The impact of government funding of povertyreduction program.docx

The impact of government funding of poverty
reduction programmes
Suhyun Jung1, Seong-Hoon Cho2, Roland K. Roberts2
1 Department of Applied Economics, University of Minnesota,
1994 Buford Ave, 337k Ruttan Hall Saint Paul, MN
55108, United States (e-mail: [email protected])
2 University of Tennessee – Agricultural and Resource
Economics, Knoxville, Tennessee, United States
(e-mail: [email protected], [email protected])
Received: 29 December 2011 / Accepted: 13 October 2013
Abstract. This research evaluates the impacts on poverty rates
of government funds for edu-
cation, health and hospitals, and public welfare allocated to
poverty reduction for counties with
persistently high poverty in the Southern United States. Our
analysis found that increases in
education funding in a poverty hot-spot county reduce the
poverty rates of that county and its
neighbouring hot-spot counties. We also found that higher
health and hospital funding in a
hot-spot county is associated with higher poverty rates in
neighbouring hot-spot counties and
that public welfare funding is not effective in mitigating
poverty either within or outside of
poverty hot-spots.
JEL classification: H75, I32, R58

Key words: Government funding, spatial panel, poverty,
southern United States
1 Introduction
1.1 Background and objective
Since President Lyndon Johnson declared war on American
poverty in 1964, policy-makers have
struggled to develop programmes to reduce poverty and
researchers have striven to determine
the effectiveness of those programmes. Through their efforts,
significant amounts of research
and government funding have been directed towards the poverty
issue. For example, the
Appalachian Regional Commission (ARC) was formed by the
federal government in 1964 to
improve the standard of living in the Appalachian region. This
programme included grants,
direct loans, guaranteed loans, and direct payments for retirees,
the unemployed and the poor
(Reeder and Calhoun 2002). Despite government spending for
poverty reduction, the poverty
rate in the United States has remained consistently above 10 per
cent over the last four decades.
For instance, the poverty rate rose from 11.3 per cent in 2000 to
15.9 per cent in 2011, its highest
level since 1993, partially due to the recession that began at the
end of 2007 (DeNavas-Walt
et al. 2007; Bishaw 2009, 2012). Poverty rates have been
persistently higher for decades in the
doi:10.1111/pirs.12089
© 2014 The Author(s). Papers in Regional Science © 2014

RSAI
Papers in Regional Science, Volume 94 Number 3 August 2015.
mailto:[email protected]
mailto:[email protected], [email protected]
Southern United States, particularly in the borderland of Texas
and Lower Mississippi Delta
(hereafter referred to as ‘poverty hot-spots’), compared to other
parts of the United States
(Poston et al. 2010). The recent increase in the US poverty rate
and the persistently higher
poverty rates in the Southern United States have revitalized
interest in understanding whether
government programmes have been effective in reducing the
poverty rate.
In our research, we analyse the government’s role in poverty-
rate reduction by evaluating
the hypotheses that government funds budgeted for health and
hospital, education, and public
welfare reduce the poverty rate. We particularly investigate the
effects of government funding
on poverty rates in poverty hot-spots using three-decade panel
data, which is unique in this
type of study. Our hypotheses were conceptually motivated and
tested using a spatial Durbin
model for panel data (Elhorst 2003, 2010). The model was
estimated using county-level data
from 16 states in the US Census Bureau’s South Division
(referred to as ‘the South’). The
data were for 1990, 2000, and 2010. Ex ante impact analysis
was performed for persistent
poverty areas in the South. The model was used to estimate

direct, indirect, and total marginal
poverty-reducing effects of government funds. We predicted
poverty rates and marginal
poverty-reducing effects of significant government funding
categories in the South and in
three poverty hot-spots (spatial clusters of counties with
persistent poverty). Our study pro-
vides important information to help policy-makers in
developing regional poverty reduction
strategies.
1.2 Review of empirical literature on poverty
Many researchers have investigated the effects of specific
government funding categories on
poverty and economic status. A number of researches have
focused on government funds to
Fig. 1. Poverty hot-spots (high-poverty counties surrounded by
high-poverty counties) in 2010 based on local indicators
of spatial association (LISA) using the poverty rate
S. Jung et al.654
improve health and education because health improvements and
increased education were found
to be highly correlated with economic growth (e.g., Triest 1997;
Bloom and Canning 2000;
Waidmann and Rajan 2000; Bhargava et al. 2001; Beale Fan et
al. 2002; Jung and Thorbecke
2003; Probst et al. 2004). A large body of evidence shows that
poverty is correlated with alcohol

and drug abuse and mental health issues, and thus the funding
of programmes that support
people with these problems should impact poverty (Baingana et
al. 2006). In other branches of
the literature, public welfare programmes targeting low-income
families with children, their
parents, and caregivers (e.g., the Temporary Assistance to
Needy Families (TANF) programme)
were found to affect poverty (Lower-Basch 2011). This finding
suggests that funding pro-
grammes through the public welfare budget should address
poverty issues.
Numerous studies have focused on the quantitative impact of
government spending on
poverty reduction. Fan et al. (2002) examined the effects on
China’s rural poverty rate of
government expenditures on rural education and infrastructure
and found positive poverty-
reducing impacts. Jung and Thorbecke (2003) explored the
impacts of increased education
expenditures, and the resulting excess supply of educated and
skilled labour, on poverty alle-
viation in Tanzania and Zambia. Afonso and Aubyn (2004)
evaluated the efficiency of govern-
ment spending on education and health among Organization for
Economic Cooperation and
Development (OECD) countries and suggested possible causes
(i.e., different resource prices,
public sector inefficiency) for varying government expenditure
outcomes in terms of poverty
indicators such as literacy, life expectancy, and infant
mortality. Glennerster (2002) reviewed
poverty measures in the United States and emphasized the need
for a variety of poverty
measures. He identified health expenditures as a crucial element

in explaining the basic neces-
sities of the poor. Smeeding (2006) compared government
spending and poverty trends in 11
developed countries and emphasized the importance of creating
incentives for low-wage
workers when increasing welfare benefits targeted at low-
income families.
A number of studies have evaluated regional poverty reduction
strategies. Triest (1997)
examined how increased educational opportunity and increased
employment of low-income
populations narrow the interregional poverty gap. Swaminathan
and Findeis (2004) found that
welfare assistance to poor workers had a positive effect on
reducing poverty in metropolitan
areas. Rupasingha and Goetz (2007) suggested that increases in
government investment in social
capital can reduce the poverty rate by easing transaction costs
paid by local associations. Allard
et al. (2003) and Blank (2005) suggested that poverty reduction
is more effective when spatially-
targeted governmental policies are implemented. Levernier et
al. (2000) found that developing
educational programmes specifically targeted at minorities and
residents in non-metropolitan
statistical areas (MSA) is a key element for reducing poverty.
Notwithstanding the importance of regional targeting of poverty
reduction policies found in
the literature, poverty reduction has rarely been explored using
a spatially-explicit framework.
Partridge and Rickman (2005) discussed spatial dependencies in
poverty rates and adjusted for
spatial autocorrelation by including weighted averages of
neighbouring-county characteristics.

