.1%
122
21.8%
90
16.1%
347
62.1%
37
35.9%
0
0.0%
66
64.1%
2
4.2%
16
33.3%
30
62.5%
7
24.1%
4
13.8%
18
62.1%
10
23.8%
16
38.1%
16
38.1%
1
6.7%
3
20.0%
11
73.3%
0
0.0%
4
11.1%
32
88.9%
16
9.2%
16
9.2%
142
81.6%
3
4.8%
12
19.4%
47
75.8%
4
22.2%
0
0.0%
14
77.8%
8
10.1%
0
0.0%
71
89.9%
2
7.1%
3
10.7%
23
82.1%
7
53.8%
0
0.0%
6
46.2%
292
20.2%
154
10.7%
998
69.1%
22
12.9%
28
16.4%
121
70.8%
19
17.8%
6
5.6%
82
76.6%
0
0.0%
3
10.7%
25
89.3%
23
15.8%
5
3.4%
118
80.8%
0
0.0%
3
8.8%
31
91.2%
5
11.6%
0
0.0%
38
88.4%
5
10.6%
0
0.0%
42
89.4%
28
16.9%
9
5.4%
129
77.7%
68
39.5%
41
23.8%
63
36.6%
72
28.5%
45
17.8%
136
53.8%
14
14.9%
12
12.8%
68
72.3%
5
50.0%
0
0.0%
5
50.0%
0
0.0%
0
0.0%
13
100.0%
8
38.1%
2
9.5%
11
52.4%
4
8.7%
0
0.0%
42
91.3%
2
14.3%
0
0.0%
12
85.7%
17
21.5%
0
0.0%
62
78.5%
4,660
19.6%
4,448
18.7%
14,713
61.8%
141
22.7%
26
4.2%
454
73.1%
81
16.7%
62
12.8%
341
70.5%
16
16.0%
16
16.0%
68
68.0%
40
14.4%
38
13.7%
200
71.9%
92
17.3%
49
9.2%
390
73.4%
336
15.3%
324
14.7%
1,543
70.0%
43
20.4%
58
27.5%
110
52.1%
96
17.1%
78
13.9%
387
69.0%
141
13.0%
126
11.6%
815
75.3%
4
4.3%
5
5.4%
84
90.3%
23
6.7%
37
10.8%
282
82.5%
24
10.3%
39
16.8%
169
72.8%
47
10.5%
38
8.5%
363
81.0%
50
6.7%
108
14.4%
592
78.9%
32
10.2%
39
12.4%
244
77.5%
9
9.6%
19
20.2%
66
70.2%
3,295
23.0%
3,271
22.8%
7,772
54.2%
8
21.1%
7
18.4%
23
60.5%
20
7.1%
35
12.5%
225
80.4%
147
20.6%
63
8.8%
504
70.6%
15
14.2%
10
9.4%
81
76.4%
100
TABLE 3.4 A
Numbers and Percentages of Female Heads of Households in Families by Children’s Age: By Town
FHH with
ALL FHH children <
in families
18
3,810
2,398
Ontario
Bristol
66
39
Canadice
54
31
Canandaigua city
605
363
Canandaigua
179
103
East Bloomfield
94
69
Farmington
446
300
Geneva city
722
509
Geneva
88
38
Gorham
150
83
Hopewell
148
93
Manchester
355
245
Naples
105
81
Phelps
274
167
Richmond
96
59
Seneca
84
21
South Bristol
45
27
Victor
224
132
West Bloomfield
75
38
1,756
1,131
Orleans
Albion
341
236
Barre
52
15
Carlton
73
48
Clarendon
101
53
Gaines
224
138
Kendall
93
60
Murray
185
132
Ridgeway
344
221
Shelby
259
172
Yates
84
56
3,545
2,382
Wayne
Arcadia
821
532
Butler
83
50
Galen
161
104
Huron
64
36
Lyons
240
154
Macedon
357
270
Marion
174
117
Ontario
420
310
Palmyra
213
151
Rose town
88
63
Savannah
73
44
Sodus
327
214
Walworth
146
105
Williamson
185
69
Wolcott
193
163
845
555
Yates
Barrington
28
11
Benton
68
49
Italy
19
10
Jerusalem
103
81
Middlesex
48
25
Milo
309
199
Potter
67
53
Starkey
168
108
Torrey
35
19
% of all
FHH in
families
62.9%
59.1%
57.4%
60.0%
57.5%
73.4%
67.3%
70.5%
43.2%
55.3%
62.8%
69.0%
77.1%
60.9%
61.5%
25.0%
60.0%
58.9%
50.7%
64.4%
69.2%
28.8%
65.8%
52.5%
61.6%
64.5%
71.4%
64.2%
66.4%
66.7%
67.2%
64.8%
60.2%
64.6%
56.3%
64.2%
75.6%
67.2%
73.8%
70.9%
71.6%
60.3%
65.4%
71.9%
37.3%
84.5%
65.7%
39.3%
72.1%
52.6%
78.6%
52.1%
64.4%
79.1%
64.3%
54.3%
FHH with own children < 18 years of age
% of FHH
FHH with % of FHH
FHH with
with
FHH with % of FHH children with children
children < 6 children < 6 chilren 0 to with chilren between 6 & between 6 &
yrs
yrs
17 yrs
0 to 17 yrs
17 only
17 only
489
20.4%
434
18.1%
1,475
61.5%
6
15.4%
0
0.0%
33
84.6%
9
29.0%
5
16.1%
17
54.8%
92
25.3%
43
11.8%
228
62.8%
24
23.3%
6
5.8%
73
70.9%
9
13.0%
18
26.1%
42
60.9%
65
21.7%
56
18.7%
179
59.7%
145
28.5%
133
26.1%
231
45.4%
0
0.0%
10
26.3%
28
73.7%
7
8.4%
7
8.4%
69
83.1%
6
6.5%
17
18.3%
70
75.3%
58
23.7%
77
31.4%
110
44.9%
18
22.2%
14
17.3%
49
60.5%
26
15.6%
19
11.4%
122
73.1%
0
0.0%
7
11.9%
52
88.1%
0
0.0%
0
0.0%
21
100.0%
2
7.4%
2
7.4%
23
85.2%
17
12.9%
8
6.1%
107
81.1%
5
13.2%
12
31.6%
21
55.3%
195
17.2%
251
22.2%
685
60.6%
62
26.3%
26
11.0%
148
62.7%
2
13.3%
4
26.7%
9
60.0%
4
8.3%
7
14.6%
37
77.1%
0
0.0%
22
41.5%
31
58.5%
18
13.0%
39
28.3%
81
58.7%
0
0.0%
0
0.0%
60
100.0%
27
20.5%
34
25.8%
71
53.8%
42
19.0%
50
22.6%
129
58.4%
30
17.4%
54
31.4%
88
51.2%
10
17.9%
15
26.8%
31
55.4%
481
20.2%
424
17.8%
1,477
62.0%
84
15.8%
98
18.4%
350
65.8%
7
14.0%
13
26.0%
30
60.0%
11
10.6%
37
35.6%
56
53.8%
14
38.9%
9
25.0%
13
36.1%
53
34.4%
22
14.3%
79
51.3%
51
18.9%
49
18.1%
170
63.0%
12
10.3%
28
23.9%
77
65.8%
62
20.0%
56
18.1%
192
61.9%
54
35.8%
8
5.3%
89
58.9%
12
19.0%
11
17.5%
40
63.5%
10
22.7%
5
11.4%
29
65.9%
43
20.1%
30
14.0%
141
65.9%
15
14.3%
0
0.0%
90
85.7%
13
18.8%
25
36.2%
31
44.9%
40
24.5%
33
20.2%
90
55.2%
109
19.6%
87
15.7%
359
64.7%
1
9.1%
5
45.5%
5
45.5%
10
20.4%
8
16.3%
31
63.3%
4
40.0%
0
0.0%
6
60.0%
9
11.1%
14
17.3%
58
71.6%
0
0.0%
8
32.0%
17
68.0%
61
30.7%
25
12.6%
113
56.8%
11
20.8%
9
17.0%
33
62.3%
11
10.2%
13
12.0%
84
77.8%
2
10.5%
5
26.3%
12
63.2%
101
TABLE 3.5 A
Numbers and Percentages of Female Heads of Households by Age: By Town
Genesee
Alabama
Alexander
Batavia city
Batavia
Bergen
Bethany
Byron
Darien
Elba
Le Roy
Oakfield
Pavilion
Pembroke
Stafford
Tonawanda Res
Livingston
Avon
Caledonia
Conesus
Geneseo
Groveland
Leicester
Lima
Livonia
Mount Morris
North Dansville
Nunda
Ossian
Portage
Sparta
Springwater
West Sparta
York town
Monroe
Brighton
Chili
Clarkson
East Rochester
Gates
Greece
Hamlin
Henrietta
Irondequoit
Mendon
Ogden
Parma
Penfield
Perinton
Pittsford
Riga
Rochester
Rush
Sweden
Webster
Wheatland
Total
2,105
30
52
921
204
61
53
64
45
67
261
102
62
92
57
34
2,167
210
136
48
202
51
69
92
298
313
306
129
16
34
31
64
42
126
37,437
1,155
823
162
415
1,122
3,749
321
1,103
2,138
162
601
360
915
1,306
493
186
20,581
77
447
1,139
182
FHH in Families
< 45 yrs
1,191
13
26
539
96
47
18
43
15
41
170
57
19
71
23
13
1,287
143
80
18
125
29
33
34
134
194
236
85
6
17
23
41
18
71
22,888
561
454
106
247
405
1,930
201
585
1,005
79
364
224
394
643
233
74
14,371
34
228
653
97
%
56.6%
43.3%
50.0%
58.5%
47.1%
77.0%
34.0%
67.2%
33.3%
61.2%
65.1%
55.9%
30.6%
77.2%
40.4%
38.2%
59.4%
68.1%
58.8%
37.5%
61.9%
56.9%
47.8%
37.0%
45.0%
62.0%
77.1%
65.9%
37.5%
50.0%
74.2%
64.1%
42.9%
56.3%
61.1%
48.6%
55.2%
65.4%
59.5%
36.1%
51.5%
62.6%
53.0%
47.0%
48.8%
60.6%
62.2%
43.1%
49.2%
47.3%
39.8%
69.8%
44.2%
51.0%
57.3%
53.3%
102
Total
3,932
89
124
1,578
409
132
49
104
104
114
647
156
74
234
86
32
3,646
475
169
110
700
69
100
237
335
361
508
144
25
39
52
106
44
172
56,132
4,281
1,490
214
710
2,314
6,597
414
2,268
5,090
435
812
658
1,874
2,756
1,404
268
20,817
164
874
2,344
348
FHH in Non-Families
45+ yrs
%
65+ yrs
3,040
77.3%
2,000
82
92.1%
56
97
78.2%
66
1,262
80.0%
888
334
81.7%
204
112
84.8%
77
33
67.3%
19
66
63.5%
39
66
63.5%
30
95
83.3%
74
420
64.9%
246
134
85.9%
70
56
75.7%
38
191
81.6%
127
65
75.6%
53
27
84.4%
13
2,671
73.3%
1,652
376
79.2%
233
137
81.1%
87
87
79.1%
67
280
40.0%
155
48
69.6%
20
82
82.0%
54
191
80.6%
121
274
81.8%
121
295
81.7%
175
435
85.6%
332
112
77.8%
55
16
64.0%
7
39
100.0%
11
49
94.2%
30
73
68.9%
55
36
81.8%
8
141
82.0%
121
38,500
68.6%
22,288
2,832
66.2%
1,759
1,088
73.0%
708
194
90.7%
156
480
67.6%
276
1,776
76.8%
1,148
5,203
78.9%
3,306
246
59.4%
130
1,367
60.3%
755
4,263
83.8%
2,982
340
78.2%
189
491
60.5%
187
474
72.0%
243
1,348
71.9%
744
2,145
77.8%
991
1,158
82.5%
732
208
77.6%
156
12,321
59.2%
6,281
133
81.1%
82
475
54.3%
274
1,733
73.9%
1,061
225
64.7%
128
%
65.8%
68.3%
68.0%
70.4%
61.1%
68.8%
57.6%
59.1%
45.5%
77.9%
58.6%
52.2%
67.9%
66.5%
81.5%
48.1%
61.8%
62.0%
63.5%
77.0%
55.4%
41.7%
65.9%
63.4%
44.2%
59.3%
76.3%
49.1%
43.8%
28.2%
61.2%
75.3%
22.2%
85.8%
57.9%
62.1%
65.1%
80.4%
57.5%
64.6%
63.5%
52.8%
55.2%
70.0%
55.6%
38.1%
51.3%
55.2%
46.2%
63.2%
75.0%
51.0%
61.7%
57.7%
61.2%
56.9%
TABLE 3.5 A
Numbers and Percentages of Female Heads of Households by Age: By Town
Ontario
Bristol
Canadice
Canandaigua city
Canandaigua
East Bloomfield
Farmington
Geneva city
Geneva
Gorham
Hopewell
Manchester
Naples
Phelps
Richmond
Seneca
South Bristol
Victor
West Bloomfield
Orleans
Albion
Barre
Carlton
Clarendon
Gaines
Kendall
Murray
Ridgeway
Shelby
Yates
Wayne
Arcadia
Butler
Galen
Huron
Lyons
Macedon
Marion
Ontario
Palmyra
Rose town
Savannah
Sodus
Walworth
Williamson
Wolcott
Yates
Barrington
Benton
Italy
Jerusalem
Middlesex
Milo
Potter
Starkey
Torrey
Total
3,810
66
54
605
179
94
446
722
88
150
148
355
105
274
96
84
45
224
75
1,756
341
52
73
101
224
93
185
344
259
84
3,545
821
83
161
64
240
357
174
420
213
88
73
327
146
185
193
845
28
68
19
103
48
309
67
168
35
FHH in Families
< 45 yrs
2,267
35
27
319
105
71
300
481
38
90
83
233
69
171
51
26
20
104
44
1,093
226
15
40
66
132
41
138
215
159
61
2,248
486
50
99
36
155
246
115
296
152
60
48
184
106
64
151
469
13
49
10
53
24
159
42
101
18
%
59.5%
53.0%
50.0%
52.7%
58.7%
75.5%
67.3%
66.6%
43.2%
60.0%
56.1%
65.6%
65.7%
62.4%
53.1%
31.0%
44.4%
46.4%
58.7%
62.2%
66.3%
28.8%
54.8%
65.3%
58.9%
44.1%
74.6%
62.5%
61.4%
72.6%
63.4%
59.2%
60.2%
61.5%
56.3%
64.6%
68.9%
66.1%
70.5%
71.4%
68.2%
65.8%
56.3%
72.6%
34.6%
78.2%
55.5%
46.4%
72.1%
52.6%
51.5%
50.0%
51.5%
62.7%
60.1%
51.4%
103
Total
6,563
64
111
1,212
358
141
510
1,175
286
198
137
691
178
418
155
113
74
602
140
2,351
418
69
105
164
259
71
244
466
409
146
5,295
1,106
90
227
90
410
375
252
507
522
149
92
540
241
365
329
1,461
40
108
44
243
61
664
47
188
66
FHH in Non-Families
45+ yrs
%
65+ yrs
4,923
75.0%
3,003
31
48.4%
21
87
78.4%
41
887
73.2%
580
272
76.0%
144
97
68.8%
65
380
74.5%
150
819
69.7%
590
245
85.7%
141
177
89.4%
115
112
81.8%
61
527
76.3%
346
152
85.4%
97
335
80.1%
212
131
84.5%
58
58
51.3%
46
64
86.5%
43
436
72.4%
