A T Still University Global Poverty in United States Articles Summary Summaries Write three summaries to three of your sources and a tentative thesis stat

A T Still University Global Poverty in United States Articles Summary Summaries

Write three summaries to three of your sources and a tentative thesis statement for your up-coming essay.

#4 Outline: Global Poverty Essay

You may use the following outline for this assignment. If you have an alternative outline that you’d like to use, please email your instructor for permission.

The intro., thesis, and topic sentences (including the first sentence of the conclusion) must be written in full sentences.

Title:

Introduction

Hook (Grab your readers’ attention):

Explain why the hook is important to your topic:

If needed, provide background information for your topic:

Thesis statement: What are you writing about and what are the major points you will make?

Body Paragraph #1

Background information on extreme poverty:

Where is the poverty?

Who is suffering from poverty?

Why does this area/country suffer from poverty?

How was the country oppressed/colonialized?

Body Paragraph #2

First major point:

Evidence to support your point:

Explain the evidence by interpreting, evaluating, or providing additional details:

Link back to how this point ties into the thesis:

Body Paragraph #3

Second major point:

Evidence to support your point:

Explain the evidence by interpreting, evaluating, or providing additional details:

Link back to how this point ties into the thesis:

Body Paragraph #4

Third major point:

Evidence to support your point:

Explain the evidence by interpreting, evaluating, or providing additional details:

Link back to how this point ties into the thesis:

Continue Body Paragraphs as needed:

Penultimate Paragraph (optional):

Are there any impediments to enacting the solution?

What evidence is there of this impediment?

What further solutions can overcome this issue?

What evidence is there of further solutions?

How does this information tie back to the thesis?

Conclusion

Conclusion summary. Summarize the main points of the essay:

Give your reader something to think about related to your topic:

If possible, connect back to the hook.

#4 Essay: Global Poverty Essay

Global Poverty Essay

Purpose –

This essay will enable you to explore issues related to extreme poverty in the Global South.
You will have freedom in how you compose this essay because you can focus on just one or all of the follow topics: causes, effects, and solutions to extreme poverty in a specific location/country.
Your research may reveal different perspectives about the causes, effects, and solutions to extreme poverty, so you will need to choose the best research to argue for your point of view.

In your paper, you need the following items:

An organized introduction with a clear thesis statement, which supports the purpose of your argument
Body paragraphs that support your thesis statement with evidence from e-books, magazines, newspapers, websites, and peer-reviewed journals.
Proper paraphrasing and quoting from at least six reliable sources, including at least one peer-reviewed article.
An organized conclusion

Your paper must adhere to the following guidelines:

4 to 5 pages in length, excluding the Works Cited page
Double-spaced, Times New Roman/Calibri, 12-point font
MLA format
1 inch margins
Use MLA in-text citations for all references to sources
Have a Works Cited page
Use at least five reliable sources. One source must be from a peer-reviewed journal.
Use correct punctuation, grammar, and mechanics
Use complete sentences
Submit a rough draft to Tutor.com and make revisions using advice from your tutor
Submit your paper to SafeAssign on Blackboard (2019) 49, 474–494
The Rust Belt, the Sunbelt, and the Concentration of
Poverty Within Large U.S. Cities?
Scott W. Hegerty
Department of Economics, Northeastern Illinois University, USA
Abstract: Previous research that has quantified the dispersion of U.S. urban poverty has often focused
on metropolitan areas rather than on central cities themselves. Detroit, for example, shares a region with
wealthy suburbs and thus appears to have concentrated poverty, but the city itself is uniformly poor and has
few pockets of wealth. This study calculates four measures of household poverty concentration across block
groups inside 74 U.S. cities, finding poor “Rust Belt” cities such as Detroit to have “diffuse” poverty. We
also isolate a group of low-poverty, high-concentration cities in the U.S. South and West, as well as outliers
that include “Sunbelt” cities with more diffuse poverty than predicted. Regression analysis finds the poverty
rate itself to explain most of the variation in poverty concentration, with ethnic composition and the share
of service employment also playing a potential role. Calculating changes in tract level poverty distributions
from 2010 to 2015, we find that the concentration of poverty has decreased overall and that growth in the
share of Hispanic residents might help explain these decreases.
Keywords: urban poverty, concentration, United States, inequality, spatial statistics
JEL Codes: I32
1.
INTRODUCTION
The widening concentration of poverty in the United States, particularly in large urban
centers, has long been a concern to researchers and policymakers alike. But while there
have been numerous studies examining the U.S. and other countries, most have addressed
metropolitan areas rather than city centers themselves. For this reason, their findings have
mainly uncovered significant urban-suburban inequalities. Rusk (2013), for example, finds
the Detroit metro area to be among the most segregated in the U.S.
An examination of the city itself, not including any suburbs, shows a different distribution, however. Within Detroit, poverty is more evenly spread out inside its urban boundaries
than in many other cities. Chicago, for example, has high-poverty neighborhoods on its West
?
The helpful comments of Dr. Patricia Smith and other participants at the 2018 Midwest Economics
Association meeting, as well as those of two anonymous referees, are greatly appreciated.
Hegerty is an Associate Professor of Economics at Northeastern Illinois University, 5500 N. Saint Louis Ave.,
Chicago, IL 60625. E-mail: S-Hegerty@neiu.edu
(c) Southern Regional Science Association 2019
ISSN 1553-0892, 0048-49X (online)
www.srsa.org/rrs
HEGERTY: THE CONCENTRATION OF POVERTY IN LARGE U.S. CITIES
475
and South Sides, but includes both wealthy areas on the North Side (near Lake Michigan)
and middle-class neighborhoods to the Northwest and Southwest. Policymakers and pundits
sometimes make comparisons between the two cities (and between Detroit and other urban
centers), when the reality is actually quite different. Wealthy neighborhoods can subsidize
poor ones, so that a less evenly poor city can avoid Detroit’s fate of drastically reduced
public services. It is therefore necessary to establish a metric for the concentration of urban
poverty, so that policies that might be beneficial for Detroit can be applied in the appropriate
context. Similar cities can also be identified, as well as cities that might be quite different
in their poverty distributions. These metrics can then inform urban policy.
Concentrated poverty, as Sharkey (2008, 2012) notes, is resilient, even in the face of residential mobility. As the vast literature on “neighborhood effects” highlights, the geographic
distribution of poverty can have a direct impact on health and other outcomes. Small and
Newman (2001) discuss some of the mechanisms through which poverty affects quality of
life; specific effects include mental health (Leventhal and Brooks-Gunn, 2003) as well as
reduced educational opportunities, wealth, and private investment and increased crime and
costs of government (Kneebone et al., 2011). Yet, as noted above, empirical studies often
focus on MSAs rather than specifically on urban cores. Some earlier examples, which model
the concentration of urban poverty and differences among socioeconomic groups, include
Madden (1996), Adelman and Jaret (1999), and Madden (2003). More recently, Aliprantis
et al. (2013) calculate Gini coefficients for large MSAs and find Rust Belt cities such as
Milwaukee and Detroit to have highly concentrated poverty.