Partridge and Rickman (2006) explored the geographic
disparities in poverty across the United
States and drew implications for integrated national poverty
reduction strategies that combine
place-based and person-based policies. They considered
interregional equilibrium and disequi-
librium perspectives in which firms are attracted to low-wage
areas and labour departs until
poverty equilibrium is reached. Under the equilibrium
perspective, local economic development
policies are unlikely to increase the utilities of the original
residents because new migration
offsets any wage gains arising from increased labour demand.
Alternatively, under the disequi-
librium perspective, local economic growth can reduce local
poverty rates because barriers to
mobility (e.g., housing market constraints, transportation costs,
migration costs, and imperfect
information) can contribute to deviations from equilibrium
poverty rates that can persist
over time.
Government funding and poverty reduction in the Southern US
655
Empirical evidence on whether poverty rates tend to follow the
interregional equilibrium
perspective (e.g., Blomquist et al. 1988; Beeson and Eberts
1989) or disequilibrium perspective
(e.g., Kaldor 1970; Krugman 1991; Glaeser et al. 1992) is
mixed. Nevertheless, spatial patterns
of poverty commonly suggest that poverty rates are persistently

unequal across regions
(Friedman and Lichter 1998; DeNavas-Walt et al. 2007; Weber
et al. 2005). For example, the
‘Southern Black Belt,’ extending from Southwest Tennessee to
East-central Mississippi and then
East through Alabama to the border with Georgia, has had
persistently higher poverty rates than
other regions within the South (Wimberley and Morris 1997,
2002).
1.3 Significance of this analysis
The aforementioned empirical literature provides insight into
four distinct aspects of poverty:
(i) the impact of government funding on poverty; (ii) the
regional targeting of poverty reduction
strategies; (iii) the spatial nature of poverty; and (iv) the
persistent nature of poverty. Neverthe-
less, research addressing all four aspects of poverty in one
framework has not been undertaken
mainly because an econometric framework accounting for both
the spatial and persistent
natures of poverty was not available until recently. By adjusting
for spatial and temporal
autocorrelations, our comprehensive econometric framework is
used to evaluate the impacts of
government poverty-related funding categories on poverty-rate
reductions in several clusters of
high-poverty counties in the South. This aspect of our study is
an important advance in
poverty-related research addressing the efficacious government
poverty-reduction strategies that
target regionally-persistent poverty.
We also emphasize that our study examines the impact of
government funding on poverty

over 20 years (i.e., 1990, 2000, and 2010) and few, if any,
previous studies explicitly considered
the spatial aspects of poverty in the context of its long-term
persistence. Combining spatial and
long-term analyses is particularly important because persistent
poverty takes considerable time
to address, and shorter-term temporal and spatial analyses
performed separately or in combi-
nation may not fully identify government’s effectiveness and
role in poverty alleviation.
Another of our innovations is to evaluate the effectiveness of
government funds allocated to
poverty reduction in clusters of high-poverty counties, defined
as poverty hot-spots. Identifying
poverty hot-spots using a spatial statistical tool (see Section
2.7) allows us to be more focused and
systematic. Given that persistent poverty is a serious problem
and a daunting challenge in some
areas, focusing resources on poverty hot-spots is being actively
pushed (Duncan 1992; Lyson and
Falk 1993; Wimberley and Morris 2002). Under increasingly
tight budgets, states could take a
more targeted approach by concentrating funding for poverty
reduction on local ‘hot-spots’. Thus,
our focus on poverty hot-spots is even more meaningful and
sought after than ever.1
2 Methods and procedures
2.1 Conceptual motivation for the empirical specification
Conceptual hypotheses about the relationships between
government funding for health care,
education, or public welfare programmes and the poverty rate
provide motivation for an

empirical model to test those hypotheses. First, government
funding for health-care programmes
1 We appreciate an anonymous referee for pointing out the
following information. The targeting of impoverished
hot-spots has been promoted by policy-makers in the health care
debate over the Affordable Care Act (Blumberg 2012;
HHS 2013; Manchikanti et al. 2011).
S. Jung et al.656
is a crucial element in explaining the basic necessities of the
poor (Glennerster 2002; Meyer and
Sullivan 2012). Poor health has contributed to US poverty
because medical expenses can
exhaust family resources. For example, about half of
bankruptcies in the United States in 2001
involved medical debt (Himmelstein et al. 2005). The strong
correlation between health-related
expenses and wealth loss is well documented (Smith 1999; Cook
et al. 2010). Consequently, we
hypothesize that an increase in government funding of health-
care programmes decreases the
poverty rate.
Second, government funding for education is correlated with
earnings. In 2006, 23 per
cent of the US population without a high school diploma had
incomes below the poverty level
while fewer than 4 per cent of the population with a college
degree had incomes below the
poverty level (Rynell 2008). Given the statistical evidence,

many believe that the persistent
cycle of poverty can be broken through education (Bhola 2006;
Perry 2006; Rodgers and
Rodgers 1993). Based on the premise that education can
alleviate poverty, the US government
has initiated programmes aimed at improving education with a
major purpose of alleviating
poverty. For example, the Individuals with Disabilities
Education Act (IDEA) and the Child
Nutrition Act (CNA), funded through the education budget, are
weighted heavily towards the
poor in their decision-making formulas (Fujiura and Yamaki
2000; Cook and Frank 2008).
Thus, our hypothesis is that increased government funding of
education programmes
decreases the poverty rate.
The hypothesis about the relationship between government
funding of public welfare pro-
grammes and the poverty rate is based on two conflicting views.
Proponents of public welfare
spending to reduce poverty argue that reduced income
inequality through welfare spending can
increase family expenditures on education and increase
incentives to work (Kenworthy 1999).
Critics argue that such programmes fail to reduce poverty
because: (i) too little money reaches
the poor (Stigler 1970; Friedman and Friedman 1979; Crook
1997); (ii) the programmes
undermine the intrinsic motivation of the poor (Murray 1984;
Butler and Kondratas 1987; Lee
1987); and (iii) the programmes reduce incentives to invest and
to work (Arrow 1979; Lindbeck
et al. 1994; Okun 1975). Our testable hypothesis is the
proponents’ view–welfare spending
reduces poverty.

2.2 Empirical model specification
For panel data, the poverty rate equation is:
P Xit it i t it= + + + +α β μ λ ε , (1)
where i represents the ith county (i = 1, 2, . . . , 1,420); t
denotes 1990, 2000, and 2010; P is the
poverty rate;α is a constant parameter; X is a vector of
explanatory variables including demo-
graphic, employment and environmental characteristics, per
capita government funding for
education (E), health and hospitals (H), and public welfare (F),
a dummy variable for whether
or not the ith county is within a poverty hot-spot (S), and the
interaction of the dummy variable
with per capita government funding (i.e., E × S, H × S, F × S); β
is a parameter vector; and ε
is an error term. The terms μ and λ respectively denote
unobserved spatial and time specific
effects. The interaction variables capture differences in the
impacts of per capita government
funding by the counties that are or are not within poverty hot-
spots.
Equation (1) allows the poverty rate to be both spatially and
time persistent, generating
spatial and temporal dependencies among the observations. We
used a spatial Durbin model
for panel data (Elhorst 2003, 2010) to estimate equation (1)
because the model controls for
both spatial and temporal dependencies. We employed a
‘specific-to-general’ approach to
compare the aspatial poverty-rate equation with the spatial
poverty-rate equation, and a

657
‘general-to-specific’ approach to compare the generalized
spatial model with the spatial error
and spatial lag models (Elhorst 2010). We also compared fixed
and random effects models.
2.3 Endogeneity issues of explanatory variables
Because per capita government funding for education, health
and hospitals, and public welfare
are largely determined by economic conditions, they are closely
associated with the poverty rate,
potentially introducing endogeneity into the poverty-rate
equation (e.g., Fan and Chan-Kang
2008). In our estimates, endogeneity is not a concern because
we use exogenous predetermined
budget allocations for 1987, 1997 and 2007 as explanatory
variables to explain the poverty
rates in 1990, 2000 and 2010, respectively (Wooldridge 2009, p.
562). Other variables, such as
employment variables, that are contemporanenous to the
poverty-rate variable may also be
endogenous. We performed a robustness analysis by estimating
a reduced-form model that
included only the most exogenous variables to see if the results
changed significantly.2
2.4 Tests for model specifications