230
113
80.7%
63
1,915
81.5%
1,260
370
88.5%
239
54
78.3%
39
67
63.8%
26
122
74.4%
60
221
85.3%
182
66
93.0%
34
171
70.1%
111
362
77.7%
215
337
82.4%
256
145
99.3%
98
4,220
79.7%
2,620
922
83.4%
627
72
80.0%
52
204
89.9%
153
74
82.2%
49
325
79.3%
210
228
60.8%
109
196
77.8%
128
429
84.6%
242
434
83.1%
250
128
85.9%
75
84
91.3%
64
414
76.7%
232
145
60.2%
66
299
81.9%
188
266
80.9%
175
1,217
83.3%
859
36
90.0%
30
98
90.7%
35
34
77.3%
13
206
84.8%
139
55
90.2%
34
527
79.4%
423
34
72.3%
15
169
89.9%
135
58
87.9%
35
%
61.0%
67.7%
47.1%
65.4%
52.9%
67.0%
39.5%
72.0%
57.6%
65.0%
54.5%
65.7%
63.8%
63.3%
44.3%
79.3%
67.2%
52.8%
55.8%
65.8%
64.6%
72.2%
38.8%
49.2%
82.4%
51.5%
64.9%
59.4%
76.0%
67.6%
62.1%
68.0%
72.2%
75.0%
66.2%
64.6%
47.8%
65.3%
56.4%
57.6%
58.6%
76.2%
56.0%
45.5%
62.9%
65.8%
70.6%
83.3%
35.7%
38.2%
67.5%
61.8%
80.3%
44.1%
79.9%
60.3%
TABLE 3.6A
Educational Attainment for Women 25 Years of Age and Older: By Town
< 9th grade
Genesee County
Alabama
Alexander
Batavia city
Batavia
Bergen
Bethany
Byron
Darien
Elba
Le Roy
Oakfield
Pavilion
Pembroke
Stafford
Tonawanda Res
Livingston
Avon
Caledonia
Conesus
Geneseo
Groveland
Leicester
Lima
Livonia
Mount Morris
North Dansville
Nunda
Ossian
Portage
Sparta
Springwater
West Sparta
York town
Monroe
Brighton
Chili
Clarkson
East Rochester
Gates
Greece
Hamlin
Henrietta
Irondequoit
Mendon
Ogden
Parma
Penfield
Perinton
Pittsford
Riga
Rochester
Rush
Sweden
Webster
Wheatland
#
1,042
39
24
420
160
33
7
0
28
40
137
59
33
38
8
16
638
43
42
21
103
4
39
61
50
104
77
28
4
14
16
4
6
22
11,712
476
232
52
136
864
1,124
59
260
1,319
31
80
120
501
338
87
31
5,452
10
113
391
36
%
5.0%
6.0%
3.0%
7.3%
7.4%
3.2%
1.2%
0.0%
2.8%
5.0%
4.9%
5.6%
4.1%
2.5%
1.0%
11.8%
3.2%
1.8%
2.8%
2.8%
5.5%
0.8%
5.2%
4.1%
2.0%
6.5%
3.8%
2.8%
1.5%
5.0%
2.7%
0.5%
1.5%
2.0%
4.6%
3.4%
2.5%
2.5%
5.5%
7.7%
3.3%
2.0%
2.3%
6.2%
1.0%
1.3%
2.4%
3.9%
2.0%
0.9%
1.7%
7.6%
0.8%
3.3%
2.9%
2.0%
9-12 grade
no diploma
#
1,879
51
38
680
173
55
39
66
99
67
223
114
54
140
63
17
1,994
199
113
55
162
46
109
116
146
323
261
103
14
30
44
109
50
114
28,341
856
686
221
244
1,527
3,373
248
770
2,280
146
430
411
707
685
341
95
13,946
83
333
855
104
%
9.1%
7.9%
4.7%
11.8%
8.0%
5.4%
6.9%
8.1%
10.0%
8.3%
8.0%
10.8%
6.7%
9.3%
7.5%
12.5%
10.1%
8.4%
7.4%
7.3%
8.6%
9.0%
14.5%
7.7%
5.9%
20.1%
12.9%
10.3%
5.4%
10.7%
7.5%
13.6%
12.8%
10.6%
11.1%
6.1%
7.3%
10.6%
9.9%
13.7%
9.8%
8.6%
6.8%
10.7%
4.9%
7.0%
8.3%
5.5%
4.1%
3.5%
5.1%
19.5%
6.8%
9.6%
6.2%
5.9%
High School
diploma
#
7,865
288
327
1,953
919
373
214
335
341
293
1,140
422
253
638
313
56
6,738
708
568
249
431
158
280
412
634
595
745
458
114
154
262
344
167
459
71,193
2,202
2,939
624
748
3,742
12,180
1,161
3,243
6,332
519
1,730
1,791
2,928
3,163
1,276
575
20,550
279
861
3,862
488
%
38.0%
44.4%
40.6%
33.9%
42.7%
36.6%
37.6%
41.1%
34.5%
36.3%
40.7%
39.8%
31.2%
42.5%
37.3%
41.2%
34.0%
29.7%
37.4%
33.0%
22.8%
30.9%
37.3%
27.4%
25.7%
37.0%
36.8%
45.8%
43.8%
55.0%
44.7%
42.8%
42.6%
42.6%
27.8%
15.6%
31.1%
30.0%
30.3%
33.6%
35.4%
40.2%
28.6%
29.8%
17.5%
28.0%
36.2%
22.9%
18.9%
13.2%
30.7%
28.8%
23.0%
24.8%
28.2%
27.5%
104
Some college
no degree
#
3,740
111
172
964
349
233
121
180
206
132
426
206
189
263
159
29
4,066
592
330
190
259
95
133
309
561
317
559
167
59
44
109
131
76
135
45,067
2,046
1,963
349
584
2,024
6,456
609
2,209
3,794
555
1,186
915
2,084
2,975
1,624
307
11,749
221
651
2,457
309
%
18.1%
17.1%
21.4%
16.7%
16.2%
22.9%
21.3%
22.1%
20.9%
16.4%
15.2%
19.5%
23.3%
17.5%
19.0%
21.3%
20.5%
24.9%
21.8%
25.2%
13.7%
18.6%
17.7%
20.5%
22.7%
19.7%
27.6%
16.7%
22.7%
15.7%
18.6%
16.3%
19.4%
12.5%
17.6%
14.5%
20.8%
16.8%
23.6%
18.2%
18.8%
21.1%
19.5%
17.9%
18.7%
19.2%
18.5%
16.3%
17.8%
16.8%
16.4%
16.5%
18.2%
18.8%
17.9%
17.4%
Associates
degree
#
2,766
79
105
733
300
122
84
107
161
105
390
143
98
200
128
11
2,335
257
187
107
199
70
52
196
447
89
147
110
35
17
72
95
41
214
26,649
1,092
1,149
234
200
1,220
4,119
331
1,705
2,226
306
769
627
1,356
1,791
778
239
5,893
115
386
1,814
299
%
13.4%
12.2%
13.0%
12.7%
13.9%
12.0%
14.8%
13.1%
16.3%
13.0%
13.9%
13.5%
12.1%
13.3%
15.3%
8.1%
11.8%
10.8%
12.3%
14.2%
10.5%
13.7%
6.9%
13.0%
18.1%
5.5%
7.3%
11.0%
13.5%
6.1%
12.3%
11.8%
10.5%
19.9%
10.4%
7.7%
12.2%
11.3%
8.1%
10.9%
12.0%
11.5%
15.0%
10.5%
10.3%
12.4%
12.7%
10.6%
10.7%
8.0%
12.7%
8.3%
9.5%
11.1%
13.2%
16.9%
Bachelors
degree
#
2,042
50
89
614
187
119
64
82
67
101
292
57
79
141
100
0
2,246
328
161
88
263
74
82
249
395
92
156
67
23
13
50
92
29
84
42,927
3,717
1,513
352
322
1,165
4,629
317
1,909
3,182
787
1,238
684
3,171
4,572
3,000
378
8,306
327
599
2,427
332
%
9.9%
7.7%
11.1%
10.6%
8.7%
11.7%
11.2%
10.1%
6.8%
12.5%
10.4%
5.4%
9.8%
9.4%
11.9%
0.0%
11.3%
13.8%
10.6%
11.7%
13.9%
14.5%
10.9%
16.5%
16.0%
5.7%
7.7%
6.7%
8.8%
4.6%
8.5%
11.4%
7.4%
7.8%
16.8%
26.3%
16.0%
16.9%
13.0%
10.4%
13.5%
11.0%
16.8%
15.0%
26.5%
20.0%
13.8%
24.8%
27.4%
31.0%
20.2%
11.6%
26.9%
17.3%
17.7%
18.7%
Graduate or
Professional
degree
#
%
1,385 6.7%
30
4.6%
50
6.2%
405 7.0%
64
3.0%
84
8.2%
40
7.0%
45
5.5%
86
8.7%
69
8.6%
193 6.9%
58
5.5%
104 12.8%
82
5.5%
68
8.1%
7
5.1%
1,793 9.1%
255 10.7%
116 7.6%
44
5.8%
472 25.0%
64 12.5%
55
7.3%
162 10.8%
238 9.6%
88
5.5%
79
3.9%
67
6.7%
11
4.2%
8
2.9%
33
5.6%
29
3.6%
23
5.9%
49
4.5%
30,024 11.7%
3,740 26.5%
956 10.1%
248 11.9%
238 9.6%
608 5.5%
2,532 7.4%
165 5.7%
1,261 11.1%
2,105 9.9%
623 21.0%
751 12.1%
406 8.2%
2,058 16.1%
3,174 19.0%
2,578 26.6%
250 13.3%
5,519 7.7%
180 14.8%
522 15.1%
1,904 13.9%
206 11.6%
TABLE 3.6A
Educational Attainment for Women 25 Years of Age and Older: By Town
< 9th grade
Ontario
Bristol
Canadice
Canandaigua city
Canandaigua
East Bloomfield
Farmington
Geneva city
Geneva
Gorham
Hopewell
Manchester
Naples
Phelps
Richmond
Seneca
South Bristol
Victor
West Bloomfield
Orleans
Albion
Barre
Carlton
Clarendon
Gaines
Kendall
Murray
Ridgeway
Shelby
Yates
Wayne
Arcadia
Butler
Galen
Huron
Lyons
Macedon
Marion
Ontario
Palmyra
Rose town
Savannah
Sodus
Walworth
Williamson
Wolcott
Yates
Barrington
Benton
Italy
Jerusalem
Middlesex
Milo
Potter
Starkey
Torrey
9-12 grade
no diploma
High School
diploma
Some college,
no degree
Associates
degree
Bachelors
degree
#
%
#
%
#
%
#
%
#
%
#
%
1,158 3.3% 2,835 8.2% 10,952 31.6% 6,945 20.0% 4,447 12.8% 4,829 13.9%
15
1.9%
71
8.9% 195 24.5% 153 19.2% 113 14.2% 158 19.8%
25
3.9%
74 11.5% 202 31.3% 127 19.7% 56
8.7%
99 15.3%
216 5.2% 317 7.6% 1,238 29.8% 876 21.1% 455 11.0% 636 15.3%
38
1.4% 165 6.3% 670 25.4% 540 20.5% 415 15.7% 406 15.4%
16
1.4%
60
5.3% 305 27.0% 247 21.8% 178 15.7% 178 15.7%
47
1.4% 229 6.6% 1,197 34.4% 738 21.2% 499 14.3% 462 13.3%
359 8.4% 535 12.5% 1,363 32.0% 706 16.6% 370 8.7% 501 11.8%
35
2.8%
92
7.3% 377 29.7% 328 25.9% 156 12.3% 164 12.9%
48
3.7% 153 11.8% 424 32.6% 219 16.9% 178 13.7% 134 10.3%
44
3.7% 123 10.2% 466 38.7% 289 24.0% 129 10.7% 106 8.8%
110 3.3% 308 9.4% 1,372 41.7% 641 19.5% 343 10.4% 325 9.9%
25
2.8%
85
9.4% 275 30.5% 166 18.4% 140 15.5% 154 17.1%
76
3.1% 239 9.8% 816 33.4% 443 18.1% 390 15.9% 274 11.2%
7
0.6%
58
5.0% 379 32.5% 291 24.9% 120 10.3% 185 15.9%
24
2.5%
60
6.3% 327 34.5% 193 20.4% 133 14.0% 121 12.8%
19
3.2%
27
4.5% 140 23.4% 112 18.7% 105 17.6% 110 18.4%
44
1.2% 170 4.8% 838 23.7% 730 20.7% 527 14.9% 703 19.9%
10
1.1%
69
7.6% 368 40.8% 146 16.2% 140 15.5% 113 12.5%
960 6.4% 2,225 14.9% 5,985 40.0% 2,431 16.2% 1,409 9.4% 1,230 8.2%
156 7.2% 253 11.6% 882 40.6% 345 15.9% 212 9.8% 218 10.0%
35
5.0%
84 12.0% 279 39.8% 124 17.7% 72 10.3% 74 10.6%
15
1.6%
98 10.4% 408 43.5% 142 15.1% 130 13.8% 98 10.4%
58
5.0% 157 13.6% 439 38.1% 215 18.6% 136 11.8% 126 10.9%
73
5.5% 157 11.9% 567 42.8% 237 17.9% 134 10.1% 97
7.3%
17
1.8%
72
7.7% 330 35.3% 217 23.2% 118 12.6% 106 11.3%
249 9.2% 786 29.0% 849 31.4% 336 12.4% 200 7.4% 155 5.7%
124 5.4% 265 11.5% 1,086 47.2% 361 15.7% 201 8.7% 142 6.2%
190 10.0% 263 13.8% 790 41.4% 302 15.8% 135 7.1% 157 8.2%
43
5.2%
90 10.8% 355 42.8% 152 18.3% 71
8.6%
57
6.9%
1,664 5.2% 3,493 11.0% 11,669 36.7% 5,919 18.6% 3,453 10.9% 3,374 10.6%
500 9.2% 701 12.9% 1,995 36.6% 783 14.4% 597 11.0% 504 9.2%
42
6.4%
78 11.9% 270 41.3% 144 22.0% 57
8.7%
47
7.2%
117 7.8% 168 11.3% 742 49.7% 241 16.1% 115 7.7%
86
5.8%
60
8.1%
98 13.2% 230 31.0% 158 21.3% 74 10.0% 93 12.5%
172 8.6% 287 14.4% 793 39.8% 287 14.4% 144 7.2% 150 7.5%
104 3.6% 219 7.6% 864 30.0% 633 22.0% 424 14.7% 413 14.3%
43
2.6% 150 9.1% 639 38.6% 336 20.3% 189 11.4% 153 9.3%
109 3.4% 231 7.1% 1,148 35.3% 682 21.0% 405 12.5% 406 12.5%
84
3.2% 366 13.8% 961 36.2% 507 19.1% 310 11.7% 216 8.1%
28
3.5% 116 14.6% 298 37.6% 183 23.1% 72
9.1%
52
6.6%
57
9.4%
95 15.7% 245 40.5% 91 15.0% 67 11.1% 28
4.6%
140 4.5% 426 13.8% 1,254 40.7% 607 19.7% 197 6.4% 284 9.2%
42
1.5% 134 4.9% 752 27.5% 508 18.6% 391 14.3% 630 23.1%
89
3.8% 166 7.0% 811 34.4% 535 22.7% 317 13.5% 218 9.3%
77
5.2% 258 17.5% 667 45.3% 224 15.2% 94
6.4%
94
6.4%
510 6.2% 988 12.1% 2,926 35.7% 1,457 17.8% 739 9.0% 789 9.6%
62 14.0% 46 10.4% 148 33.3% 76 17.1% 50 11.3% 30
6.8%
77
9.1%
98 11.5% 286 33.7% 170 20.0% 44
5.2% 101 11.9%
13
4.0%
57 17.3% 118 35.9% 62 18.8% 23
7.0%
25
7.6%
14
1.0% 130 8.9% 538 36.7% 253 17.3% 120 8.2% 201 13.7%
7
1.5%
45
9.5% 151 31.9% 99 20.9% 57 12.1% 57 12.1%
129 5.1% 379 14.9% 942 37.1% 484 19.1% 205 8.1% 206 8.1%
63 11.7% 40