Other studies incorporate a more varied mix of geographical areas. Examples include
Dawkins (2004), who calculates a set of Gini coefficients specifically for the city of Atlanta;
Cook and Marchant (2006), who find increased poverty in Northeastern urban cores and the
inner-ring suburbs of growing Sunbelt regions; Weinberg (2011), who discovers a negative
relationship between Gini coefficients and household income in states and metros of various
sizes; and Howell and Timberlake (2014), who examine time trends and racial disparities in
suburban poverty. Rusk (2013) notes that “elastic” cities, which have been able to incorporate and annex neighboring suburban areas, have lower concentrations of poverty than
“inelastic” cities in the Northeast and Midwest. Most relevant to the current study, Ades
and Apparicio (2012) construct a number of measures, based on earlier work by Massey and
Denton (1988), for eight large Canadian metro areas and evaluate changes in these measures over five-year periods. Dwyer (2012) calculates segregation measures during the 1990s,
during which poverty had become less concentrated, and Kavanagh et al. (2016) evaluate
changes in poverty concentrations in four Scottish cities between 2001 and 2011.
We seek to expand the use of statistical methods for a large sample of central cities, so
that we may examine underlying differences between older cities such as Detroit and fastergrowing places that still experience considerable poverty. We do so for a set of more than 70
large U.S. cities, analyzing the cities themselves rather than their surrounding metropolitan
areas. We focus on four main goals: 1) Measuring the distribution of poverty by calculating
and comparing four separate statistics; 2) finding relevant socioeconomic variables that explain variations in the distribution of poverty among cities; 3) describing and attempting to
explain changes in these distributions; and 4) noting avenues for future research, particularly
for “outlier” cities with unexpectedly concentrated or “diffuse” poverty.
c Southern Regional Science Association 2019.
476
The Review of Regional Studies 49(3)
Overall, we find that our main block-group-based concentration measures are most closely
related to the poverty rate itself, with explanatory variables such as population, its change,
and city elasticity having little significant effect. Ethnic composition (particularly the share
of White and Hispanic residents), as well as the share of service (but not professional)
employment, also contribute in many cases. We note differences in the poverty distributions
of Detroit and other “Rust Belt” cities, especially when compared to booming cities such
as Plano (Tex.), and find outliers that may warrant further analysis. Tract-based poverty
concentration mostly decreases from 2010 to 2015, but this seems not to be explained by
any economic characteristics.
This paper proceeds as follows. Section 2 discusses a theory of poverty diffusion, while
Section 3 outlines the paper’s methodology. Section 4 describes the results and Section 5
concludes, highlighting three key avenues for further research.
2.
A BASIC THEORY OF POVERTY DIFFUSION
While this study is primarily empirical, we begin with a simple theoretical framework to
explain both the diffusion process within cities and differences between cities. First, we
expect that, all else equal, high poverty cities will have higher dispersions of residents in
poverty. One way to visualize this is to borrow a science example and imagine the diffusion
of a gas in a jar: a large number of atoms will fill the available space evenly due the transfer
of energy. Here, however, the diffusion process is based on economic forces and poverty is
contained by city borders, where tax and other policies differ (and where other, less benign,
tactics are sometimes employed).
A hypothetical city with 1,000 people and five neighborhoods, for example, could have
a 40 percent poverty rate and high segregation if all 400 poor residents lived in one neighborhood and 150 non-poor residents lived in each of the other four neighborhoods. But
we would expect poverty to spread across the borders between the rich and poor areas and
poverty rates to therefore equalize over time. Poor neighborhoods would drive down nearby
property values to the point where low income residents can afford to leave their old neighborhoods and move to these more desirable areas. This process would proceed as long as
these differentials existed, but city borders (such as the well-known example of Detroit’s
Eight Mile Road) might provide a sufficiently powerful boundary and thus contain poverty.
While positive changes in the other direction are possible, their effects are limited. Gentrification, which is currently taking place in Detroit and elsewhere, might only take place
in certain areas within the city and be insufficiently large-scale to lower the dispersion of
poverty overall.