We conducted a spatial Lagrange multiplier (LM) test (Burridge
1980; Anselin 1988) and a
robust LM test (Anselin et al. 1996) to compare the aspatial and
spatial models in the context of
the specific-to-general approach. The robust and non-robust LM
statistics (248 and 11 respec-
tively) for the spatial lag model and the corresponding LM
statistics (597 and 360 respectively)
for spatial error model indicated that the aspatial model was
rejected at the 5 per cent level
(hereafter referred to as ‘significant’) in favour of the spatial
lag model or the spatial error
model. The spatial model estimates were based on 1,420 × 3
observations assuming a hybrid of
the first-order Queen continguity weight matrix and the inverse
distance weight matrix (here-
after ‘the hybrid weight matrix’).
In the context of the general-to-specific approach, we performed
Wald and likelihood ratio
(LR) tests using the framework of a spatial Durbin model for
panel data (SDMP) that employs
both spatial lag and spatial error components (LeSage and Pace
2009) with incorporation of
temporally lagged dependent variable.
P P w P w P X w Xit it ij jt
j
N
ij jt
j
N

it ij jt
j
N
= + + + + +− −
= = =
∑ ∑ ∑α γ δ ρ β φ1 1
1 1 1
++ + +μ λ εi t it, (2)
where j represents the jth county, γ is a parameter of poverty
rates for the lagged time period
(Pit‒1), wij is element (i, j) of the N × N spatial weight matrix
W, w Pij jt
j
N
=
∑
1
is poverty rates within
the neighbours defined by the hybrid weight matrix, δ is a
parameter for spatially lagged poverty
rate for the lagged time period (WPjt‒1), ρ is a parameter for
spatially lagged poverty rates for the
present time period (WPjt), ϕ is a parameter vector of spatially
lagged independent variables for
the present time period (WXjt), and μi and λt represent the

spatial-specific time-invariant effect
and time-specific spatial-invariant effect, respectively. The
Wald and LR test statistics compar-
ing the spatial Durbin model against the spatial lag model were
515 and 468, respectively, and
the Wald and LR statistics comparing the spatial Durbin model
against the spatial error model
2 Alternatively, endogeneity tests could be done; however,
because of the burden of choosing valid instrumental
variables, we chose the robustness test. We particularly thank
an anonymous referee for bringing this issue to the
authors’ attention.
S. Jung et al.658
were 215 and 225, respectively. These statistics rejected the
hypothesis that the SDMP can be
simplified to the spatial lag model or spatial error model. The
same tests were performed with
consistent results using hybrid weight matrices that included
second- and third-order Queen
contiguity weight matrices. Based on these test results, the
poverty-rate equation was estimated
using the SDMP expressed in Equation (2).
2.5 Panel data model specification
The panel data model can be specified in two ways. One way
deals with whether to include time-
and/or spatial-specific effects, and the other is whether to treat
unobserved effects as random or

fixed effects. First, we perform an LR test to investigate
whether to include a time period fixed
effect and a spatial fixed effect. An LR statistic of 134 using
estimates from the SDMP rejected
the hypothesis that the temporal effects are jointly insignificant.
An LR statistic of 3,219 using
estimates from the SDMP without the time-invariant variables
(i.e., urban influence codes and
natural amenity scales) rejected the hypothesis that the spatial
effects are jointly insignificant. As
a result, we included a time-specific effect and a spatial-
specific effect in the model.
Second, we tested the hypothesis that the unobserved effects
can be treated as random effects
using Hausman’s specification test based on the SDMP without
the time-invariant variables
(Hausman 1978; Lee and Yu 2010a). A Hausman statistic of 136
rejected the hypothesis,
suggesting the estimation of the SDMP with fixed effects.
Consequently, the poverty-rate
equation was estimated using a fixed-effect SDMP model.
Time-invariant variables were
excluded from both tests because the coefficients of the time-
invariant variables cannot be
estimated for spatial fixed-effect models using panel data
(Greene 2010).
2.6 Estimation and marginal effects
The SDMP poverty-rate equation specified with time period and
spatial fixed effects using the
hybrid weight matrix was estimated by maximum likelihood
following Elhorst (2003, 2010).
Once the parameters were estimated, the total marginal effect of
a change in the kth explanatory

variable xk on the South’s average poverty rate in county i = 1
up to N at a given time t was
estimated by:
∂
∂
∂
∂
⎡
⎣ ⎢
⎤
⎦ ⎥
= −( ) +[ ]−
P
x
P
x
W I W
k Nk t
k N k
1
1� I ρ β φ . (3)
Alternatively, the marginal effect that incorporates γ and δ (i.e.,
1 1−( ) − +( )[ ]−γ ρ δI W
+[ ]β φI Wk N k ) (Debarsy et al. 2012; Elhorst 2012) could be

used. By ignoring γ and δ, our
marginal effects estimated by Equation (3) are perceived as a
short-term effect because they do
not account for the temporal dynamics of the poverty rate and
spatially lagged poverty rate.
Despite the potential for obtaining long-term effects, we chose
to use the short-term effects
because of three observations made by Elhorst (2012) about the
dynamic spatial Durbin model’s
long-term effects (i.e., identification problem, lack of empirical
evidence, and overfitting due to
model complexity).
Because spatial spillover effects play a significant role in the
total marginal effect, the total
marginal effect was decomposed into a direct marginal effect
(hereafter, referred to as direct
effect) and an indirect marginal effect (hereafter, referred to as
indirect effect) (LeSage and Pace
2009). The direct effect is the effect on county i’s poverty rate
and the indirect effect is the effect
on the poverty rate outside county i. We followed LeSage and
Pace (2009) to calculate the direct
and indrect effects (The details are laid out in Appendix 2).
659
We identified poverty ‘hot-spots’ using local indicators of
spatial association (LISA) analy-
sis (see the following subsection for the details), and

summarized the marginal effects of
significant government funding categories across the poverty
hot-spots. These summaries quan-
tify the relative importance of the funding categories in
alleviating poverty, and they also provide
a means to examine how these effects changed over time. We
used the estimated parameters of
the SDMP model expressed in Equation (2) to predict poverty
rates for the entire South and
across the three poverty hot-spots for 1990, 2000, and 2010.
2.7 Identifying clusters of high-poverty counties
We used 2010 average county poverty rates to identify clusters
of high-poverty counties. LISA
values (Anselin 1995) were estimated to identify spatial clusters
of poverty in
the South. The LISA values indicate the extent of spatial
autocorrelation between the poverty
rate in a particular county and the poverty rates of the counties
surrounding it. Poverty
hot-spots were identified through plotting sets of contiguous
locations for which LISA values
were significant (Anselin 1995). These clusters can include a
single county and its con-
tiguous neighbours or a larger set of contiguous counties for
which the LISA values
are significant. The county LISA values for poverty rates for
2010 were defined as:
LISA y y y w y yi i i
n
i j
n
ij j= −( ) ∑[ ]⋅ ∑ −( )= =1 2 1 , where n is the sample size, yi is