7.4% 187 34.8% 102 19.0% 75 13.9% 40
7.4%
112 9.9% 138 12.1% 408 35.9% 131 11.5% 126 11.1% 99
8.7%
33
7.9%
55 13.1% 148 35.3% 80 19.1% 39
9.3%
30
7.2%
105
Graduate or
Professional
degree
#
%
3,498 10.1%
92 11.5%
63
9.8%
416 10.0%
403 15.3%
147 13.0%
307 8.8%
429 10.1%
116 9.1%
143 11.0%
47
3.9%
190 5.8%
57
6.3%
208 8.5%
127 10.9%
89
9.4%
85 14.2%
523 14.8%
56
6.2%
734 4.9%
108 5.0%
33
4.7%
48
5.1%
22
1.9%
59
4.5%
74
7.9%
133 4.9%
124 5.4%
71
3.7%
62
7.5%
2,236 7.0%
371 6.8%
16
2.4%
24
1.6%
30
4.0%
157 7.9%
226 7.8%
144 8.7%
269 8.3%
208 7.8%
43
5.4%
22
3.6%
175 5.7%
275 10.1%
219 9.3%
57
3.9%
784 9.6%
32
7.2%
73
8.6%
31
9.4%
208 14.2%
57 12.1%
195 7.7%
31
5.8%
123 10.8%
34
8.1%
TABLE 3.10A
Median Income in Dollars by Selected Households: By Town
Genesee
Alabama
Alexander
Batavia city
Batavia
Bergen
Bethany
Byron
Darien
Elba
Le Roy
Oakfield
Pavilion
Pembroke
Stafford
Tonawanda Res
Livingston
Avon
Caledonia
Conesus
Geneseo
Groveland
Leicester
Lima
Livonia
Mount Morris
North Dansville
Nunda
Ossian
Portage
Sparta
Springwater
West Sparta
York town
Monroe
Brighton
Chili
Clarkson
East Rochester
Gates
Greece
Hamlin
Henrietta
Irondequoit
Mendon
Ogden
Parma
Penfield
Perinton
Pittsford
Riga
Rochester
Rush
Sweden
Webster
Wheatland
Total
Households
$40,542
$40,223
$43,500
$33,484
$38,449
$49,412
$45,450
$49,722
$48,844
$46,161
$39,690
$41,579
$48,837
$41,266
$49,516
$25,208
$42,066
$43,971
$46,359
$48,200
$40,660
$46,797
$41,230
$48,774
$51,197
$32,813
$32,519
$40,665
$46,563
$32,500
$43,155
$43,059
$40,789
$43,229
$44,891
$52,066
$55,097
$53,438
$39,221
$45,709
$48,355
$49,987
$51,081
$45,276
$76,369
$59,240
$53,189
$63,223
$69,341
$88,232
$58,842
$27,123
$67,632
$44,151
$58,746
$55,239
Families
$47,771
$45,947
$51,364
$42,460
$43,425
$54,012
$50,234
$56,927
$50,844
$51,058
$49,189
$45,506
$51,750
$46,495
$54,667
$36,563
$50,513
$54,315
$50,607
$53,125
$62,206
$48,828
$46,652
$57,127
$55,382
$38,015
$41,519
$44,677
$50,938
$38,750
$48,333
$49,716
$44,583
$50,136
$55,900
$70,436
$61,481
$58,212
$48,553
$53,964
$57,102
$55,020
$60,803
$55,493
$87,827
$64,606
$60,686
$74,959
$80,606
$102,215
$65,106
$31,257
$73,269
$58,750
$69,629
$63,297
FAMILIES
Married
couples
$52,765
$47,885
$52,632
$52,161
$46,949
$57,552
$54,375
$60,500
$54,808
$55,758
$54,784
$50,368
$55,769
$47,669
$55,408
$39,338
$56,057
$59,073
$56,078
$57,917
$77,091
$52,202
$51,587
$62,935
$60,369
$46,776
$50,888
$51,677
$52,500
$43,333
$50,469
$53,320
$48,125
$53,045
$66,406
$77,900
$66,327
$60,851
$56,371
$58,682
$62,930
$61,083
$65,181
$60,687
$95,389
$69,043
$65,674
$81,401
$85,980
$105,980
$67,366
$48,400
$76,315
$67,117
$75,639
$67,031
106
FHH
$25,223
$20,000
$36,667
$21,600
$26,280
$32,250
$33,542
$24,464
$26,563
$33,542
$27,137
$22,500
$32,500
$27,955
$23,625
$22,143
$23,639
$19,904
$25,208
$45,192
$24,545
$27,750
$32,188
$27,143
$28,542
$19,818
$14,872
$21,359
$40,000
$23,125
$13,958
$29,688
$37,500
$24,904
$25,265
$39,757
$36,604
$30,889
$23,661
$35,182
$32,115
$23,984
$36,424
$32,397
$43,250
$41,052
$26,583
$37,669
$37,813
$43,177
$37,500
$17,953
$43,194
$32,128
$41,520
$37,115
Non-FAMILIES
FHH 65+ yrs,
living alone
FHH
$19,968
$14,436
$14,338
$12,857
$19,808
$18,571
$16,939
$14,397
$24,792
$14,125
$20,417
$15,114
$24,792
$14,688
$21,750
$11,641
$25,833
$18,269
$20,417
$12,115
$22,813
$14,118
$16,563
$11,346
$25,500
$13,958
$21,167
$17,344
$27,500
$16,250
$15,417
$8,250
$19,634
$15,847
$19,966
$17,128
$20,972
$16,696
$21,429
$15,313
$19,722
$16,435
$26,042
$19,500
$18,684
$16,500
$20,515
$17,981
$18,009
$15,677
$17,596
$14,615
$17,339
$13,423
$21,250
$15,893
$32,917
$14,375
$14,375
$10,000
$17,000
$16,477
$18,000
$13,750
$26,071
$8,750
$20,667
$16,964
$22,796
$16,237
$31,881
$21,992
$25,191
$19,446
$14,087
$12,717
$22,119
$16,691
$20,887
$14,759
$23,585
$17,312
$20,934
$17,500
$25,978
$17,402
$22,500
$14,987
$30,352
$14,219
$27,835
$18,068
$29,667
$21,321
$30,938
$18,627
$30,446
$19,388
$37,037
$28,287
$23,889
$16,731
$18,296
$13,081
$29,500
$25,000
$19,348
$13,622
$25,944
$16,801
$28,534
$19,099
TABLE 3.10A
Median Income in Dollars by Selected Households: By Town
Ontario
Bristol
Canadice
Canandaigua city
Canandaigua
East Bloomfield
Farmington
Geneva city
Geneva
Gorham
Hopewell
Manchester
Naples
Phelps
Richmond
Seneca
South Bristol
Victor
West Bloomfield
Orleans
Albion
Barre
Carlton
Clarendon
Gaines
Kendall
Murray
Ridgeway
Shelby
Yates
Wayne
Arcadia
Butler
Galen
Huron
Lyons
Macedon
Marion
Ontario
Palmyra
Rose town
Savannah
Sodus
Walworth
Williamson
Wolcott
Yates
Barrington
Benton
Italy
Jerusalem
Middlesex
Milo
Potter
Starkey
Torrey
Total
Households
$44,579
$53,250
$44,893
$37,197
$57,978
$52,176
$49,863
$31,600
$44,234
$43,138
$41,604
$39,154
$36,813
$47,246
$50,536
$48,007
$52,313
$59,349
$43,347
$37,972
$34,028
$44,545
$40,660
$46,667
$32,604
$49,821
$38,672
$35,206
$34,091
$39,803
$44,157
$37,755
$38,462
$36,098
$41,389
$39,322
$48,640
$48,205
$51,399
$45,542
$41,205
$35,913
$39,528
$64,611
$51,068
$33,516
$34,640
$36,184
$37,500
$33,750
$38,488
$43,618
$31,102
$42,784
$29,337
$39,453
Families
$52,698
$60,172
$52,596
$47,388
$65,170
$56,171
$54,769
$41,224
$58,350
$45,917
$46,452
$47,117
$42,566
$53,854
$54,306
$51,705
$56,346
$71,526
$52,206
$42,830
$37,188
$47,411
$42,028
$52,064
$38,523
$50,952
$45,184
$41,696
$40,972
$42,165
$51,495
$46,239
$41,587
$45,175
$47,656
$47,593
$55,218
$52,484
$61,281
$54,076
$46,000
$41,466
$46,286
$67,830
$56,757
$40,265
$40,681
$38,864
$43,988
$36,250
$45,254
$47,763
$38,547
$47,188
$34,453
$40,350
FAMILIES
Married
couples
$58,627
$64,265
$54,405
$56,423
$67,658
$60,549
$59,833
$51,129
$60,640
$51,154
$49,276
$52,176
$48,450
$61,573
$57,717
$53,355
$63,269
$77,220
$58,382
$49,013
$44,808
$49,352
$44,773
$53,504
$42,458
$54,274
$52,888
$47,364
$48,542
$47,109
$57,267
$54,280
$44,837
$51,300
$51,726
$51,343
$61,814
$54,741
$70,097
$57,974
$48,226
$47,188
$51,524
$71,012
$61,367
$48,150
$46,290
$41,528
$47,143
$38,750
$48,811
$51,667
$44,732
$52,670
$43,208
$42,679
107
FHH
$27,271
$29,000
$30,500
$30,066
$32,337
$18,750
$26,653
$20,308
$35,417
$32,727
$24,405
$19,519
$21,250
$24,839
$29,265
$44,375
$23,281
$44,167
$36,563
$22,647
$17,120
$34,583
$19,659
$19,338
$24,167
$33,438
$26,467
$21,513
$25,438
$20,893
$25,437
$25,995
$25,417
$22,386
$19,167
$25,909
$23,403
$29,107
$28,333
$26,477
$34,167
$30,313
$22,596
$29,545
$32,569
$18,375
$20,022
$18,500
$23,750
$14,750
$30,139
$22,143
$19,522
$22,639
$16,204
$20,313
Non-FAMILIES
FHH 65+ yrs,
living alone
FHH
$21,370
$15,822
$30,500
$13,750
$19,531
$12,679
$17,889
$15,625
$23,534
$21,974
$22,917
$13,063
$27,330
$19,375
$16,949
$13,615
$21,500
$20,563
$20,385
$14,152
$20,150
$17,417
$19,232
$16,397
$20,000
$11,447
$22,206
$16,823
$26,250
$15,000
$18,542
$12,778
$24,167
$19,167
$31,250
$19,120
$24,028
$14,444
$18,558
$14,953
$18,077
$16,688
$18,295
$15,625
$21,761
$10,938
$28,382
$15,000
$17,734
$16,892
$24,688
$16,875
$23,403
$13,664
$19,133
$14,556
$14,840
$14,071
$16,667
$13,214
$18,134
$12,830
$17,500
$13,117
$12,059
$11,771
$14,330
$13,375
$15,882
$13,125
$18,558
$16,458
$27,292
$16,875
$20,086
$13,365
$21,122
$12,629
$17,458
$13,571
$18,631
$13,229
$13,214
$12,273
$16,333
$9,359
$31,319
$18,654
$16,836
$12,148
$13,531
$11,653
$16,675
$13,982
$18,750
$18,750
$21,667
$18,482
$14,792
$13,125
$20,446
$21,071
$17,344
$17,857
$14,835
$13,015
$25,469
$12,917
$15,500
$12,016
$22,500
$16,042
Table 3.12A-1
Median Earnings for Men and Women, 16 Years of Age and Older, in Dollars
FULL-TIME, YEAR ROUND
Median
Earnings
Genesee
Alabama
Alexander
Batavia city
Batavia
Bergen
Bethany
Byron
Darien
Elba
Le Roy
Oakfield
Pavilion
Pembroke
Stafford
Tonawanda Res
Livingston
Avon
Caledonia
Conesus
Geneseo
Groveland
Leicester
Lima
Livonia
Mount Morris
North Dansville
Nunda
Ossian
Portage
Sparta
Springwater
West Sparta
York town
Monroe
Brighton
Chili
Clarkson
East Rochester
Gates
Greece
Hamlin
Henrietta
Irondequoit
Mendon
Ogden
Parma
Penfield
Perinton
Pittsford
Riga
Rochester
Rush
Sweden
Webster
Wheatland
$30,009
$28,448
$30,890
$27,919
$28,063
$33,667
$29,417
$33,490
$29,569
$31,640
$30,820
$29,882
$28,422
$30,625
$30,313
$21,406
$31,316
$31,774
$32,189
$36,061
$35,392
$24,456
$31,431
$31,775
$35,346
$28,268
$28,464
$29,760
$28,816
$24,737
$30,718
$30,669
$30,121
$28,036
$35,699
$40,733
$37,422
$35,404
$30,374
$34,433
$36,192
$31,947
$35,665
$36,577
$50,540
$40,029
$37,912
$45,059
$48,913
$60,084
$40,633
$27,304
$43,171
$32,920
$42,503
$35,929
Median
Earnings
Male
$34,430
$32,454
$37,553
$32,091
$32,098
$36,913
$32,113
$38,828
$35,000
$37,244
$36,810
$32,355
$31,074
$32,487
$35,548
$31,250
$36,599
$40,654
$37,287
$40,313
$42,218
$24,353
$35,450
$40,607
$40,800
$31,940
$31,503
$32,483
$32,313
$29,000
$35,938
$36,633
$31,781
$34,048
$41,279
$46,292
$45,156
$41,397
$32,094
$38,815
$41,563
$39,722
$39,636
$41,463
$59,750
$46,145
$42,566
$52,282
$60,587
$70,780
$47,083
$30,521
$48,802
$39,850
$50,263
$41,733
108
Median
Earnings
Female
$23,788
$23,456
$21,020
$23,289
$24,404
$26,571
$24,643
$24,877
$23,278
$24,688
$23,024
$23,403
$25,125
$25,046
$24,375
$12,159
$25,228
$25,559
$28,159
$25,000
$25,969
$26,477
$26,420
$26,316
$30,578
$20,625
$25,500
$22,660
$23,977
$18,846
$23,693
$22,472
$25,982