We also expect that national and international economic forces, and particularly industrial decline, will play a large role in regional differences in the dispersion of U.S. urban
poverty. Since a plant closing will be detrimental to a large surrounding area, we expect its
effects to be concentrated in entire cities in the Northeast and Midwest. Even outmigration
by residents could leave the “poorest of the poor” behind. This contributes to the dispersion
of poverty if the most relatively well-off leave for better neighborhoods.
In addition, many “Rust Belt” cities also are surrounded by incorporated suburbs and
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HEGERTY: THE CONCENTRATION OF POVERTY IN LARGE U.S. CITIES
477
therefore contain a larger proportion of high-density “core” areas than cities in the South and
West. A high-poverty neighborhood in a more elastic city could be surrounded by suburblike areas that would be part of a separate city elsewhere, so this type of “overbounded” city
would appear to have a higher concentration of poverty, all else equal. Racial and ethnic
factors can also play a key role in distributions of poverty. Housing policy has long helped to
restrict African-Americans to central cities. The corresponding outmigration of Whites has
led to wide urban-suburban divergences in the Northeast and Midwest. Immigration, often
from Mexico, could lead to both population growth and changes in incomes, so “Sunbelt”
cities and others in the South and West might exhibit differing distributions of nonwhite
populations than do “Rust Belt” cities. We can test these theories, for all regions, in our
empirical analysis.
3.
EMPIRICAL METHODOLOGY
Data for this study come from the U.S. Census American Community Survey (ACS). We use
2014 data (5-year estimates) at the block-group level and 2010 and 2015 ACS data at the
tract level. We also use population data for Census-designated places, basing our evaluation
only on the 77 cities which have a 2014 population estimate of 250,000 or greater. Not
only does this threshold represent a common definition of “large” cities, it also balances
a sufficiently large sample size with a sufficient number of block groups within each city.
Because some of these cities might be contiguous (such as Minneapolis-St. Paul, or major
cities with large suburbs), we examine visually whether any pairs or groups share a poverty
“cluster” across city borders. In three cases, we treat two cities together, while in others, we
leave them separate.1
Using data for a total of 74 major cities (including three city pairs), we calculate four
metrics to capture the concentration of poverty. First, we calculate the Gini coefficient
based on five groups of roughly equal numbers of households. Next, following Massey and
Denton (1988), Ades and Apparicio (2012), and others, we also include two nonspatial indices
and one spatial measure of poverty concentration. These are the Isolation Index (IS ), the
Exposure Index (xPX ), and the Absolute Concentration Index (ACO). These measures all
take values between zero and one, and generally compare each block group’s share of the
city’s total households in poverty with its percentages of households not in poverty or its
overall share of total population. The ACO measure also includes land area in its calculation.
Our four measures are as follows:
1. The Gini coefficient is calculated by placing tracts, in order of increasing poverty rates,
into five groups of equal population size. Cumulative proportions of households in poverty
for each quintile form a Lorenz curve (see Figure 1), which is compared against a 45-degree
line and the triangle below using simple geometry. A value of zero represents perfectly
distributed poverty and a value of one represents perfectly concentrated poverty. The bestknown of the four measures used here, it is commonly found in country studies of economic
1
These are: Los Angeles/Long Beach, CA; Denver/Aurora, CO; and San Diego/Chula Vista, CA. At the
same time, Dallas, Ft. Worth, and Arlington (TX), as well as Minneapolis and St. Paul, MN (among
others), are evaluated individually.
c Southern Regional Science Association 2019.
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The Review of Regional Studies 49(3)
development (where a value of one implies that one person has all of a country’s income and
a value of zero represents perfect income equality).
2. The Isolation Index, which uses the number of households in poverty (xi ) and population
(ti ), with X and T representing citywide totals of each group:
n
IS =
1 X xi t1 ? xi
?
2 i=1 X
T ?X
(1)
Conceptually, this score is made up of the difference of two components and concentration rises if constituent block groups or tracts contain large shares of the total number of
households in poverty (the left part of the difference) or large shares of the total number of
households not in poverty (the absolute value of the right part).
3. The Exposure Index, which interacts each block group’s or tract’s poverty rate with its
percentage of the city’s total number of households in poverty:
xP x =