the poverty rate in
county i with sample mean , and wij is an n × n contiguity
weight matrix with diagonal elements
of 0 and off-diagonal elements of 1 for all counties j that are
contiguous to county i. The LISA
clustering was done for poverty rates in 1990 and 2000 as well,
but the poverty hot-spots did not
change appreciably because of the persistent temporal and
spatial clustering of poverty rates.
See Figure 1 for identified three poverty hot-spots.
3 Study area and data description
3.1 Study area
This study focuses on 1,420 counties in 16 states (i.e.,
Arkansas, Alabama, Delaware, Florida,
Georgia, Kentucky, Louisiana, Maryland, Mississippi, North
Carolina, Oklahoma, South Caro-
lina, Tennessee, Texas, Virginia, and West Virginia) in the
South. The South was selected
because its poverty rates have been persistently higher than
other regions, making poverty an
important issue in the South compared with other US regions. In
2010, the South had the highest
poverty rate at 16.9 per cent, compared with 12.8 per cent in the
Northeast, 13.9 per cent in the
Midwest, and 15.3 per cent in the West (US Census Bureau
2011a). In addition, the South was
the only region with a significant increase in its poverty rate
(about 10 per cent increase from
15.7 per cent to 16.9 per cent) between 2009 and 2010 (US
Census Bureau 2011a).
3.2 Data

The study employs five county-level geographical information
system (GIS) datasets: (i)
poverty data for 1989, 1999, and 2009 (i.e., the percentage of
individuals with incomes below
the US Census Bureau’s 1991, 2001, 2011 poverty threshold
(US Census Bureau 2011d) based
on family size and the ages of its members, adjusted for
inflation using the consumer price
index) to represent poverty rates in 1990, 2000, and 2010,
respectively; (ii) demographic data
(i.e., population percentages of Whites, Asian-Pacific Islanders,
Blacks and other races, with the
S. Jung et al.660
population percentage of Blacks as the reference category;
population percentages below 18
years of age, between 18 and 24 years of age, 65 years of age or
older, with the population
percentage between 25 and 64 years of age as the reference
category; female-headed house-
holds; difficulty speaking English; at least some college
education; and living in households
with three or more workers) for 1990, 2000, and 2010 (US
Census Bureau 2001a, 2011b); (iii)
employment data (i.e., employment variables that include
population percentages of unem-
ployed workers 16 years of age or older; employment in
agriculture, forestry and fisheries;
employment in manufacture, mining and construction;
employment in transportation, commu-

nication and other public utilities; employment in wholesale and
retail activities; and employ-
ment in finance, insurance and real estate) for 1990, 2000, and
2010 (US Census Bureau 2001b,
2011c; US Department of Labour 2011); (iv) employment in the
arts and environmental data
(i.e., natural amenity scales and urban influence codes) for 1993
and 2003 (ERS USDA 2004,
2007); and (v) funds from federal and state governments
allocated by state governments to
county budget categories for education, health and hospitals,
and public welfare – budget codes
of C21, C42 and C79 for 1987, 1997 and 2007, respectively (US
Census Bureau 2008).
County budget categories for revenues allocated by state
governments were used because
they include all government funds flowing into the budgets of
municipalities, townships, special
districts, and independent school districts (US Census Bureau
2008). Because the US Census
Bureau does not publish spending from these budget categories,
in using these data, we assume
these funds are spent as intended. As can be seen from their
descriptions below, these county
funding categories are heavily weighted toward programmes
directly affecting the poor.
The county budget category for education (hereafter, ‘education
funding’) includes funds
received by the counties from the state to support local schools
and state redistribution of federal
aid for education; handicapped, special and vocational
education and rehabilitation; student
transportation; equalization aid; school health; local community
colleges; adult education;

school buildings; and property tax relief related strictly to
school funding. The IDEA and CNA
programmes are examples of programmes funded through
county education budgets (Salisbury
2004; Trohanis 2008;). These budget categories for education
can help increase the income
levels of individuals, which may affect the poverty rate of a
county. For example, federal aid for
handicapped, special and vocational education and
rehabilitation can provide incentives for
disabled and low income people to work and earn more income
to lift themselves out of poverty.
A significant marginal effect implies that an increase in
education funding reduces the poverty
rate.
The county budget category for health and hospitals (hereafter,
‘health and hospital
funding’) includes funds received from the state to support
county programmes for local health;
maternal and child health; alcohol, drug abuse and mental
health; environmental health;
superfunds; nursing aid; hospital financing; and hospitalization
of patients in local government
hospitals. Health and hospital funding does not include
Medicaid spending, which is included
in the budget category for public welfare. Health and hospital
funding can promote a better
environment for the poor or help increase the ability of
individuals to earn income. For instance,
funding for alcohol, drug abuse and mental health can help drug
or alcohol addicts and people
with mental disease to overcome addictive behaviour, increasing
their ability to earn income and
lift themselves out of poverty.

The county budget category for public welfare (hereafter,
‘public welfare funding’) includes
funds received from the state for public welfare purposes;
medical care including Medicaid and
related administration under public assistance programmes; care
in nursing homes not associ-
ated with hospitals; federal categorical assistance; non-
categorical assistance and administration
of local welfare programmes. Public welfare funding is more
directly related to increasing the
income of the poor than the other two funding categories. The
TANF programme is an example
of a programme funded through the public welfare budget.
661
Urban influence codes for 1993 were used as proxies for
rurality in 1990 and urban influence
codes for 2003 were used as proxies for rurality in 2000 and
2010. Natural amenity scales for
1993 were used as proxies for the physical characteristics of
counties that enhance their appeal
as places to live (ERS USDA 2004). Variable names,
definitions, and descriptive statistics for the
variables used in the analysis are presented in Table 1.
Table 1. Variable names, descriptions, and statistics
Variable Description 1990 mean
(SD)

2000 mean
(SD)
2010 mean
(SD)
Poverty variable
Individual poverty rate Percentage of individuals with incomes
below the
US Census Bureau poverty threshold based on
the family size and the age of its member
adjusted for inflation using the consumer price
index in 1989, 1999, and 2009 (%)
20.04 16.94 18.26
(8.47) (6.74) (6.61)
Spatial lag of individual
poverty rate
Spatial lag of individual poverty rate in 1989,
1999, and 2009 (%)
20.01 16.89 18.13
(6.92) (5.33) (5.02)
Demographic variable
White White population alone divided by total
population (%)
79.89 77.52 76.11
(16.95) (17.26) (17.65)

Asia-Pacific Asia–Pacific population divided by total
population (%)
0.44 0.65 0.96
(0.74) (1.05) (1.52)
Other races Native Americans and other races excluding
white, Asian-Pacific, and Black population
divided by total population (%)
3.05 5.08 6.24
(6.11) (6.43) (5.98)
Age 0–17 years Population aged below 18 years divided by total
population (%)
26.61 25.27 23.53
(3.50) (3.15) (3.10)
Age 18–24 years Population aged between 18 and 24 divided by
total population (%)
9.85 9.25 9.10
(3.53) (3.55) (3.59)
Age over 65 years Population aged 65 or older divided by total
population (%)
14.33 14.14 15.27
(4.18) (3.83) (3.89)
Female-headed Population living in female-headed households
divided by total households (%)
15.17 17.04 13.60
(5.67) (6.22) (4.22)