$20,430
$29,553
$35,171
$29,903
$25,795
$26,953
$29,024
$29,864
$24,698
$30,271
$30,937
$37,309
$30,438
$29,381
$33,365
$36,113
$39,336
$31,202
$25,139
$39,357
$27,103
$33,197
$30,733
Percentage of
Female Earnings
to Male
69%
72%
56%
73%
76%
72%
77%
64%
67%
66%
63%
72%
81%
77%
69%
39%
69%
63%
76%
62%
62%
109%
75%
65%
75%
65%
81%
70%
74%
65%
66%
61%
82%
60%
72%
76%
66%
62%
84%
75%
72%
62%
76%
75%
62%
66%
69%
64%
60%
56%
66%
82%
81%
68%
66%
74%
Table 3.12A-1
Median Earnings for Men and Women, 16 Years of Age and Older, in Dollars
FULL-TIME, YEAR ROUND
Median
Earnings
Ontario
Bristol
Canadice
Canandaigua city
Canandaigua
East Bloomfield
Farmington
Geneva city
Geneva
Gorham
Hopewell
Manchester
Naples
Phelps
Richmond
Seneca
South Bristol
Victor
West Bloomfield
Orleans
Albion
Barre
Carlton
Clarendon
Gaines
Kendall
Murray
Ridgeway
Shelby
Yates
Wayne
Arcadia
Butler
Galen
Huron
Lyons
Macedon
Marion
Ontario
Palmyra
Rose town
Savannah
Sodus
Walworth
Williamson
Wolcott
Yates
Barrington
Benton
Italy
Jerusalem
Middlesex
Milo
Potter
Starkey
Torrey
$31,421
$33,684
$34,231
$29,512
$34,401
$33,413
$32,147
$28,025
$30,900
$29,482
$27,385
$29,139
$31,313
$30,630
$30,961
$30,124
$36,515
$43,372
$30,553
$28,818
$29,430
$29,271
$29,754
$30,036
$27,080
$30,738
$30,385
$26,752
$28,333
$26,895
$31,904
$29,077
$29,474
$29,304
$30,833
$27,994
$34,233
$32,830
$37,355
$31,458
$29,268
$27,179
$30,898
$39,348
$36,088
$27,485
$25,962
$26,500
$25,726
$25,403
$26,838
$30,234
$24,203
$27,217
$24,933
$27,292
Median
Earnings
Male
$36,732
$38,182
$38,523
$31,950
$41,302
$43,438
$39,645
$31,315
$39,186
$35,658
$30,575
$32,444
$34,508
$34,792
$36,114
$31,301
$40,909
$51,006
$37,340
$32,450
$31,960
$32,284
$32,188
$34,432
$30,709
$36,959
$32,795
$33,189
$32,087
$32,586
$36,825
$32,602
$32,617
$31,900
$34,205
$31,447
$41,166
$40,086
$41,771
$36,631
$34,028
$31,364
$34,935
$44,552
$39,250
$32,617
$29,671
$29,301
$26,951
$25,795
$31,071
$34,464
$28,475
$31,111
$28,750
$31,625
109
Median
Earnings
Female
$26,139
$27,900
$26,118
$26,538
$28,191
$24,336
$26,097
$23,054
$23,108
$23,682
$23,354
$25,041
$23,333
$26,498
$26,461
$27,146
$29,853
$35,208
$26,410
$22,605
$23,500
$21,193
$23,448
$22,545
$21,917
$24,309
$26,113
$20,881
$21,299
$20,224
$26,470
$24,419
$21,723
$25,789
$25,500
$25,990
$28,924
$24,545
$30,000
$24,083
$22,167
$23,125
$26,296
$32,292
$31,250
$21,898
$21,566
$18,482
$23,250
$24,167
$25,115
$23,676
$20,109
$22,500
$20,583
$24,083
Percentage of
Female Earnings
to Male
71%
73%
68%
83%
68%
56%
66%
74%
59%
66%
76%
77%
68%
76%
73%
87%
73%
69%
71%
70%
74%
66%
73%
65%
71%
66%
80%
63%
66%
62%
72%
75%
67%
81%
75%
83%
70%
61%
72%
66%
65%
74%
75%
72%
80%
67%
73%
63%
86%
94%
81%
69%
71%
72%
72%
76%
Table 3.12A-2
Median Earnings for Men and Women, 16 Years of Age and Older, in Dollars
PART-TIME
Median
Earnings
Genesee
Alabama
Alexander
Batavia city
Batavia
Bergen
Bethany
Byron
Darien
Elba
Le Roy
Oakfield
Pavilion
Pembroke
Stafford
Tonawanda Res
Livingston
Avon
Caledonia
Conesus
Geneseo
Groveland
Leicester
Lima
Livonia
Mount Morris
North Dansville
Nunda
Ossian
Portage
Sparta
Springwater
West Sparta
York town
Monroe
Brighton
Chili
Clarkson
East Rochester
Gates
Greece
Hamlin
Henrietta
Irondequoit
Mendon
Ogden
Parma
Penfield
Perinton
Pittsford
Riga
Rochester
Rush
Sweden
Webster
Wheatland
$9,182
$8,963
$10,275
$8,470
$9,800
$11,883
$8,536
$9,691
$9,508
$8,607
$9,281
$8,241
$10,878
$7,425
$9,266
$8,875
$6,670
$8,675
$9,076
$9,719
$3,813
$6,631
$11,097
$8,184
$11,687
$9,985
$8,381
$9,375
$11,250
$7,212
$10,179
$10,244
$9,688
$10,064
$9,650
$13,715
$10,365
$7,789
$9,458
$10,065
$10,206
$9,645
$6,380
$10,954
$14,766
$9,989
$10,500
$10,151
$12,490
$8,798
$10,404
$9,143
$9,232
$6,032
$10,664
$9,641
Median
Earnings
Male
$10,733
$11,413
$11,726
$9,006
$14,605
$12,400
$12,917
$11,324
$11,071
$12,250
$10,223
$9,676
$13,571
$8,611
$8,482
$3,636
$6,975
$9,545
$10,531
$11,750
$3,877
$6,380
$10,450
$7,453
$16,875
$12,353
$9,219
$11,571
$10,625
$11,250
$13,281
$12,917
$12,813
$7,100
$9,752
$15,512
$10,202
$8,622
$9,294
$10,790
$10,905
$8,884
$5,604
$11,561
$12,267
$10,109
$11,103
$10,784
$12,491
$10,728
$9,955
$9,668
$5,278
$6,839
$11,330
$8,103
110
Median
Earnings
Female
$8,443
$7,500
$9,031
$8,193
$7,826
$11,625
$7,344
$9,087
$8,841
$6,917
$8,676
$7,585
$9,818
$7,310
$9,609
$10,938
$6,389
$8,388
$8,339
$8,698
$3,783
$10,958
$11,771
$8,670
$10,816
$8,730
$7,876
$8,168
$11,442
$6,780
$8,092
$9,745
$6,875
$10,448
$9,589
$12,624
$10,440
$7,265
$9,567
$9,728
$9,878
$9,919
$7,233
$10,597
$15,563
$9,903
$10,323
$9,851
$12,490
$7,418
$10,543
$8,774
$11,676
$5,393
$10,444
$10,225
Percentage of
Female Earnings
to Male
79%
66%
77%
91%
54%
94%
57%
80%
80%
56%
85%
78%
72%
85%
113%
301%
92%
88%
79%
74%
98%
172%
113%
116%
64%
71%
85%
71%
108%
60%
61%
75%
54%
147%
98%
81%
102%
84%
103%
90%
91%
112%
129%
92%
127%
98%
93%
91%
100%
69%
106%
91%
221%
79%
92%
126%
Table 3.12A-2
Median Earnings for Men and Women, 16 Years of Age and Older, in Dollars
PART-TIME
Median
Earnings
Ontario
Bristol
Canadice
Canandaigua city
Canandaigua
East Bloomfield
Farmington
Geneva city
Geneva
Gorham
Hopewell
Manchester
Naples
Phelps
Richmond
Seneca
South Bristol
Victor
West Bloomfield
Orleans
Albion
Barre
Carlton
Clarendon
Gaines
Kendall
Murray
Ridgeway
Shelby
Yates
Wayne
Arcadia
Butler
Galen
Huron
Lyons
Macedon
Marion
Ontario
Palmyra
Rose town
Savannah
Sodus
Walworth
Williamson
Wolcott
Yates
Barrington
Benton
Italy
Jerusalem
Middlesex
Milo
Potter
Starkey
Torrey
$8,945
$9,799
$11,205
$8,698
$10,432
$10,773
$10,249
$5,210
$8,750
$9,834
$10,557
$9,703
$10,137
$10,027
$9,978
$8,538
$11,344
$10,621
$10,154
$8,310
$8,253
$9,099
$9,201
$9,387
$8,237
$10,294
$7,168
$8,427
$7,338
$8,886
$9,280
$8,797
$7,857
$7,607
$6,656
$8,151
$10,979
$7,894
$10,386
$10,316
$12,604
$7,550
$9,541
$9,805
$8,408
$9,197
$7,089
$9,183
$6,887
$8,229
$4,432
$9,607
$7,641
$10,066
$7,247
$10,189
Median
Earnings
Male
$9,831
$10,682
$14,583
$9,572
$12,663
$11,656
$10,772
$6,115
$8,697
$9,680
$10,469
$11,685
$11,583
$10,827
$10,726
$7,542
$13,194
$12,526
$10,000
$10,085
$9,704
$13,750
$9,202
$12,841
$7,347
$11,781
$10,625
$9,760
$8,772
$21,875
$10,188
$9,922
$8,523
$7,844
$6,862
$7,063
$13,678
$9,375
$10,171
$13,531
$13,523
$10,054
$9,421
$7,981
$9,425
$11,266
$8,544
$9,844
$5,903
$9,821
$5,897
$9,306
$8,829
$11,167
$8,333
$13,472
111
Median
Earnings
Female
$8,360
$9,493
$9,420
$7,977
$9,767
$9,821
$9,953
$4,644
$8,917
$9,955
$10,607
$8,202
$9,625
$9,468
$9,181
$9,393
$9,643
$9,508
$10,227
$7,385
$7,315
$6,563
$9,201
$8,918
$8,611
$7,225
$6,325
$7,727
$6,729
$6,828
$8,761
$7,876
$7,457
$7,418
$6,364
$8,932
$9,547
$6,899
$10,534
$8,730
$12,105
$7,174
$9,580
$10,230
$7,241
$7,480
$6,436
$8,125
$7,393
$7,298
$4,082
$9,712
$7,155
$9,276
$6,776
$7,500
Percentage of
Female Earnings
to Male
85%
89%
65%
83%
77%
84%
92%
76%
103%
103%
101%
70%
83%
87%
86%
125%
73%
76%
102%
73%
75%
48%
100%
69%
117%
61%
60%
79%
77%
31%
86%
79%
87%
95%
93%
126%
70%
74%
104%
65%
90%
71%
102%
128%
77%
66%
75%
83%
125%
74%
69%
104%
81%
83%
81%
56%
Table 3.12A-3
Median Earnings for Men and Women, 16 Years of Age and Older, in Dollars
FULL-TIME & PART-TIME TOGETHER
Median
Earnings
Genesee
Alabama
Alexander
Batavia city
Batavia
Bergen
Bethany
Byron
Darien
Elba
Le Roy
Oakfield
Pavilion
Pembroke
Stafford
Tonawanda Res
Livingston
Avon
Caledonia
Conesus
Geneseo
Groveland
Leicester
Lima
Livonia
Mount Morris
North Dansville
Nunda
Ossian
Portage
Sparta
Springwater
West Sparta
York town
Monroe
Brighton
Chili
Clarkson
East Rochester
Gates
Greece
Hamlin
Henrietta
Irondequoit
Mendon
Ogden
Parma
Penfield
Perinton
Pittsford
Riga
Rochester
Rush
Sweden
Webster
Wheatland
$21,641
$20,927
$20,973
$19,478
$20,767
$25,191
$22,179
$23,613
$22,232
$22,250
$23,181
$21,787
$25,294
$21,604
$25,091
$12,454
$18,841
$24,029
$25,160
$24,694
$5,116
$10,034
$25,494
$20,015
$29,201
$18,971
$20,319
$21,933
$24,792
$17,829
$21,496
$22,307
$22,279
$21,415
$25,602
$32,271
$28,659
$27,391
$22,074
$26,845
$27,188
$24,327
$19,689
$28,470
$36,960
$29,060
$27,484
$31,848
$35,196
$35,254
$30,409
$20,220
$32,200
$14,901
$31,564
$27,532
Median
Earnings
Male
$28,327
$28,384
$31,395
$24,562
$26,456
$30,994
$29,777
$32,625
$30,098
$30,368
$30,414
$29,274
$29,263
$27,660
$30,814
$20,972
$24,814
$32,481
$30,838
$35,182
$6,917
$8,867
$30,259
$28,342
$36,623
$27,179
$25,136
$29,745
$29,688
$26,296
$30,042
$30,161
$28,000
$29,141
$32,040
$39,305
$37,438
$37,215
$25,893
$33,069
$35,912
$32,280
$21,396
$35,364
$51,129
$40,334
$36,869
$43,673
$50,129
$55,236
$40,417
$22,326
$39,250
$21,069
$42,380
$31,322
112
Median
Earnings
Female
$15,787
$14,356
$13,843
$15,703
$13,862
$19,779
$15,529
$17,188
$15,787
$15,430
$16,807
$15,154
$19,528
$13,733
$16,806
$11,833
$13,703
$16,800
$20,407
$16,741
$4,565
$19,167
$19,741
$14,304
$19,122
$12,281
$16,500
$16,167
$20,132
$12,344
$16,458
$18,729
$16,397
$15,731
$20,501
$26,766
$21,570
$18,629
$20,189
$21,328
$20,743
$18,759
$17,697
$22,115
$29,095
$20,579
$20,522
$23,398
$25,095
$21,464
$20,591
$17,907
$25,826
$10,964
$23,049
$24,292
Percentage of
Female Earnings
to Male
56%
51%
44%
64%
52%
64%
52%
53%
52%
51%
55%
52%
67%
50%
55%
56%
55%
52%
66%
48%
66%
216%
65%
50%
52%
45%
66%
54%
68%
47%