n h
X
xi i xi
i=1
X
ti
(2)
This score, being the sum of two products, increases if its constituent areas both have large
shares of the total number of households in poverty and have high poverty rates. Since it is
difficult for many constituent block groups or tracts to have both if poverty is concentrated,
high scores represent more diffuse poverty.
4. The Absolute Concentration score, which compares the land area taken up by households
in poverty versus the total population. Here, households are sorted by population and divided
into groups with a breakpoint where the cumulative population equals X. This is done both
from the smallest to largest (n1 ) and from largest to smallest (n2 ). T1 is the cumulative
population sum from 1 to n1 and T2 is this sum from n2 to n. Ai is the land area of each
tract:
Pn
ACO = 1 ?
xi A i
i=1 X
Pn ti Ai
i=n2 T2
?
?
Pn1
ti Ai
i=1 T1
Pn1 ti Ai
i=1 T1
(3)
Noting that it subtracts the left-hand-side component, we see that the ACO score decreases
(representing more diffuse poverty) if constituent areas have large shares of the total number
of households in poverty, in a large land area, relative to their shares of the total population.
Because the denominator of this component captures overall population concentration, the
ACO score is higher if disproportionately large shares of total poverty, relative to population,
can be found in small land areas.
These four measures are conceptually similar to a certain extent. All are constructed to
take values between zero and one, and in three cases (all but the xPX score), low values
represent diffuse poverty. These measures also exhibit differences from one another. The
Gini coefficient as calculated here applies a set number of component subgroups, which
can vary from study to study. While five is a common choice, results might be sensitive
to the application of a larger number. The calculations of the other scores have their own
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HEGERTY: THE CONCENTRATION OF POVERTY IN LARGE U.S. CITIES
479
idiosyncrasies as well. Only the IS score compares households in poverty with those not in
poverty and only the ACO score incorporates a spatial component. The xPX score stands
out for its “opposite” behavior from the others. At the same time, only xPX includes block
group- or tract-level poverty rates (xi /ti ).
Because each of these measures includes slightly different component variables, none can
be said to be “superior” to the others, and to our knowledge, there is no systematic comparison of the four. Likewise, no “norms,” such as appropriate means or standard deviations,
are known to exist and the economic interpretation of a one-hundredth-point increase or
decrease is also unknown. We therefore estimate all four and note interesting statistical
properties and differences among measures as part of our analysis, leaving comparisons for
future research.
Based on these four measures, we next examine the relationship between poverty and
poverty concentration for our set of 74 U.S. cities. Poverty concentration appears to be
closely related to poverty rates and we seek to explain additional causes of variation through
regression analysis. After finding population change since 1960 to be insignificant in a baseline regression, we model each concentration measure as a function of various combinations
of the following variables: the poverty rate itself; the population in 2014 (to test whether
city size affects plays a role on poverty distributions); the percentages of White, Black, and
Hispanic residents (expecting that the effects might be opposite in sign; since these variables
are shown to be correlated with one another, they are entered separately in additional specifications); the percentages of service and professional jobs in each city (it is expected that
the former are lower-paying and might lead to more diffuse poverty, while the latter might
encourage “enclaves” or gentrification); and two separate dummy variables, equaling 1 for
each of our identified “Rust Belt” and “Sunbelt” cities, respectively. We also include city
“elasticity” (increase in land area since 1960) to one of the better-performing models in an
eighth specification. This measures a city’s ability to expand its borders through annexation. In models using this variable, we only include cities that were among the 100 largest
in both 1960 and 2014, leaving a sample of 54 cities for the OLS estimations that include
this variable. Since it is difficult to interpret the coefficient estimates in a meaningful way
(because point scores are the dependent variable and no known norms exist), we focus on
sign and significance to identify the main contributors to the distribution of urban poverty.
Next, we calculate tract-level distributions for all four measures and their changes from
2010 to 2015. Our main focus is to detect overall shifts in the measures (increased concentration or diffusion) over this period. In a set of bivariate analyses, we also compare changes in
the IS score against variables representing income and ethnic composition. Overall, except
for ethnic makeup, their explanatory power is limited, and fe…
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