English speaking Population aged between 16 and 64 with
difficulty
speaking English divided by total population
(%)
1.30 1.97 3.70
(2.97) (3.10) (5.22)
Some college education Population with at least some college
education
divided by total population aged 25 or older
(%)
31.15 35.73 42.94
(10.49) (10.00) (10.71)
Three or more workers Population living in households with 3
or more
workers divided by total population (%)
10.80 9.27 4.54
(2.80) (2.17) (1.74)
Employment variable
Unemployment rate Unemployed workers aged 16 years or older
divided by total population aged 16 years or
older (%)
6.62 4.58 9.82
(3.01) (1.66) (2.85)
Agriculture Population of employment in agriculture, forestry,
and fisheries divided by total employed
population (%)

6.24 4.54 5.78
(5.94) (4.85) (6.30)
Manufacturing Population of employment in manufacture,
mining, and construction divided by total
employed population (%)
30.98 27.06 21.54
(10.31) (8.61) (7.20)
Public utility Population of employment in transportation,
communications, and other public utility
divided by total employed population (%)
6.66 7.16 7.12
(2.06) (3.95) (2.25)
Wholesale and retail trade Population of employment in
wholesale and retail
trade divided by total employed population (%)
19.17 13.01 14.17
(3.47) (4.15) (2.70)
S. Jung et al.662
Table 1. Continued
Variable Description 1990 mean
(SD)

2000 mean
(SD)
2010 mean
(SD)
Finance and insurance Population of employment in finance,
insurance,
and real estate divided by total employed
population (%)
4.18 4.44 4.67
(1.67) (1.74) (1.90)
Arts Population of employment in art, design,
entertainment, performance, sports, and related
workers divided by total employment (arts
occupation in 1990 for 1990 and 2000 for 2000
and 2010) (%) (ERS USDA 2007)
0.60 0.62 0.62
(0.37) (0.38) (0.38)
Environmental variable
Urban influence code Measure of rurality ranges between 1 and
12, 1
being large metro area of 1+ million residents
and 12 being noncore not adjacent to metro or
micro area and does not contain a town of at
least 2,500 residents (urban influence code in
1993 for 1990 and 2003 for 2000 and 2010)
(ERS USDA 2007)
5.33 4.89 4.89
(2.65) (3.21) (3.21)

Natural amenity scale Measure of physical characteristics of a
county
area that enhance the location as a place to live,
which is constructed by combining six
measures of climate, typography, and water
area that reflect environmental qualities most
people prefer: warm winter, winter sun,
temperate summer, low summer humidity,
topographic variation, and water area (Natural
amenity scale measured over the years between
1970 and 1996 used for 1990, 2000, and 2010)
(ERS USDA 2004)
0.37 0.37 0.37
(1.37) (1.37) (1.37)
Governmental funding variable
Health and hospitals State aid for local health programmes;
maternal
and child health; alcohol, drug abuse, and
mental health; environmental health;
superfunds; nursing aid; hospital financing
(including construction); and hospitalization of
patients in local government hospitals in 1987,
1997, and 2007, respectively, for 1990, 2000,
and 2010 ($/capita)
6.67 15.65 20.13
(13.67) (41.96) (57.03)
Education State aid for support of local schools;
redistribution of Federal aid for education;
handicapped, special, and vocational education
and rehabilitation; student transportation;

equalization aid; school health; local
community colleges; adult education; school
buildings; and property tax relief related strictly
to school funding. in 1987, 1997, and 2007,
respectively, for 1990, 2000, and 2010
($/capita)
395.25 684.91 1,046.33
(131.66) (217.37) (349.44)
Public welfare State aid for public welfare purposes; medical
care and related administration under public
assistance programmes (including Medicaid)
even if received by a public hospital; care in
nursing homes not associated with hospitals;
Federal categorical assistance; non-categorical
assistance (e.g., home relief, emergency
assistance); and administration of local welfare
programmes in 1987, 1997, and 2007,
respectively, for 1990, 2000, and 2010
($/capita)
5.32 12.67 16.71
(13.14) (30.52) (52.87)
Notes: The data are at the county level for 1990, 2000, and 2010
unless indicated otherwise.
663
4 Empirical results

4.1 Regression results
The results of the SDMP with spatial- and time-fixed effects
using the hybrid weight matrix are
presented in Table 2.3 The positive effect of the spatially
lagged poverty rate indicates that a 1
per cent increase in the poverty rate in neighbouring counties
increases the own poverty rate by
0.27 per cent. This finding reaffirms the spatial clustering of
poverty in the South. Conversly, the
coefficient for the time lagged poverty rate is not significant
while the coefficient for the
spatially lagged time lagged poverty rate is positive and
significant, which means that a 1 per
cent increase in the time lagged poverty rate in surrounding
counties increased the own poverty
rate by 0.06 per cent. The insignificance of the coefficent for
the time lagged poverty rate
variable is unexpected given the persistent nature of the
poverty. This result may be explained
by the persistent spatial clustering of poverty, captured by the
spatially lagged poverty rate,
overshadowing the time-lagged effect of poverty. Because the
spatial spillover effects make
interpretation of parameters difficult, we focus below on
interpreting the direct, indirect, and
total marginl effects of the variables.
The direct effects of all demographic variables except other
races variable were significant
and the signs were as expected. The direct effects indicate that
counties with less White and
Asian-Pacific population, higher percentages of youth, seniors,
people living in female-headed
households, people with difficulty speaking English, people

without any college education, and
people living in households with less than three workers had
higher poverty rates. These results
suggest that, in addition to having an economically active
population, a county’s human-capital
capacity was important in explaining lower poverty rates. The
total effects differ from the direct
effects for some of the variables. For example, a 1 percentage
point increase in the Asian-Pacific
population in a county decreases the poverty rate of the own
county by 0.27 per cent, while it
decreases the poverty rate in the entire South by 0.56 per cent,
when summing the direct and
spillover (indirect) effects. As another example, a 1 percentage
point increase in people living in
female-headed households increases the poverty rate of the own
county by 0.06 per cent, but has
no effect on the poverty rate in the entire South, after
accounting for the spatial spillover. These
differences in direct and total effects reaffirm the importance of
understanding and accounting
for spatial spillover effects.
The direct effects were significant for four of seven
employment variables. Increases in the
percentages of employments in the manufacturing sector and the
finance and insurance sector of
a county decrease the poverty rate in the own county. In
contrast, increases in the percentages
of the population unemployed and employment in agriculture
increase the poverty rate in the
own county. Again, these direct effects are quite different from
the total effects that take into
account of spatial spillover, reflecting the broader impacts on
the South as a whole. The total
effects indicate that 1 percentage point increases in employment

in the agriculture sector, the
manufactor sector, and art sector decrease the poverty rate in
the entire South by 0.19 per cent,
0.20 per cent, and 2.11 per cent, respectively. The higher total
effect of employment in the art
sector reaffirms the positive effect of growth in creative
employment that is highly associated
with poverty mitigation and rural economic development (Cho
et al. 2007). On the other hand,
employment in the finance and insurance sector did not have a
significant total effect on the
poverty rate. The differences between the direct and total
effects of the employment rates in
different sectors indicate differences between the local and
global effects of the employment
structure on poverty.
3 The side-by-side presentation of the parameters for the full
and reduced-form models is in Appendix 1 (robustness
analysis). The comparison between the two models shows no
significant change in terms of sign and significance of the
non-spatially lagged parameters, suggesting no substantial
endogeniety bias caused by the employment variables.
S. Jung et al.664
Table 2. Parameter estimates of the spatial Durbin panel model
with spatial fixed and time fixed effects
Variables Parameter of
non-lagged

variable
Parameter of
spatially-lagged
variable
Direct
effect
Indirect
effect
Total
effect
Poverty variable
Individual poverty rate 0.2684*
(0.0202)
Time lag of individual poverty rate –0.0212 0.0631* –0.0178
0.0737* 0.0559
(0.0183) (0.0282) (0.0181) (0.0349) (0.0362)
White –0.1231* 0.0306 –0.1230* –0.0022 –0.1252*
(0.0216) (0.0354) (0.0207) (0.0427) (0.0452)
Asia–Pacific –0.2520* –0.1614 –0.2672* –0.2971 –0.5644*
(0.1107) (0.1775) (0.1095) (0.2111) (0.2180)
Others –0.0356 0.1407* –0.0277 0.1734* 0.1457*
(0.0306) (0.0479) (0.0290) (0.0581) (0.0571)