55%
62%
59%
54%
64%
68%
58%
50%
78%
64%
58%
58%
83%
63%
57%
51%
56%
54%
50%
39%
51%
80%
66%
52%
54%
78%
Table 3.12A-3
Median Earnings for Men and Women, 16 Years of Age and Older, in Dollars
FULL-TIME & PART-TIME TOGETHER
Median
Earnings
Ontario
Bristol
Canadice
Canandaigua city
Canandaigua
East Bloomfield
Farmington
Geneva city
Geneva
Gorham
Hopewell
Manchester
Naples
Phelps
Richmond
Seneca
South Bristol
Victor
West Bloomfield
Orleans
Albion
Barre
Carlton
Clarendon
Gaines
Kendall
Murray
Ridgeway
Shelby
Yates
Wayne
Arcadia
Butler
Galen
Huron
Lyons
Macedon
Marion
Ontario
Palmyra
Rose town
Savannah
Sodus
Walworth
Williamson
Wolcott
Yates
Barrington
Benton
Italy
Jerusalem
Middlesex
Milo
Potter
Starkey
Torrey
$23,554
$26,970
$25,764
$21,902
$27,002
$25,610
$24,657
$13,799
$24,779
$22,523
$21,720
$23,465
$23,500
$24,702
$24,646
$25,048
$26,903
$32,599
$24,666
$20,269
$18,354
$21,474
$21,390
$24,258
$21,045
$25,227
$17,327
$18,995
$19,340
$20,698
$24,259
$22,353
$20,931
$22,603
$21,395
$21,899
$26,703
$22,263
$28,241
$23,874
$23,486
$21,294
$22,695
$30,971
$27,131
$19,697
$17,457
$18,424
$20,951
$16,780
$13,325
$22,114
$17,331
$21,559
$15,516
$19,306
Median
Earnings
Male
$30,037
$36,300
$31,902
$25,772
$35,337
$35,523
$33,049
$18,881
$30,402
$28,656
$25,772
$28,588
$31,042
$29,940
$30,066
$28,218
$35,028
$44,809
$29,964
$26,832
$20,734
$28,426
$26,161
$31,240
$26,538
$32,545
$29,073
$26,373
$27,023
$30,733
$30,652
$27,557
$25,435
$28,500
$26,439
$25,807
$32,320
$32,771
$36,356
$32,033
$30,699
$26,875
$27,904
$39,060
$31,966
$23,729
$22,528
$25,078
$23,459
$21,875
$21,422
$26,991
$21,517
$27,095
$19,362
$24,500
113
Median
Earnings
Female
$18,208
$19,570
$20,449
$17,926
$19,529
$19,603
$19,682
$11,045
$19,383
$20,107
$18,904
$18,805
$15,625
$19,966
$19,929
$20,047
$20,259
$21,568
$19,500
$15,034
$16,217
$14,318
$16,164
$18,026
$15,653
$19,726
$11,257
$15,254
$13,859
$11,480
$18,903
$17,073
$17,234
$17,176
$18,250
$19,267
$20,305
$17,425
$21,276
$17,336
$19,342
$17,667
$19,491
$23,929
$21,366
$14,504
$13,013
$15,074
$17,977
$11,652
$9,049
$16,250
$12,574
$14,612
$12,791
$14,102
Percentage of
Female Earnings
to Male
61%
54%
64%
70%
55%
55%
60%
58%
64%
70%
73%
66%
50%
67%
66%
71%
58%
48%
65%
56%
78%
50%
62%
58%
59%
61%
39%
58%
51%
37%
62%
62%
68%
60%
69%
75%
63%
53%
59%
54%
63%
66%
70%
61%
67%
61%
58%
60%
77%
53%
42%
60%
58%
54%
66%
58%
TABLE 3.15 A
Numbers and Percentages of Females in Poverty by Age: By Town
Total
Female
Population
In poverty
Genesee
Alabama
Alexander
Batavia city
Batavia
Bergen
Bethany
Byron
Darien
Elba
Le Roy
Oakfield
Pavilion
Pembroke
Stafford
Tonawanda Res
Livingston
Avon
Caledonia
Conesus
Geneseo
Groveland
Leicester
Lima
Livonia
Mount Morris
North Dansville
Nunda
Ossian
Portage
Sparta
Springwater
West Sparta
York town
Monroe
Brighton
Chili
Clarkson
East Rochester
Gates
Greece
Hamlin
Henrietta
Irondequoit
Mendon
Ogden
Parma
Penfield
Perinton
Pittsford
Riga
Rochester
Rush
Sweden
Webster
Wheatland
2,631
72
105
1,124
343
73
45
76
65
71
232
152
50
134
57
32
3,459
246
133
78
1,149
74
93
106
203
355
479
174
9
64
87
121
60
28
44,654
1,220
641
163
362
986
2,746
280
1,476
1,808
175
258
344
771
769
478
137
30,184
13
844
858
141
% of
# of Females
% of
# of Females
% of
# of Females
% of
Females in
< 18 in
Females <
18-64 in Females 1865+
Females 65+
poverty of
poverty
18 in
poverty
64 in
in poverty in poverty
all in
poverty
poverty
poverty
58.5%
55.4%
61.8%
60.5%
60.6%
55.7%
50.0%
61.8%
67.0%
44.9%
54.0%
61.3%
37.9%
61.5%
64.0%
58.2%
57.5%
55.7%
58.6%
65.5%
60.1%
58.7%
49.7%
57.9%
52.9%
62.6%
54.7%
63.7%
18.0%
53.8%
52.7%
53.8%
56.1%
50.0%
56.3%
58.7%
65.8%
56.6%
58.5%
61.0%
61.0%
49.6%
48.5%
64.7%
69.7%
51.8%
56.4%
61.2%
57.6%
65.7%
65.9%
55.2%
76.5%
54.8%
57.8%
77.5%
727
17
45
321
98
20
13
25
10
28
43
34
9
47
9
8
721
60
44
26
103
31
21
21
68
82
103
63
0
17
30
41
11
0
14,570
239
199
46
72
165
824
73
321
376
61
56
137
193
173
104
11
11,206
0
71
182
61
114
27.6%
23.6%
42.9%
28.6%
28.6%
27.4%
28.9%
32.9%
15.4%
39.4%
18.5%
22.4%
18.0%
35.1%
15.8%
25.0%
20.8%
24.4%
33.1%
33.3%
9.0%
41.9%
22.6%
19.8%
33.5%
23.1%
21.5%
36.2%
0.0%
26.6%
34.5%
33.9%
18.3%
0.0%
32.6%
19.6%
31.0%
28.2%
19.9%
16.7%
30.0%
26.1%
21.7%
20.8%
34.9%
21.7%
39.8%
25.0%
22.5%
21.8%
8.0%
37.1%
0.0%
8.4%
21.2%
43.3%
1,534
43
48
689
188
34
28
42
44
35
122
100
37
73
36
15
2,463
169
85
44
1,034
41
68
65
124
221
284
100
9
42
53
62
42
20
25,136
695
382
85
250
578
1,299
197
1,046
781
72
174
158
389
486
155
84
17,011
13
711
502
68
58.3%
59.7%
45.7%
61.3%
54.8%
46.6%
62.2%
55.3%
67.7%
49.3%
52.6%
65.8%
74.0%
54.5%
63.2%
46.9%
71.2%
68.7%
63.9%
56.4%
90.0%
55.4%
73.1%
61.3%
61.1%
62.3%
59.3%
57.5%
100.0%
65.6%
60.9%
51.2%
70.0%
71.4%
56.3%
57.0%
59.6%
52.1%
69.1%
58.6%
47.3%
70.4%
70.9%
43.2%
41.1%
67.4%
45.9%
50.5%
63.2%
32.4%
61.3%
56.4%
100.0%
84.2%
58.5%
48.2%
370
12
12
114
57
19
4
9
11
8
67
18
4
14
12
9
275
17
4
8
12
2
4
20
11
52
92
11
0
5
4
18
7
8
4,948
286
60
32
40
243
623
10
109
651
42
28
49
189
110
219
42
1,967
0
62
174
12
14.1%
16.7%
11.4%
10.1%
16.6%
26.0%
8.9%
11.8%
16.9%
11.3%
28.9%
11.8%
8.0%
10.4%
21.1%
28.1%
8.0%
6.9%
3.0%
10.3%
1.0%
2.7%
4.3%
18.9%
5.4%
14.6%
19.2%
6.3%
0.0%
7.8%
4.6%
14.9%
11.7%
28.6%
11.1%
23.4%
9.4%
19.6%
11.0%
24.6%
22.7%
3.6%
7.4%
36.0%
24.0%
10.9%
14.2%
24.5%
14.3%
45.8%
30.7%
6.5%
0.0%
7.3%
20.3%
8.5%
TABLE 3.15 A
Numbers and Percentages of Females in Poverty by Age: By Town
Total
Female
Population
In poverty
4,168
Ontario
Bristol
63
Canadice
63
Canandaigua city
630
Canandaigua
264
East Bloomfield
70
Farmington
304
Geneva city
1,207
Geneva
74
Gorham
124
Hopewell
160
Manchester
410
Naples
172
Phelps
197
Richmond
48
Seneca
67
South Bristol
74
Victor
192
West Bloomfield
49
2,518
Orleans
Albion
521
Barre
73
Carlton
128
Clarendon
215
Gaines
240
Kendall
78
Murray
185
Ridgeway
527
Shelby
444
Yates
107
4,527
Wayne
Arcadia
1,022
Butler
120
Galen
236
Huron
180
Lyons
260
Macedon
358
Marion
123
Ontario
331
Palmyra
299
Rose town
69
Savannah
167
Sodus
624
Walworth
119
Williamson
168
Wolcott
451
1,800
Yates
Barrington
120
Benton
175
Italy
70
Jerusalem
222
Middlesex
64
Milo
588
Potter
110
Starkey
373
Torrey
78
% of
# of Females
% of
# of Females
% of
# of Females
% of
Females in
< 18 in
Females <
18-64 in Females 1865+
Females 65+
poverty
poverty
18 in
poverty
64 in
in poverty in poverty
poverty
poverty
58.7%
1,187
28.5%
2,397
57.5%
584
14.0%
47.7%
13
20.6%
42
66.7%
8
12.7%
53.8%
11
17.5%
44
69.8%
8
12.7%
61.8%
150
23.8%
341
54.1%
139
22.1%
65.7%
29
11.0%
165
62.5%
70
26.5%
51.1%
16
22.9%
34
48.6%
20
28.6%
51.5%
92
30.3%
190
62.5%
22
7.2%
58.3%
441
36.5%
661
54.8%
105
8.7%
66.7%
17
23.0%
57
77.0%
0
0.0%
47.1%
23
18.5%
85
68.5%
16
12.9%
65.0%
57
35.6%
85
53.1%
18
11.3%
53.5%
117
28.5%
269
65.6%
24
5.9%
68.3%
62
36.0%
75
43.6%
35
20.3%
65.7%
54
27.4%
111
56.3%
32
16.2%
48.0%
22
45.8%
18
37.5%
8
16.7%
71.3%
7
10.4%
37
55.2%
23
34.3%
57.4%
15
20.3%
56
75.7%
3
4.1%
65.1%
61
31.8%
91
47.4%
40
20.8%
61.3%
0
0.0%
36
73.5%
13
26.5%
57.5%
887
35.2%
1,466
58.2%
165
6.6%
58.3%
149
28.6%
330
63.3%
42
8.1%
54.9%
18
24.7%
48
65.8%
7
9.6%
42.5%
37
28.9%
83
64.8%
8
6.3%
79.3%
70
32.6%
138
64.2%
7
3.3%
61.5%
112
46.7%
103
42.9%
25
10.4%
54.5%
26
33.3%
52
66.7%
0
0.0%
55.1%
77
41.6%
88
47.6%
20
10.8%
53.4%
198
37.6%
305
57.9%
24
4.6%
61.8%
159
35.8%
264
59.5%
21
4.7%
51.9%
41
38.3%
55
51.4%
11
10.3%
57.1%
1,389
30.7%
2,434
53.8%
704
15.6%
54.0%
382
37.4%
509
49.8%
131
12.8%
62.5%
40
33.3%
60
50.0%
20
16.7%
54.0%
46
19.5%
158
66.9%
32
13.6%
67.7%
54
30.0%
63
35.0%
63
35.0%
50.6%
61
23.5%
146
56.2%
53
20.4%
60.9%
132
36.9%
208
58.1%
18
5.0%
46.2%
37
30.1%
76
61.8%
10
8.1%
61.8%
91
27.5%
185
55.9%
55
16.6%
61.5%
61
20.4%
181
60.5%
57
19.1%
49.6%
20
29.0%
33
47.8%
16
23.2%
53.7%
70
41.9%
79
47.3%
18
10.8%
58.4%
210
33.7%
318
51.0%
96
15.4%
65.4%
19
16.0%
68
57.1%
32
26.9%
53.2%
16
9.5%
103
61.3%
49
29.2%
61.5%
150
33.3%
247
54.8%
54
12.0%
58.6%
674
37.4%
941
52.3%
185
10.3%
51.3%
58
48.3%
55
45.8%
7
5.8%
54.2%
103
58.9%
63
36.0%
9
5.1%
50.7%
22
31.4%
47
67.1%
1
1.4%
60.2%
77
34.7%
122
55.0%
23
10.4%
71.9%
17
26.6%
33
51.6%
14
21.9%
63.2%
149
25.3%
344
58.5%
95
16.2%
62.5%
48
43.6%
58
52.7%
4
3.6%
56.9%
158
42.4%
185
49.6%
30
8.0%
50.0%
42
53.8%
34
43.6%
2
2.6%
115
TABLE 3.16 A
Numbers and Percentages of Children in Poverty by Age: By Town
Genesee
Alabama
Alexander
Batavia city
Batavia
Bergen
Bethany
Byron
Darien
Elba
Le Roy
Oakfield
Pavilion
Pembroke
Stafford
Tonawanda Res
Livingston
Avon
Caledonia
Conesus
Geneseo
Groveland
Leicester
Lima
Livonia
Mount Morris
North Dansville
Nunda
Ossian
Portage
Sparta
Springwater
West Sparta
York town
Monroe
Brighton
Chili
Clarkson
East Rochester
Gates
Greece
Hamlin
Henrietta
Irondequoit
Mendon
Ogden
Parma
Penfield
Perinton
Pittsford
Riga
Rochester
Rush
Sweden
Webster
Wheatland
# of
preschoolers
children
(< 5 yrs)
507
5
16
269
59
8
13
21
5
13
26
25
18
29
0
0
610
45
45
7
77
11
28
11
66
85
120
35
0
10
26
34
10
0
9,865
152
67
14
43
107
436
39
179
240
8
39
58
135
60
54
15
7,993
4
58
147
17
% of
preschoolers
children
(< 5 yrs)
11.3%
3.6%
7.6%
22.7%
14.2%
3.0%
11.6%
9.9%
2.2%
6.3%
5.7%
9.7%
10.3%
7.4%
0.0%
0.0%
15.1%
10.1%
13.0%
5.7%
22.4%
12.6%
14.6%
4.6%
11.0%
28.9%
33.7%
12.4%
0.0%
13.9%
22.6%
19.1%
9.1%
0.0%