Age 0–17 years 0.5069* –0.0845 0.5096* 0.0684 0.5780*
(0.0412) (0.0699) (0.0412) (0.0852) (0.0944)
Age 18–24 years 0.2865* –0.1988* 0.2762* –0.1568 0.1193
(0.0520) (0.0872) (0.0519) (0.1113) (0.1280)
Age over 65 years 0.2353* 0.1999* 0.2512* 0.3451* 0.5963*
(0.0403) (0.0661) (0.0392) (0.0804) (0.0870)
Female-headed 0.0626* –0.1390* 0.0556* –0.1575* –0.1019
(0.0263) (0.0423) (0.0273) (0.0536) (0.0598)
English speaking 0.1926* –0.0770 0.1898* –0.0333 0.1566*
(0.0300) (0.0438) (0.0280) (0.0517) (0.0512)
Some college education –0.1155* 0.0927* –0.1114* 0.0805* –
0.0309
(0.0157) (0.0241) (0.0152) (0.0291) (0.0298)
Three or more workers –0.2741* –0.0611 –0.2822* –0.1750* –
0.4572*
(0.0304) (0.0503) (0.0301) (0.0621) (0.0678)
Employment variable
Unemployment rate 0.1839* –0.0378 0.1864* 0.0125 0.1989*
(0.0322) (0.0457) (0.0320) (0.0531) (0.0467)
Agriculture 0.0743* –0.2133* 0.0613* –0.2518* –0.1905*
(0.0229) (0.0374) (0.0229) (0.0457) (0.0456)
Manufacturing –0.0640* –0.0790* –0.0705* –0.1246* –0.1951*
(0.0177) (0.0295) (0.0177) (0.0364) (0.0378)
Public utility –0.0039 –0.0194 –0.0061 –0.0227 –0.0287
(0.0275) (0.0461) (0.0272) (0.0574) (0.0615)

Wholesale and retail trade –0.0195 –0.0492 –0.0236 –0.0685 –
0.0920
(0.0218) (0.0383) (0.0215) (0.0493) (0.0530)
Finance and insurance –0.2670* 0.1226 –0.2631* 0.0708 –
0.1923
(0.0474) (0.0936) (0.0479) (0.1237) (0.1423)
Arts –0.2244 −1.3036* –0.3214 −1.7880* −2.1094*
(0.2128) (0.4382) (0.2232) (0.5663) (0.6520)
Time period fixed effects variable
1990 2.4721
(4.3698)
2000 –0.4658
(4.1937)
2010 −2.0063
(4.1941)
665
None of the time period fixed effects (i.e., 1990, 2000, 2010
dummy variables) are signifi-
cant, indicating that poverty rates do not have any significant
temporal pattern. The lack of
temporal variation in poverty rates reaffirms the persistence of
poverty in the South over the

1990–2010 period. The significant direct, indirect, and total
effects of the poverty hot-spot
dummy variable indicates that poverty rates are higher in
counties within and around poverty
hot-spots compared with other counties in the South.
The education funding variable was significant, as were the
interactions with the poverty
hot-spot dummy variable for two government funding variables
(i.e., health and hospital funding
and education funding). The significant direct effect and non-
significant total effect of the
education funding variable show that education funding in a
county is a significant factor in
poverty mitigation in that county but not in the South as a
whole. The significant interactions
suggest that health and hospital funding and education funding
have significant effects on
poverty rates within the poverty hot-spots.
A $1 increase in per capita education funding in a hot-spot
county decreases poverty rates
by 0.0018 per cent and 0.0022 per cent within the own county
and within all hot-spot counties,
respectively. On the other hand, a $1 increase in a county in the
South decreases poverty rates
by 0.0006 per cent in the own county but has no effect on the
poverty rate in the entire South,
after accounting for the spatial spillover effects. These results
support the hypothesis that
education funding effectively reduced poverty rates within
poverty hot-spots as a whole, and
confirm the findings of other researchers that education
expenditures are correlated with earn-
ings and can decrease the poverty rate (Rodgers and Rodgers
1993; Bhola 2006; Perry 2006;

Rynell 2008). In addition, the indirect effect of education
funding is not significant. The
implication is that most of the total effect occurs within the
county where the funds are allocated.
Table 2. Continued
Variables Parameter of
non-lagged
variable
Parameter of
spatially-lagged
variable
Direct
effect
Indirect
effect
Total
effect
Poverty hot-spot
Poverty hot-spot 2.7294* 0.2068 2.7888* 1.1945* 3.9833*
(0.4090) (0.5443) (0.4029) (0.5991) (0.5335)
Health and hospitals –0.0003 –0.0031 –0.0004 –0.0038 –0.0043
(0.0013) (0.0026) (0.0011) (0.0034) (0.0039)

Education –0.0006* 0.0004 –0.0006* 0.0003 0.0003
(0.0003) (0.0004) (0.0003) (0.0005) (0.0006)
Public welfare 0.0019 0.0020 0.0021 0.0032 0.0052
(0.0014) (0.0024) (0.0014) (0.0031) (0.0035)
Governmental funding variable – in poverty hot-spots
Health and hospitals 0.0028 0.0129* 0.0037 0.0179* 0.0216*
(0.0025) (0.0053) (0.0026) (0.0069) (0.0080)
Education –0.0018* 0.0001 –0.0018* –0.0004 –0.0022*
(0.0004) (0.0006) (0.0004) (0.0006) (0.0006)
Public welfare –0.0005 –0.0046 –0.0010 –0.0062 –0.0072
(0.0048) (0.0102) (0.0047) (0.0128) (0.0136)
Notes: Spatial fixed effects are not presented (40 county fixed
effect variables were significant out of 1420 counties).
* p < 0.05.
S. Jung et al.666
A $1 increase in per capita health and hospital funding in a hot-
spot county increases the
poverty rates by 0.0179 per cent and 0.0216 per cent in
neighbouring counties and in all hot-spot
counties, respectively. The positive sign for the indirect effect
implies that health and hospital
funding in a hot-spot county is associated with higher poverty
rates in neighbouring hot-spot
counties. The results may indicate that counties with more

heavily subsidized health and
hospital systems are regional healthcare centres within poverty
hot-spots that are surrounded by
more rural, less populated counties with lower costs of living.
Every county within a poverty
hot-spot cannot have a well-developed healthcare and hospital
system because they do not have
sufficient population to efficiently support one. Nevertheless,
those counties may have lower
costs of living, providing the poor with opportunities to live
more comfortably in surrounding
counties and still be within driving distance of a good
healthcare system. This finding coincides
with previous research. Andrulis and Duchon (2007) found
disparities in the numbers of
hospitals between urban and suburban areas and the number of
suburban hospitals in high
poverty areas had been decreasing the most. Felland et al.
(2009) found that there is an increased
demand from suburban low-income people for urban healthcare
facilities because of several
barriers such as limited transportation and insufficient
awareness.
The insignificance of both the public welfare variable and the
interaction of the poverty
hot-spot dummy variable with public welfare funding suggests
rejection of the hypothesis that
public welfare funding provides a poverty-reducing effect. This
finding implies a critical view
of public welfare spending as a poverty-reducing measure in the
South both within and outside
of poverty hot-spots, and supports the view of critics of the
poverty-reducing effects of public
welfare programmes (Stigler 1970; Okun 1975; Arrow 1979;
Friedman and Friedman 1979;

Murray 1984; Butler and Kondratas 1987; Lee 1987; Lindbeck
et al. 1994; Crook 1997). Our
results are worthy of consideration given recent attention to the
Medicaid programme in debates
about budget reductions and the Affordable Care Act
(Manchikanti et al. 2011; Sommers and
Epstein 2011; Blumberg 2012; HHS 2013). Even though our
public welfare variable is not
designed to isolate the effect of Medicaid spending, the
information is worthy of attention in
reference to policy-making for poverty reduction.
4.2 Predicted poverty rates and average marginal effect of
education funding
The predicted poverty rates and average marginal effect of
education funding inside and outside
of poverty hot-spots are presented in Table 3. The predicted
poverty rate, averaged across the
three years and the three poverty hot-spots (28.72%), is higher
than the average outside the
poverty hot-spots (16.89%) and in the entire South (18.42%). In
particular, the average across
Table 3. Average predicted poverty rates inside and outside of
poverty hot-spots
Predicted poverty rate (%) Average marginal effect
Regions No. of
Obs.
1990 2000 2010 Average of
1990, 2000, 2010
Education funding

Poverty hot-spots Texas 15 40.17 34.55 32.15 35.62 –0.00219
Mississippi-Delta 128 30.31 26.18 26.83 27.77 –0.00223
Central- Appalachia 40 32.75 27.12 27.60 29.16 –0.00223
Sum of all hot-spots 183 31.65 27.07 27.43 28.72 –0.00223
Outside of poverty hot-spots 1,237 18.32 15.44 16.91 16.89 –
0.00031
The entire South 1,420 20.04 16.94 18.27 18.42 –0.00031
667
the three years is the highest in the Texas poverty hot-spot
(35.62%). The higher predicted
poverty rates in the Texas poverty hot-spot may be explained by
Texas having the highest
percentage of immigrants among the states in the South. These
immigrants lack health insurance
and their unemployment rate is higher compared with other
residents, leading to higher poverty
rates (Rector 2006; Camarota 2012).
County-average predicted poverty rates decreased by 5.62 per
cent, 4.13 per cent, 5.63 per
cent, 2.88 per cent, and 3.10 per cent between 1990 and 2000 in
the Texas, Mississippi Delta,
and Central Appalachia poverty hot-spots, outside of the three
poverty hot-spots, and in the
entire South, respectively. The decrease in the entire South
corresponds with US poverty trends

of the 1990s (Lichter and Campbell 2005). Between 2000 and
2010, the county-average
predicted poverty rate increased slightly by 0.65 per cent and
0.48 per cent in the Mississippi-
Delta and Central-Appalachia poverty hot-spot, respectively,
while the average poverty rate
decreased by 2.4 per cent in the Texas poverty hot-spot.
The marginal effect of education funding differs slightly across
poverty hot-spots. The
counties in the Texas poverty hot-spots had an average marginal
poverty-reducing effect of
0.00219 per cent for a $1 per capita increase in funds allocated
to education. The other poverty
hot-spots had an average marginal poverty-reducing effect of
0.00223 per cent, slightly higher
than the Texas poverty hot-spot. The smaller marginal effect of
education funding in the Texas
hot-spot reflects the highest government education funding and
poverty rate among the three
poverty hot-spots for all three time periods. This might result
from Texas having a higher rate
of immigration (i.e., 14% of undocumented immigrants in the
nation lived in Texas in 1996;
HRO 2001) and being located on the border with Mexico. The
higher rate of immigration likely
increased the poverty rate in the region, triggering higher
spending for education because both
legal and illegal immigrants’ children are entitled for education
unlike other government
expenditures (Bernsen 2006).
5 Conclusions
This research evaluated the effects on poverty rates of state and
federal government funding

allocations to county governments for education, health and
hospitals, and public welfare. We
employed a recently developed econometric framework that
incorporates both the spatial and
persistent natures of poverty and used data from 1990, 2000,
and 2010 for 1,420 counties across
16 states in the South. Based on the estimates from the
empirical model, we discussed marginal
effects of government funding allocations on poverty rates in a
subset of counties identified
through LISA analysis as poverty hot-spots. Our use of LISA
statistics is an advantage of our
research in that prior knowledge of large poverty hot-spots
allowed us to focus more efficiently
on the spatial relationship between government funding and
poverty reduction in large multi-
county poverty hot-spots where persistent poverty is an issue.
Our analysis found that increases in education funding in a hot-
spot county reduce the
poverty rates of the county and the neighbouring hot-spot
counties. We also found that higher
health and hospital funding in a hot-spot county is associated
with higher poverty rates in
neighbouring hot-spot counties and that public welfare funding
is not effective in mitigating
poverty either within or outside of poverty hot-spots. These
findings suggest that education is the
only budget category with significant poverty reducing effects
within poverty hot-spots while
health and hospital funding in a hot-spot county is associated
with higher poverty rates in
neighbouring hot-spot counties. Our findings also show that the
marginal effects of education
funding differ across the three poverty hot-spots.

We learn from these implications that: (i) providing education
funding specifically targeting
impoverished hot-spots can reduce poverty; (ii) funding state-
supported regional healthcare and
S. Jung et al.668
hospital systems within a poverty hot-spot can provide a benefit
to poor families in lower-cost-
of-living counties surrounding the major healthcare hub; (iii)
redesigning of public welfare
programmes aimed at mitigating poverty may be needed if
poverty reduction is truly their
objective; and (iv) using site-specific information may be useful
in prioritizing funding decisions
among the three poverty hot-spots.
Future research could incorporate the bias-corrected maximum
likelihood estimator or the
quasi-maximum likelihood estimator for spatial dynamic panel
data proposed by Lee and Yu
(2010b) and Yu et al. (2008). The estimator of the SDMP with
fixed effects based on Elhorst
(2003, 2010) we used in our study is time-consistent and
asympototically normal when time t is
large for given number of cross-sectional units n (Lee and Yu
2010b). However, the approach of
estimating the SDMP with fixed effects applied in our case may
yield inconsistent parameter
estimates when n is large and t is small (e.g., n = 1,420 and t =
3 in our case) (Lee and Yu 2010b;
Lee and Yu 2010c). Thus, the bias correction procedure may

provide consistent parameter
estimates.
Also, future research could focus more attention on smaller
clusters of counties with
persistent poverty. For example, of the 320 counties in the 16
states that were classified as
persistent-poverty counties by ERS in 2010 (ERS USDA 2008),
156 counties were included in
the poverty hot-spots identified by the LISA analysis. Thus,
more detailed regional poverty
reduction strategies can be evaluated in future research by
closely examining smaller poverty
clusters. Likewise, the effectiveness of more detailed
government funding for poverty-related
programmes could be investigated in future research. Although
isolating the effect of funding for
a single programme such as Medicaid is beyond the scope of our
research, our modelling
framework could be used for such analysis. Medicaid funding
was not included in our analysis
because data were not available for 1987 (CMS 2013), and we
were interested in the effects of
broad categories of funding directed at poverty reduction. As
more consistent historical data
become available, such analysis can be undertaken.
669
Appendix 1