17.7%
6.8%
3.4%
2.8%
8.5%
5.8%
6.8%
4.9%
7.7%
7.4%
1.2%
3.0%
5.0%
5.4%
1.7%
2.7%
3.3%
39.9%
1.9%
9.3%
5.1%
4.5%
# of
schoolaged
children
(6-11 yrs)
490
24
29
178
52
10
15
17
10
31
22
40
37
13
4
8
422
49
17
11
19
25
16
13
52
46
101
29
5
14
18
0
7
0
11,437
133
129
31
81
76
574
106
180
285
50
54
81
101
205
42
9
9,043
0
81
138
38
% of
schoolaged
children
(6-11 yrs)
9.5%
11.1%
11.9%
14.2%
10.4%
3.3%
10.2%
6.4%
3.4%
13.7%
3.9%
13.3%
15.8%
3.4%
2.1%
27.6%
8.5%
8.2%
4.3%
5.6%
4.9%
17.2%
9.0%
3.7%
7.0%
16.8%
18.4%
12.7%
6.5%
19.7%
12.2%
0.0%
7.5%
0.0%
17.1%
5.1%
5.2%
4.6%
15.0%
3.5%
6.9%
10.7%
6.8%
7.1%
5.5%
3.0%
5.7%
3.2%
4.6%
1.7%
1.7%
39.6%
0.0%
8.6%
3.9%
8.1%
116
# of teenagers % of teenagers
# of
children
children
all children
(12-17 yrs)
(12-17 yrs)
(0-17 yrs)
443
7.7%
1,440
12
4.7%
41
36
12.5%
81
171
13.3%
618
48
9.3%
159
9
3.1%
27
9
5.0%
37
13
5.8%
51
5
1.6%
20
29
13.1%
73
54
6.4%
102
15
4.3%
80
4
1.5%
59
20
4.2%
62
10
4.8%
14
8
21.6%
16
473
8.2%
1,505
49
7.1%
143
39
7.4%
101
24
8.5%
42
54
14.8%
150
19
12.7%
55
2
0.9%
46
18
4.0%
42
29
3.7%
147
73
14.9%
204
17
3.5%
238
42
12.4%
106
15
19.0%
20
14
15.6%
38
25
17.2%
69
40
17.1%
74
13
11.8%
30
0
0.0%
0
8,075
12.9%
29,377
60
2.8%
345
85
3.5%
281
41
6.9%
86
55
10.4%
179
211
8.5%
394
600
7.0%
1,610
55
5.1%
200
186
6.5%
545
220
5.6%
745
20
2.4%
78
34
1.7%
127
102
6.7%
241
123
3.9%
359
119
2.8%
384
103
4.2%
199
13
2.3%
37
5,891
33.4%
22,927
0
0.0%
4
51
4.6%
190
69
2.0%
354
37
7.5%
92
% of
all children
(0-17 yrs)
9.3%
6.7%
10.9%
16.6%
11.1%
3.1%
8.4%
7.3%
2.4%
11.2%
5.4%
8.8%
8.6%
5.0%
2.3%
16.5%
10.2%
8.3%
8.0%
7.0%
13.6%
14.4%
7.7%
4.0%
6.9%
19.3%
17.1%
12.5%
10.0%
16.3%
17.0%
11.7%
9.6%
0.0%
15.9%
4.9%
4.0%
4.9%
11.4%
6.1%
6.9%
7.0%
7.0%
6.7%
3.3%
2.5%
5.9%
4.0%
3.1%
2.9%
2.4%
37.9%
0.5%
7.1%
3.6%
6.9%
TABLE 3.16 A
Numbers and Percentages of Children in Poverty by Age: By Town
Ontario
Bristol
Canadice
Canandaigua city
Canandaigua
East Bloomfield
Farmington
Geneva city
Geneva
Gorham
Hopewell
Manchester
Naples
Phelps
Richmond
Seneca
South Bristol
Victor
West Bloomfield
Orleans
Albion
Barre
Carlton
Clarendon
Gaines
Kendall
Murray
Ridgeway
Shelby
Yates
Wayne
Arcadia
Butler
Galen
Huron
Lyons
Macedon
Marion
Ontario
Palmyra
Rose town
Savannah
Sodus
Walworth
Williamson
Wolcott
Yates
Barrington
Benton
Italy
Jerusalem
Middlesex
Milo
Potter
Starkey
Torrey
# of
preschoolers
children
(< 5 yrs)
887
11
8
95
13
6
72
409
0
30
22
102
30
32
15
0
8
34
0
578
142
12
21
29
68
24
29
156
73
24
1,030
285
15
43
23
34
134
12
70
97
9
40
138
7
26
97
493
55
82
11
18
7
127
31
130
32
% of
preschoolers
children
(< 5 yrs)
11.7%
5.5%
8.2%
11.4%
2.4%
2.1%
7.8%
36.4%
0.0%
10.6%
10.6%
14.8%
18.2%
6.8%
6.5%
0.0%
8.1%
3.9%
0.0%
17.3%
22.6%
7.5%
11.4%
8.2%
25.3%
12.6%
7.4%
26.3%
17.9%
14.6%
14.1%
25.3%
11.0%
12.5%
18.5%
9.1%
16.4%
3.5%
9.3%
17.4%
5.1%
23.8%
21.6%
0.8%
5.9%
26.2%
25.6%
37.4%
36.4%
13.9%
7.9%
9.6%
25.2%
14.5%
35.8%
34.8%
# of
schoolaged
children
(6-11 yrs)
910
25
14
71
27
18
114
309
13
17
28
145
34
42
18
0
13
22
0
691
127
12
69
73
79
23
55
144
83
26
994
266
21
61
57
48
93
38
42
25
5
40
144
19
34
101
466
42
66
24
70
11
98
31
102
22
% of
schoolaged
children
(6-11 yrs)
10.2%
10.1%
9.2%
8.5%
4.1%
6.3%
10.6%
27.9%
5.3%
5.3%
9.0%
18.4%
14.6%
5.8%
6.3%
0.0%
10.9%
2.2%
0.0%
17.7%
21.1%
5.4%
24.0%
20.3%
23.7%
8.6%
11.2%
23.6%
17.5%
10.1%
11.3%
21.3%
11.2%
13.2%
30.0%
9.9%
10.4%
7.3%
4.5%
4.0%
2.0%
19.3%
16.7%
2.2%
5.3%
24.9%
22.3%
29.8%
28.4%
25.8%
21.0%
8.3%
19.8%
14.4%
29.7%
21.8%
117
# of teenagers % of teenagers
children
children
(12-17 yrs)
(12-17 yrs)
620
7.2%
14
6.5%
11
8.0%
96
11.2%
38
5.4%
0
0.0%
26
2.5%
153
15.8%
29
9.6%
44
11.5%
24
9.0%
52
6.7%
20
9.8%
20
3.1%
20
5.3%
15
7.1%
15
10.9%
39
4.7%
4
2.1%
548
13.4%
37
6.6%
18
10.1%
42
15.3%
0
0.0%
75
16.8%
8
2.1%
65
12.9%
114
15.9%
138
26.1%
51
19.3%
811
8.9%
222
14.8%
36
17.3%
39
9.5%
4
2.6%
57
12.2%
43
5.8%
41
7.0%
28
2.8%
31
4.2%
33
11.7%
40
27.8%
114
14.3%
32
3.7%
13
1.7%
78
17.5%
410
18.2%
28
17.7%
39
13.7%
19
17.0%
65
21.4%
6
4.9%
109
18.6%
27
12.8%
82
27.2%
35
20.2%
total # of
children
2,417
50
33
262
78
24
212
871
42
91
74
299
84
94
53
15
36
95
4
1,817
306
42
132
102
222
55
149
414
294
101
2,835
773
72
143
84
139
270
91
140
153
47
120
396
58
73
276
1,369
125
187
54
153
24
334
89
314
89
% of children
9.6%
7.5%
8.5%
10.4%
4.1%
2.7%
7.0%
27.2%
5.7%
9.2%
9.4%
13.2%
14.0%
5.1%
5.9%
2.2%
10.1%
3.5%
0.7%
16.1%
17.1%
7.5%
17.7%
10.8%
21.1%
6.6%
10.8%
21.6%
20.9%
14.7%
11.3%
20.0%
13.5%
11.8%
17.9%
10.5%
11.0%
6.3%
5.2%
8.0%
6.6%
23.1%
17.2%
2.2%
4.0%
22.6%
21.9%
28.0%
25.2%
19.0%
17.7%
7.3%
21.1%
13.9%
31.1%
24.3%
TABLE 3.19 A
Percentages of Households At or Below the Federal Poverty Threshold
Families
Genesee
Alabama
Alexander
Batavia city
Batavia
Bergen
Bethany
Byron
Darien
Elba
Le Roy
Oakfield
Pavilion
Pembroke
Stafford
Tonawanda Res
Livingston
Avon
Caledonia
Conesus
Geneseo
Groveland
Leicester
Lima
Livonia
Mount Morris
North Dansville
Nunda
Ossian
Portage
Sparta
Springwater
West Sparta
York town
Monroe
Brighton
Chili
Clarkson
East Rochester
Gates
Greece
Hamlin
Henrietta
Irondequoit
Mendon
Ogden
Parma
Penfield
Perinton
Pittsford
Riga
Rochester
Rush
Sweden
Webster
Wheatland
Non-Families
% of Total
% of Married
Couples
% of FHH
% of MHH
% of FHH
% of MHH
7.8%
6.8%
6.6%
11.7%
10.2%
5.0%
4.4%
4.7%
4.3%
5.5%
6.1%
7.8%
4.3%
4.1%
5.1%
16.9%
9.8%
6.1%
4.5%
5.0%
24.5%
7.6%
6.3%
6.6%
5.8%
13.7%
16.5%
8.7%
5.5%
13.3%
9.0%
8.5%
7.3%
1.2%
10.8%
6.3%
3.5%
4.8%
8.9%
6.3%
5.4%
6.5%
8.6%
5.8%
3.1%
3.0%
3.8%
3.2%
3.3%
2.4%
5.0%
23.5%
0.6%
12.0%
4.5%
3.0%
2.8%
2.6%
2.5%
2.9%
6.7%
0.9%
1.0%
2.9%
0.7%
2.3%
2.3%
1.9%
3.7%
1.9%
2.3%
4.3%
2.4%
3.9%
0.8%
1.6%
4.1%
3.3%
2.3%
0.0%
1.2%
3.2%
2.9%
1.5%
2.5%
6.9%
5.0%
2.3%
1.6%
1.4%
2.6%
1.5%
1.2%
0.7%
1.6%
2.5%
1.9%
2.5%
1.2%
1.7%
2.4%
1.1%
1.2%
1.6%
0.9%
0.6%
1.5%
8.2%
0.0%
2.1%
1.5%
0.0%
20.0%
20.0%
21.2%
28.3%
14.7%
4.9%
11.3%
12.5%
24.4%
23.9%
8.8%
31.4%
0.0%
5.4%
0.0%
23.5%
23.2%
25.7%
22.8%
33.3%
22.3%
19.6%
18.8%
27.2%
10.1%
31.6%
37.3%
20.2%
0.0%
17.6%
41.9%
12.5%
28.6%
0.0%
27.3%
12.4%
9.8%
20.4%
18.8%
15.0%
12.8%
15.3%
13.5%
14.3%
0.0%
7.2%
11.1%
8.9%
8.0%
10.5%
17.2%
39.8%
10.4%
14.3%
8.4%
17.0%
11.7%
20.0%
19.4%
19.4%
0.0%
6.9%
21.4%
0.0%
0.0%
24.4%
10.7%
0.0%
12.2%
7.9%
19.4%
#DIV/0!
11.1%
0.0%
7.7%
0.0%
82.8%
36.0%
9.4%
7.6%
2.8%
5.2%
17.3%
13.6%
50.0%
12.9%
15.0%
19.2%
15.8%
0.0%
13.7%
4.1%
0.0%
11.5%
4.5%
3.4%
5.2%
12.5%
9.7%
7.9%
0.0%
9.6%
2.5%
4.7%
4.9%
12.0%
0.0%
25.3%
0.0%
6.7%
8.3%
0.0%
15.4%
15.7%
14.5%
15.1%
18.1%
17.4%
12.2%
16.3%
23.1%
4.4%
14.1%
17.9%
14.9%
12.4%
23.3%
21.9%
20.9%
4.4%
6.5%
12.7%
44.7%
14.5%
9.0%
10.1%
16.7%
24.1%
24.4%
20.1%
12.0%
33.3%
21.2%
20.8%
20.5%
2.9%
16.1%
9.3%
6.8%
15.0%
16.3%
11.6%
11.7%
15.9%
17.6%
12.0%
8.0%
8.3%
8.8%
6.4%
8.9%
7.0%
13.4%
24.6%
0.0%
28.1%
10.8%
5.7%
9.2%
9.7%
7.3%
12.2%
16.0%
12.2%
6.7%
0.0%
6.6%
4.0%
5.0%
13.2%
0.0%
3.8%
4.5%
26.1%
16.9%
8.4%
7.9%
4.2%
39.0%
1.2%
14.6%
22.6%
13.2%
10.9%
30.3%
16.7%
15.6%
20.0%
7.2%
17.0%
1.6%
0.0%
15.1%
11.5%
10.0%
6.8%
7.0%
8.9%
6.2%
8.2%
21.3%
4.4%
3.1%
4.1%
9.0%
4.7%
5.6%
3.3%
6.6%
22.7%
0.0%
19.3%
8.2%
4.4%
118
TABLE 3.19 A
Percentages of Households At or Below the Federal Poverty Threshold
Families
Ontario
Bristol
Canadice
Canandaigua city
Canandaigua
East Bloomfield
Farmington
Geneva city
Geneva
Gorham
Hopewell
Manchester
Naples
Phelps
Richmond
Seneca
South Bristol
Victor
West Bloomfield
Orleans
Albion
Barre
Carlton
Clarendon
Gaines
Kendall
Murray
Ridgeway
Shelby
Yates
Wayne
Arcadia
Butler
Galen
Huron
Lyons
Macedon
Marion
Ontario
Palmyra
Rose town
Savannah
Sodus
Walworth
Williamson
Wolcott
Yates
Barrington
Benton
Italy
Jerusalem
Middlesex
Milo
Potter
Starkey
Torrey
Non-Families
% of Total
% of Married
Couples
% of FHH
% of MHH
% of FHH
% of MHH
7.4%
4.8%
7.7%
10.1%
7.2%
5.9%
5.3%
15.6%
2.8%
4.4%
7.3%
7.7%
10.9%
4.4%
3.0%
3.6%
5.6%
3.6%
3.8%
9.1%
13.8%
5.3%
7.9%
5.2%
7.7%
4.8%
6.0%
13.2%
9.6%
6.8%
8.5%
10.8%
11.2%
10.5%
10.8%
9.2%
6.0%
5.1%
5.8%
7.1%
6.0%
14.1%
12.3%
2.6%
6.1%
16.0%
10.7%
10.3%
8.2%
10.6%
8.1%
6.0%
13.6%
8.6%
13.3%
7.6%
2.1%
1.0%
1.8%
2.0%
1.3%
0.0%
2.4%
5.8%
0.8%
3.7%
1.1%
2.3%
5.9%
1.3%
1.0%
2.4%
4.0%
0.7%
0.9%
3.3%
5.6%
3.5%
5.7%
0.0%
3.9%
2.7%
0.5%
4.7%
2.7%
2.6%
2.8%
3.4%
3.9%
3.9%
6.0%
2.1%
2.1%
2.5%
1.2%
2.3%
2.3%
9.3%
5.2%
0.5%
1.7%
6.1%
4.8%
11.0%
6.5%
6.7%
1.5%
1.0%
4.8%
4.0%
5.9%
6.0%
19.4%
31.8%
16.7%
15.4%
13.4%
7.4%
11.9%
34.8%
11.4%
8.0%
18.9%
29.9%
27.6%
12.0%
10.4%
0.0%
4.4%
19.2%
12.0%
26.9%
34.3%
13.5%
27.4%
22.8%
23.7%
16.1%
21.1%
28.5%
30.5%
26.2%
20.7%
23.0%
18.1%
24.8%
21.9%
17.5%
28.6%
5.7%
12.1%
23.9%
13.6%
12.3%
23.9%
10.3%
12.4%
42.5%
31.8%
21.4%
17.6%
26.3%
36.9%
16.7%
34.3%
34.3%
36.9%
25.7%
7.4%
16.7%
9.5%
18.6%
0.0%
15.5%
0.0%
18.2%
0.0%
0.0%
0.0%
3.9%
0.0%
5.2%
0.0%
0.0%
13.8%
3.1%
0.0%
11.4%
15.8%
5.6%
7.8%
0.0%
0.0%
24.3%
7.9%
21.4%
13.6%
4.2%
11.9%
20.8%
10.2%
0.0%
8.6%
14.7%
11.6%
10.7%
4.1%
7.1%
11.1%
32.7%
15.8%
0.0%
0.0%
20.9%
11.3%
0.0%
0.0%
0.0%
28.2%
6.7%
3.2%
16.7%
30.9%
0.0%
15.3%
7.8%
20.7%
19.8%
30.4%
15.6%
7.6%
21.8%
5.9%
5.6%
22.6%
11.1%
18.5%
13.2%
5.2%
18.6%
13.5%
6.0%
10.0%
13.0%
12.9%
11.6%
8.6%
14.0%
9.7%
0.0%
12.3%
18.9%
13.4%
8.9%
18.2%
13.8%
34.4%
23.8%
30.0%
15.6%
8.3%
9.5%
16.4%
15.7%
11.4%
25.0%
30.6%
20.3%
18.6%
28.9%
20.0%
7.5%
13.9%
29.5%
21.4%
13.1%
25.0%
17.0%
12.2%
6.1%
9.9%
4.2%
12.3%
10.0%
12.7%
20.9%
12.6%
13.5%
2.6%
4.6%
12.0%
10.4%
10.9%
0.4%
7.3%
0.0%
4.7%
9.9%
7.9%
11.7%
18.5%
6.0%
7.5%
10.3%
4.2%
4.5%
8.6%
20.5%
8.5%
11.3%
11.1%
16.7%
15.5%
18.3%
11.0%
14.7%
2.5%
13.1%
10.9%
7.2%
8.8%
12.3%
10.9%
0.0%
14.1%
11.3%
9.1%
3.8%
7.0%
10.8%
2.0%
13.5%
13.0%
2.8%
13.0%
9.3%
119
Table 3.20 A
Percentages of Families With and Without Children Under the Age of 18
At or Below the Federal Poverty Threshold
Genesee
Alabama
Alexander
Batavia city
Batavia
Bergen
Bethany
Byron
Darien
Elba
Le Roy
Oakfield
Pavilion
Pembroke
Stafford
Tonawanda Res
Livingston
Avon
Caledonia
Conesus
Geneseo
Groveland
Leicester
Lima
Livonia
Mount Morris
North Dansville
Nunda
Ossian
Portage
Sparta
Springwater
West Sparta
York town
Monroe
Brighton
Chili
Clarkson
East Rochester
Gates
Greece
Hamlin
Henrietta
Irondequoit
Mendon
Ogden
Parma
Penfield
Perinton
Pittsford
Riga
Rochester
Rush
Sweden
Webster
Wheatland
Families living WITH children < 18
% of Married
Couples
% of FHH
% of MHH
3.7%
27.4%
16.5%
3.0%
30.8%
25.9%
5.0%
39.3%
19.4%
3.3%
40.8%
29.5%
10.3%
20.8%
0.0%
1.8%
6.3%
10.8%
2.1%
20.7%
35.3%
4.6%
17.8%
0.0%
1.3%
21.1%
0.0%
1.5%
40.0%
33.3%
3.2%
12.6%
16.7%
3.0%
32.8%
0.0%
4.3%
0.0%
17.2%
3.7%
5.6%
13.7%
1.2%
0.0%
29.2%
0.0%
30.0%
0.0%
2.8%
30.4%
11.9%
3.0%
30.2%
0.0%
1.6%
26.7%
9.6%
1.5%
48.5%
0.0%
6.4%
25.8%
0.0%
5.0%
27.0%
40.9%
4.4%
22.4%
0.0%
0.0%
42.0%
19.2%
2.2%
15.7%
4.2%
3.3%
53.2%
7.3%
2.6%
42.4%
35.1%
3.1%
25.7%
20.0%
2.1%
0.0%
0.0%
10.1%
22.2%
14.3%
4.7%
50.0%
27.3%
3.7%
17.4%
20.3%
3.0%
26.3%
14.8%
0.0%
0.0%
0.0%
3.6%
35.6%
17.9%
2.2%
19.2%
0.0%
1.6%
14.9%
0.0%
0.0%
27.3%
12.5%
3.6%
27.2%
0.0%
2.3%
22.7%
3.2%
2.6%
17.6%
8.8%
3.7%
22.8%
9.3%
1.6%
22.7%
13.9%
1.5%
21.7%
17.7%
2.2%
0.0%
0.0%
0.9%
7.4%
16.9%
2.4%
15.2%
4.0%
2.0%
15.3%
7.3%
0.8%
12.7%
8.9%
0.5%
15.1%
17.6%
0.0%
22.2%
0.0%
12.2%
47.1%
31.4%
0.0%
21.1%
0.0%
2.4%
16.3%
0.0%
1.8%
9.7%
14.3%
0.0%
28.7%
0.0%
120
Families living WITHOUT children < 18
% of Married
Couples
% of FHH
% of MHH
1.8%
5.4%
2.5%
2.1%
11.8%
0.0%
0.0%
0.0%
0.0%
2.6%
5.3%
7.1%
3.9%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
1.3%
0.0%
0.0%
0.0%
26.9%
0.0%
3.1%
0.0%
0.0%
1.6%
0.0%
0.0%
0.6%
28.6%
0.0%
3.2%
0.0%
0.0%
0.2%
0.0%
0.0%
3.1%
0.0%
0.0%
7.4%
14.3%
0.0%
2.0%
3.9%
9.5%
4.5%
0.0%
0.0%
0.0%
0.0%
0.0%
1.8%
0.0%
0.0%
2.1%
11.8%
100.0%
1.9%
0.0%
0.0%
0.7%
10.0%
38.5%
0.0%
9.5%
0.0%
0.3%
0.0%
0.0%
3.2%
0.0%
0.0%
3.2%
0.0%
0.0%
0.0%
0.0%
0.0%
2.9%
0.0%
100.0%
3.5%
12.5%
0.0%
5.2%
22.2%
0.0%
1.3%
0.0%
0.0%
0.0%
30.4%
18.2%
2.9%
0.0%
0.0%
1.7%
7.0%
7.4%
1.0%
3.1%
11.8%
0.8%
0.0%
0.0%
1.7%
0.0%
0.0%
0.0%
0.0%
8.8%
2.6%
6.0%
3.8%
1.2%
4.5%
0.0%
0.9%
0.0%
25.8%
0.9%
2.0%
6.5%
1.8%
4.9%
0.0%
2.6%
0.0%
0.0%
1.3%
6.8%
0.0%
0.0%
0.0%
0.0%
1.1%
0.0%
0.0%
1.0%
1.3%
0.0%
0.6%
0.0%
0.0%
3.0%
10.3%
0.0%
4.1%
12.3%
15.3%
0.0%
0.0%
0.0%
1.7%
10.5%
14.3%
1.2%
5.8%
0.0%
0.0%
0.0%
0.0%
Table 3.20 A
Percentages of Families With and Without Children Under the Age of 18
At or Below the Federal Poverty Threshold
Families living WITH children < 18
% of Married
Couples
% of FHH
2.8%
26.1%
Ontario
Bristol
1.4%
36.2%
Canadice
0.0%
29.0%
Canandaigua city
2.0%
22.1%
Canandaigua
1.0%
21.8%
East Bloomfield
0.0%
9.9%
Farmington
2.8%
15.8%
Geneva city
10.0%
44.1%
Geneva
1.8%
19.2%
Gorham
8.3%
12.8%
Hopewell
2.3%
19.0%
Manchester
2.3%
37.9%
Naples
7.5%
32.5%
Phelps
0.7%
18.1%
Richmond
2.2%
16.9%
Seneca
2.1%
0.0%
South Bristol
6.0%
6.3%
Victor
1.1%
22.5%
West Bloomfield
0.0%
6.5%
4.7%
36.9%
Orleans
Albion
6.5%
42.0%
Barre
4.7%
35.0%
Carlton
8.0%
41.7%
Clarendon
0.0%
35.9%
Gaines
5.8%
35.3%
Kendall
3.3%
19.7%
Murray
1.1%
28.1%
Ridgeway
7.3%
40.5%
Shelby
4.7%
39.0%
Yates
3.5%
39.3%
3.2%
28.2%
Wayne
Arcadia
2.9%
32.4%
Butler
4.9%
25.9%
Galen
4.4%
36.4%
Huron
9.1%
31.8%
Lyons
0.0%
24.1%
Macedon
2.3%
34.8%
Marion
4.0%
8.0%
Ontario
0.7%
16.1%
Palmyra
1.9%
32.7%
Rose town
2.8%
18.5%
Savannah
13.2%
16.4%
Sodus
8.1%
32.9%
Walworth
0.8%
13.3%
Williamson
0.8%
25.6%
Wolcott
10.4%
46.3%
7.9%
42.4%
Yates
Barrington
15.4%
38.5%
Benton
11.5%
22.6%
Italy
13.9%
41.7%
Jerusalem
2.1%
43.7%
Middlesex
0.0%
28.6%
Milo
6.2%
47.9%
Potter
6.7%
38.3%
Starkey
11.7%
46.7%
Torrey
10.4%
40.9%
% of MHH
9.7%
18.5%
13.3%
17.2%
0.0%
27.5%
0.0%
27.1%
0.0%
0.0%
0.0%
4.5%
0.0%
7.7%
0.0%
0.0%
0.0%
0.0%
0.0%
14.8%
18.8%
11.1%
12.1%
0.0%
0.0%
9.7%
9.4%
30.9%
24.5%
5.3%
15.8%
25.3%
16.1%
0.0%
17.6%
18.6%
16.7%
17.1%
5.7%
0.0%
12.5%
42.5%
20.5%
0.0%
0.0%
20.5%
15.4%
0.0%
0.0%
0.0%
47.8%
10.0%
3.9%
25.0%
39.5%
0.0%
121
Families living WITHOUT children < 18
% of Married
Couples
% of FHH
% of MHH
1.5%
5.1%
3.2%
0.6%
21.1%
0.0%
2.6%
0.0%
0.0%
2.0%
3.2%
22.5%
1.5%
0.0%
0.0%
0.0%
0.0%
0.0%
1.9%
0.0%
0.0%
2.4%
6.2%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
18.8%
0.0%
2.3%
9.8%
0.0%
4.8%
9.1%
0.0%
1.8%
0.0%
0.0%
0.0%
0.0%
0.0%
2.7%
0.0%
0.0%
2.6%
0.0%
28.6%
0.3%
14.0%
5.0%
1.4%
20.7%
0.0%
2.0%
4.5%
2.7%
4.5%
14.6%
0.0%
2.2%
0.0%
0.0%
3.7%
0.0%
0.0%
0.0%
0.0%
0.0%
1.8%
0.0%
0.0%
2.2%
0.0%
100.0%
0.0%
0.0%
0.0%
2.6%
0.0%
0.0%
1.1%
12.2%
0.0%
1.8%
0.0%
0.0%
2.5%
0.0%
1.9%
3.7%
0.0%
0.0%
3.1%
0.0%
0.0%
3.4%
0.0%
0.0%
3.8%
0.0%
0.0%
3.5%
0.0%
0.0%
1.9%
0.0%
0.0%
0.9%
0.0%
0.0%
1.6%
0.0%
0.0%
2.7%
0.0%
15.2%
1.8%
0.0%
0.0%
5.9%
0.0%
0.0%
2.9%
0.0%
0.0%
0.0%
0.0%
0.0%
2.6%
0.0%
0.0%
3.1%
0.0%
25.0%
2.4%
4.6%
0.0%
7.6%
6.7%
0.0%
2.5%
0.0%
0.0%
0.7%
0.0%
0.0%
1.1%
0.0%
0.0%
1.8%
0.0%
0.0%
3.7%
5.1%
0.0%
1.1%
0.0%
0.0%
0.9%
10.9%
0.0%
2.1%
0.0%
0.0%
Table 3.24 A
Median Household Incomes for Female Heads of Households Not in Families: By Town
Genesee
Alabama
Alexander
Batavia city
Batavia
Bergen
Bethany
Byron
Darien
Elba
Le Roy
Oakfield
Pavilion
Pembroke
Stafford
Tonawanda Res
Livingston
Avon
Caledonia
Conesus
Geneseo
Groveland
Leicester
Lima
Livonia
Mount Morris
North Dansville
Nunda
Ossian
Portage
Sparta
Springwater
West Sparta
York town
Monroe
Brighton
Chili
Clarkson
East Rochester
Gates
Greece
Hamlin
Henrietta
Irondequoit
Mendon
Ogden
Parma
Penfield
Perinton
Pittsford
Riga
Rochester
Rush
Sweden
Webster
Wheatland
All FHH in nonfamilies
$19,968
$14,338
$19,808
$16,939
$24,792
$20,417
$24,792
$21,750
$25,833
$20,417
$22,813
$16,563
$25,500
$21,167
$27,500
$15,417
$19,634
$19,966
$20,972
$21,429
$19,722
$26,042
$18,684
$20,515
$18,009
$17,596
$17,339
$21,250
$32,917
$14,375
$17,000
$18,000
$26,071
$20,667
$22,796
$31,881
$25,191
$14,087
$22,119
$20,887
$23,585
$20,934
$25,978
$22,500
$30,352
$27,835
$29,667
$30,938
$30,446
$37,037
$23,889
$18,296
$29,500
$19,348
$25,944
$28,534
122
FHH 65+ yrs
living alone
$14,436
$12,857
$18,571
$14,397
$14,125
$15,114
$14,688
$11,641
$18,269
$12,115
$14,118
$11,346
$13,958
$17,344
$16,250
$8,250
$15,847
$17,128
$16,696
$15,313
$16,435
$19,500
$16,500
$17,981
$15,677
$14,615
$13,423
$15,893
$14,375
$10,000
$16,477
$13,750
$8,750
$16,964
$16,237
$21,992
$19,446
$12,717
$16,691
$14,759
$17,312
$17,500
$17,402
$14,987
$14,219
$18,068
$21,321
$18,627
$19,388
$28,287
$16,731
$13,081
$25,000
$13,622
$16,801
$19,099
FHH 65+ yrs
living with another
$22,500
$16,071
$41,250
$60,104
$24,107
$32,708
$24,750
$16,250
$23,750
$38,750
$18,750
$41,250
$16,250
$26,250
$33,654
$50,417
$67,656
$101,581
$30,000
$46,250
$27,019
$177,361
$17,045
$24,688
$13,750
$38,333
$31,250
$177,361
$90,957
Table 3.24 A
Median Household Incomes for Female Heads of Households Not in Families: By Town
Ontario
Bristol
Canadice
Canandaigua city
Canandaigua
East Bloomfield
Farmington
Geneva city
Geneva
Gorham
Hopewell
Manchester
Naples
Phelps
Richmond
Seneca
South Bristol
Victor
West Bloomfield
Orleans
Albion
Barre
Carlton
Clarendon
Gaines
Kendall
Murray
Ridgeway
Shelby
Yates
Wayne
Arcadia
Butler
Galen
Huron
Lyons
Macedon
Marion
Ontario
Palmyra
Rose town
Savannah
Sodus
Walworth
Williamson
Wolcott
Yates
Barrington
Benton
Italy
Jerusalem
Middlesex
Milo
Potter
Starkey
Torrey
All FHH in nonfamilies
$21,370
$30,500
$19,531
$17,889
$23,534
$22,917
$27,330
$16,949
$21,500
$20,385
$20,150
$19,232
$20,000
$22,206
$26,250
$18,542
$24,167
$31,250
$24,028
$18,558
$18,077
$18,295
$21,761
$28,382
$17,734
$24,688
$23,403
$19,133
$14,840
$16,667
$18,134
$17,500
$12,059
$14,330
$15,882
$18,558
$27,292
$20,086
$21,122
$17,458
$18,631
$13,214
$16,333
$31,319
$16,836
$13,531
$16,675
$18,750
$21,667
$14,792
$20,446
$17,344
$14,835
$25,469
$15,500
$22,500
123
FHH 65+ yrs
living alone
$15,822
$13,750
$12,679
$15,625
$21,974
$13,063
$19,375
$13,615
$20,563
$14,152
$17,417
$16,397
$11,447
$16,823
$15,000
$12,778
$19,167
$19,120
$14,444
$14,953
$16,688
$15,625
$10,938
$15,000
$16,892
$16,875
$13,664
$14,556
$14,071
$13,214
$12,830
$13,117
$11,771
$13,375
$13,125
$16,458
$16,875
$13,365
$12,629
$13,571
$13,229
$12,273
$9,359
$18,654
$12,148
$11,653
$13,982
$18,750
$18,482
$13,125
$21,071
$17,857
$13,015
$12,917
$12,016
$16,042
FHH 65+ yrs
living with another
$15,795
$26,250
$13,750
$13,750
$10,000
$29,583
$17,083
$36,250
$38,750
$56,250
$50,417
$34,479
$21,250
$33,750
$45,625
$33,750
$36,250
$21,964
$85,489
$33,750
$46,250
$28,750
$90,957
$38,839
$22,500
$8,750
$41,250
$23,571
$16,250
$36,250
$46,250
$23,750
$13,750
APPENDIX B
Self-Sufficiency Standard
For Selected Family Types
For Each of the
Seven Study Counties.