Table A1. Comparison of parameter estimates of the spatial
Durbin panel model with and without the
employment and demographic data variables
Variables Full model Reduced-form model
No employment data No employment, race, and age
Parameter of
non-lagged
variable
Parameter of
spatially-lagged
variable
Parameter of
non-lagged
variable
Parameter of
spatially-lagged
variable
Parameter of
non-lagged
variable
Parameter of
spatially-lagged

variable
Poverty variable
Individual poverty rate 0.2684* 0.3314* 0.3865*
(0.0202) (0.0196) (0.0186)
Time lag of individual
poverty rate
–0.0212 0.0631* –0.0281 0.0533 0.0253 0.0137
(0.0183) (0.0282) (0.0186) (0.0280) (0.0189) (0.0278)
White –0.1231* 0.0306 –0.1386* 0.0433
(0.0216) (0.0354) (0.0219) (0.0361)
Asia–Pacific –0.2520* –0.1614 –0.3125* –0.1099
(0.1107) (0.1775) (0.1132) (0.1787)
Others –0.0356 0.1407* –0.0393 0.1626*
(0.0306) (0.0479) (0.0312) (0.0489)
Age 0–17 years 0.5069* –0.0845 0.5217* –0.0420
(0.0412) (0.0699) (0.0414) (0.0695)
Age 18–24 years 0.2865* –0.1988* 0.3210* –0.1879*
(0.0520) (0.0872) (0.0527) (0.0871)
Age over 65 years 0.2353* 0.1999* 0.2598* 0.2780*
(0.0403) (0.0661) (0.0410) (0.0661)
Female-headed 0.0626* –0.1390* 0.0621* –0.1646* 0.1349* –
0.1700*
(0.0263) (0.0423) (0.0268) (0.0427) (0.0265) (0.0412)

English speaking 0.1926* –0.0770 0.1981* –0.1657* 0.2437* –
0.2676*
(0.0300) (0.0438) (0.0303) (0.0431) (0.0295) (0.0410)
Some college education –0.1155* 0.0927* –0.1232* 0.0952* –
0.1329* 0.1104*
(0.0157) (0.0241) (0.0154) (0.0184) (0.0159) (0.0191)
Three or more workers –0.2741* –0.0611 –0.2914* –0.1661* –
0.2942* –0.2438*
(0.0304) (0.0503) (0.0310) (0.0493) (0.0323) (0.0498)
Employment variable
Unemployment rate 0.1839* –0.0378
(0.0322) (0.0457)
Agriculture 0.0743* –0.2133*
(0.0229) (0.0374)
Manufacturing –0.0640* –0.0790*
(0.0177) (0.0295)
Public utility –0.0039 –0.0194
(0.0275) (0.0461)
Wholesale and retail trade –0.0195 –0.0492
(0.0218) (0.0383)
Finance and insurance –0.2670* 0.1226
(0.0474) (0.0936)
Arts –0.2244 −1.3036*
(0.2128) (0.4382)

Time period fixed effects variable
1990 2.4721 1.6270 1.6872
(4.3698) (3.8951) (0.8686)
2000 –0.4658 –0.3559 –0.4146
(4.1937) (3.8438) (0.9582)
2010 −2.0063 −1.2711 −1.2725
(4.1941) (3.7991) (0.9214)
S. Jung et al.670
Table A1. Continued
Variables Full model Reduced-form model
No employment data No employment, race, and age
Parameter of
non-lagged
variable
Parameter of
spatially-lagged
variable
Parameter of
non-lagged

variable
Parameter of
spatially-lagged
variable
Parameter of
non-lagged
variable
Parameter of
spatially-lagged
variable
Poverty hot-spot
Poverty hot-spot 2.7294* 0.2068 2.6742* –0.1890 3.2504*
0.7977
(0.4090) (0.5443) (0.4192) (0.5561) (0.4395) (0.5579)
Health and hospitals –0.0003 –0.0031 0.0024 –0.0013 0.0008 –
0.0030
(0.0013) (0.0026) (0.0025) (0.0027) (0.0014) (0.0028)
Education –0.0006* 0.0004 –0.0008* 0.0005 –0.0006* –0.0002
(0.0003) (0.0004) (0.0003) (0.0004) (0.0003) (0.0005)
Public welfare 0.0019 0.0020 0.0014 0.0006 0.0019 0.0014
(0.0014) (0.0024) (0.0014) (0.0024) (0.0015) (0.0025)
Governmental funding variable – in poverty hot-spots

Health and hospitals 0.0028 0.0129* 0.0024 0.0097 0.0009
0.0052
(0.0025) (0.0053) (0.0025) (0.0054) (0.0027) (0.0056)
Education –0.0018* 0.0001 –0.0017* 0.0008 –0.0023* –0.0006
(0.0004) (0.0006) (0.0004) (0.0006) (0.0005) (0.0006)
Public welfare –0.0005 –0.0046 0.0008 –0.0042 0.0008 –0.0033
(0.0048) (0.0102) (0.0049) (0.0104) (0.0052) (0.0109)
Notes: Spatial fixed effects are not presented. * p < 0.05.
Appendix 2
From Equation (3), the total marginal effect of kth explanatory
variable x in a given county
(i = 1) on P is:
∂
∂
= −( ) +[ ]−
P
x
W I W
k
k N k
1
1
I ρ β φ , (A1)

which can be reexpressed as:
∂
∂
= −( )
+⎡
⎣
⎢
⎢
⎢
⎢
⎤
⎦
⎥
⎥
⎥
⎥
−P
x
W
w
w
w
k

k k
N
1
1
11
21
1
I ρ
β φ
φ
φ
�
. (A2)
671
Let sij be an (i, j) element of (I − ρW)−1, then Equation (A2)
becomes:
∂

∂
=
+
+
+
=
=
∑
∑P
x
s s w
s s w
s s w
k
k k j j
j
n
k k j j
j
n

n k k nj
1
11 1 1
1
21 2 1
1
1
β φ
β φ
β φ
�
jj
j
n
1
1=
∑
⎡
⎣
⎢

⎢
⎢
⎢
⎢
⎢
⎢
⎢
⎢
⎢
⎤
⎦
⎥
⎥
⎥
⎥
⎥
⎥
⎥
⎥
⎥
⎥
. (A3)
The first element of the matrix in Equation (A3) is the direct
effect of the kth explanatory
variable x on P in a given county (i = 1) while the other
elements are the indirect effects of kth
explanatory variable x on P in other counties. Therefore, the
total marginal effect is the sum of
all elements in the matrix.
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© 2015 The Author(s). Papers in Regional Science © 2015
RSAI
doi:10.1111/pirs.12089
:
Resumen. Esta investigación evalúa los impactos en las tasas de
pobreza de los fondos del go-
bierno para la educación, la salud y los hospitales, y el
bienestar público asignado a la reducción de
la pobreza para los condados con una alta pobreza persistente en
el sur de los Estados Unidos.
Nuestro análisis encontró que el aumento de fondos para la
educación en un condado candente
en cuanto a la pobreza reduce las tasas de pobreza de ese
condado y de los condados candentes
vecinos. Asimismo, encontramos que el aumento de la fi
nanciación para la salud y los hospitales
en un condado candente en pobreza se asocia con mayores tasas
de pobreza en los condados
candentes vecinos y que la fi nanciación del bienestar público
no es efi caz en la mitigación de la
pobreza, ya sea dentro o fuera de los puntos candentes de

pobreza.
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