124
Self-Sufficiency Standard for Selected Family Types for the Seven Study Counties58
Monthly Costs
The Self-Sufficiency Standard for Genesee County59
Adult
Adult +
Adult +
Adult +
Infant
Infant +
School Age
Preschooler
+ Teenager
Housing
Child Care
Food
Transportation
Health Care
Miscellaneous
Taxes
Earned Income Tax Credit (-)
Child Care Tax Credit (-)
Child Tax Credit (-)
Monthly Self-Sufficiency Wage
Hourly Self-Sufficiency Wage
Annual Income
Monthly Costs
59
60
$609
$422
$241
$190
$214
$168
$300
-54
-44
-42
$2,004
$11.56
$24,048
$609
$881
$325
$190
$235
$224
$436
0
-80
-83
$2,737
$15.79
$32,844
$609
$281
$429
$190
$240
$175
$213
-187
-46
-47
$1,857
$10.71
$22,284
The Self-Sufficiency Standard for Livingston County60
Adult
Adult +
Adult +
Adult +
Infant
Infant +
School Age
Preschooler
+ Teenager
Housing
Child Care
Food
Transportation
Health Care
Miscellaneous
Taxes
Earned Income Tax Credit (-)
Child Care Tax Credit (-)
Child Tax Credit (-)
Monthly Self-Sufficiency Wage
Hourly Self-Sufficiency Wage
Annual Income
58
$501
0
$164
$185
$83
$93
$216
0
0
0
$1,242
$7.17
$14,904
$501
0
$164
$185
$103
$95
$224
0
0
0
$1,272
$7.34
$15,264
$609
$422
$241
$190
$221
$168
$307
-50
-44
-42
$2,022
$11.67
$24,264
$609
$881
$325
$190
$242
$225
$439
0
-80
-83
$2,748
$15.85
$32,976
$609
$281
$429
$190
$247
$176
$216
-184
-46
-47
$1,871
$10.79
$22,452
The Self-Sufficiency Standard for New York http://www.sixstrategies.org/files/Resource-StandardReport-NY.pdf
The Self-Sufficiency Standard for New York, page 54, Table 27
The Self-Sufficiency Standard for New York, page 54, Table 28
125
Adult +
Infant +
Preschooler
+School Age
$781
$1,163
$437
$190
$256
$283
$630
0
-80
-125
$3,535
$20.39
$42,420
Adult +
Infant +
Preschooler
+School Age
$781
$1,163
$437
$190
$262
$283
$633
0
-80
-125
$3,544
$20.45
$42,528
Monthly Costs
The Self-Sufficiency Standard for Monroe County61
Adult
Adult +
Adult +
Adult +
Infant
Infant +
School Age
Preschooler
+ Teenager
Housing
Child Care
Food
Transportation
Health Care
Miscellaneous
Taxes
Earned Income Tax Credit (-)
Child Care Tax Credit (-)
Child Tax Credit (-)
Monthly Self-Sufficiency Wage
Hourly Self-Sufficiency Wage
Annual Income
Monthly Costs
62
$609
$530
$241
$190
$221
$179
$382
0
-42
-42
$2,268
$13.08
$27,216
$609
$1,093
$325
$190
$242
$246
$537
0
-80
-83
$3,079
$17.76
$36,948
$609
$368
$429
$190
$247
$184
$260
-145
-44
-74
$2,024
$11.68
$24,288
The Self-Sufficiency Standard for Ontario County62
Adult
Adult +
Adult +
Adult +
Infant
Infant +
School Age
Preschooler
+ Teenager
Housing
Child Care
Food
Transportation
Health Care
Miscellaneous
Taxes
Earned Income Tax Credit (-)
Child Care Tax Credit (-)
Child Tax Credit (-)
Monthly Self-Sufficiency Wage
Hourly Self-Sufficiency Wage
Annual Income
61
$501
0
$164
$185
$103
$95
$224
0
0
0
$1,272
$7.34
$15,264
$501
0
$164
$185
$103
$95
$224
0
0
0
$1,272
$7.34
$15,264
$609
$530
$241
$190
$221
$179
$382
0
-42
-42
$2,268
$13.08
$27,216
The Self-Sufficiency Standard for New York, page 55, Table 29
The Self-Sufficiency Standard for New York, page 55, Table 30
126
$609
$1,093
$325
$190
$242
$246
$537
0
-80
-83
$3,079
$17.76
$36,948
$609
$368
$429
$190
$247
$184
$260
-145
-44
-74
$2,024
$11.68
$24,288
Adult +
Infant +
Preschooler
+School Age
$781
$1,461
$437
$190
$262
$313
$771
0
-80
-125
$4,010
$23.13
$48,120
Adult +
Infant +
Preschooler
+School Age
$781
$1,461
$437
$190
$262
$313
$771
0
-80
-125
$4,010
$23.13
$48,120
Monthly Costs
The Self-Sufficiency Standard for Orleans County63
Adult
Adult +
Adult +
Adult +
Infant
Infant +
School Age
Preschooler
+ Teenager
Housing
Child Care
Food
Transportation
Health Care
Miscellaneous
Taxes
Earned Income Tax Credit (-)
Child Care Tax Credit (-)
Child Tax Credit (-)
Monthly Self-Sufficiency Wage
Hourly Self-Sufficiency Wage
Annual Income
Monthly Costs
64
$609
$422
$241
$190
$214
$168
$300
-$54
-$44
-$42
$2,004
$11.56
$24,048
$609
$881
$325
$190
$235
$224
$436
$0
-$80
-$83
$2,737
$15.79
$32,844
$609
$281
$429
$190
$240
$175
$213
-$187
-$46
-$47
$1,857
$10.71
$22,284
The Self-Sufficiency Standard for Wayne County64
Adult
Adult +
Adult +
Adult +
Infant
Infant +
School Age
Preschooler
+ Teenager
Housing
Child Care
Food
Transportation
Health Care
Miscellaneous
Taxes
Earned Income Tax Credit (-)
Child Care Tax Credit (-)
Child Tax Credit (-)
Monthly Self-Sufficiency Wage
Hourly Self-Sufficiency Wage
Annual Income
63
$501
$0
$164
$185
$83
$95
$216
$0
$0
$0
$1,244
$7.18
$14,928
$501
0
$164
$185
$103
$95
$224
0
0
0
$1,272
$7.34
$15,264
$609
$530
$241
$190
$221
$179
$382
0
-42
-42
$2,268
$13.08
$27,216
The Self-Sufficiency Standard for New York, page 56, Table 31
The Self-Sufficiency Standard for New York, page 56, Table 32
127
$609
$1,093
$325
$190
$242
$246
$537
0
-80
-83
$3,079
$17.76
$36,948
$609
$368
$429
$190
$247
$184
$260
-145
-44
-74
$2,024
$11.68
$24,288
Adult +
Infant +
Preschooler
+School Age
$781
$1,163
$437
$190
$256
$283
$630
$0
-$80
-$125
$3,535
$20.39
$42,420
Adult +
Infant +
Preschooler
+School Age
$781
$1,461
$437
$190
$262
$313
$771
0
-80
-125
$4,010
$23.13
$48,120
Monthly Costs
The Self-Sufficiency Standard for Yates County65
Adult
Adult +
Adult +
Adult +
Infant
Infant +
School Age
Preschooler
+ Teenager
Housing
Child Care
Food
Transportation
Health Care
Miscellaneous
Taxes
Earned Income Tax Credit (-)
Child Care Tax Credit (-)
Child Tax Credit (-)
Monthly Self-Sufficiency Wage
Hourly Self-Sufficiency Wage
Annual Income
65
$400
0
$164
$185
$103
$85
$183
0
0
0
$1,120
$6.46
$13,440
$480
$422
$241
$190
$221
$155
$230
-103
-48
-42
$1,746
$10.07
$20,952
The Self-Sufficiency Standard for New York, page 74, Table 64
128
$480
$881
$325
$190
$242
$212
$380
-17
-80
-83
$2,530
$14.60
$30,360
$480
$281
$429
$190
$247
$163
$155
-241
-50
-11
$1,643
$9.48
$19,716
Adult +
Infant +
Preschooler
+School Age
$621
$1,163
$437
$190
$262
$267
$563
0
-80
-83
$3,340
$19.27
$40,080
APPENDIX C
Survey Instrument for
One-On-One Interviews
129
This is a survey sponsored by the Women’s Foundation of Greater Genesee Valley in Rochester. This
agency gives money to organizations that help women to support themselves and their children.
We want to know more about what actually helps women, like you, to find work, pay bills, buy food, find
housing and transportation, as well as, care for their children. The time and information that you
provide us will be very helpful in finding out what services and supports are
needed in your community.
All the information that you share in this survey will be kept strictly confidential, we do not want your
name or address.
How many children do you have living with you? _______________ (please write in the number)
1.
What social services do you use? (please select as many as apply)
Response
Mark Answer(s) “X”
Cash Assistance
Food Stamps
Medicaid
HUD
Work Study
Subsidized Child Care
Catholic Charities
Wilson Commencement Park
Cooperative Extension Programming
(ESNY/EFNEP)
Chances and Changes
WIC
Family Resource Center
Legal Assistance of the Finger Lakes
Planned Parenthood
Other Services/Supports/Programs
(please tell us what those are)
2.
How would you describe the programs that you are enrolled in?
(mark your answer “x” in the box provided)
□
□
□
Extremely inadequate in helping me to make ends meet.
Somewhat inadequate in helping me to make ends meet.
Adequate in helping me to make ends meet.
130
3.
In your opinion, how difficult is it for you to get the services you need to take care of your daily living
expenses? (mark your answer “x” in the box provided)
□
□
□
□
4.
Impossible
Very difficult
Somewhat difficult
Fairly easy
What do you do when you have a crisis (for example, your car breaks down, you can’t pay rent or
you run out of food)? (select as many as apply)
Response
Mark Answers “X”
Wait it out
Call the Department of Social Services
Call a Community Agency for help
Call a family member
Call a friend
Call a local church
If you have other emergency strategies,
please write them in to the right…
5.
Within Box A and Box B, please tell us what your greatest need is, your second greatest need and
your third greatest need?
BOX A
Response
BOX B
Mark answers
1=greatest need
2=second greatest need
3=third greatest need
Housing
Transportation
Childcare
High School
Education
Job Training
Self-Esteem
Counseling
6.
Response
Mark answers
1=greatest need
2=second greatest need
3=third greatest need
Housing
Transportation
Childcare
High School Education
Job Training
Self-Esteem Counseling
When you ask for assistance from the Department of Social Services, how are you treated?
(mark your answer “x” in the box provided)
□
□
□
□
In a hostile way
Rudely
Warmly
With respect
131
7.
When you ask for assistance from a community-based program, how are you treated?
(mark your answer “x” in the box provided)
□
□
□
□
8.
Rudely
Warmly
With respect
Some people say that a single mother with 2 children needs to make at least $17.49/hour to cover all
expenses (that comes out to $3,078/month and $36,963/year), do you believe this amount is:
(mark your answer “x” in the box provided)
□
□
□
□
9.
In a hostile way
Too little
About right
Slightly too high
Far too high
When you consider the amount of money you make each year, how close would you say your income
is to this amount ($36,963)? (mark your answer “x” in the box provided)
□
□
□
□
□
Not even close (under $10,000/year)
Far below ($10,000-24,999/year)
Fairly close ($25,000-30,000/year)
Close ($30,000-36,962/year)
Exactly the same or more ($36,963 or more in a year)
10. How would you describe your financial situation?
(mark your answer “x” in the box provided)
□
□
□
□
Extremely bad (no housing, little food)
Somewhat bad
Somewhat stable
Very stable (can pay all the bills each month)
11. How would you compare your current financial situation with your situation in the past?
(mark your answer “x” in the box provided)
□
□
□
□
□
Much worse
Somewhat worse
About the same
Somewhat better
Much better
If your situation is different from last year, what is different?
132
12. What do you do to try to make ends meet? (please select as many as apply)
Response
Mark Answer(s) “X”
Use Food Stamps
Clip coupons
Work more than one job
Skip meals
Go without meeting your own
basic needs
Other things you do to make it to
the end of the month?
13. How important is the way you feel about yourself and your ability to be financially stable.
(mark your answer “x” in the box provided)
□
□
□
□
Extremely unimportant
Relatively unimportant
Relatively important
Extremely important
14. Circle the option that best describes where you live.
a. Farm
b. Rural, non-farm
c. Small town
d. Suburb
e. City
15. Circle the option that best describes your housing.
a. Apartment
b. Trailer that you rent
c. Trailer that you own
d. House that you rent
e. House that you own
f. I live with my parents or other family.
16. Circle the option that best describes your present marital status.
a. Single, never married
b. Married
c. Divorced
d. Separated
e. Widowed
17. Circle the option that best describes your employment status.
a. Unemployed
b. Disabled
c. Actively seeking employment (Work Study Program)
d. Part-time
e. Full-time
133
18. Circle the option that describes your highest level of education completed.
a. No formal education
b. Some grade school
c. Completed grade school
d. Some high school
e. Completed high school
f. Some trade school
g. Completed trade school
h. Some college
i. Completed college
19. Circle the option that best describes your current transportation status.
a. Own a car
b. Own a car but can’t insure it
c. Own a car but it is broken
d. Bus
e. Taxi
f. Friend or family provides rides
g. No driver’s license
20. Did you respond to the 2000 Census?
□
□
Yes
No
21. Are there any services that you know about that you don’t use? (Why?)
22. What kinds of help do you think you need in order to get to the place that you can take care of
yourself and your family financially?
134
23. What causes you the most problems in your life?
24. What is the one piece of advice you would offer to a younger woman?
County of Residence________________________ Location of Interview _______________________
THANK YOU FOR YOUR TIME AND FOR YOUR PARTICIPATION!
©KATHRYN A. BOWEN, PHD. BECS, INC. 102 DESMOND STREET, SUITE #2, SAYE PA 18840
135